THEMAIN LA MILLOR TOTHAIMAHALLI HATAN US 20180096432A1 ( 19) United States (12 ) Patent Application Publication ( 10) Pub . No.: US 2018 /0096432 A1 Varma et al. (43) Pub. Date : Apr. 5 , 2018 (54 ) METHODS AND SYSTEMS FOR Publication Classification REPRESENTING RELATIONAL INFORMATION IN 3D SPACE (51) Int. Ci. G06Q 40 /06 ( 2006 .01) (71) Applicant: RECONSTRUCTOR HOLDINGS (52 ) CPC U .S. CI . LLC , Greenwich , CT (US ) ...... . . . . . .. G06Q 40 /06 (2013 .01) ( 72 ) Inventors : Samir Varma, Cos Cob , CT (US ); (57 ) ABSTRACT Michael Wayne Shore , Dallas, TX Systems and techniques are described herein for graphically (US ) representing a number of data objects in three dimensional space . In one example , multiple data objects may be graphi (21) Appl. No.: 15 /833, 986 cally represented and manipulated in a three ormore dimen sional space, with each axis of a three dimensional grid (22 ) Filed : Dec . 6 , 2017 representing one or more variables or variables of the data objects . A graphical user interface may be provided that Related U .S . Application Data enables configuration of the display (visual, or alpha (60 ) Continuation - in -part of application No. 15 /097,855 , numeric , or both ) of any number of variables associated with filed on Apr. 13, 2016 , now abandoned , which is a the data objects of interest. The graphical user interface may continuation of application No. 13 /942 ,145 , filed on also provide for controls that enable manipulation of the data objects themselves and their representation in the three Jul. 15 , 2013, now abandoned , which is a division of dimensional space. In some aspects , the graphical user application No . 13 /288, 660 , filed on Nov . 3 , 2011 , interface may provide for animations of the data objects , now Pat. No. 8 ,606 ,672. representing changes in values of the variables associated (60 ) Provisional application No .61/410 ,237 , filed on Nov . with the data objects over time to more efficiently convey 4 , 2010 . trend information . nomhuspeh** Enne Network a.rtisipas * -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ???????? ??????? ????? Patent Application Publication Apr. 5, 2018 Sheet 1 of12 US 2018/ 0096432 A1 *???? * * ? . FFIG ?1 • •? ??i?? • - •??• ???? ?????? ??? ?????? ** ???***4 # & ?a ???? Patent Application Publication Apr . 5 , 2018 Sheet 2 of 12 US 2018/ 0096432 A1 * - *- - *- * -* -*- * - *-* - *-* - *- - *- * * * * “ … … … … … … … … … …. www … … . … … … … . * , ?? , " } , " 1 ,????????????? * . - - - - - - -wwwt / "," www. ' | ' , ??????????????? … - SS 222| , ?? MANYAMAMAMAMANYWAY APPYHHHHHHHHHHHHHATSAIN … … * CAS ?? ?? * ; *-*- -* -*.…. .“ *. .- * ??? … . … . … . ?? ? . * * ??? . * ? 2 . FIG ?? * - , 4,, , , ?? - - * - . * HTTP :/ / … . yy . . www com www www wreture : www www .sine a more ?? ? “ ? ? come r c i a i s Patent Application Publication Apr. 5 , 2018 Sheet 3 of 12 US 2018 /0096432 A1 algow DOE O - -4 .4 . 4 .4 . . 4 .4 . 4 . * . . ??? 1 { } 315 viai . writRe-able Lowaw AN?M RAUMAR t' W Ho $ 3004d320 IUntserfeacre YAWA FIG 3 . Buouy AVRUV .ww VNN S4Y Y uoneorunuwo Interace . . . . . .4 4 44 * * * *.4 . V . * * . * . * * *. . . . * * * * * * ** * * * WON * * ** ** * * ** * * ** * Patent Application Publication Apr. 5 , 2018 Sheet 4 of 12 US 2018 /0096432 A1 jueusang Advice * WAMS.YA 410 2012- 11 anas mom need to ensure * ** .. . * . * . . * . * . . * . * . * . .* . * * . * * 14 . . . . Y ! 44 wwwwwwwwwwwwwwwwwwwwwwwww AS * * U U ' . DVescritpoirn un 406 * : 44Y ????? { 1 $ ???11 } . . . . Wat w ' * . - * ** * * * * * . - '. ' . . ' ' we r *. . . * . * . * . * * * ' ' . ? . ww wwwwwwww pos FRienqaunedsatl jual8sanulVariables 400 vQod0uosAe wvwawvww*-wvwwwv OuizunjeWON ? ? 4A AANADAA www . - - ? WOMAN ? WAMASAN * *** / 19 ***** * * * ** * * * * ** * w wwviwvwiw www ? Database ** * * * ** * * * Mission Patent Application Publication Apr. 5 , 2018 Sheet 5 of 12 US 2018 /0096432 A1 Start ' ''''' '''''''' ' $ 500 Receive financial Request and WW Investment w Valables vit -479- * * 2 itir * . Al Variables **** *07: *** Received ? ***** **** *** * ***** ****** * * Generate Description Vectors * * ** wwwwwwwwwwwwwwww FIG . 5 .. . * * * * * * * * * * ** * . .. . .. .. *** S506 Normalize Investment Variables wwwwwwwwwwwwwwwwwwwwwwwwwwang 8508 Calculato Similarity Value . Wwmiw w w * * * . Determine Investment W Advice ow * * ** * * * * ** * wwww * * !! h * ithin ?! ** * : . Additional *** * *** Financial * * * * * wwwwwwwwwwwwwwwwwwwwas Requestshe :- *. 14tht * * ** ** * * * ** * * * * ** * ***** * * ONS Patent Application Publication Apr. 5 , 2018 Sheet 6 of 12 US 2018 /0096432 A1 600 Receive selection of data point object(s )formodeling (e.g., investments )602 Obtain data formodeling data point object( s) 604 wwwwwwwwlingen Receive definition of variable ! metric for evaluating data point objeci(s ) 606 Determine variable /metric for evaluating data point objectis ) Son Additional Yes variables metrics selected 610 No Determine target for each variablelmetric and associate as reference data point object 612 Determine distance between data points /objects (and reference point object based on target s ) and variable /metrics) for each data point/object 614 Translate data point/object(s) and reference point/object to 3D model space 616 FIG . 6 Patent Application Publication Apr. 5 , 2018 Sheet 7 of 12 US 2018 /0096432 A1 Receive request for display of different information relating to at least one data point object 702 Yes mineralen Receive Obtain requested information graphicalmodification of from database 704 data point/object 708 No Display obtained data in 3D Yes space 706 Determine new values of one or more variables/metrics based on graphicalmodification 710 Access data object in database Modify and store new values in data objectbased on determined new values 714 End 716 FIG . 7 Patent Application Publication Apr. 5 , 2018 Sheet 8 of 12 US 2018 /0096432 A1 wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww x Y -- -* » --------- 03 83 X3 ) , ( -- - ~ --- --- ~ - -- --- --- --- --- - -- --- -- - e me e re o ~ ~ ~ w w ww w w w w w w w w ww ~ ~ ~ ~ ~ z1 x1 ) , { yi 802 - ) 05 , 85 ~ 5 ( ~ ~ ~ . 8 FIG ~ ~ 810 - ~ ~ ~ ~ - - - - - --* * * * 804) 02 , 92 , x2 ( ) Ø4 84 , x4 ( ~ ~ ~ kawe wa w ewa ww www www . sa - ine w 800 Patent Application Publication Apr. 5 , 2018 Sheet 9 of 12 US 2018 /0096432 A1 - - - -- - - - -- - - - - - - -- - -- - - -- - - - - -- - - - - ti -- - - - - - - - - - - - - - - - - - - - - 218 _ Q 218 Q - - - - - - - - - - -- - - -- - - -- - - 812 % FIG 9 . - - - - - .www 908 . ww de 208 6 812 w . - - 808QQ018248 www. * 8127 900RROOOOOOOOOOO0O0oo90o0oPogostoprodogodowod w 218 -Q - - - wwe - - -- w w w 808 Q & pogo - - - - -- - - -21a -- - - -- - - -- - -- -- mmw-- - -- -- - Q - - --- -- -- -- -218 8087 O . 800 Patent Application Publication Apr. 5 , 2018 Sheet 10 of 12 US 2018 /0096432 A1 ++ - - - ww wtut w w - * * * ww w - * - - . - . we w } 1 018 - ere h - - - - - - -com ww - - - O Zoom - - . - * * ** - . - - . - . -. * • - * ** FIG 10 . - * * alimen wwwwww ww ww - - -- -- www 810 0 - - - - - - - VARTAL * . am . RIVALLAVILA - w V ** *** l ro- -- - - - -- -- - -- - . popooooooooooodooodoomodogopao w w www - 2 - . - -- - -- - - - 8047 - -* *- -ten - - pod 800 Patent Application Publication Apr. 5 , 2018 Sheet 11 of 12 US 2018 /0096432 A1 ?? ?? ? ?? ? ? ?? ?? . ?? ?? . ?? - - . . - - -- - - - - €o* *-- 908 -* * ---- -man -* * ** , * * * - ------wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww ? ww ww w ? ? ? ? ? ? ? 806278 ? ? ? ? 802 - ) 0 , x ( ? ? ? 810a FIG . 11 804a 8106 ? ? ? wwww ??? .-- - - - ---804 1808 .. . ww f - - -- - - - - -- - -- - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - -- - - - - -- 808am 800 Patent Application Publication Apr. 5 , 2018 Sheet 12 of 12 US 2018 /0096432 A1 - camaa ----- - * ** - - -- * - - --les- - - - - - - - - - -- * - - -- - - - - - - -- -- - -- - - - - - - - - - - ww w w w - - 802 12 . FIG 800 4*i are * • • • • • • • • 804 • • • • • • • • • • • • • • • 808 - -- - -- -- -wwwwww To ls Pan Rotate Zoom MoveStart40men 1212-20SetPointofView 1208 Resize Configuration Configuration Configuration Variable 1214 Animation 1216 Disaptlay 1218 -1204 1206 1210 1202 1200 US 2018/0096432 A1 Apr. 5 , 2018 METHODS AND SYSTEMS FOR the future . In addition , a need exists for more useful sports REPRESENTING RELATIONAL information analysis and a way to access and represent that INFORMATION IN 3D SPACE analysis . CROSS REFERENCE TO RELATED SUMMARY APPLICATIONS [0006 ] In order to solve at least the above -noted problems, [0001] This application is a continuation - in -part applica the present advancement relates to information processing apparatus and associated method for analyzing investment tion of U .S . patent application Ser. No. 15 /097 ,855 filed Apr. information relating to a financial indicator. An input unit 13 , 2016 , which is a continuation application of U . S . patent receives a financial request and one or more investment application Ser. No . 13 / 942 , 145 filed Jul. 15 , 2013 , which is variables relating to the financial indicator and a description a divisional of U . S . patent application Ser. No . 13 /288,660 , vector unit generates at least two vectors containing the one filed Nov . 3 , 2011 , now U . S . Pat. No . 8 ,606 ,672 , which is or more investment variables based on the financial request. based upon and claims the benefit of priority from the U . S . A description vector similarity unit then calculates a simi Provisional Application No . 61/410 ,237 , filed on Nov. 4 , larity value by comparing the at least two vectors and an 2010 ; the entire contents of all the above-listed priority investment analysis unit analyzes the similarity value and documents of which are incorporated herein by reference . the financial request, and determines investment advice based on results of the analysis . FIELD [0007] In some aspects, the data and analysis associated [0002] The embodiments discussed herein relate to a with the data may be graphically represented via a fully method and system for analyzing and displaying information customizable and configurable graphical user interface . In graphically . More specifically , a method and system that some implementations, the data may include financial data . provides comparisons , via a three or more dimensional plot In other implementations, the data may include other types or virtual reality environment, of information , such as of data , such as sport related information (player stats, fantasy league, etc . ). The user interface may represent the investment or sports information , based on an analysis of the data in three dimensional ( 3D ) space , for example via a information is described . display device or virtual reality or augmented reality device or devices . Each axis of the 3D space defines or represents BACKGROUND one or more variables of interest. In some cases, more than 10003] Financialmarkets , such as the stock market, pro one variable may be represented by a single axis , such that vide a vehicle for consumers to make financial investments values of the two or more variables may be combined . In one with the hope of monetary gain . For example, stocks of a example , an ideal value ( e . g ., for comparison , as a goal, etc.) corporation purchased by a consumer can provide monetary for a number of selected variables may be obtained . These gains for the consumer if the stock value increases over time. values may be graphed or plotted onto a 3D plane , such as Before making an investment in the stock market, a con a 3D grid or space . Values of interest, such as investment sumer should undertake a considerable amount of time to assets or opportunities, may be obtained , such as for research the investment. However, consumers are easily example , from one or more databases, or via other means . overwhelmed by the amount of factors that can be taken into These values may also be graphed or plotted in the 3D space . account when attempting to make a smart investment deci The values may then be compared to the ideal value , and a sion and may only wish to focus on customized factors that vector representing the difference may be generated. The are most relevant to a consumer ' s portfolio needs. Further, vector may define the distance of the values to the ideal it is difficult for consumers to determine the risk and reward value , both in the 3D space and in the parent N -dimensional levels of various investments as well as the similarity and/ or space . The data , and more specifically the relative value of differences between various investments based on these multiple variables may be more readily graphically repre sented and visually understood , via the relative distances factors . between points in the 3D space . Information , such as relat 10004 ) Financial markets can also be extremely volatile ing to financial investments, sports teams performance, and which can result in sudden losses with respect to a consum other data may find particular applicability with the er 's investment . This sudden loss can cause the consumer to described systems and methods. dispose of the investment regardless of potential future [0008 ] In some aspects, four dimensions may be repre gains. For example , a consumermay notice a sudden drop in sented and visualized in the three dimensional space, via a stock price and proceed to sell the stock to avoid further changing of the values of interest ( and in some cases, also losses . However, the stock price over a longer period of time the ideal value ) in the space , e . g ., via movement within the may show a significant increase thereby resulting in a three dimensional space . The fourth dimension may repre missed opportunity by the consumer for long - term monetary sent time, or any other variable or combination of variables . gain . In some aspects , the three or more dimensional display may [0005 ] A need exists for a method and system for analyz be displayed , controlled , and/ or manipulated using virtual ing investment information that allows a consumer to gen reality or augmented reality . Controls may be provided that erate customized investment analysis and advice based on a navigate through the 3D space , for example based on variety of factors identified by the consumer. Further, a need gestures . exists for providing risk and reward information as well as [00091 In some examples, more dimensions may be rep comparison information between investments based on the resented via other visual indicators , such as different colors , above -noted factors in a way that intelligently informs a shapes , textures, line dashing, flashings , and so on . In some consumer of investment value in the past, present and into cases, selecting or hovering over a data point (representing US 2018/0096432 A1 Apr. 5 , 2018 a value of interest), for example that represents a potential [0016 ] FIG . 3 is a schematic drawing of a mobile device investment, may display additional information of the according to an exemplary embodiment of the present investment. In some examples, selection or touching of a advancement; data point may reveal more information , expand its view , [0017 ]. FIG . 4 is a functional drawing of the investment contract its view , turn it on its axis to reveal other informa analysis system according to an exemplary embodiment of tion about the data point, change its color, size , brightness, the present advancement; contrast and other characteristics to reveal information about [0018 ] FIG . 5 is an algorithmic flow chart of analyzing the data point. In some cases , each data point may have or investment information according to an exemplary embodi be associated with different touch points that do different ment of the present advancement. things - for example touch a stock ' s representation from the [0019 ]. FIG . 6 is a flow diagram illustrating an example left and it does/ shows one piece of information or metric , but process for graphically representing data including a number touch it from the top and it does or shows another piece of of values in a multi-dimensional space . information , metric , etc . [0020 ] FIG . 7 is a flow diagram illustrating an example [ 0010 ] In some aspects , the user interface may provide process for modifying data through graphicalmodifications controls to expand the field of view to see more detail about in a multi - dimensional. a data point or group of data points, such as a stock or group 10021 ] FIGS . 8 - 11 illustrate example multi-dimensional of stocks, based on user movements , such as moving hands spaces for representing multiple values. or arms apart in some given volume of space (breaststroke [0022 ] FIG . 12 illustrates an example display of a multi outwards ) . The user interface may also provide controls to dimensional space, representing multiple values, with user contract the field of view to see more data points or stocks interface controls for manipulating the multi -dimensional in less detail based on user movements , for example by space . movement of arms or hands in a reverse breaststroke . DETAILED DESCRIPTION [ 0011 ] In some examples, 3D or virtual reality controls may include projecting “ touch pads” or other control objects [0023] In the following , the present advancement will be on the walls/ceilings/floor or even in the volume of a room discussed by describing a preferred embodiment with ref that allow a user to manipulate the data and the perspective erence to the accompanying drawings . However, those of the 3D space . By using an entire room ( e. g ., and office , skilled in the art will realize other applications and modifi conference room , or other physical room ), for example , as a cations within the scope of the disclosure as defined in the VR platform , detailed examinations ofmultiple investments enclosed claims. made be made simultaneously, including the relations [0024] In embodiments, any desired network configura between them . In other cases, any amountof physical space tion , hardware , programming architecture , or a combination may be used to represent the data in VR ( e . g ., a space of a of both may be used to implement a system for analyzing pre - defined , but located outside). In one example , these investment information . controls may include virtual " keyboards," or other similar [0025 ] FIG . 1 is a schematic diagram of a system for controls that can detect user movements ( e . g ., projecting a analyzing investment information according to an exem laser image of a keyboard onto a tabletop ). plary embodiment of the present advancement. In FIG . 1 , a [0012 ] In some aspects , the user interface may provide for computer 2 is connected to a server 4 , a database 6 and a controls to add other attributes to a point in the 3D space , mobile device 8 via a network 10 . The server 4 represents such as stock information . For example , this may include one or more servers connected to the computer 2 , the making stocks that trade easily feel be light and stocks that database 6 and the mobile device 8 via the network 10 . The trade with difficulty feel heavy - e .g., easy to push /pull database 6 represents one or more databases connected to versus a bit harder to push /pull in VR , in manipulating or the computer 2 , the server 4 and the mobile device 8 via navigating through the 3D space . network 10 . The mobile device 8 represents one or more mobile devices connected to the computer 2 , the server 4 and BRIEF DESCRIPTION OF THE DRAWINGS the database 6 via the network 10 . The network 10 represents one or more networks, such as the Internet , connecting the [0013] A more complete appreciation of the present computer 2 , the server 4 , the database 6 and the mobile advancements and many of the attendant advantages thereof device 8 . will be readily obtained as the same becomes better under 10026 ] The server 4 , the computer 6 and /or the mobile stood by reference to the following detailed description device 8 can be utilized as a central hosting site for the when considered in connection with the accompanying investment analysis system . Users of the computer 6 and drawings. However, the accompanying drawings and their mobile device 8 can also access the investment analysis exemplary depictions do not in any way limit the scope of system on the server 4 via network 10 . Accordingly , the the advancements embraced by this specification . The scope server 4 may notify users of the investment information of the advancements embraced by the specification and through the network 10 to the computer 6 or mobile device drawings are defined by the words of the accompanying [0027 ] Next, a hardware description of the information claims. processing apparatus for analyzing investment information [0014 ] FIG . 1 is a schematic diagram of a system for according to exemplary embodiments is described with analyzing investment information according to an exem reference to FIG . 2 . In FIG . 2 , the information processing plary embodiment of the present advancement; apparatus includes a CPU 200 which performs the processes [0015 ] FIG . 2 is a schematic diagram of an information described above. The process data and instructions may be processing apparatus according to an exemplary embodi stored in memory 202 . These processes and instructions may ment of the present advancement . also be stored on a storage medium disk 204 such as a hard US 2018/0096432 A1 Apr. 5 , 2018 drive (HDD ) or portable storage medium or may be stored f0034 ] The general purpose storage controller 222 con remotely . Further, the claimed advancements are not limited nects the storage medium disk 204 with communication bus by the form of the computer - readable media on which the 224 , which may be an ISA , EISA , VESA , PCI, or similar, for instructions of the inventive process are stored . For example , interconnecting all of the components of the information the instructions may be stored on CDs, BLU -RAY,DVDs, in processing apparatus. A description of the general features FLASH memory, RAM , ROM , PROM , EPROM , and functionality of the display 212 , keyboard and /ormouse EEPROM , hard disk or any other information processing 216 , as well as the display controller 210 , storage controller device with which the information processing device com 222 , network controller 208 , sound controller 226 , and municates, such as a server or computer. mi general purpose I/O interface 214 is omitted herein for 10028 ]. Further, the claimed advancements may be pro brevity as these features are known . vided as a utility application , background daemon , or com [0035 ] FIG . 3 is a block diagram of the mobile device 8 . ponent of an operating system , or combination thereof, The mobile device 8 , which may be a smart phone such as executing in conjunction with CPU 200 and an operating an iPhone from Apple , Inc. of America , includes a processor system such as Microsoft Windows 7 , UNIX , Solaris, 320 used to control the functions of the mobile device 8 and LINUX , Apple MAC -OS and other systems known to those to run applications thereon , such as an electronic address skilled in the art . book , internet browser, etc . Processor 320 may be an ARM [0029] CPU 200 may be a Xenon or Core processor from processor or a processor such as a Core 2 Duo from Intel Intel of America or an Opteron processor from AMD of Corporation of America . Alternatively , Processor 320 may America , or may be other processor types that would be be implemented on an FPGA, ASIC or using discrete logic recognized by one of ordinary skill in the art. Alternatively , circuits, as would be recognized by one of ordinary skill in the CPU 200 may be implemented on an FPGA , ASIC , PLD the art. or using discrete logic circuits, as one of ordinary skill in the [0036 ] A user interface 310 , which may include a micro art would recognize . Further, CPU 200 may be implemented phone, speaker, touch screen , keyboard or any combination as multiple processors cooperatively working in parallel to thereof, allows the processor 320 to receive input from a perform the instructions of the inventive processes described user of the mobile device 300 , and a display 315 provides above . feedback to the user . Display 315 may be a color or [ 0030 ] The information processing apparatus in FIG . 2 monochrome LCD display or any other display that would also includes a network controller 208, such as an Intel be recognized by one of ordinary skill in the art. Ethernet PRO network interface card from Intel Corporation [0037] Mobile device 8 also includes a communication of America , for interfacing with network 10 . As can be interface 305 and antenna 335 to communicate with other appreciated , the network 10 can be a public network , such as devices, such as the server 4 or another mobile device 8 . the Internet, or a private network such as an LAN or WAN Further, mobile device 8 may communicate with these other network , or any combination thereof and can also include devices wirelessly using the cellular network ( including PSTN or ISDN sub -networks. The network 10 can also be EDGE, 3G , 4G , etc .,) a WiFi connection , a Bluetooth wired , such as an Ethernet network , or can be wireless such connection or any other wireless form of communication as a cellular network including EDGE , 3G and 4G wireless that is known. Mobile device 8 may also communicate cellular systems. The wireless network can also be WiFi, through a wired connection to communication interface 305 , Bluetooth , or any other wireless form of communication that such as a USB connection . is known. 10038 ] In some embodiments, mobile device 8 includes [0031 ] The information processing apparatus further read -only memory , ROM 330 , to store low -level functions includes a display controller 210 , such as a NVIDIA and processes necessary to support core functionality, and GeForce GTX or Quadro graphics adaptor from NVIDIA re -writable memory 325 , which stores an operating system , Corporation of America for interfacing with display 212 , drivers , applications, application data and user data . Re such as a Hewlett Packard HPL2445w LCD monitor. A writable memory 325 may be dynamic or static random general purpose I/O interface 214 interfaces with a keyboard access memory (RAM ), FLASH memory , EEPROM and/ or mouse 216 as well as a touch screen panel 218 on or memory , and the like . Further, portions of the re -writable separate from display 212 . General purpose I/O interface memory 325 may be removable . also connects to a variety of peripherals 220 including [ 0039 ] For example , re -writable memory 325 may store an printers and scanners, such as an OfficeJet or DeskJet from application for communicating with server 4 to identify Hewlett Packard . mobile device 8 and to provide the server 4 with access to [0032 ] In some aspects display controller 210 may be data stored therein . Such application may be downloaded , designed to interface with display 212 and/ or one or more via any of the communication methods described above , other peripheral devices 220 such as virtual reality (VR ) or from the server 4 , from a software repository or from an augmented reality devices. These may include devices made online store , such as the App Store from Apple, Inc . of by various different companies . As will be described in America . Further, such an application may be an electronic greater detail below , display controller 220 and /or other address book , or electronic personal information manager components of the system of FIG . 2 may be configured to (PIM ). However, the application may also be a plug- in for an extract and manipulate data to create new data useful for electronic address book or PIM already installed on mobile displaying graphical representations of the data and various device 8 , a background application , driver and the like . relationships between the data . [0040 ] As one of skill in the art would recognize , the [0033] A sound controller 226 is also provided in the above descriptions of the server 4 and mobile device 8 are information processing apparatus , such as Sound Blaster merely examples and other server configurations and mobile X - Fi Titanium from Creative , to interface with speakers / devices may be used without departing from the scope of the microphone 228 thereby providing sounds and /or music. present advancements . US 2018/0096432 A1 Apr. 5 , 2018 [0041] According to some embodiments, a “module ” keep calculations consistent. More information with respect refers to hardware architecture or one ormore programming to the normalizing unit 404 is provided with respect to FIG . architectures, or a combination of both , configured to per form one or more designated functions. [00461 The description vector similarity unit 406 calcu [ 0042 ] FIG . 4 describes the functionality of the investment lates the similarities between description vectors created by analysis system according to one embodiment of the present the description vector unit 402 and normalized by the normalizing unit 404 . As described later , at least one way to advancement. FIG . 4 illustrates investment variables and a determine the similarity between description vectors is to financial request 400 , the database 6 , the server 4 , the calculate the Euclidean distance between the description investment advice 410 , a description vector unit 402, nor vectors. However, another metric for determining the simi malizing unit 404 , description vector similarity unit 406 and larity is to use an absolute value metric or any other metric an investment analysis unit 408. The description vector unit defined by the user of the investment analysis system . The 402, normalizing unit 404 , description vector similarity unit results of the description vectors similarity unit are then 406 and an investment analysis unit 408 are illustrated transmitted to the investment analysis unit 408 . within the server 4 but in other embodiments could be [0047] The investment analysis unit 408 analyzes the included within the computer 6 or the mobile device 8 . results of the description vector similarity unit 406 to Investment variables and a financial request 400 relating to provide the user with investment advice based on the an investment, or financial indicator, are received from a investment variables and the financial request 400 defined user depending on the investment information the user wants by the user. To do this , the investment analysis unit 408 to obtain . Example investment variables 400 include P /E compares similarity information from the description vector ratios, debt to equity , total sales, and temporal resampled similarity unit 406 based on what type of investment infor risk . The financial request represents a request from the user mation the user is interested in obtaining for his or her to obtain certain financial information . A variety of non portfolio . Non - limiting examples of investment information limiting examples of financial requests are described in include a list of a best or worst investments , a comparison detail later. The investment variables and financial request of investments, and risk and reward information . More 400 are then transmitted to the server 4 via the network 10 information on the investment information determined by as described in FIG . 1. the investment analysis unit 408 is described later with [0043] The description vector unit 402 of the server 4 respect to FIG . 5 . defines a description vector based on the investment vari 10048 ] The investment advice 410 of the investment ables and the financial request 400 . The description vector analysis unit 408 are provided to the database 6 to be stored can represent a multidimensional point in multidimensional for possible later analysis and are provided to the user via at space and contains the investment variables 400 . The least a computer 2 or mobile device 8 as described in FIG . description vector defines what type of investment informa tion , such as risk or reward information , the user is seeking [0049 ] FIG . 5 is an algorithmic flow chart of analyzing to obtain for his or her portfolio needs. More information investment information according to an exemplary embodi with respect to the description vector is provided later with ment of the present advancement. The process of analyzing respect to FIGS. 5 and 6 . investment information starts at step S500 by having a user input a customized ranking of factors , or investment vari 10044 ] The normalizing unit 404 of the server 4 normal ables 400 , relating to the investment (ple ratios, debt to izes all of the investment variables 400 of the description equity , etc ), and a financial request 400 . The system then vector defined by the description vector unit 402 . Normal determines whether all the variables have been received at ization is required so that all of the investment variables 400 step S502 . If NO at step S502 , the system continues to have an equal weight with respect to each other when accept investment variables 400 until the user has finished . investment calculations are performed . One type of normal If YES at step S502 , the system proceeds to step S504 to ization is 1/N , where N is the number of investment vari generate a description vector based on the investment vari ables 400 . Another type of normalization is to use the ables and the financial request 400 . standard deviation of all the information in database 6 . For [0050 ] The description vector DV generated by the this type ofnormalization , the information analyzing system description vector unit 402 is defined as follows. DV = ( si , calculates the standard deviation for each investment vari able 400 contained in the database 6 and then divides the s2 , s3 , . . . , sn ), where sl through sn are investment variables investment variable 400 of the particular investment by the 400 that describe an investment or benchmark in question . standard deviation . For example , if the user selects IBM as Therefore , if the user wanted to measure the risk of a single a particular investment and chooses the P / E Ratio as one of stock and determines that the most important factors are the the investment variables 400 , the investment analysis system ple ratio , the 21 day resampled risk of the maximum locates all of the P / E Ratios in the database 6 , calculates the drawdown exceeding 10 % , and the Quick Ratio , then the standard deviation , and then divides the P / E Ratio of IBM by description vector for this particular stock , i.e . for this the standard deviation to obtain a normalized value. particular calculation , would be an “ Investment Risk Vec tor " defined as IRV = (ple ratio , 21 day resampled risk of the [ 0045 ] The present advancement, however, does not maximum drawdown exceeding 10 % , Quick Ratio ). A cor require the input investment variables to be equally responding “ Benchmark Risk Vector” would be defined as weighted . As long as the sum of all the normalization BRV = (p / e ratio , 21 day resampled risk of the maximum weights is 1, different investment variables 400 could have drawdown exceeding 10 % , Quick Ratio ). While both vec different weights . Any additional description vector being tors contain the same investment variables 400 , the values of compared to the description vector defined by the descrip the investment variables 400 will likely be different based on tion vector unit 204 would also have to be normalized to the corresponding value of each investment variable 400 in US 2018/0096432 A1 Apr. 5 , 2018 the database 6 with respect to the investment or benchmark . formance of a security , mutual fund or investment manager Once the description vector is defined , the process proceeds can be measured . Generally , broad market and market to step S506 to normalize each of the investment variables segment stock and bond indexes are used for this purpose . 400 . For example, the Benchmark could be a recognized bench [0051] The investment variables 400 must be normalized mark like the S & P 500 , it could be a sector index , it could to ensure they each provide the appropriate amount of be an ETF , or it could be some custom composite created by weight to the investment analysis calculations. For example , the user. the absolute value of a ple ratio is generally in the tens or [0055 ] The Investment Reward Vector IReward is defined twenties. The absolute value of a 21 day resampled risk , as follows. IReward = ( IReward1 , IReward2 , IReward3, . . . however, is between zero and 1 . If the two variables were not , IRewardn ), where the investment variables 400 are vari normalized, then any future calculation would be almost ables that the user thinks affect the expected return of the entirely dominated by the p / e ratio . investment. The investment could be for a stock , a portfolio , [0052] One option is for the user to specify how to or a mixed bag of various kinds of investments . It should be normalize each variable based on the user ' s needs. However , noted that the Investment Reward Vector should not be the a natural, default, normalization for each piece of data is same vector as Investment Risk Vector IRisk . The Invest supplied for the ease of the user. As noted above, one such ment Risk Vector IRisk represents what the user thinks of as natural normalization is to use the standard deviation of all the financial information that goes wrong, or might go such pieces of information in the database 6 . So , for wrong, or that which indicates an elevated level of risk . example , if the user requested analysis on a particular stock , IReward represents that financial information which the user and the user ' s choices were the ple ratio , the p / s ratio , and thinks might go right thereby providing a favorable return the chance of a 5 day maximum drawdown exceeding 5 % , for the user. all the ple ratios would be located in the database 6 , their [0056 ) The Benchmark Reward Vector is defined as fol standard deviation would be calculated , and then that par lows. BReward = (BReward1 , BReward2 , BReward3, . . . , ticular stock ' s ple ratio would be divided by this standard deviation . Then all the p /s ratios would be located in the BRewardn ) where the investment variables 400 correspond to the investment variables 400 defined in the Investment database 6 , their standard deviation would be calculated , and Reward Vector IReward if the Benchmark and the Invest this stock 's p /s ratio would be divided by that standard ment Reward Vector IReward are being compared . It should deviation . This process is repeated until all the investment be noted that BReward represents the “ ideal” investment variables 400 of the description vector are normalized . according to the user. For example , if the user is a stock [0053] Returning to the description vector, non - limiting investor and is interested in value, the user may think the examples of the description vector include an Investment best kinds of investments are those where the ple ratio is less Risk Vector , Benchmark Risk Vector, Investment Reward than 10 , the price /book is less than 1 , the current ratio is Vector and an Benchmark Reward Vector. The description greater than 1 , and the profit margin is greater than 5 % . vector can also be any other vector chosen by the user. As Thus, the user would create a BReward with those four such , the names “ Benchmark Risk Vector” , “ Benchmark investment variables 400 and normalize the investment Reward Vector ” , “ Investment Risk Vector ” and “ Investment variables 400 . A comparison of proposed investments to this Reward Vector” are for convenience and the user is free to “ ideal” investment will be described later. specify any number of vectors with customized naming conventions so as to make whatever comparison the user [0057 ] Returning to FIG . 5 , after the investment variables sees fit between whatever instruments , portfolios or groups 400 have been normalized at step S506 , the similarities the user wants . The Investment Risk Vector IRisk is defined between the description vectors are calculated at step S508 . as follows. IRisk = ( IRisk1, IRisk2, IRisk3 , . . . , IRiskn ), There are differentways of defining a similarity between two where IRisk1 through IRiskn are the normalized investment description vectors . One option is to take the Euclidean variables 400 that describe the investment in question . For distance between two multidimensional vectors generated example, IRiskl= p / e ratio , IRisk2 = 21 day resampled risk , by the description vector unit 402 . This would be repre IRisk3 =total sales of company/total sales in industry , etc , to sented by , for example, ||IRisk -Brisk || or ||IReward IRiskn . Alternatively , IRisk1 = 5 day resampled risk ofmaxi BReward?, which represents taking each normalized invest mum drawdown > 5 % , IRisk2 = chance of hitting a fixed stop ment variable 400 from each description vector generated by loss of 3 % over a one month period , etc , to IRiskn . The the description vector unit 402, subtract one from the other, investment could be anything from an individual financial square the result , sum them together, and take the square instrument such as a stock , to a group of financial instru root. For example , to calculate the risk of an investment, the ments such as a group of stocks , to an entire investment following calculation is performed : (IRisk1 - BRiski)^ 2 + portfolio made up of any number of financial instruments (IRisk2 - BRisk2)^ 2 + (IRisk3 - BRisk3) ^2 + . . . + ( IRiskN such as stocks, bonds , mutual funds, commodities, options, BRiskN ) 2 . The square root of the resulting sum is then OTC derivatives , currencies , contingent claims, derivatives , calculated to obtain the similarity value. balance sheet items, options , futures, exchange traded [0058] Another method of calculating the similarity is by instruments , and over the counter instruments. using an absolute value similarity metric . Using the absolute [0054] The Benchmark Risk Vector Brisk is defined as value metric , each normalized investment variable from follows. BRisk = (BRiski, BRisk2 , BRisk3 , . . . , BRiskn ) each description vector generated by the description vector where BRiskl through BRiskn are the same investment unit 402 is subtracted from one another and the resultant variables 400 as IRisk1 through IRiskn , but for the Bench values are added to obtain a distance between the description mark rather than the Investment if the Benchmark Risk vectors . The absolute value of the distance is then deter Vector BRisk is used in comparison to the Investment Risk mined to obtain a similarity value . For example , to calculate Vector. A Benchmark is a standard against which the per - the risk of an investment, the following calculation is US 2018/0096432 A1 Apr. 5 , 2018 performed : |(IRisk1 - BRisk1)1 + ( IRisk2 - BRisk2)1+ 1 investments are then ranked by the equation “ (1 /reward (IRisk3 -BRisk3)I + . . . + (IRiskN -BRiskN )I. value)/( risk value)” to determine the better investments. [0059] It should be noted that when more variables are [0065 ] A user can also build a portfolio with an optimal added , the absolute value of the result will increase . While risk /reward . In this case , an optimization routine would find the results for each calculation will be unaffected by this , it the portfolio that maximized the “ ( 1/reward )/risk ” ratio might cause some confusion to a user. Therefore , one subject to user defined constraints . The user defined con non - limiting example to keep intuition correct from one straints can represent certain filters within a portfolio with similarity calculation to the next for the user, is to divide respect to the investment information . For example , the user each “ similarity ” result by VN , where N is the number of could define constraints such that no single investment can variables in the vector. have more than a 10 % weight in the portfolio , the top 5 [0060 ] Investment advice is then determined by the invest investments must account for at least 25 % of the totalweight ment analysis unit 408 based on the results of step S508 and in the portfolio , currencies must represent a fraction between the types of description vectors compared . Provided below 3 % and 5 % of the portfolio , and gold must represent are non-limiting examples of investment advice determined between 1 % and 3 % of the portfolio . To define best port by the investment analysis unit 408 based on a variety of folio , similarity to a benchmark is used for optimization . financial requests 400 received that relate to investment [0066 ] In other words, for a portfolio , there would be a information . For example , upon receiving a financial request weight (wi) for each investment. Each variable that went to identify the risk of an investment, the investment analysis into either the investment risk vector or the investment system calculates the similarity between the investment risk reward vector would be weighted by that weight ( wi) as vector and the benchmark risk vector. For this analysis , the follows: greater the number calculated from the similarity between [0067] IReward =w1* IRewardV1+ w2* IRewardV2 + the investment risk vector and the benchmark risk vector, the w3* IRewardV3 and so on , with the sum of the w 's = 1, where higher the risk of the investment. each of the IRewardV 's is a vector. BReward is set to the [0061] An investment comparison can also be calculated user 's idea of the ideal investment . IRisk = w1* IRiskV1 + to provide the user with information on how the current w2 * IRiskV2 +w3* IRiskV3 and so on , while having the same investment compares to an ideal investment. An ideal invest w ' s as IReward . An optimization routine to maximize ment is a predefined investment by the user based on the ( 1/|||Reward -BReward||)/(|||Risk - BRiskl) by varying the user ' s financial requirements . This is determined by calcu w 's , subject to constraints, would then be used to rank the lating the similarity between the investment reward vector various investments based on the user constraints . and the benchmark reward vector. For this analysis, the [0068 ] In another example according to the present smaller the number calculated from the similarity between the investment reward vector and the benchmark reward advancement, when trying to choose amongst 10 stocks that vector, the closer the current investment is to the ideal have been identified via a stock screen or some other associated method, it is important to know which of these investment and therefore the better the current investment. stocks gives the most return with respect to the risk . If it is [0062] For a given group of investments, a list of the best believed that the best stocks have Earnings Growth in the and worst investments out of the group can be calculated . current quarter of50 % to 75 % , Annual earnings growth over This list is determined by calculating the risk and reward for the last 3 years of 15 % - 25 % , and as close to new highs as each investment in the database 6 . Next, for each invest possible , the BReward vector is defined as “ 62 .5 /normal ment, the ratio " (1/ reward )/(risk )” is calculated and the ization (investment variable , ), 20 /normalization (investment investments can be ranked by this number. Accordingly , the variable ,), 0 /normalization ( investment variablez).” The investments with the highest ranks are the best and the value 62.5 represents the midpoint of 50 to 75 , the value 20 investments with the lowest ranks are the worst. The best or represents the midpoint of 15 % -25 % , and 0 represents a worst investments can be any type of investment meeting stock trading at a new high , ie, (( current price -highest predetermined constraints by the user. For example , a best price )/current price )/ normalization (investment variablez). investment could be one which makes the most short term The same process is then repeated for each of the ten stocks gain whereas the worst investment could be one with the to get their IReward vectors . The ten distances, or similarity quickest loss short term . values, are then calculated as dReward1 through dReward10 [0063] Alternative investments that have a similar risk by obtaining the reward similarity values between IReward1 profile to the current investment can also be calculated . This and BReward1 , IReward2 and BReward2 and so forth analysis is performed by calculating the risk for all the through IReward10 and BReward10 . investments in the database 6 and finding those investments [0069] Similarly, if it is believed that the most worrisome with a risk value that is closest to the risk value of the current risks are such concerns as the risk that this stock will fall investment. Similarly, alternative investments that have a more than 10 % in the next week , the probability that this similar reward profile to the current investment can also be stock will hit a stop loss placed 15 % away, and the risk that calculated . This analysis is performed by calculating the this stock will fall more than 20 % from a purchase price at reward for all the investments in the database 6 and finding any time over the next year, the IRisk vector is defined as the those investments with a reward value that is closest to the normalization of the above noted risk values . Further, the reward value of the current investment. BRisk vector with respect to the Benchmark i. e . the S & P [0064 ] For a user interested in one investment, better 500 contains the same investment variables 400 . Again , the investments with similar risk profiles can be calculated . ten distances , or similarity values, are then calculated as First, and as explained above , all other investments that have dRiskl through dRisk10 by obtaining the risk similarity a similar risk profile to the one investment are located from values between IRisk1 and BRisk1, IRisk2 and BRisk2 and the database 6 and their reward values are calculated . These so forth through IRisk10 and BRisk10 . US 2018/0096432 A1 Apr. 5 , 2018 [0070 ] Next, the ten Reward /Risk statistics are calculated then the high correlation to the S & P 500 has nothing to do as “ ( 1 /Reward Similarity Value?i])/Risk Similarity Value?i] with being a “ closet indexer” and the customer 's concerns " , with i going from 1 to 10 . Given the above -noted can be allayed . considerations and based on the results, the highest ranked 10075 ]. According to another example of the present stock would be the best and the lowest ranked stock would advancement, the usermay determine that a particular stock be the worst. For example , the highest ranked stock has the used to be a " value” stock but is now a " growth ” stock lowest calculated value and the worst stock has the highest because the stock has raised in value significantly . As such , calculated value based on the equation : “ ( 1 /Reward [i ])/Risk the user may have a problem convincing an investment [i].” committee to sell the stock unless the user can show that the [0071 ] In another example according to the present stock should not be in the user 's portfolio . advancement, sometimes the user is not worried about risk [0076 ] To solve this issue, a BReward Value and a but is instead interested in the stocks that best match a stock BReward Growth are calculated by the investment analysis display screen of the user. In other words, the user may be system . Using these calculations, the user can demonstrate interested in stocks that best match a stock display screen that three years ago when the user bought the stock , its defining that earnings growth must be >60 % in the last BReward Value number was much smaller than its BReward quarter , sales growth must be > 25 % in the last quarter, and Growth number. However, now the situation is reversed in average annual earnings per share must be > 25 % over the that the BReward Value is very much larger than BReward last 3 years . Growth . It is important to note in this situation that the [ 0072 ] A problem with this scenario is that a user will BReward Value and BReward Growth vectors need not have typically just adjust the values displayed on the stock display the same factors thereby making comparisons very simple screen screens until a certain number of stocks come up for users well versed in the differences between value and when no stocks meet the original stock display screen growth stocks . criteria . This causes problems because often the user is [0077] According to another example of the present looking for a “ common - sense ” or “ close - enough ” match to advancement, assume a user wants to invest in companies the screen and adjusts the screens, which can move the user local to where the user lives because the user believes he away from obtaining a meaningful screen match . In other would have a better understanding of information with words , there isn 't much of a real difference between a stock respect to those local companies from reading local news that grew at 25% and one that grew at 24 . 5 % . For this case , papers , talking to local company employees, etc . However, BReward is defined as (60, 25, 25). determining which companies are local can be troublesome 10073] Next, all the stocks in the database 6 are processed using conventional methods. A user must struggle with an and for each one the IReward is calculated as “ min (Earnings overwhelming amount of search options to determine Growth ,60 ), min (Sales Growth , 25 ), min (Average Annual whether a company is local such as a company ' s official Earnings,25 ) .” Therefore , criterion that are specified as address , local revenue , the number of employees that live “ must be greater than ” or “ must be less than " are replaced near the user, the percentage of the company' s suppliers that by any numbers that exceed ( are less than ) the threshold by are local, and the percentage of the company 's customers the threshold . In other words, if the earnings growth is more that are local. Therefore , unless the user takes all these than 60 , it is replaced with 60 . As such , if there are N stocks, things into account, the user will find it difficult to get useful N distances N [ i] are calculated between BReward?i ] and information with respect to his investment needs. IReward [i]. When the N distances N [i] are ranked in ascend [0078 ] The use of description vectors of the present ing order, the lowest N ' s have the bestmatch to your criteria . advancement overcomes this problem . First, all of the Therefore , the present advancement provides a more useful above -noted information with respect to each company is and reliable stock screen than those provided by conven collected and stored in the database 6 . For example , the tional methods. distance of “ headquarters ” of a company from the geo [ 0074 ] According to another example of the present graphic center of where the user lives, the percent of the advancement, assume that a user is a portfolio manager who company ' s employees that work within 25 miles of where owns a variety of stocks and has the S & P 500 as a bench the user lives, the percentage of the company ' s revenue that mark . Because of this situation , it may seem that the user is is generated in the state where the user lives , the amount of massively correlated to the S & P 500 and is a closet indexer. the company ' s supplies that come from companies in the This is a problem because customers may ask why they are user' s state, and the percentage of the company' s revenue payingmoney for services when it seems like the user is just that comes from other states can be included in an BReward following the S & P 500 . While this may just be a coinci description vector such as BReward (0 , 100 , 100 , 100 , 100 ). dence, there needs to be a way to prove the coincidence to This value is then compared by the investment analysis the customer. One solution afforded by the present advance system to the IReward of every company in the database 6 ment is to pick a set of salient investment variables 400 such using the same variables as those in the BReward descrip as the P / S ratio , the P / E ratio , average daily trading volume, tion vector to calculate the distances therebetween . Based on and market cap relating to a financial request to determine a the results and the customization and calculation by the financial correlation . The weighted average P /S , P /E , aver investment analysis system , the user is provided with infor age daily trading volume and market cap of the user ' s mation describing that the lowest distances represent the portfolio (weighted by their percentage weight in the port most “ local” companies . folio ) is then calculated based on their values in the database [0079 ] According to another example of the present 6 and are compared to the values from the database 6 of the advancement, the best stocks in the " energy ” sector can be same input variables 400 with respect to the S & P 500 . In calculated by customizing a BRisk and BReward and per other words, the BReward and the IReward are calculated forming the above -noted procedures for a ranking . If a user and compared . If they ' re substantially different than zero , is interested in stocks that are “ like ” , or “ closest” to South US 2018/0096432 A1 Apr. 5 , 2018 ern Co , the user determines user- customized investment the distance of IRiskGE versus BRiskIBM with IRiskF variables 400 such as Debt/Equity , Price /Sales , Quick Ratio , versus BRiskIBM . The one with the highest distance is the and Market Cap and creates a BReward with these invest riskiest. ment variables 400 for Southern Co . These results are then [0083] According to another example of the present compared by the investment analysis system to all the advancement, the user may believe that countries with the IRewards in the database 6 and the top N matches can be following set of characteristics (S ) have currencies , or determined . Similarly , if a user is interested in stocks like stocks, that will appreciate . The investment analysis system “ RHAT” , but wants Risk to be taken into account, the user can then provide the best investments from a list of countries defines investment variables 400 such as market cap , debt in the database 6 by generating BReward and IReward and ratios, share numbers , common owners , financial metrics , ranking by ascending order of distance . and similar past performance for Reward (call these x1, x2 , 10084 ] Referring back to FIG . 5 , once the investment x3, . . . xn ) and appropriate investment variables (y1, y2 , y3 advice is returned , it is determined whether there are any . . . , yn ) for Risk . The user then creates a BRisk description additional financial requests at step S512 . If YES at step vector with y1 through yn and a BReward description vector S512 , then the process returns to step S500 to receive with xl through xn . The normal above -noted ranking pro different investment variables and financial requests 400 . If cedure is then applied to determine the best stocks that are NO at step S512 , the analysis process is completed and the like “ RHAT.” Further, if the user is interested in stocks that process ends . are like “ RHAT” butonly within the “ technology” sector, the [0085 ] According to one embodiment of the present investment analysis system follows the above procedure advancement, any investment information determined by the while restricting the stocks in consideration to “ technology ” analysis investment system is organized and stored virtually stocks . in a plurality of folders . For example , analysis results can be [0080 ] According to another example of the present stored on a recording medium of a PC or in the database 6 advancement, a user may wish to determine whether his connected to the network 10 . A user can then use his mutual fund is exhibiting “ style drift” based on the managers computer 2 or connect to the network 10 to obtain previously investment strategy . Style drift is when a manager has bad calculated investment information based on previously cus performance and changes his method of managing his fund tomized description vectors. to try to catch up . This is usually a disaster for the investor. 10086 ]. Websites hosted by servers 4 can also provide users To solve this problem , the user takes a portfolio of the fund with online access to the features afforded by the analysis from a past disclosure and picks the salient investment investment system . These websites provide general access variables 400 ( these will depend on the type of fund ). The features to all users as well as increased access and func investment analysis system then determines the old IReward tionality when a user registers with the website . A user who and BReward based on these past investment variables 400 is registered could access old financial investment informa values stored in the database 6 and calculates the distance . tion based on previously customized description vectors via The investment analysis system then takes the portfolio of a search of the virtual folders. This old investment informa the fund today and makes the same IReward and BReward tion could then be compared against current values obtained but with the current values . At this point the investment by running the previously customized description vectors. A analysis system can inform the user whether the distance has user can also use customized description vectors to deter changed appreciably . The investment analysis tool can also mine investment information based on previous market repeat this process for every past quarter to determine conditions at a specific date or automatically update the old whether the distance calculation suddenly spikes up or drifts investment information based on current market conditions. up every quarter. 10087 ) Though the above discussion has been made with [0081] According to another example of the present reference to the stock market, other financial markets or advancement, a user that wants to buy government debt can investment structures may also be used without departing determine how much riskier that government debt is as from the spirit of this advancement. compared to other government debt. For example , assume [0088 ] The techniques described above are usefulin deter the user wants to buy Italian government debt but believes mining the best match of an investment option when com that German government debt should be taken to be “ risk pared to an ideal or target value , or for comparing invest free” . The user then wants to know how much riskier the ment information without a target ( e . g ., landscape analysis ) . Italian government debt is as compared German government The same principles can be applied to other types of infor debt . Here, the user determines that the investment variables mation , including sports performance , such as on the team , 400 are “ percent of debt maturing in the next year” , the league , or player level. The described techniques can be used " debt/GDP ratio ” , the “ current accountdeficit as a percent of to find all those situations in the past data that are “ similar ” GDP ” , and “ percentage of debt held by foreigners” . The to the current situation , and rank that data by their similarity investment analysis system then constructs the BRisk with to the target or ideal. Using past data , predications can then German statistics and the IRisk with Italian Statistics. The be made for future performance . In some cases, comparison calculated distance measures show how much riskier Italian made between the data can be between current versus past Debt is versus German Debt. situations, or past versus past or present versus present [0082 ] According to another example of the present situations. advancement, the user may want to buy GE bonds and 10089] A primary difference between this idea , and what is determine how much riskier the GE bonds are than F bonds? currently done in the professional sports , is that the state of If the user believes that IBM bonds are almost “ risk free ” the art in sports analysis is to find " same" situations. So , how and should be used as the benchmark , the user picks salient many yards will an NFL team gain offensively , if it has a top investment variables 400 and the investment analysis tool ranked QB , and he' s facing a bottom ranked defense ? How constructs IRiskGE , IRiskF and BRiskIBM and compares often does a team win the game when its yards/ pass attempt US 2018/0096432 A1 Apr. 5 , 2018 are greater than 7 ? And so on . This will obviously not scale (0094 ] Sports statistics by and large help with linear to additional variables,because as more variables are added , problems. For example, concerning football, the side with less and less past examples are available for comparison . more yards /pass attempt wins 75 % of the time; a side with [ 0090 ] For example, how often does a team win the game a turnover differential of + 1 wins 68 % of the time, a side when its yards /pass attempt is greater than 7 , it ' s yards /rush with a turnover differential of + 2 wins 82 % of the time, a is less than 3 .5 , its defense is in the bottom half of the league side with a turnover differential of + 3 or more wins 93 % of rankings, the temperature is below 45 F , the game is at home, the time; a side with a net field position advantage of 2 yards and the other team is using its second string safety ? Under wins one extra game per season , and so on . But, coaches this hypothetical example, it 's unlikely that there are any generally consider tens of variables, such as weather, what past situations to draw from for direct comparison . kind of field it is, time difference , how statistically strong weak their defense /offense is , the other team ' s points per [ 0091] But, a similarity metric can be created that com scoring opportunity, number ofpenalties we have in a game pares this situation to every past situation and found those versus them , how many personal fouls , etc . To manage this that are similar. For example , there may be past situations large amount of variables, they use their past experience to where all the criteria are met but the temperature was 51 F . adjust their base expectations to the current game and Or, the criteria were met but the game was away , not home. matchup . Or it was a first string safety , the temperature was 70 F but [0095 ] The same thing has to be done by a fantasy player. otherwise everything was the same. Using the similarity Which running back should I play today ? The one with the metric , it is possible to rank all past situations by their highest yards /carry ? The guy with the most outside runs ? overall similarity to the situation in question . And by defi The one who does best on grass fields ? They guy with the nition then , the shorter the distance , the higher the similarity . lowest number of fumbles/ carry ? And again , a fantasy player This information can then be used to predict future perfor takes all these variables,his past experience , and tries to pick mance based on those past similarities . For example, a that lineup that he thinks maximizes his points for the weighted sum of all past futures , would make a forward upcoming matchup 24up .. prediction . Or a distribution of possible futures could be [0096 ] The current technology, described herein , can solve output along with their probabilities of occurrence . A " fair all of these problems. It' s applicable to any sport. It auto value” for a bet could be computed and under and overval matically handles nonlinearities . It allows as many variables ues bets could be found. The possible futures would be of as a person wants to all be simultaneously considered . It interest to the announcers and commentators because they automatically draws from past experience . It provides pre would greatly inform their opinions and commentary . The cise numerical support for each decision for any number of possible futures would also be of great interest to coaches input variable for any sport . that were game planning and / or calling the game in real [0097 ] The idea is the user picks those statistics he or she time. thinks are relevant to his question for they can be pre 10092] One of the major issues in statistics is the issue of picked — different statistics for different questions or,most nonlinearity . What this means is that quite often , there will likely , both ]. For example , the following statistics are cho be a relationship between one or more variables but yet, sen : yards/pass attempt, temperature of the game, the type of traditional analysis (which essentially assume linearity ) field , the rank of the defense in points/ game allowed , the doesn 't pick it up . For a concrete example, think of buying number of defensive penalties, and the number of offensive a stock that’ s fallen a lot. As a general rule , it isn 't a good personal fouls. This is 6 variables. As described herein , a idea to buy stocks that have fallen , because they will tend to vector with these 6 variables may be created , for example , keep going down . However, it 's quite common to reason , by an application . It then goes into all past games and finds “ the stock ’s fallen , and so ordinarily it wouldn 't be a good those games thatmost closely match this vector: that is, their idea to buy it , but look , it' s gone down so much and for so total distance from 6 variables is the smallest (i.e., they 're long that it might just be a good buy.” This is an example of the best matches ). The results of those past matches are a nonlinearity — the straightforward relationship , “ don 't buy likely outcomes for the current game. This principle : i) is as it goes down” has become, " don 'tbuy as it goes down but analogous to what skilled players and coaches do : they use if it goes down a lot and for a long time, consider it.” What their experience ; ii) fast to implement on a computer ; iii) this example illustrates is that human decisionmaking is rife automatically handles as many variables as available ; and with : i) dealing with nonlinearities ; and ii) pattern recogni iv ) gives a sensible answer even when (as is likely if many tion . In the above example , a pattern is recognized : “ if it variables are being considered ) there are no past situations goes down a lot, maybe you should buy it” . that are the same. [0093] A similar principle occurs in sports. For example , [0098 ] This procedure can then answer any question that a coach could think : “ the team with the higher yards per pass the user poses : who might win , which side might run more , attempt wins 75 % of the time, but we aren 't that good at how will the temperature affect the passing game, which passing, so perhaps we should run the ball . [So far, linear defense will score more points, etc . This has the potential to thinking ). But, the other team 's pass defense is really bad , be of immense use both to coaches and fantasy fans. we are at home, and they ' re a cold weather team coming to [0099 ] This procedure can also be adapted as an in game a hot part of the country , so maybe it would be good for us decision support system for a coach , from football to base to game plan for a lot of passes.” This is nonlinear thinking ball and other sports . The system can go back through a and pattern recognition . Obviously, in the past, this coach database of all past games (or a subset if you so choose ) and has had experiences that have informed his decision making find those games that most closely match the current situ and he recognizes the similarities between the current situ ation , and provide results of what happened in the past in ation and his past experience , which he is using to reason via similar situations. Similarly , it can be used to inform deci induction . sions that are made before a game stats , such as helping a US 2018/0096432 A1 Apr. 5 , 2018 coach to decide on a best lineup and batting order before the and/ or a virtual reality or augmented reality visualization of game even begins. Is it really best to put your home run the data , as will be described below in reference to FIGS. hitters at positions 3 and 4 in the batting order ? Should the 8 - 12 . conditions that obtain at the start of the game (any number [0104] Process 600 may begin at operation 602 , where the of variables that you think are important: from size of field application , program , etc ., may receive one or more selec and temperature to the ERA of the opposing pitcher and the tions of data points or objects for modeling. Operation 602 on base percentage of the opposing team 's leadoff hitter ) may include receiving stock names, investment names or affect your starting lineup and batting order ? If so , how ? The other identification information , sports team names, players ' range of questions that can be answered is limited entirely by names, and so on . the imagination . 0105 ] At, at operation 604 , the data corresponding to the received selections of data points or objects may be [ 0100 ] In another example , the systems and techniques obtained , for example , via one or more databases , either may be used to show support for decisions that are chal local or remote to the device on which process 600 is being lenged in the future . Suppose you are the coach of a football performed . In some aspects , process 500 may include pro program that has made controversial decisions in the past viding selection options or other items, for defining exactly and the athletic director and alumni are unhappy with you . what information concerning the data point/object should be The system will make it possible for you to show that your obtained . decisions were in accord with past experience : in similar f0106 ] At operation 606 , the application or program may situations in the past, the decisions you made were the right request, and receive definitions of variables to be used for ones . Or imagine you are the athletic director trying to evaluating the data points/ objects . In some aspects , opera decide if to fire a losing coach : did he not have enough tion 606 may include receiving various information in talent, or did he make bad decisions during games ? The various different forms, for example defining a risk variable , system will help you figure that out. a reward variable, or other variable, as described above . In [0101] Or cricket, the second most popular sport in the some cases, one or more of the variables may include a statistic or other value that is directly obtainable via public world . Is it really true that you should put your best batsman information , such as dividend yield , book value for share , in at # 3 ? Is that true whether you bat first or second ? Is that etc ., for stocks or other information . In some examples , the still true if you are chasing a total or setting at total ? What one or more variables may include any number of operations about if it 's a short form match versus a long form ? Is your ( e . g ., in the form of equations and the like ) to be performed highest average batsman really your best or does he mostly on certain pieces of data . make his runs in low pressure situations, and if so , should [0107 ] At operation 608 , the values of the variables you send him higher up the order or lower ? Is it a good tactic selected /defined at operation 606 may be determined . This for a right hand bowler to bowl " around the wicket” to a left may include performing any number of calculations , as handed batsman who is well set, and on a score of 33 ? Do defined in operation 606 . At operation 610 , it may be batsmen bat differently when getting close to a score of 50 determined if any more variables have been selected . If so , or 100 because those are big psychologicalmilestones ? If so , process 600 may return to operation 606 and continue to how can you bowl or set your field to take advantage of this loop through operations 606, 608, and 610 , until no more difference ? When should the wicket-keeper stand up to a fast variables are selected . At this point, process 600 may bowler and what ' s the success rate of this tactic ? Is it really optionally continue to operation 612 , where a target for each true that in a limited overs match you should have your variable may be determined or received and associated with faster bowlers bowl the final overs ? If so , which types are a reference or target data point or object. The target point or most successful: swing, seam , or fast ? Does the size of the objectmay define an ideal or target combination of variable ground change whether or not you should think about using values in which a user wants to compare other data points or your slower bowlers at the end ? What about temperature and objects to . This may include , for example , an ideal invest condition of the pitch ? ment profile, select statistics of an ideal sports player, team , [0102] As illustrated above, the granularity and focus of etc . In some aspects , the target may include one or more the data and analysis , is completely configurable , only benchmarks, as described above in more detail . limited by the data that is available . The system can auto [0108 ] At operation 614 , the distance between the selected matically adjust and adapt to give sensible answers that data appoints or objects , and in some cases, the distance accord with human intuition and expertise . It produces a between the target point or object, may be determined . The precise , quantifiable result for support for decisions and /or distance determination may include a geometric or Euclid to make them in the first place . It can quantify and support ean distance . making one decision versus another with a precise measure [0109 ] At operation 616 , the data points/ objects, and in ment. some cases , the target point or object,may be translated into [0103 ] FIG . 6 illustrates an example process 600 for 3D space . Example 3D spaces are described below in greater graphically representing data including a number of values detail, in reference to FIGS. 8 - 12 . in a multi - dimensional space . As illustrated in FIG . 6 , [0110] In some aspects , process 600 may be utilized to dashed lines indicate optional operations, such that process display current information , without the addition or com 600 may be performed as described without including one or parison to a target set of values or target data point/ object more of those operations. Process 600 may be performed by (e . g ., omitting operation 612 ). This may be particularly software, such as an application , program , etc ., operating useful for representing a landscape of information , such as one or more physical devices (e .g., desktop , laptop , smart investments /stocks, sports statistics, and so on . phone, tablet , etc .), and /or may interface with other [0111 ] A specific example of a software code or an algo resources, such as cloud computing resources, external rithm for mapping stock variables to a list of distances , databases, etc ., as described above . Process 600 may be which can then be used to a map the data to 3D space , is performed in conjunction with a graphical user interface provided below : US 2018/0096432 A1 Apr. 5 , 2018 multipleFinancialData [ sym _ , listOfProperties _ ] : = Join [(sym ), FinancialData [sym , # ] & @ listOfProperties ]; areAnyMissingQ [ list _ ] := If [Length [ Cases [ list, x _ /; MissingQ [x ] ] ] > 0 , True , False ]; normalizeList[ list _ ] := (list - Mean [list]) / Standard Deviation [ list]; stockSymbols = Wolfram Alpha[“ Dow Jones members” , {{“ Result ” , 1 } , " ComputableData ” , Podstates ? {“ Result_More " } ]; members = WolframAlpha [" company " # , “ Wolfram Result” , TimeConstraint ? 60 ] & / @ stockSymbols ; mem = Entity Value [# , “ Symbol” ] & / @ Cases [members, Except [_Missing ]]; testdata0 = multipleFinacialData[# 1, {" Price ”, “ BookValuePerShare” , “ EarningsPerShare” , “ ShortRatio ” , “ ForwardEarnings” , “ QuarterForwardEarrrings” , “ Dividend Yield ” , “ Volatility 20Day” , “ FractionalChange50 Day" } ] & / @ mem ; (* Note that the first column in testdata is the symbol* ) testdata = Cases [test .data0 , x _ /; : areAnyMissingQ [x ]]); header = { {" Symbol” , “ BookToPrice” , “ Earnings To Price” , “ ShortRatio " , “ Forward Earnings To Price” , “ QuarterForward EarningsToPrice” , “ Dividend Yield ” , “ Volatility 20Day ” , “ FractionalChange50 Day" } }; (* Assemble Value Stock Data * ) testdata2 = {testdata [[All, 3 ]] / testdata [[All, 2]] testdata [[A11, 4 ]] / testdata [[ A11, 2 ]], testdata [[All, 5 ]], testdata [[All , 6 ]] / testdata [ [All , 2 ]], testdata [[ All , 7 ]] / testdata [[All , 2 ]], testdata [[All, 8 ]] }; testdata3 = Map [normalize List, testdata2]; benchdata3 = {Max [testdata2[[1 ]]], Max [ testdata2 [[2 ]]], Min?testdata3 ||3111, Max [testdata2 [[4 ]]], Max [testdata2 [[5 ]]], Max[testdata2[[6 ]]} ; (* Assemble Risky Stocks Data ) (*Using Min [ ] in benchdata means the riskiest stocks have the highest distance values * ) testdata4 = {testdata [(All, 5 ]], testdata[[All, 9 ]], testdata [[All, 10 ]]}; benchdata4 = {Min [ testdata[[All, 5 ]]], Min[ testdata[ [ All, 9 ]]], Min [testdata [[All, 10 ]]]} ; distFromBench [ benchdat_ , testdat_ ] : = Module [ {devbench3 , distbench3sq , distbench3, distanceso , distances }, devbench3 = Map [benchdat – # & , Transpose (testdat] ]; distbench3sq = Map [ # 2 & devbench3 , { 2 } ]; distbench3 = Map [ ( Apply [ Plus , # ] ( 1 / 2) & , distbench3sq ]; distances0 = Transpose [Join [ { testdata [[All , 1 ]] } , { distbench3 } ]] ; distances = Sort [distanceso , # 2 [ [2 ] ] > # 1 [ [2 ] ] & ]; Return [distances] ; valueStocks = distFromBench [ benchdata3, testdata3 ]; riskyStocks = distFromBench [ benchdata4, testdata4 ]; riskyStocks { {NYSE :GS, 0 .12148 }, {NYSE :JPM , 0 .403459} , {NYSE :DD , 0 .455702), {NASDAQ :AAPL , 0 .636283}, {NASDAQ: MSFT, 0 .682117 }, {NYSE : VZ , 0 .787088 ) , { NYSE :DIS , 0 .820887 } , {NYSE :NKE , 1.01138 }; {NASDAQ :CSCO , 1.04392 } , ? {{? NYSE ? ::GE NYSE , 1. 10033 }, {NYSE :PFE , 1.10589 } , {NYSE :JNJ, 1. 14212} , {NYSE :MCD , 1.26595} , XOM , 1. 40164 } , {NYSE:UTX , 1.41244 } , {NYSE :HD , 1.60277 } , {NYSE :KO , 1 .63053 }, {NYSE :UNH , 2 .08112 } , {NYSE :BA , 2 . 14153 }, {NYSE :CVX , 2 .55133 }, {NYSE : V, 2 .78094 }, {NASDAQ :INTC , 2.90289 } , {NYSE : TRV, 3 .00248 }, {NYSE :PG , 3. 24057 } , NYSE : AXP, 3. 35114 } , {NYSE:CAT, 3 .63133 }, {NYSE :IBM , 3. 99038 } , ( NYSE :WMT, 4 .59021 } } valueStocks { {NYSE :UTX , 1.43643} , {NYSE : DIS , 1.47293 } , {NYSE :JNJ, 1.57288 } , (NASDAQ :CSCO , 1.59676 } , {NYSE : JPM , 1.86101} , {NYSE :PFE, 1.90903 } , (NASDAQ:MSFT, 2 .05909 }, {NYSE :NKE, 2 .12723 }, {NYSE :GE , 2 ,16135 } , {NYSE :XOM , 2 .22498 } , {NASDAQ :AAPL, 2. 32894 }, {NYSE:UNH , 2 .34948 ) , { NYSE :HD , 2 .69554 , (NYSE :MCD , 2 .821 } , {NASDAQ : INTC , 2.92357 }, {NYSE:GS, 2 .92436 } , {NYSE :KO , 3.06058 }, {NYSE :AXP, 3.07284 } , {NYSE: PG , 3.09618 } , {NYSE:VZ , 3 . 13575 ), { NYSE: BA, 3.14188 }, { NYSE: CVX, 3.28311 }, { NYSE : TRV, 3. 45459 }, {NYSE :DD , 3.76645 } , {NYSE :WMT, 3. 82656 } , {NYSE :V , 3 .9478 } , {NYSE :CAT, 4 . 17869 }, {NYSE : IBM , 5 .56528 ) [ 0112 ] In some aspects , once a data object is selected , it exchange on which traded , corporate address . In other may be stored in one or more tables for specific use with the examples, an object may represent a bond , such that coupon , described system and process for translating information interest rate , yield to maturity , and so on may be stored for corresponding to a data object to 3D space . The specific type each object. In other examples , an object may represent a of data stored for each data object depends on user selection currency, such that central bank interest rate, inflation rate, and preference, as described above . Each data object would daily trading volume, etc .,may be stored for each object. In be associated with any number of attributes, such as for other examples, an object may represent a commodity , such investments , any of: market cap , historical prices and trading that then storage costs, yield , backwardation /contango , etc ., volumes , price/ earnings ratios, revenue, gross margin , may be stored for each object. It should be appreciated that US 2018/0096432 A1 Apr. 5 , 2018 a data object may represent a vast variety of information , accessed , for example , via one or more databases as defined by any number of different characteristics. described above , and the new variable information associ [0113] The data structure for storage , in someaspects,may ated with and stored as the object entry in the database , at be optimized for more efficient and quicker distance calcu operation 714 . Process 700 may continue to loop through lation s between data objects. The data may be stored or operation 708 through 714 , until no more modifications are represented as a tree type structures such as kd -trees and received , at which point process 700 may end. vp -trees , such as described in “ Data Structures and Algo [0118 ] In some aspects, the financial, sports, or other rithms for Nearest Neighbor Search in General Metric information , and the analysis thereof may be graphically Spaced ” , by Peter N . Yianilos, NEC Research Institute , 4 displayed or represented as a three or more dimensional Independence Way , Princeton , New jersey 08540, the con model, such as model 800 illustrated in FIG . 8 . The model tents of which are incorporated by reference in their entirety . 800 may be generated via process 600 described above in In other cases , the data may be stored in other ways . In some reference to FIG . 6 , and /or may be modified via process 700 aspects, more than one table may be utilized to store described above in reference to FIG . 7 . information for a data object, such as through referential 101191. As illustrated , an ideal or target set of variables tables and the like. It should be appreciated that the way in may be determined and defined as a reference point, and which the data is stored is only a feature of, and not a mapped to a three dimensional grid or space 800 as point or defining aspect of the systems and techniques described sphere 802 . The target set of variables may define a point or herein . As such , the described system and methods may be object in the 3D space ( can be a three dimensional object, implemented using any of a variety of data storage tech such as a sphere , cube, rectangular prism , pyramid , etc .) niques not described herein . Potential stock or other investment options, defined by any [0114 ] In any data storage implementation , the data is number of user variables , may similarly be mapped to the accessible such that it may be retrieved and or modified via 3D space 800 , in relative location to the ideal point or target interaction ' s with 3D /VR space , as will be described in 802 , as points /objects 804 , 806 , 808 , 810 , etc. As illustrated , greater detail below . each of the x , y , and z axis of the space 800 may define a [0115 ] FIG . 7 illustrates an example process 700 for certain variable , such as one or more of Risk , Reward , a modifying data through graphical modifications in a multi variety of financial or investment statistics , sports team or dimensional space. Process 700 may be performed by soft player statistics , etc ., as described above. In some cases , one ware , such as an application , program , etc ., operating one or or more axis may represent a combined variable . The more physical devices (e.g ., desktop , laptop , smartphone , combination of variables may be configurable , and may tablet, etc .), and/ or may interface with other resources , such include addition , averaging, normalization then averaging , as cloud computing resources , external databases, etc . Pro weighted addition , weighted addition after normalization, cess 700 may be performed in conjunction with a graphical and so on . In some aspects combining one or more variables user interface and/ or a virtual reality or augmented reality or metrics may include using principal component decom visualization of the data , as will be described below in position techniques, and /or selecting top variables or metrics reference to FIGS. 8 - 12 . Process 700 may also , preferably , for the 3 axes . In some cases, selection options may be be implemented in conjunction with process 600 described automatically generated based on industry standards or above . common practices, and the choice of which variables to [0116 ] Process 700 may begin at operation 702 , where it indicate on which axes may be left to user determination . It may be determined if a request for displaying different should be appreciated that in some cases , the actual variables information relating to at least one data point/ object is configured for the visualization are under the control of the received , for example through a UI, as described below in user, to provide a better way to visual data and data reference to FIG . 12 . This may include receiving a touch or relationships and analytics . selection of an object, zooming , etc . If a request is received , [0120] In one example , various different types of infor then the new information requested may be retrieved from a mation may be displayed within 3D space 800 , such as database or other storage location (or via an automatic representations of values, coordinates of various points, internet search , etc . ) at operation 706 , and displayed in the vector relationships, and so on , by virtue of special relation 3D space at operation 706 . In some cases, the database may ships in the 3D space . In one example , 3D coordinates ( e.g., be local or remote from the device on which process 700 is x , y, z ) or vector information (e.g ., distance, angle , 1 , angle being performed . Process may continue to cycle through 2 , defining a vector from the data point to the target point ) operations 702 , 704 , and 706 , until no more requests for may be displayed next to each data point/object. In some displaying different information are received aspects , other visual indicators may be used to represent [0117 ] At this point, process 700 may continue to opera additional variables, such that may be user defined . These tions 708 , in which it may be determined if a graphical may include representing, for example , a risk or reward modification of a data point object has been received . This variable , or other variable via a texture or characteristic of may include moving a data object in the 3D space and other a point or sphere , such as fill pattern , line weight, line texture operations, as described below in reference to FIG . 12 . If a such as dashing, shape, color, size , and so on . The different request is received , a new value or values may be deter variables may be defined , and selected and visually config mined for one or more metrics / variables based on the ured by a user, via a user interface as described in more graphical modification , at operation 710 . For example , if a detail below , to customize the space to communicate the data object is moved a certain distance along the x axis and most pertinent information to a specific user. the y axis of the 3D space , then based on the new position, [0121 ] In this way, the relative difference between differ the new variable information would be determined (the 3D ent investment options, and the relative difference between space accurately represents via distances the different values investment options and an ideal may be more readily con for each axis). At operation 712 , the data object may be veyed via a much more effective and efficient way — via the US 2018/0096432 A1 Apr. 5 , 2018 13 representation of distance in the 3D space 800 . Per the other on the walls/ceilings/ floor or even in the volume of a room visual indications above, categorical variables ( e.g ., defined that allow a user to manipulate the data and the perspective in ranges, binary , etc .) may alternatively or in addition be of the 3D space . In one example , these controls may include visually represented via texture , color, shape , dimensions or virtual “ keyboards,” or other similar controls use a projec size, flashing or blinking or other movement type indicators, tion to detect user movements ( e. g., projecting a laser image and so on , to convey a large amount of information that may of a keyboard onto a tabletop or directly in the volume of the be practically immediately comprehended by a user. In this room ). way , much more information may be represented and com [0126 ] It should be appreciated that these gesture controls municated to a user graphically , while minimizing the are only given by way of example . Various other gestures for amount of time and space required to communicate and zoom and other controls are contemplated herein . represent such information . [ 0122 ] In some aspects , various tools, buttons, selections , [0127] As illustrated in FIG . 11 , another variable , metric , options, controls, etc ., may enable manipulation of the 3d or dimension, such as change of one ormore variables , may space 800 , such as by rotation about one or more axis , as be represented by movement in the 3D space . For example illustrated in FIG . 9 . It should be appreciated that the 3D data points /objects 802 , 804 , 806 , 808 , and 810 may move space modeled in FIGS. 8 and 9 can be adapted for display over time, through an animation , to new locations 802a , in a traditional computer screen environment, or in a virtual 804a , 806a , 808a , and 810a within 3D space 800 . This or augmented reality environment. As illustrated in FIG . 9 , feature , to an even greater extent, offers the ability to a large number of data objects 812 are illustrated . It should represent large amounts of data ( e . g ., 3 or more variables at be appreciated that any number of data points or objects may multiple points in time) in an extremely efficient way. With be displayed within 3D space 800. this feature , trends in the data visualized can be illustrated in [0123 ] As illustrated in FIG . 10 , a zooming control or matters of seconds in one screen space, whereas to display controls 814 may be implemented to modify perspective of that amount of data in a table or chart, or to read and the 3D space 800 . In some aspects, zooming into a region or comprehend that amount of data alone, let alone to derive focal point in the 3D space 800 may provide more detail of trend information , would take multiples of that amount of data points or objects in the field of view ( and zooming out, time. In yet some aspects, change of movement over time less detail). In some aspects, zooming may simply display may also be represented ( e . g., acceleration ) to yet enable the more or less data points or objects in the field of view . As more efficient display, and corresponding more efficient illustrated , FIG . 10 represents the data objects of FIG . 9 , but communication of data , or trends of trend data . zoomed in to a higher degree , such that only a few data [0128 ] In some examples , time, or a fourth dimensions in points are displayed and the size ofthe data objects is larger. the 3D space , can be configured via a user interface , to However, it should be appreciated that the data objects may change the time period illustrated by animation in the 3D stay the same size for a zoom adjustment. These and other space , change the rate at which time is modeled via anima various configuration parameters may be adjusted by a user tion (how quickly a certain timeperiod is modeled or length through a user interface , as described in more detail below . of the animation ), and so on . In some cases, different In some cases, these controls may be implemented via changes in the data , for example , corresponding to one or standard zooming controls ( such as a bar or other selectable more data objects , may be represented differently . In addi item 814 via a pointer or mouse ). In other cases , zooming tion to or alternative to movement in the 3D space , an object controls may be linked to gestures in VR spaces, such as could change shape or color through an animation to indi moving hands or arms away to zoom ( e . g ., breaststroke type cate degrees of change in one or more variables . The types movement), and moving hands or arms together ( reverse of changes indicated , the way in which they are indicated , breastroke) to zoom out. thresholds for different visual changes , etc., may all be [0124] In some aspects , zooming controls may addition configured by the user through user interface controls . For ally or alternatively include changing the point in the 3D example , a first data object may represent a stock invest space from which the 3D space is viewed (changing in ment. The stock may initially be red and represented by a essence the user location within the 3D space ). This may be small sphere , representing a small cap stock losing money . accomplished via a display screen , or via 3D movement in Upon starting an animation over a given time period , the a virtual reality or augmented reality space. In this scenario , same data object may transform to being green and a much selection of the nextperspective location may be selected by bigger triangular prism , to represent a large cap stock that is touching the screen or double tapping on a location in the making money but has changed sector . UI controls may virtual reality space . In some implementations, virtual real enable almost endless configuration of changes of different ity of the 3D space may include visualization within the variables represented in a variety of visual ways, such as space the size of a room , for example , to provide a immer changes in color, shape, texture , size, and so on . sive experience. This type of visualization would enable a [0129 ] FIG . 12 illustrates an example graphical user inter user to walk around the various data objects, manipulate face (GUI) 1200 that may be used to manipulate and objects , access more information of objects by touching configure the 3D space 800 illustrated and described in them , changing scale, size , color, density , weight, bright reference to FIGS . 8 - 11 above. These controls may include ness , contrast, spacing, size, shape, etc . In some aspects , a panning 1202 , rotating , 1204 , zooming 1206 , and / or re target object may be moved in the space , and its values sizing 1208 of the perspective view of 3D space 800 . These re - calculated , and displayed , for example , to enable real controls may also include an item 1210 to start a simulation time or near -real time evaluation of assumption changes, of variables taken over a configured time period . In some such as for investment evaluation purposes . aspects , the point of view may be set via control 1212, for 0125 ] In some examples , 3D or virtual reality controls example , via selecting a point in or around the 3D space . In may include projecting “ touch pads” or other control objects this way , for example, the relative change of certain vari US 2018/0096432 A1 Apr. 5 , 2018 ables over time may be highlighted or discounted (e. g., determining at least three variables for evaluating the changing the perspective to align with one axis ). multiple data objects ; [ 0130 ] In some aspects, the controls may enable configu determining a target value for each of the at least three ration of different variables, via item 1214 , shown in the 3D variables ; space and precisely how they are shown . This may include determining a distance between each of the multiple data configuring certain colors, shapes, sizes, etc ., to represent objects ; and different values or different variables. The GUI 1200 may translating and displaying the multiple data objects into also include selection options for configuring animation three dimensional space as the three -dimensional 1216 , including time period , length of animation and so on . model at least in part based on the determined distance , [0131] The GUI 1200 may also include controls or selec wherein the distance between each of the multiple data tion options 1218 for configuring what data is displayed and objects accurately represent difference in values of the exactly how it is displayed (via visual indicators , via at least three variables . expanding a data object , etc.). 2 . The non -transitory computer readable storage medium [0132 ] GUI 1200 is configured to be used primarily with of claim 1 , wherein the instructions further cause the at least a display device, such as a screen ( e. g., as part of a desktop , one professor to perform the additional operations of: laptop , smart phone, tablet, etc .). However, it may be generating a graphical user interface , wherein the graphi adapted for use in virtual or augmented reality by using cal user interface comprises selection items for at least similar controls accessed via certain gestures . In some cases, one of configuring or manipulating the three dimen one or more additional sensors may be used to project one sional model. or more selection options onto a surface . In some cases other 3 . The non -transitory computer readable storage medium devices and /or sensors may be used to detect relative of claim 1 , wherein the instructions further cause the at least motion , such as the Vive motion controllers from HTC , the one professor to perform the additional operations of: Oculus Touch as part of the Oculus Riff VR system , or determining a target value for each of the at least three Mixed Reality Controllers by Microsoft . In some examples, 3D or virtual reality controls may include projecting " touch variables ; pads ” or other control objects on the walls/ ceilings/floor or determining a distance between each of the multiple data even in the volumeof a room that allow a user to manipulate objects and the at least three target values for each of the data and the perspective of the 3D space . It should be the multiple data objects ; appreciated that any of a variety of display and virtual or translating and displaying the at least three target values augmented reality systems, devices, hardware , interface into three dimensional space as part of the three software , etc ., may be used with the described systems and dimensional model, wherein the distance between each techniques to enhance the user experience and to better of the multiple data objects and the at least three target represent data analytics and relationship between data , as values accurately represent difference in values of the described herein . at least three variables . [0133] Any processes , descriptions or blocks in flowcharts 4 . The non -transitory computer readable storage medium described herein should be understood as representing mod of claim 1 , wherein obtaining the data corresponding to ules, segments, or portions of code which include one or multiple data objects formodeling comprises obtaining data more executable instructions for implementing specific logi corresponding to multiple points in time of an object to be cal functions or steps in the process, and alternate imple modeled ; and wherein the instructions further cause the at mentations are included within the scope of the exemplary least one professor to perform the additional operations of: embodiment of the present advancements in which functions animating movement of at least one of the multiple data may be executed out of order from that shown or discussed , objects based on the obtained data corresponding to including substantially concurrently or in reverse order multiple points in time, wherein the movement repre depending upon the functionality involved . sents a change in at least one value over a time period [ 0134 ] Obviously , numerous modifications and variations captured by the multiple points in time. of the present advancements are possible in light of the 5 . The non -transitory computer readable storage medium above teachings . In particular, while the application of the of claim 1, wherein displaying the multiple data objects and present advancement has been described with respect to the at least three target values into three dimensional space events such as conventions, sports and concerts , other appli comprises displaying in a virtual reality or augmented reality cations are within the scope of the appended claims. For space . example , without limitation, the present advancement may 6 . The non -transitory computer readable storage medium be applied to video games , TV , cell phones , tablets, web of claim 1 , wherein the instructions further cause the at least applications , and any other platform as would be understood one processor to perform the additional operations of: by one of ordinary skill in the art. It is therefore to be generating a graphical user interface, wherein the graphi understood that within the scope ofthe appended claims, the cal user interface comprises selection items for config present advancements may be practiced otherwise than as uring additional information of the multiple data specifically described herein . objects and how the additional information is dis 1 . A non - transitory computer readable storage medium played . having stored thereon instructions that, upon execution by at 7 . The non -transitory computer readable storage medium least one processor , cause the at least one processor to of claim 6 , wherein the selection items further comprise perform operations for generating a three - dimensional (3D ) items for configuring different graphical representations of model, the operations comprising: the addition information . obtaining data corresponding to multiple data objects for 8 . The non -transitory computer readable storage medium modeling; of claim 7 , wherein the different graphical representations of US 2018/0096432 A1 Apr. 5 , 2018 the addition information comprise at least one of color, tribution of the sports performance , the one or more shape, texture , size, fill pattern , movement, or brightness . variables having values identified by the benchmark , 9 . The non -transitory computer readable storage medium or of claim 1 , wherein at least one of the at least three variables a benchmark risk vector containing the one or more for evaluating the multiple data objects comprises a combi sports performance variables which the user believes nation of two or more variables. relate to the level of risk of the expected possible 10 . The non - transitory computer readable storage medium future outcome distribution of the sports perfor of claim 1, wherein each of the multiple data objects mance , the one or more variables having values represents investments . identified by the benchmark . 11 . The non -transitory computer readable storagemedium 14 . An information processing apparatus for analyzing of claim 1, wherein each of the multiple data objects possible future sports outcomes , comprising: represents at least one of a sports team or a sports player. a processor programmed to receive a possible future 12 . The non -transitory computer readable storage medium sports outcome request identifying sports outcome of claim 1 , wherein determining the distance between each information a user wishes to obtain relating to a pos of the multiple data objects is based on a user selectable sible future sports outcome, and one or more different metric . types of sports performance variables relating to at least 13 . An information processing apparatus for analyzing a one of the possible future sports outcomes and a distribution of possible future sports outcomes , comprising: benchmark , a processor programmed to receive a possible future generate at least two vectors containing the one or more sports outcome distribution request identifying sports sports performance variables based on the possible outcome distribution information a user wishes to future sports outcome request , obtain relating to a possible future sports outcome calculate a similarity value by calculating a distance distribution , and one or more different types of sports between the at least two vectors, performance variables relating to at least one of the and analyze the similarity value and the possible future possible future sports outcomes and a benchmark , sports outcome request, and generate at least two vectors containing the one or more determine output information , as possible future sports sports performance variables based on the possible outcome advice for a user, correlating to the possible future sports outcome distribution request , future sports outcome request and the similarity value calculate a similarity value by calculating a distance based on results of the analysis, between the at least two vectors, wherein each vector is one of the following types : and analyze the similarity value and the possible future a sports performance reward vector containing one or sports outcome request, and more sports performance variables which the user determine output information , as a distribution of possible believes relate to an expected possible future out future sports outcome advice for a user, correlating to come of the sports performance variables , the possible future sports outcome distribution request a sports performance risk vector containing one or and the similarity values based on results of the analy more sports performance variables which the user sis , believes relate to a level of risk of the sports perfor wherein each vector is one of the following types: mance , a sports performance reward vector containing one or a benchmark reward vector containing the one or more more sports performance variables which the user sports performance variables which the user believes believes relate to an expected possible future out relate to the expected possible future outcome of the come distribution of the sports performance vari sports performance , the one ormore variables having ables , values identified by the benchmark , or a sports performance risk vector containing one or a benchmark risk vector containing the one or more more sports performance variables which the user sports performance variables which the user believes believes relate to a level of risk of the sports perfor relate to the level of risk of the expected possible mance outcome distribution , future outcomeof the sports performance , the one or a benchmark reward vector containing the one ormore more variables having values identified by the sports performance variables which the user believes benchmark . relate to the expected possible future outcome dis * * * * *