DETAILED ACTION
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 .
Status of Claims
In response to the Amendment filed on June 18th, 2026, claims 1-7 and 9-20 have been amended. Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 103
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 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osman et al. (US 2018/0001205 A1) in view of Artificial Intelligence and Games (NPL document “Artificial Intelligence and Games” by Georgios N. Yannakakis and Julian Togelius, published January 26, 2018, hereinafter referred to as AI and Games).
Regarding claims 1, 9, and 17, Osman discloses a method, comprising:
executing a plurality of instances of a video game, each of the plurality of instances being from a plurality of gameplays of a plurality of users playing the video game (see par. [0053], As shown, profiler engine 145 is able to collect and analyze game plays of a plurality of users 115 playing a plurality of gaming applications, especially when the game cloud system 210 is executing instances of the gaming applications for the users 115);
collecting state data from the plurality of game plays of the video game (see par. [0042], For example, profiler engine 145 is configured to collect data from game plays of multiple users playing the particular gaming application, and specifically addressing a task (e.g., difficult task) that may be perplexing to the user);
analyzing the state data to identify a plurality of success criteria associated with the plurality of game plays (see par. [0054], In addition, profiler engine 145 is able to collect and analyze user data when a corresponding user 115A is playing any gaming application);
training an AI model using the state data and the plurality of success criteria, the AI model being optimized for a plurality of scenarios in the video game (see par. [0042], Specifically, as more data is collected from game plays of multiple users, the profiler engine 145 is able to learn (e.g., by applying deep learning or artificial intelligence techniques) how a particular gaming application should be played (generally, or within the context of a given level, sub-level, or given problem), or is being played by successful users, and/or other unsuccessful users (e.g., learning from mistakes)); and
using the AI model to automatically generate a next game input sequence to drive interactivity with a particular scenario of the video game in a game play of the user playing the video game to achieve a predefined objective of the particular scenario in response to detecting that the game play of the user fails to satisfy at least one success criteria of the plurality of success criteria (see par. [0055], For example, game play controller 171 is able to control the game play to achieve a desirable result as requested and defined by the user 115A, as previously described; also see par. [0097], In one embodiment, the method includes detecting that the user is in need of assistance. For example, failures of the user when addressing a particular task may be monitored. After a threshold amount of failed attempts, a query may be generated for display to the user asking if the user needs assistance (e.g., in the form of hints or game play control, etc.)).
However, Osman does not explicitly disclose using the AI model automatically and without input from a user.
AI and Games teaches that an AI model can automatically, without input from a user, assist a player upon detecting repeated failures (see pg. 95, last par. through pg. 96, first par., “The game simply takes over the controls and plays for you for about 10 to 20 seconds, and lets you continue afterwards”). It would have been obvious to one of ordinary skill in the art to modify the method of Osman to use the automatic assistance of AI and Games as this is merely automating a manual activity (modifying Osman so that input is not required) to achieve the same result of obtaining assistance from the AI model (see MPEP 2144.04, Section III. AUTOMATING A MANUAL ACTIVITY, “The court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art”).
Regarding claims 2 and 18, Osman discloses generating a recommendation on how to play the particular scenario to achieve the predefined objective of the particular scenario (see par. [0037], For example, a sequence of input commands may be provided through the interface as generated by the game play controller 171, wherein the input commands automatically control the game play of the user as currently executed by an instance of a corresponding gaming application).
Regarding claims 3, 11, and 19, Osman discloses receiving a first user input to take back control of the game play from the AI model, wherein the game play is controlled by the AI model prior to the receiving of the first user input (see par. [0019], When the user wishes to return to active play, the user can stop the AI character, and allow the user to continue the game).
Regarding claims 4 and 12, Osman discloses wherein the first user input includes instructions to hand back control of the game play after a period of time (see par. [0019], When the user wishes to return to active play, the user can stop the AI character, and allow the user to continue the game).
Regarding claim 5 and 13, Osman discloses receiving a second input to take over control of the game play using the AI model to drive interactivity with another scenario, wherein the second user input is received after a period of time after receiving the first user input (see par. [0019], In still another embodiment, the game play controller is configured to play the gaming application after the user pauses the game play, such that an AI character is turned on to perform specific tasks; the claim simply describes toggling the AI model back on after being off).
Regarding claims 6 and 14, Osman discloses wherein the training the AI model includes collecting a subset of the state data that is associated with corresponding game plays of the particular scenario of the video game; identifying second success criteria through analysis of the subset of the state data; and training the AI model for the particular scenario using the subset of the state data and the second success criteria, wherein the AI model that is trained provides a plurality of outputs for a plurality of inputs for the particular scenario (see par. [0058], In particular, the AI personal assistant 120 is configured for monitoring game play of the user 115S and collecting user data that can be used for artificial intelligence (AI) purposes. For example, in a game play, the user 115S may have a defined task 301 to accomplish. The task may include a single action or sub-task 302, or multiple actions or sub-tasks, as indicated by the hash marks 302 within task 301. Also, the task may include no actions or sub-tasks. Further, the task is defined by a task type, such that similar tasks may be grouped under a single task type. These tasks may have similar goals, perform similar actions or sub-tasks to complete the task, or share other similarities. By grouping tasks under a task type, game plays of users addressing tasks of the task type may be analyzed to learn appropriate actions to take, the actions to avoid, the best styles of game plays, and the most efficient styles of game play for successfully completing those tasks for a particular gaming application, for a genre of gaming applications, or for gaming applications in general).
Regarding claims 7 and 15, Osman discloses wherein the training the AI model includes receiving first state data from the game play of the user; receiving a game response in the game play of the user based on the first state data; generating a predicted degree of success for the user playing the particular scenario in the game play of the user based on the game response; and adjusting the success criteria to achieve incrementally better results in driving interactivity with the particular scenario based on the predicted degree of success (see par. [0074], As previously described, the output nodes may identify problems, tasks, task types, difficult tasks, approaches to complete a specific task within a gaming application, other game play difficulties within a gaming application, predicting a rate of success for a user regarding a particular type of task or a particular task, predicting a rate of success for a team of members that are given a set of tasks to accomplish, determine a proficiency score for a user and/or team, determine a recommendation, provide assistance, provide appropriate responses to take regarding a presented task or task type according to a particular user's game play style, learn appropriate responses or approaches to take with respect to a particular task, etc.).
Regarding claims 8 and 16, Osman discloses wherein the plurality of success criteria corresponds to the plurality of scenarios in the video game (see par. [0059], The game plays include a plurality of tasks to address and/or accomplish. Each of the tasks monitored is defined by a corresponding task type. In that manner, tasks of the same type can be analyzed to determine the gaming style, gaming habits, and proficiency of the user. That information is helpful in building a game play profile for that user 115S, wherein the game play profile simulates the game play of the user 115S when encountering a task of a particular task type).
Regarding claims 10 and 20, Osman discloses wherein the Al model comprises a global AI model configured to play the video game, and wherein training the AI model further comprises training a personal AI model for the user by applying input state data associated with game play of the user to a deep learning engine to modify the global AI model (see par. [0090], Further, profiler engine 145 is configured to collect data from a plurality of users playing a plurality of gaming applications to build one or more default game play profiles, and to build a personalized game play profile for a corresponding user).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLEN CHAN whose telephone number is (571)270-5529. The examiner can normally be reached Monday-Friday, 11:00 AM EST to 7:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dmitry Suhol can be reached at (571) 272-4430. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLEN CHAN/Primary Examiner, Art Unit 3715 7/1/2026
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