Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This non-final Office action is in response to applicant’s communication received on December 16, 2024, wherein claims 1-20 are currently pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Step 1 (MPEP 2106.03) of the subject matter eligibility test per MPEP 2106.03:
Claims 1-7 are directed to a method (i.e., process), claims 8-14 are directed to computer readable storage medium (i.e., product; (note that Applicant separates “storage medium” from “signal medium” at paragraphs 0029-0031 of the specification and the claims are directed specifically to tangible storage medium)), and claims 15-20 are directed to a system (i.e. machine). Accordingly, claims 1-20 are directed to one of the four statutory categories of invention.
(Under Step 2) The claimed invention (in claims 1-20) is directed to an abstract idea without significantly more.
(Under Step 2A, Prong 1 (MPEP 2106.04)) The independent claims (1, 8, 15) and dependent claims (2-7, 9-14, 16-20) recite receiving/obtaining abstract information/data information (operation/event (operation is left very broad by the Applicant – see specification paras. 0003), input information from a human/person (where dependent claims state can be user biometrics, user performance data, state data), feedback, etc.,), data analysis and manipulation to determine more abstract information/data (e.g. comparing information, predicting (using mathematical concepts), matching, etc.,), and providing/displaying this determined data for further analysis and decision-making (providing cues (recommendations/suggestions)). The claimed invention further uses mathematical steps to analyze and determine further data (e.g. prediction using predictive models).
The limitations of the independent claims (1, 8, 15) and dependent claims (2-7, 9-14, 16-20), under the broadest reasonable interpretation, covers methods of organizing human activity (managing personal behavior or relationships or interactions between people (following rules or instructions in an open ended broadly stated operation/event)), mental process (observation and evaluation with feedback (opinion))), and mathematical concepts (e.g. using mathematical techniques for predicting). If a claims limitation, under its broadest reasonable interpretation, covers the performance of the limitation as fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including scheduling, social activities, teaching, and following rules or instructions), then it falls within the “organizing human activities” grouping of abstract ideas. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, pages 50-57). If claim limitations, under its broadest reasonable interpretation, cover the performance of the limitation as concepts performed in the human mind (including an observation, evaluation, judgment, opinion), the claim limitations fall within the Mental process grouping of abstract ideas. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, pages 50-57). If a claims limitation, under its broadest reasonable interpretation, covers the performance of the limitation as mathematical relationships, mathematical formulas or equations, mathematical calculations then it falls within the Mathematical concepts grouping of abstract ideas. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance - Federal Register, Vol. 84, Vol. 4, January 07, 2019, pages 50-57).
Accordingly, since Applicant's claims fall under organizing human activities grouping, mental process grouping, and mathematical concepts grouping, the claims recite an abstract idea.
(Under Step 2A, prong 2 (MPEP 2106.04(d))) This judicial exception is not integrated into a practical application because but for the recitation of old/well-known generic/general-purpose computing/technology components/elements/terms (see listing below), in the context of the independent claims (1, 8, 15) and dependent claims (2-7, 9-14, 16-20), the independent claims (1, 8, 15) and dependent claims (2-7, 9-14, 16-20) encompass the above stated abstract idea (organizing human activity (managing personal behavior or relationships or interactions between people (following rules or instructions in an open ended broadly stated operation/event)), mental process (observation and evaluation with feedback (opinion))), and mathematical concepts (e.g. using mathematical techniques for predicting)). The old/well-known generic/general-purpose computing/technology components/elements/terms/limitations used in the claims (and in the specification) by the Applicant are in the following list/listing (additional elements):
computer-implemented, computing devices, etc., (in Independent claim 1 and its dependent claims 2-7);
computer program product (software) residing on a computer readable storage medium, processors, etc., (in independent claim 25); and
computing system, processors, memories, etc., (independent claim 20).
(hereinafter the above list/listing will be referred to as “generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above)” or “additional elements (see list/listing above)” in the rest of the §101 rejection – i.e. whenever “generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above)” or “additional elements (see list/listing above)” is used/stated in the rest of the §101 rejection it is referring to and incorporates the above list/listing).
As shown above, the independent claims (1, 8, 15) and dependent claims (2-7, 9-14, 16-20) and specification recite generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above) which are recited at a high level of generality performing generic/general purpose computer/computing functions. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, page 53-55). The generic/general-purpose computing/technology components/elements/terms/limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above). The CAFC has stated that it is not enough, however, to merely improve abstract processes by invoking a computer merely as a tool. Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020). The focus of the claims is simply to use computers and a familiar network as a tool to perform abstract processes (discussed above) involving simple information exchange. Carrying out abstract processes involving information exchange is an abstract idea. See, e.g., BSG, 899 F.3d at 1286; SAP America, 898 F.3d at 1167-68; Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1261-62 (Fed. Cir. 2016). And use of standard computers and networks to carry out those functions—more speedily, more efficiently, more reliably—does not make the claims any less directed to that abstract idea. See Alice Corp., 573 U.S. at 222-25; Customedia, 951 F.3d at 1364; Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1092-93 (Fed. Cir. 2019); SAP America, 898 F.3d at 1167; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1314 (Fed. Cir. 2016); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353, 1355 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 1370 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014). Accordingly, the additional elements (see list/listing above) do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
(Under Step 2B (MPEP 2106.05)) The independent claims (1, 8, 15) and dependent claims (2-7, 9-14, 16-20) do not include additional elements (see list/listing above) that are sufficient to amount to significantly more than the judicial exception because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The independent claims (1, 8, 15) and dependent claims (2-7, 9-14, 16-20) recite using known generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above). For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of "well-understood, routine, [and] conventional activities previously known to the industry." Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014), at 2359 (quoting Mayo, 132 S. Ct. at 1294 (internal quotation marks and brackets omitted)). These activities as claimed by the Applicant are all well-known and routine tasks in the field of art – as can been seen in the specification of Applicant’s application (for example, see Applicant’s specification at, for example, Figs. 1-2 [showing general-purpose/generic computers/processors/etc., and generic/general-purpose computing components/devices/etc.,]; and ¶¶ 0029, 0032-0033, 0037-0038, 0040, 0042 [e.g. paragraphs where Applicant recites general-purpose/generic computers/processors/etc., and generic/general-purpose computing components/devices/etc., in Applicant’s specification]) and/or the specification of the below cited art (used in the rejection below and on the PTO-892) and/or also as noted in the court cases in §2106.05 in the MPEP. Further, "the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention." Alice at 2358. None of the hardware offers a meaningful limitation beyond generally linking the system to a particular technological environment, that is, implementation via computers. Adding generic computer components to perform generic functions that are well‐understood, routine and conventional, such as gathering data, performing calculations, and outputting a result would not transform the claims into eligible subject matter. Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might impede innovation more than it would promote it. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional elements (see list/listing above) or combination of elements in the claims other than the abstract idea per se amount(s) to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc. (758 F.3d 1344 (2014) discussing U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, the independent claims (1, 8, 15) and dependent claims (2-7, 9-14, 16-20) do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the independent claims and dependent claims do not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
The dependent claims (2-7, 9-14, 16-20) further define the independent claims and merely narrow the described abstract idea, but not adding significantly more than the abstract idea. The above rejection fully includes and details the discussion of dependent claims and the above rejection applies to all the dependent claim limitations. In summary, the dependent claims (2-7, 9-14, 16-20) further state using obtained data/information (where the information itself is abstract in nature – as shown above), data analysis and manipulation to determine more abstract information/data (e.g. comparing information, predicting (using mathematical concepts), matching, etc.,), and providing/displaying this determined data for further analysis and decision-making (providing cues (recommendations/suggestions)). The claimed invention further uses mathematical steps to analyze and determine further data (e.g. prediction using predictive models). The limitations of the dependent claims (2-7, 9-14, 16-20), under the broadest reasonable interpretation, covers methods of organizing human activity (managing personal behavior or relationships or interactions between people (following rules or instructions in an open ended broadly stated operation/event)), mental process (observation and evaluation with feedback (opinion))), and mathematical concepts (e.g. using mathematical techniques for predicting). This judicial exception is not integrated into a practical application because the claims and specification recite generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above) performing generic computer/computing/technology functions. (MPEP 2106.04 and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, page 53-55). The additional elements (see list/listing above) do not integrate the abstract idea in to a practical application because they does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities. The dependent claims merely use the same general technological environment and instructions to implement the abstract idea without adding any new additional elements. Also, the dependent claims also do not include additional elements that are sufficient to amount to significantly more than the juridical exception because the additional elements (see list/listing above) either individually or in combination are merely an extension of the abstract idea itself. See detailed rejection above.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gorski et al., (US 2023/0034337).
As per claim 8, Gorski discloses a computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations (¶¶ 0029-0031, 0074, 0095) comprising:
receiving input data from a user participating in a live simulation scenario; receiving input data from the live operation (¶¶ 0011 [live…event], 0091-0094 [athlete…simulation…human…Player A…simulation scenarios; see with 0062-0064 [live…event…live content], 0036 [prediction…simulated…inputs], 0039 [signals/sensors and continual data inputs], 0041-0042 [transmit data…communication…real-time or near real-time…real-time health statistics]]; see also 0045-0051);
analyzing the input data from the user participating in the live operation and the input data from the live operation (see citations above and also see ¶¶ 0036 [data inputs… computations, derivations, extractions, extrapolations, simulations, creations, modifications, enhancements, estimations, evaluations, inferences, establishments, determinations, deductions, observations], 0039 [data input and analyze information including calculations, computations, predictions, probabilities, possibilities, estimations, evaluations, etc.,], 0051 [speculation system…one or more signals or readings (e.g., including sets of signals of readings) utilized in one or more simulations, computations, or analyses; (9) as part of one or more simulations, an output of which directly or indirectly engages with one or more users; (10) to recommend one or more actions], 0042 [persons…real-time…statistics…assessment system]); and
providing feedback to the user participating in the live operation based upon, at least in part, analyzing the input data from the user participating in the live operation and the input data from the live operation (see citations above and also see ¶¶ 0051 [one or more signals or readings (e.g., including sets of signals of readings) utilized in one or more simulations, computations, or analyses; (9) as part of one or more simulations, an output of which directly or indirectly engages with one or more users; (10) to recommend one or more actions…assessment system…strategy…recommendation (feedback)…based upon user interaction], 0062-0068 [live…event…user…live content…assessment system…evaluate…simulation…analyses…recommend one or more action (feedback)…speculation system…evaluate/calculate…data…live streams…of event…real-time…monitoring…provide one or more recommendations]).
As per claim 1, claim 1 discloses substantially similar limitations as claim 8 above; and therefore claim 1 is rejected under the same rationale and reasoning as presented above for claim 8.
As per claim 15, claim 15 discloses substantially similar limitations as claim 8 above; and therefore claim 15 is rejected under the same rationale and reasoning as presented above for claim 8.
As per claim 9, Gorski discloses the computer program product of claim 8, wherein the input data from the user participating in the live operation includes at least one of user biometrics and user performance data, and wherein the input data from the live operation includes live state data (see citations above for claim 8 and see with ¶¶ 0070-0072 [biological data… biochemical composition data, biochemical structure data, pulse data, oxygenation data, core body temperature data, skin temperature data, galvanic skin response data, perspiration data, location data, positional data, audio data, biomechanical data, hydration data, heart-based data, neurological data…indicator…player…team…indicator related to…performance], 0083-0084 [performance and performance changes: current performance]).
As per claim 2, claim 2 discloses substantially similar limitations as claim 9 above; and therefore claim 2 is rejected under the same rationale and reasoning as presented above for claim 9.
As per claim 16, claim 16 discloses substantially similar limitations as claim 9 above; and therefore claim 16 is rejected under the same rationale and reasoning as presented above for claim 9.
As per claim 10, Gorski discloses the computer program product of claim 8, wherein the operations further comprise predicting performance of the user in the live operation to generate a predicted performance of the user (see citations above for claim 8 and see with, for example, ¶¶, 0083-0085 [predicting performance…predictive indicator aimed at predicting fatigue (or the likelihood that fatigue will occur at any given time based on exercise patterns) for users… predictive indicator for any given user in order to predict current or future biological performance]).
As per claim 3, claim 3 discloses substantially similar limitations as claim 10 above; and therefore claim 3 is rejected under the same rationale and reasoning as presented above for claim 10.
As per claim 17, claim 17 discloses substantially similar limitations as claim 10 above; and therefore claim 17 is rejected under the same rationale and reasoning as presented above for claim 10.
As per claim 11, Gorski discloses the computer program product of claim 10, wherein predicting performance of the user in the live operation to generate the predicted performance of the user includes processing, using a trained predictive model, at least one of input data from the user participating in a past simulation scenario, input data from the user participating in a past live operation, the input data from the user participating in the live operation and the input data from the live operation processed using feature engineering data (see citations above for claims 8 and 10; and see with ¶¶ 0083-0085 [prediction…training the system to understand these one or more performance changes and the variables associated with the changes (e.g., which can occur via one or more neural networks), a user can re-create data (e.g., using one or more methodologies including within one or more simulation scenarios) to predict future events or occurrences based on, for example, a historical performance understanding of the subject, the historical impact of the one or more inputs, current performance, the current impact of the one or more inputs, and trends seen by the system for similar events, subjects, and inputs. Advantageously, the one or more inputs can be biological data. In particular, computing subsystem or the wagering system or the probability assessment system can create one or more artificially-generated animal data sets, computed assets, or predictive indicators, which may occur via one or more simulations that utilize at least a portion of the predictive indicator, computed asset, the real collected animal data, and/or its one or more derivatives. This may occur utilizing one or more artificial intelligence techniques (e.g., one or more trained neural networks, machine learning systems) or statistical models; with 0085 [data relevant for understanding past behaviors to predict future performance] and with 0040-0041 [sensor data, wearable device data, sensor (camera-based) data, information from computing and communication devices and other technical gadget/device data (all engineering data)]]; see also 0086, 0095-0098).
As per claim 4, claim 4 discloses substantially similar limitations as claim 11 above; and therefore claim 4 is rejected under the same rationale and reasoning as presented above for claim 11.
As per claim 18, claim 18 discloses substantially similar limitations as claim 11 above; and therefore claim 18 is rejected under the same rationale and reasoning as presented above for claim 11.
As per claim 12, Gorski discloses the computer program product of claim 8, wherein providing feedback to the user participating in the live operation includes triggering an intervention event in the live operation based upon, at least in part the input data from the user participating in the live operation and the input data from the live operation (see citations above for claim 8 and see with some examples at ¶¶ 0073 [many example – e.g. adjustments; mandating a…stoppage], 0086 [examples: adjusting outcomes; an automotive or aircraft manufacturer may want to run simulations to fine-tune the predictive indicator in order to provide one or more responses related to a subject within the vehicle or aircraft to mitigate or prevent a risk; someone that is exhibiting specific physiological or biomechanical characteristics while driving a vehicle may be at risk for causing an accident…vehicle may take one or more actions (e.g., stop the car, pull over, drive to the hospital) based upon the predictive indicator; person is having a heart attack based on collected sensor data; the vehicle may stop itself if it is determined that the likelihood of a person having a heart attack with a given profile and characteristics; airline may monitor the real-time biological characteristics of its one or more pilots via one or more source sensors while flying and take one or more actions (e.g., notify the airline, take control away from the pilot, put the plane on autopilot, enable control of the plane to the airline or airline manufacturer remotely) based upon the probability of an occurrence happening]).
As per claim 5, claim 5 discloses substantially similar limitations as claim 12 above; and therefore claim 5 is rejected under the same rationale and reasoning as presented above for claim 12.
As per claim 19, claim 19 discloses substantially similar limitations as claim 12 above; and therefore claim 19 is rejected under the same rationale and reasoning as presented above for claim 12.
As per claim 13, Gorski discloses the computer program product of claim 12, wherein the operations further comprise matching one of a predicted performance of the user, the input data from the user participating in the live operation, and the input data from the live operation to a rule of a plurality of rules (see citations above for claims 8, 10-12; and see with ¶¶ 0051-0057 [e.g. – predictions, probabilities, or possibilities; (5) to formulate one or more strategies; (6) to take one or more actions; (7) to mitigate or prevent one or more risks; (8) as one or more signals or readings (e.g., including sets of signals of readings) utilized in one or more simulations, computations, or analyses; (9) as part of one or more simulations, an output of which directly or indirectly engages with one or more users… observation of a user's interaction with the data, from which computing subsystem or the wagering system or the probability assessment system may dynamically create, enhance, or modify a wagering market or odds, a product that is acquired or consumed, a strategy, a prediction, a recommendation, and the like based upon the user interaction with the data…action…mitigate risk; see with 0083-0085 [prediction…training the system to understand these one or more performance changes and the variables associated with the changes (e.g., which can occur via one or more neural networks), a user can re-create data (e.g., using one or more methodologies including within one or more simulation scenarios) to predict future events or occurrences based on, for example, a historical performance understanding of the subject, the historical impact of the one or more inputs, current performance, the current impact of the one or more inputs, and trends seen by the system for similar events, subjects, and inputs…computing subsystem…the probability assessment system…data sets, computed assets, or predictive indicators, which may occur via one or more simulations that utilize at least a portion of the predictive indicator, computed asset, the real collected…data, and/or its one or more derivatives…utilizing one or more artificial intelligence techniques (e.g., one or more trained neural networks, machine learning systems) or statistical models; with 0085 [data relevant for understanding past behaviors to predict future performance] and with 0040-0041 [sensor data, wearable device data, sensor (camera-based) data, information from computing and communication devices and other technical gadget/device data (all engineering data)]]; see also 0083-0086, 0089-0090).
As per claim 6, claim 6 discloses substantially similar limitations as claim 13 above; and therefore claim 6 is rejected under the same rationale and reasoning as presented above for claim 13.
As per claim 20, claim 20 discloses substantially similar limitations as claim 13 above; and therefore claim 20 is rejected under the same rationale and reasoning as presented above for claim 13.
As per claim 14, Gorski discloses the computer program product of claim 12, wherein triggering the intervention event includes providing at least one of a visual cue, an audio cue, a virtual cue, and a virtual intervention (¶¶ 0073 [many example – e.g. adjustments; mandating a…stoppage], 0086 [examples: adjusting outcomes; an automotive or aircraft manufacturer may want to run simulations to fine-tune the predictive indicator in order to provide one or more responses related to a subject within the vehicle or aircraft to mitigate or prevent a risk; someone that is exhibiting specific physiological or biomechanical characteristics while driving a vehicle may be at risk for causing an accident…vehicle may take one or more actions (e.g., stop the car, pull over, drive to the hospital) based upon the predictive indicator; person is having a heart attack based on collected sensor data; the vehicle may stop itself if it is determined that the likelihood of a person having a heart attack with a given profile and characteristics; airline may monitor the real-time biological characteristics of its one or more pilots via one or more source sensors while flying and take one or more actions (e.g., notify the airline, take control away from the pilot, put the plane on autopilot, enable control of the plane to the airline or airline manufacturer remotely) based upon the probability of an occurrence happening]; 0103 [e.g. – audio control (e.g., voice control), a physical cue (e.g., head movement, eye movement, or hand gesture)]).
As per claim 7, claim 7 discloses substantially similar limitations as claim 14 above; and therefore claim 7 is rejected under the same rationale and reasoning as presented above for claim 14.
Conclusion
The prior art made of record on the PTO-892 and not relied upon is considered pertinent to applicant's disclosure. For example, some of the pertinent prior art is as follows:
Ghanchi et al., (US 2018/0369699): Relates to virtual reality simulation, and more specifically, in some implementations, to devices, systems, and methods for use in a virtual reality sports simulation. A system for virtual reality simulation may include an accessory (e.g., one or more of a bat, a glove, or a helmet) for interacting with a virtual reality environment. The accessory may provide the user with haptic feedback that emulates sensations that the user would experience when playing a live-action sport to provide the user with a more meaningful and realistic experience when playing a virtual reality game. Further, virtual reality simulations disclosed herein may include incorporating data from a live-action event (e.g., a live-action sporting event) into a virtual reality environment to provide a user with a realistic experience.
Stefik et al., (US 2018/0060796): Provides for monitoring parking enforcement officer performance with the aid of a digital computer is provided. A time-based active representational model of the city is created by fusing sensory data collected from various sources around a city with numerical data gleaned from historical and on-going activities, including parking regulation citation and warning numbers, resource allocations, and so on. The model can be used to form quantitative predictions of expected violations, revenue stream, and so forth, that can then be used as recommendations as to where to enforce and when, so as to maximize the utilization of the limited resources represented by the officers on the street. Moreover, the performance of the officers can be weighed against expectations of performance postulated from the quantitative predictions.
Alford (US 2023/0172510): Discusses indexing of data through the observance of neural network divergence presents a means of identifying important moments and periods of change within extremely large and complex datasets. Performance divergence, especially when occurring across multiple data types, indicates that the individual's patterns and habits have changed relative to previously generated models. As divergence occurs, old models are no longer performant in analyzing and predicting the content of new data. By generating indexes for these specific moments of divergence, the system can identify, present, preserve, and share these events as a set of subjectively valuable data subset to optimize the search and review of biographical datasets.
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/Gurkanwaljit Singh/
Primary Examiner, Art Unit 3625