DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to claims filed on 6/4/2024 in relation to application 18/716,458.
The instant application claims benefit to foreign application GB # 2117715.9 with a priority date of 12/8/2021.
Claims 1,3, 5,6,9-16, 18-21 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/16/2025 has been entered.
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 1,3, 5,6,9-16, 18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more (Step 1: YES).
Claims 1,3, 5,6,9-16, 18-21 are directed to a method of providing adaptive training to a user by receiving user biometric data, generating a personality biometric profile comprising one or more personality characteristics and generating a training scenario for the user based on the user biometric profile. A training is conducted using the generated training scenario, wherein user behavioral data is collected in real time. The involve steps are drawn to concept categorized as an actions of observing, identifying, generating, evaluating and judging of a biometric profile. A concept that are mental processes, Again by including receiving user biometric profile and updating the training scenario for the user subsequent to the training session based on the updated user profile, the processing is like organizing of certain human activities. The use of revision by machine-learned model could also be categorized as a use of mathematical calculations within some machine models. They are generally categorized as a grouping of an abstract idea (Step 2A: Prong 1 YES).
The independent and dependent claims 2-21 with pre- and post- solution activities to control training scenarios do not include additional elements that are sufficient to be significantly more than the judicial exception because the limitations of “a computer system with interface display”, “a processor’, “a memory’, "network remote storage", "databases of digital content with predetermined profile”, “aggregation of environmental information”, “personality characteristics control based on the analysis of the user biometric data” are done by merely use of generic computer functions and computer parts. The amendments that signifies generating the user biometric profile , analyzing the user biometric data by a machine learning algorithm, and the machine learning algorithm generating the personality biometric profile and the one or more personality characteristics of the user based on the analysis of the user biometric data. The generation of the training scenario based on the personality biometric profile and the one or more personality characteristics of the user, and the machine learning agent controls a plurality of non-player characters in the training scenario; and the training of the user in the training session comprises the user being trained in a VR environment. These involve selecting portions of profile from input and determining from storage only a corresponding known machine filtering for evaluation and results. It may also involve some mathematical relationships with formulas and calculations. Hence not indicative of integration of a practical application (Step 2A: Prong 2 No).
The steps in the recited claims that are highlighted are a well-understood, routine, and conventional activities known in art. Fig.1 of the instant specification discloses computing device with generic hardware to implement the process claimed here. Application paragraphs 0050-0052 of specification indicate a conventional machine learning algorithm navigating various training environment of the embodiment presented to users in the invention. The navigational control, here in the instant case, are activities done on generic personal computers, server computer systems, use of hand-held or laptop devices by users, multiprocessor systems, network PCs. Storing, analyzing and retrieval of routine sessions from working panes are already known activities in art. For example in case of Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, the activities of storing and retrieving of information in a memory of consumer electronic for a field of use purposes are recognized to be computer functions well-understood, routine, and conventional, when they are claimed in a merely generic manner. Further, there found to be no additional elements here in the claim recitation that improves the functioning of a computer itself to overcome the abstract idea rejection (Step 2B: No).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1,3, 5,6,9-16, 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Number US 11416651 B2 Roberts et al.( Roberts) in view of US Patent Application Publication Number US 20190156222 A1 to Emma et al. (Emma).
Claim 1. Roberts teaches a method of providing adaptive training to a user (col.4 lines 5-10 adaptive simulating training), the method comprising:
receiving user biometric data from a plurality of user devices (col.4 lines 4-5 biometric data use);
generating a personality biometric profile based on the user biometric data, the personality biometric profile comprising one or more personality characteristics of the user based on the user biometric data (col.10 lines 1-19 storing biometric profile);
generating a training scenario for the user based on the user biometric profile (col.15 lines 2-5, 36-55 prediction module on biometric data; training to better predict participant behavior );
training the user using the generated training scenario in a training session, wherein user behavioral data is collected in real time during the training session (col.3 lines 49-67; col.4 lines 11 real-time collection with predictable adaptability);
updating the user biometric profile based on the collected user behavioral data, and updating the training scenario for the user subsequent to the training session based on the updated user profile (col.15 lines 56-65 updating the training data).
Roberts teaches generating of biometric profile analyzing the user biometric data by a machine learning algorithm, and the machine learning algorithm generating the personality biometric profile and the one or more personality characteristics of the user based on the analysis of the user biometric data (Fig.9A element 901,Fig. 9B element 903,905; col.2 lines 11-26 stress level as one of the biometric profile analyzed by machine learning).
the generating of the training scenario for the user comprises a machine learning agent generating the training scenario based on the personality biometric profile and the one or more personality characteristics of the user, and the machine learning agent controls a plurality of non-player characters in the training scenario; and the training of the user in the training session comprises the user being trained in a VR environment.
Roberts does not explicitly identify the generating of the training scenario for the user comprises a machine learning agent generating the training scenario based on the personality biometric profile and the one or more personality characteristics of the user, and the machine learning agent controls a plurality of non-player characters in the training scenario; and the training of the user in the training session comprises the user being trained in a Virtual Reality environment. Emma, however, teaches the identification of generating training scenario based on the personality biometric profile and the one or more personality characteristics of the user, (¶0067, 0082 user answers to unknown questions are stored in the user profile and are retrieved in future scenarios relevant to that question; they are operable to encourage the character's personality traits in the user; that is a machine learning agent controls a plurality of non-player characters in the training scenario; and the training of the user in the training session comprises the user being trained in a Virtual Reality environment). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the generating of the training scenario for the user comprises a machine learning agent generating the training scenario based on the personality biometric profile and the one or more personality characteristics of the user, and the machine learning agent controls a plurality of non-player characters in the training scenario; and the training of the user in the training session comprises the user being trained in a Virtual Reality environment, as taught by Emma, into the system of Roberts, in order to provide relevant training scenarios efficiently. Contributing faster towards an overall objectives.
Note; The art Robert allows for more accurate prediction models to be created by the predictions module using machine learning and/or stochastic simulation (col.11 lines 15-32). The system is capable of executing updated machine readable program instructions following a pre-simulation information , as described in greater detail with regard to FIGS. 4-6. The secondary art, Emma et al. on the other hand illustrates artificial intelligence (AI) toy with improved conversational dialogue and personality development such that the AI toy determines responses to stimuli based on user profiles and personality profiles that could be developed further through user interaction and external media inputs.
Claim 3. (original) The method of providing adaptive training to a user according to wherein the generating of the personality biometric profile additionally comprises assigning a weighted score to each of the one or more personality characteristics by the machine learning algorithm, and wherein the generating of the training scenario for the user is also based on the score for each personality characteristic (col.10 lines 20-46 subsequent simulation could be based on stochastic and other sub-sub-module query that could contain weighted score base).
Claim 5. Roberts teaches the method of providing adaptive training to a user according to claim 3, the method additionally comprising: modifying, in real time during the training session, the training scenario based on the user's behavior during the training, wherein the machine learning agent modifies the training scenario (col.9 lines 46-55 modification of simulation environment) .
Claim .6. (currently amended) The method of providing adaptive training to a user according to claim 4, wherein the machine learning agent collects the user behavioral data during the training session (col. 10 lines 11-15 behavioral data by machine learning agent).
Claim 9. (original) The method of providing adaptive training to a user according to wherein the modifying of the training scenario includes modifying the VR environment (col.12 lines 43-59 modification involved with virtual environment).
Claim 10. Roberts teaches the method of providing adaptive training to a user according toclaim 1, wherein the plurality of user devices are wearable devices (col.4 lines 12-16 wearable devices).
Claim 11. Roberts teaches the method of providing adaptive training to a user according toclaim 1,wherein the personality biometric profile is additionally iteratively updated based on user biometric data collected from the plurality of user devices during the training session (col.2 lines 16-48 personality biometric profile generated iteratively).
Claim 12. Roberts teaches the method of providing adaptive training to a user according toclaim 1,wherein the one or more personality characteristics comprises one or more of Stressed, Assertive, Leadership, Conscientiousness, Openness, and Receptive (col.2 lines 16-18 stress levels identified as one of the personality characteristics ) .
Claim 13. Roberts teaches the method of providing adaptive training to a user according toclaim 1,wherein the generated personality biometric profile is stored in a memory, and optionally wherein each updated personality biometric profile is also stored in the memory (Fig.10 element 119 persistent storage).
Claim 14. Roberts teaches a system comprising: one or more processors; a non-transitory memory; and storing one or more programs, more processors, (Fig.2 col.4 lines 5-10 adaptive simulating training with the use of processor and memory programs).
Robert provide adaptive training to a user such that the system is at least configured to: receive user biometric data from a plurality of user devices ; generate a personality biometric profile based on the user biometric data, the personality biometric profile comprising one or more personality characteristics of the user based on the user biometric data; generate a training scenario for the user based on the user biometric profile; train the user using the generated training scenario in a training session, wherein user behavioral data is collected in real time during the training session; update the user biometric profile based on the collected user behavioral data, and update the training scenario for the user subsequent to the training session based on the updated user profile (Fig.4-6, Fig.9A element 901,Fig. 9B element 903,905; col.2 lines 11-26 stress level as one of the biometric profile analyzed by machine learning algorithm to adapt and update) .
generating a personality biometric profile based on the user biometric data, the personality biometric profile comprising one or more personality characteristics of the user based on the user biometric data (col.10 lines 1-19 storing biometric profile);
generating a training scenario for the user based on the user biometric profile (col.15 lines 2-5, 36-55 prediction module on biometric data; training to better predict participant behavior );
training the user using the generated training scenario in a training session, wherein user behavioral data is collected in real time during the training session (col.3 lines 49-67; col.4 lines 11 real-time collection with predictable adaptability);
updating the user biometric profile based on the collected user behavioral data, and updating the training scenario for the user subsequent to the training session based on the updated user profile (col.15 lines 56-65 updating the training data).
Roberts teaches generating of biometric profile analyzing the user biometric data by a machine learning algorithm, and the machine learning algorithm generating the personality biometric profile and the one or more personality characteristics of the user based on the analysis of the user biometric data (Fig.9A element 901,Fig. 9B element 903,905; col.2 lines 11-26 stress level as one of the biometric profile analyzed by machine learning )
Note; The art Robert allows for more accurate prediction models to be created by the predictions module using machine learning and/or stochastic simulation (col.11 lines 15-32). The system is capable of executing updated machine readable program instructions following a pre-simulation information , as described in greater detail with regard to FIGS. 4-6.
In some embodiments, prediction module may also consider the effects of altering one or more roles assigned to simulation participants and predict the effects of a role change on each of the stress levels of each simulation participants.
Roberts does not explicitly identify the generating of the training scenario for the user comprises a machine learning agent generating the training scenario based on the personality biometric profile and the one or more personality characteristics of the user, and the machine learning agent controls a plurality of non-player characters in the training scenario; and the training of the user in the training session comprises the user being trained in a Virtual Reality environment. Emma, however, teaches the identification of generating training scenario based on the personality biometric profile and the one or more personality characteristics of the user, (¶0067, 0082 user answers to unknown questions are stored in the user profile and are retrieved in future scenarios relevant to that question; they are operable to encourage the character's personality traits in the user; that is a machine learning agent controls a plurality of non-player characters in the training scenario; and the training of the user in the training session comprises the user being trained in a Virtual Reality environment). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the generating of the training scenario for the user comprises a machine learning agent generating the training scenario based on the personality biometric profile and the one or more personality characteristics of the user, and the machine learning agent controls a plurality of non-player characters in the training scenario; and the training of the user in the training session comprises the user being trained in a Virtual Reality environment, as taught by Emma, into the system of Roberts, in order to provide relevant training scenarios efficiently. Contributing faster towards an overall objectives.
Claim 15 Roberts teaches a non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which, when executed by an electronic device with one or more processors, cause the electronic device to perform any of the methods of claim 1 (Fig.2 col.4 lines 5-10 adaptive simulating training with the use of processor and storage programs)
Claim 16. Robert teaches the system according to claim 14, wherein the generation of the personality biometric profile additionally comprises assignment of a weighted score to each of the one or more personality characteristics by the machine learning algorithm, and wherein the generation of the training scenario for the user is also based on the score for each personality characteristic (col.10 lines 20-46 subsequent simulation could be based on stochastic and other sub-sub-module query that could contain weighted score base).
Claim 18. Robert teaches the system according to claim 14, wherein training of the user in the training session comprises the user being trained in a VR environment (col.12 lines 43-59 modification involved with virtual environment).
Claim 19. Robert teaches the system according to claim 14, wherein the plurality of user devices are wearable devices ((col.4 lines 12-16 wearable devices).
.
Claim 20. Robert teaches the system according to claim 14,wherein the system is further configured to additionally iteratively update the personality biometric profile based on user biometric data collected from the plurality of user devices during the training session (col.2 lines 16-48 personality biometric profile generated iteratively).
.
Claim 21. Robert teaches the system according to claim 14, wherein the one or more personality characteristics comprises one or more of Stressed, Assertive, Leadership, Conscientiousness, Openness, or Receptive ((col.2 lines 16-18 stress levels identified as one of the personality characteristics ) .
Response to Arguments/Remarks
Applicant's arguments/amendments filed on 6/16/2025 have been considered and found not to be persuasive to the overcome 35USC§102 rejections.
However, upon further consideration, further statement is made as necessitated by amendments changing the scope and claim numbers.
Applicant's amendment(s) and argument/clarifications of the claims necessitated the new rejection statement as presented in this Office action.
35USC101
Examiner respectfully traverses applicant’s assertion on pages 8-16 to find amendments directed towards the abstract idea of using a generic machine learning technique in a particular a Virtual Reality training environment without significant inventive concept. Court in a similar recent case, Recentive, 692 F. Supp. 3d at 451 found that the asserted claims were “directed to the abstract ideas of producing network maps and event schedules, respectively, but by using known generic mathematical techniques.” It also found that because the machine learning limitations were no more than “broad, functionally described, well-known techniques” they could generally be claimed “only generic and conventional computing devices,” as of the instance case.
The receiving, generating, analyzing of training scenario after updating appears a part of mental process in terms of collecting data (e.g., profile, behavioral), analyzing that data (e.g., selecting a model for scenario), and providing an output based on that analysis (e.g., generate the next scenario based on personality biometric profile) and thereby abstract under, e.g., Electric Power Group. And/or abstract as a method of teaching/training human beings (see MPEP for citation on that). The “model” claiming appears to be a AI model and possibly one in the form of machine learning. This in addition to the abstract idea and cite their limited disclosure in the specification as evidence for Berkheimer finding that it must be well-known, routine, and conventional and would not be “significantly more” and/or claim a “practical application” for that matter.
Previously on pages 11,12 of remarks applicant asserted that Claim 1 as amended further recites the machine learning algorithm is involved in generating the personality biometric profile and the one or more personality characteristics of the user (based on the analysis of the user biometric data), it can be retrained to that specific user, such that each iterative update more accurately reflects the user in question (see para. [0046] of the original specification of the present application) and thus the invention of claim 1 provides technical improvement. The invention of claim 1 therefore provides technical improvements such as time savings, efficiency and greater accuracy..
Examiner respectfully traverses and finds that the specification of the instant case do not provide technical instructions or details of machine learning algorithm on generating using a personality biometric profile could “only plausibly mean that the patent applicant drafted the specification understanding that a person of ordinary skill in the art knew what profile update was, how to include it on a biometric reading devices, and that using it for the purposes disclosed in the patent was routine, conventional, and well-understood. 35USC101 rejection is maintained.
35USC 102
Applicant’s argument from pages 16-20 found to be convincing for biometric data generating personality profile. A secondary prior art Emma et al. illustrates how interaction with in order to generate potential ideas, concepts, and scenarios for a story.
Upon further consideration, rejection statement is made as necessitated by amendments changing the scope and claim numbers.
Previously applicant asserted that art Roberts fails to disclose "wherein generating the user biometric profile comprises analyzing the user biometric data by a machine learning algorithm, and the machine learning algorithm generating the personality biometric profile and the one or more personality characteristics of the user based on the analysis of the user biometric data" as recited by independent claim 1 as amended and its dependents.
Applicant on pages 13,14 of previous argument/remark asserted that the distinguishing features of claim 1 as amended allow for the building of a personality biometric profile for a specific user, so that that profile can be used to right away create a unique and tailored session for the user, thereby removing the need of using default values and settings and removing the unnecessary initial time to learn/collect user behavior during a training session (para. [0066] of the original specification of the present application). This technical benefit is not appreciated by Roberts, which requires such an initial "data collection" period.
Examiner respectfully traverses and indicated machine learning algorithm to analyze user biometric data to generate a personality biometric profile and updates as required by claim 1 has been adequately explained in art Roberts. Though the machine learning employed in Roberts is in the prediction module, it is used to predict the effects of modifying a pre-simulation environment on the stress levels of each simulation participant and forwardly allow for the building of a personality biometric profile by machine learning and stochastic simulations (see col. 14, lines 36-58 of Roberts) . Some other arts like US 20190147760 A1 Bruckner et al.(Bruckner) also provides machine learning algorithm based on analysis of user biometric data. 35USC102 is maintained.
Conclusion
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.Z/Examiner, Art Unit 3715 September 20, 2025
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715