CTNF 18/819,278 CTNF 83393 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 2. 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. 3. Claims 1-19 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. 4. Step 1 Claims 1-19 are directed to methods meeting the requirements for Step 1. 5. Step 2A Prong 1 Independent Claim 1 (below) recites “training a machine learning model using the gameplay data.” Independent Claim 14 recites a similar limitation. This limitation recites a mathematical calculation, which is a mathematical concept, and thus, an abstract idea. A method, comprising: recording gameplay data from one or more sessions of a video game, the one or more sessions defined for interactive gameplay of a user; training a machine learning model using the gameplay data , wherein the training configures the machine learning model to imitate the interactive gameplay of the user; after the training, exposing the machine learning model to one or more scenarios of the video game, such that the machine learning model generates gameplay decisions in response to the one or more scenarios; evaluating the gameplay decisions of the machine learning model in response to the one or more scenarios to determine one or more descriptive features of the user's gameplay; using the determined descriptive features of the user's gameplay to provide a recommendation to the user. 6. Step 2A Prong II There is no discernable additional element (or combination of elements) recited in Claims 1 or 14 that have integrated the judicial exception into a practical application. In making this determination Examiner relies on the two step guidance from “Advance notice of change to the MPEP in light of Ex Parte Desjardins (December 5, 2025) wherein the claims were directed to a machine learning model on a series of tasks. First the specification must be evaluated to decide if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement “ in the functioning of a computer, or an improvement to other technology or a technical field.” (Emphasis in original). Second, if the specification sets forth an improvement, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement i.e., the components or steps of the invention that provide the improvement described in the specification. In Desjardins , the specification explained how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Moreover, the specification disclosed enumerated improvements, namely: the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. “Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation” according to the guidance. Here, examination of the specification indicates no such improvements in the functioning of a computer, or of an improvement to other technology or a technical field. According to Applicant in his specification the machine learning model is operating in a highly conventional computing environment. “It will be appreciated that the hardware utilized for the video game experience can vary in various implementations. For example, the computing device can be a game console, personal computer, laptop, tablet, cell phone, or any other type of computing device that is capable of executing a video game as described herein.” ([0038]). And that the learning model is recited as a tool to be applied where “In other implementations, the machine learning model 112 can utilize other types of artificial intelligence or machine learning techniques (e.g. support vector machine, linear regression, logistic regression, naive Bayes, decision trees, k nearest neighbor, etc.) that can be configured to imitate the gameplay of a user, in accordance with implementations of the present disclosure.” ([0050]). But instead of describing improvements to the model itself on par with the articulated improvements of Desjardins , the specification describes uses, namely: how an AI trained bot can be deployed to play a game on the user’s behalf ([0053-0054]); provide for new experiences in game play to play against other’s AI bots ([0055]); to cover for a human user when the human user is not available during group play ([0056]); and for a user to borrow another player’s AI bot to help the player overcome a difficult section of a game ([0058]). In contrast to Desjardins , the Federal Circuit held machine learning claims ineligible in Recentive Analytics, Inc. v. Fox Corp. , No. 23-2437 (Fed. Cir. 2025). In Recentive, the claims were directed towards providing parameters to a machine learning model to train the model to identify relationships between different event parameters and target features using historical data corresponding to one or more previous series of live events, and changing conditions, and, using machine learning, dynamically generate optimized maps and schedules. However, the Court found that the technology described in the specification and recited in the claims was conventional. Neither the specification nor the claims described and recited, respectively, how any such improvements in AI was accomplished i.e., by an articulated specific technological improvement to the underlying machine learning method not just claimed uses in new environments. Consequently, the Court held the claims failed to provide “significantly more” than the abstract idea of generating event schedules and network maps through the application of machine learning. In weighing these considerations, Examiner deems that Applicant’s disclosure and claims unlike those of Desjardins and similar to those of Recentive because the specification describes a conventional computing environment and a litany of machine models that can be performing as designed therein. Also, the specification and the claims, while expressing the benefits of using machine learning “to provide a recommendation to the user” (See Claim 1), fail to express and claim any technological improvement in the operation of the machine learning itself e.g., an improvement to the algorithm; reduction in complexity; or a savings via efficient use of storage space as in Dejardins . Examiner deems the balance of the claimed steps to record video game play data from one of more sessions; to train the model on the data; to expose the model to one or more scenarios of the video game; to evaluate the game play for descriptive features; and to provide a recommendation to a user to be, in totality, a conventional recitation of the machine learning model and model training process applied to express news uses in the technological field of video gaming. Thus, these remaining claimed elements are deemed to be conventional machine model training and a technological field of use. Thus, Claims 1 and 14 lack a practical application. 7. Step 2B According to the advanced guidance to be reflected in MPEP 2106, in addition to the considerations discussed in Step 2A, an additional consideration indicative of an inventive concept (aka “significantly more”) is the addition of a specific limitation other than what is well-understood, routine, conventional activity in the field (MPEP 2106.05(d)). Conversely, an additional consideration not indicative of an inventive concept is simply appending well-understood, conventional activities previously known to the industry, specified at a high level of generality, to the abstract idea (MPEP 2106.05(d) and Berkheimer Memo, April 20, 2018). Thus, the additional elements evaluated under Step 2A are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. Examiner notes there are no extra-solution elements but merely limitations of a highly generic video game environment field of use. Thus, Claims 1 and 14 lack an innovative concept and are ineligible. 8. Dependent Claims 2-13 and 15-19 Claims 2-9, 12-13, and 15-19 are features, skills, techniques, tendencies, opponents, styles, paths, skill levels, subsequent session recommendations, video, video inputs, game states, and image frames related to either the inputs or the outputs of the trained model. Claim 10 recites a well-known game controller device. Claim 11 recites a species of well-known machine learning models. Claim Objections 07-29-01 AIA 9. Claim s 1 and 14 are objected to because of the following informalities: a. Claim 1, Line 10: Change “gameplay:” to – gameplay; and --. b. Claim 14, Line 10: Change “gameplay;” to – gameplay; and --. Appropriate correction is required. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 10. 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 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. 07-20-aia AIA 11. 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. 07-23-aia AIA 12. 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. 07-21-aia AIA 13. Claim s 1-8, 10, 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. Pub. No. 2006/0246973 to Thomas in view of U.S. Pat. Pub. No. 2009/0210078 to Crowley . In Reference to Claims 1, 14, and 17 Thomas discloses a method (Fig.1, [0024]), comprising: recording gameplay data from a first session of a video game ([0023, 0035]), the first session defined for interactive gameplay of a user (Fig. 2a, see also [0023, 0025]); training a machine learning model using the gameplay data, wherein the training causes the machine learning model to imitate the interactive gameplay of the user {mimic the tendencies of the user} (Fig. 2b, see simulation mode where simulator 120 retrieves input data of a first user to simulate the first user’s behavior to play a second user ([0026]); after the training (in the simulation mode [0007]), exposing the machine learning model to one or more scenarios of the video game {skill level} ([0026 second user wants to interact with first user, see also events 685 and situations 655 [0028]; skill level [0041]), such that the machine learning model generates decisions in response to the one or more scenarios ([0026 simulates the first user’s behavior; “in the simulator 120 is in simulation mode, and a particular event and/or situation occurs, the simulator 120 can invoke a corresponding reaction based on the stored data, thereby simulating the user” [0030]); and evaluating the gameplay decisions {analyzing outcomes} of the machine learning model in response to the one or more {predefined} scenarios (see actions include events and circumstances/situations ([0006, 0025, 0028, 0041, 0044]) to determine one of more descriptive features of the user’s gameplay (See means for players to engage in any possible configuration; to practice against and develop skills; simulate the unique, individual behaviors, weaknesses, strategies and tendencies of actual human opponents within a videogame [0044]). Thomas discloses his invention in the context of a game such as basketball game ([0028, 0029]) or football ([0031]) among other games but is not explicit as to using the descriptive features of the user’s gameplay to provide a recommendation to the user. One of skill in the art would be aware of the teachings of Crowley. Crowley teaches of athletic performance analysis to determine a skill level of an athlete with respect to one of more actions (Abstr., [0019]) to include providing recommendations to the user ([0044]). Crowley provides this invention to provide feedback for an athlete so that they can improve their performance ([0007]). The Supreme Court in KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007) identified a number of rationales to support a conclusion of obviousness (A) Combining prior art elements according to known methods to yield predictable results; (B) Simple substitution of one known element for another to obtain predictable results; (C) Use of known technique to improve similar devices (methods, or products) in the same way; and (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results. Here, one of skill in the art would look to Crowley to modify the invention of Thomas to utilize the information based on the evaluations to inform users to help improve their performance. The Courts have held that combining prior art elements according to known methods to yield predictable results to be indicia of obviousness. In Reference to Claim 2 Thomas discloses wherein the descriptive features of the user's gameplay identify a style or tendency of the user during gameplay ([0044]). In Reference to Claim 3 Thomas discloses analysis of a user’s behavior, tendencies, and inclinations for a given event of situation ([0044, 0051]) and Crowley teaches of providing feedback and constructive criticism ([0010, 0044]). In Reference to Claim 4 Crowley discloses wherein the recommendation identifies an opponent for the user to play against where a report generator 210 compiles basic data on particular skills and corresponding data for the athlete to match players with respect to a curtained skill and provide feedback to let the athlete know where they should focus their training. In Reference to Claim 5 Crowley teaches wherein the recommendation identifies a style of play for the user to develop as in a manner in which an opponent dribbles a basketball strong to the left and weak to the right ([0092]). In Reference to Claim 6 Crowley teaches wherein the recommendation identifies a path of development for the user where an athlete is tested in multiple time periods so as to assess the progress of the athlete and to generate a prediction of the near term and longer term expected progress ([0037]). In Reference to Claims 7 and 15 Thomas discloses wherein the gameplay data includes user inputs during the interactive gameplay (input data [0026]) and Crowley discloses capturing video highlights ([0076]). In Reference to Claim 8 Thomas discloses wherein training the machine learning model uses the user inputs to cause the machine learning model to respond to a given portion of the video by generating inputs similar to the user inputs that were generated in response to the given portion of the video during the first session ([0026]). One of skill in the art would also employ training the model using the video of Crowley ([0076]) as well since it would be obvious to try to achieve the predictable result of expanding the information profile of the player. In Reference to Claim 10 Thomas discloses wherein the user inputs are defined from a controller device operated by the user during the first session ([0029]). In Reference to Claim 13 Thomas discloses wherein the gameplay data includes game state data from the first session of the video game (user inputs data in storage is game state data from prior play ([0006, 0026]). In Reference to Claim 16 Thomas discloses a user profile can contain sub-profiles of events ([0028]) wherein the tendencies of the user in the interactive gameplay are defined by activity and non-activity of the user in the interactive gameplay wherein for example, “[a]n event 685, as mentioned above, can be defined by a particular action taken place either by a user or by the computer program 110 itself. An action can be movement by an avatar 687, special moves invoked 689, stoppage of time 691, such as a timeout, etc.” [0028]. In Reference to Claim 18 Thomas discloses during operation, the simulator 120 will continue to process ([0030]) during a video game. Crowley teaches of providing recommendations both quick and minimal and longer and more in-depth ([0071]) and of comparing data on skills from an earlier time to a current time. One of skill in the art would be aware of providing timely feedback, even during the current time to modify a next game of Thomas to provide any feedback. The Courts have held that the use of a known technique to improve similar devices (methods, or products) in the same way to be indicia of obviousness . 07-21-aia AIA 14. Claim s 9, 11, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thomas, Crowley, further in view of U.S. Pat. Pub. No. 2018/0243656 to Aghdaie . In Reference to Claim 9 Thomas discloses the invention substantially as claimed. However, the reference discloses the events ([0025, 0028]) of the video game but does not explicitly disclose the actual images of the video game. Aghdaie discloses machine learning [0052] in the context of game play data to include images and other data ([0171]). Here, one of skill in the art would recognize that events in a In Reference to Claims 11 and 19 Aghdaie teaches that gameplay parameters employ machine learning algorithms to include neural networks [0052]. One of skill in the art would be aware that the machine learning of Thomas would be carried out by one of the methods to include neural networks of Aghdaie. The Courts have held that use of known technique to improve similar devices (methods, or products) in the same way is indicia of obviousness. In Reference to Claim 12 Thomas discloses events {scenarios} of the game ([0028]) of a subsequent plurality of drill ([0013]) which Examiner construes as "not defined from the first session”. Aghydaie teaches of said game data which is image data . Double Patenting 08-33 AIA 15. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). 16. A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). 17. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp. 08-34 AIA 18. Claim s 1-2, 4-5, 7-17, and 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1 and 5-18 of U.S. Patent No. 12,102,921 . Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims express minor phraseology differences and are anticipated by the previously granted claims which further determine a classification of the machine learning model . U.S. Pat. No. 12,102,921 U.S. Pat. App. No. 18/819,278 1. A method, comprising: recording gameplay data from a first session of a video game, the first session defined for interactive gameplay of a user; training a machine learning model using the gameplay data, wherein the training causes the machine learning model to imitate the interactive gameplay of the user; after the training, exposing the machine learning model to one or more scenarios of the video game, such that the machine learning model generates gameplay actions in response to the one or more scenarios; and evaluating the gameplay actions of the machine learning model in response to the one or more scenarios to determine a classification of the machine learning model , wherein the classification is indicative of descriptive features of the user's gameplay including a style of gameplay; using the classification of the machine learning model to provide a coaching recommendation to the user. and similarly for Claim 14 but also states that the model mimics tendencies. 5. The method of claim 1, wherein the coaching recommendation includes recommendation regarding the style of gameplay. 6. The method of claim 1, wherein the coaching recommendation includes recommendation of an opponent based on the classification. 7. The method of claim 1, wherein the gameplay data includes video of the first session and user inputs during the interactive gameplay. 8. The method of claim 7, wherein training the machine learning model uses the video and the user inputs to cause the machine learning model to respond to a given portion of the video by generating inputs similar to the user inputs that were generated in response to the given portion of the video during the first session. 9. The method of claim 8, wherein the given portion of the video is defined by one or more image frames of the video. 10. The method of claim 7, wherein the user inputs are defined from a controller device operated by the user during the first session. 11. The method of claim 1, wherein the machine learning model is a neural network. 12. The method of claim 1, wherein the one or more scenarios of the video game are defined by one or more image frames of the video game, that are not defined from the first session. 13. The method of claim 1, wherein the gameplay data includes game state data from the first session of the video game. 15. The method of claim 14, wherein the gameplay data includes video and user inputs from the user sessions of the video game. 16. The method of claim 14, wherein the tendencies of the user in the interactive gameplay are defined by activity and non-activity of the user in the interactive gameplay. 17. The method of claim 14, wherein performing the evaluation of the trained machine learning model is configured to determine a skill level of the user, and wherein the coaching is based on the determined skill level of the user. 18. The method of claim 14, wherein the machine learning model is a neural network. 1. A method, comprising: recording gameplay data from one or more sessions of a video game, the one or more sessions defined for interactive gameplay of a user; training a machine learning model using the gameplay data, wherein the training configures the machine learning model to imitate the interactive gameplay of the user; after the training, exposing the machine learning model to one or more scenarios of the video game, such that the machine learning model generates gameplay decisions in response to the one or more scenarios; evaluating the gameplay decisions of the machine learning model in response to the one or more scenarios to determine one or more descriptive features of the user's gameplay; using the determined descriptive features of the user's gameplay to provide a recommendation to the user. and similarly for Claim 14 and Claim 2 where 2. The method of claim 1, wherein the descriptive features of the user's gameplay identify a style or tendency of the user during gameplay 5. The method of claim 1, wherein the recommendation identifies a style of play 25 for the user to develop. 4. The method of claim 1, wherein the recommendation identifies an opponent for the user to play against. 7. The method of claim 1, wherein the gameplay data includes video of the first session and user inputs during the interactive gameplay. 8. The method of claim 7, wherein training the machine learning model uses the video and the user inputs to cause the machine learning model to respond to a given portion of the video by generating inputs similar to the user inputs that were generated in response to the given portion of the video during the first session. 9. The method of claim 8, wherein the given portion of the video is defined by one or more image frames of the video. 10. The method of claim 7, wherein the user inputs are defined from a controller device operated by the user during the first session. 11. The method of claim 1, wherein the machine learning model is a neural network. 12. The method of claim 1, wherein the one or more scenarios of the video game are defined by one or more image frames of the video game, that are not defined from the one or more sessions. 13. The method of claim 1, wherein the gameplay data includes game state data from the first session of the video game. 15. The method of claim 14, wherein the gameplay data includes video and user inputs from the user sessions of the video game. 16. The method of claim 14, wherein the tendencies of the user in the interactive gameplay are defined by activity and non-activity of the user in the interactive gameplay. 17. The method of claim 14, wherein the descriptive features include a skill level of the user, and wherein the recommendation is based on the determined skill level of the user. 19. The method of claim 14, wherein the machine learning model is a neural network . Conclusion 07-96 AIA 19. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is in the Notice of References Cited . 20. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Paul A. D’Agostino whose telephone number is (571) 270-1992. 21. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. 22. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Vasat can be reached on (571) 270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-270-2992. /PAUL A D'AGOSTINO/ Primary Examiner, Art Unit 3715 Application/Control Number: 18/819,278 Page 2 Art Unit: 3715 Application/Control Number: 18/819,278 Page 3 Art Unit: 3715 Application/Control Number: 18/819,278 Page 4 Art Unit: 3715 Application/Control Number: 18/819,278 Page 5 Art Unit: 3715 Application/Control Number: 18/819,278 Page 6 Art Unit: 3715 Application/Control Number: 18/819,278 Page 7 Art Unit: 3715 Application/Control Number: 18/819,278 Page 8 Art Unit: 3715 Application/Control Number: 18/819,278 Page 9 Art Unit: 3715 Application/Control Number: 18/819,278 Page 10 Art Unit: 3715 Application/Control Number: 18/819,278 Page 11 Art Unit: 3715 Application/Control Number: 18/819,278 Page 12 Art Unit: 3715 Application/Control Number: 18/819,278 Page 13 Art Unit: 3715 Application/Control Number: 18/819,278 Page 14 Art Unit: 3715 Application/Control Number: 18/819,278 Page 15 Art Unit: 3715 Application/Control Number: 18/819,278 Page 16 Art Unit: 3715 Application/Control Number: 18/819,278 Page 17 Art Unit: 3715
Prosecution Insights