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 .
Claim Rejections - 35 USC § 102
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 1-2, 4-10, 12-17, 21-22 are rejected under 35 U.S.C. 102a1 as being anticipated by Eatedali (US 2020/0197811).
Claim 1. Eatedali discloses a system (system can be a computer system or server; paragraphs 49, 54) comprising:
at least one processor (paragraph 49); and
memory encoding computer-executable instructions that, when executed by the at least one processor (paragraphs 49, 60-61), cause the at least one processor to perform operations comprising:
instantiating a first agent in a gameplay session with a user for a first game wherein behavior of the first agent is defined by the first game as a non-playable character (NPC) of the first game (An “existing on-player character” is modified in video game. The NPC is modified based on tracked and monitored player’s profile. The NPC is modifies “on the fly” and continuously, and therefore the NPC is instantiated prior to being modified. The existing NPC can be the original or default NPC or also referred to as a baseline NPC. See paragraphs 30-31, 35, 66, 68, 72, 86. Eatedali explicitly discloses, “In some embodiments, the initial attributes of the NPC are not based on any tracked human player attributes and are rather default attributes provided by the game; paragraph 86.);
receiving an indication for control of the first agent according to a personalized agent of the user (According to Applicant’s specification, “As used herein, an NPC “possessed” by the player's personalized agent means the NPC takes on characteristics of the personalized agent.” Therefore, this limitation is interpreted that the NPC takes on characteristics of the personalized agent. Eatedali discloses that the NPC takes on the characteristics of the personalized agent/NPC profile personalized by player game play; 30-35, 65-81); and
controlling the first agent based upon one or more machine learning models (AI engine or machine learning process is trained and used in the NPC application to control the NPC: paragraphs 20, 53, 70, 81.) comprising a generative machine learning model (Generate data representative of a behavior of the NPCs by applying a neural network or machine learning process to the human player data and game data; paragraphs 20-21) configured to process user input to generate model output based thereon (generate data representative of a behavior of the NPC by applying a neural network or machine learning process and updating the NPC in real time or in the next game; paragraphs 20, 70, 77, 86, 79-90), thereby overriding the behavior of the first agent (modify the existing NPC; paragraphs 30-35, 65-81 ) defined by the game (Eatedali explicitly discloses, “In some embodiments, the initial attributes of the NPC are not based on any tracked human player attributes and are rather default attributes provided by the game; paragraph 86.), the controlling comprising:
receiving, by the first agent, a user interaction during gameplay (NPC application track player’s profile and monitor the profile during the playing of the game by the player; paragraphs 65-75);
generating, via the one or more machine learning models, a response to the user interaction for the first agent (generate data representative of a behavior of the NPC by applying a neural network or machine learning process and updating the NPC in real time or in the next game; paragraphs 20, 70, 77, 86, 79-90); and
instructing the first agent to perform the generated response (68-78); thereby controlling the non-player character via the personalized agent of the user (As indicated above, according to Applicant’s specification, “As used herein, an NPC “possessed” by the player's personalized agent means the NPC takes on characteristics of the personalized agent.” Therefore, this limitation is interpreted that the NPC takes on characteristics of the personalized agent. Eatedali discloses that the NPC takes on the characteristics of the personalized agent/NPC profile personalized by player game play; 30-35, 65-81).
Claim 2. Eatedali discloses the system of claim 1, wherein at least one of the one or more machine learning models is trained using reinforcement learning to control agent gameplay in the first game (machine learning algorithms including reinforcement learning; paragraphs 14, 24, 70, 81).
Claim 4. Eatedali discloses the system of claim 1, wherein the first agent is controlled based on one or more of: information about the first game; information about the non-player character (NPC);identification of a specific agent; or identification of specific agent characteristics (Eatedali discloses the NPC management engine request or select and retrieves one or more NPC from the database; paragraph 76. The agent is control based on indent6fication of a specific agent or agent characteristics one NPC is a retrieval of the specific agent/NPC. Eatedali also disclose that the AI engine identifies the NPC and identifies or selects the preferred NPC behavior; paragraphs 70, 77. NPC profile is generated for current game play or later selection; paragraphs 78, 80.).
Claim 5. Eatedali discloses the system of claim 4, wherein the controlling further comprises: determining a genre for the first game; and identifying agent personalization data related to the genre (Eatedali discloses the games can be different genre or same game type, paragraphs 15-17, 66, 69. An agent or NPC is selected based on the genre/type of game. For example, shooting games “snipers” role game, sports game with “sports arena” racing game with “racetracks”, etc.; paragraph 69.).
Claim 6. Eatedali discloses the system of claim 4, wherein the controlling further comprises determining one or more NPC characteristics, wherein NPC characteristic comprise one or more of: NPC abilities; NPC party role; or NPC characteristics; and identifying agent personalization data related to the one or more NPC characteristics (Game play information are used to determine and modify and evolve the NPC(s); paragraph 69. The game play information includes NPC or player characteristics including types of roles, characteristics such as items or weapons, ability, traits, attributes, metrics; paragraph 8, 69-70, 73, 75).
Claim 7. Eatedali discloses the system of claim 4, wherein the controlling further, comprises identifying the specific agent and identifying a the subset of agent personalization data comprises identifying personalization data associated with the specific agent (Eatedali discloses the NPC management engine request or select and retrieves one or more NPC from the database; paragraph 76. The retrieval of one NPC is a retrieval of a specific agent/NPC. Eatedali also disclose that the AI engine identifies the NPC and identifies or selects the preferred NPC behavior; paragraphs 70, 77. The personalization data is interpreted as behavior data of the NPC. The NPC profile attributes and game play actions are also considered personalization data. Therefore, the subset of agent personalization data is identified by identifying the specific agent characteristics from a plurality of agent characteristics; paragraphs 78).
Claim 8. Eatedali discloses the system of claim 4, wherein the controlling further comprises identifying the specific agent characteristics and identifying the subset of agent personalization data comprises identifying the specific agent characteristics from a plurality of agent characteristics (Eatedali discloses the NPC management engine request or select and retrieves one or more NPC from the database; paragraph 76. The retrieval of one NPC is a retrieval of a specific agent/NPC. Eatedali also disclose that the AI engine identifies the NPC and identifies or selects the preferred NPC behavior; paragraphs 70, 77. The specific agent characteristics is interpreted as behavior data of the NPC. The NPC profile attributes and game play actions are also considered agent characteristics; paragraphs 78).
Claim 9. see rejection for claim 1 above.
Claims 10, 12-16. See rejection for claims 2, 4-8 above.
Claim 17. Eatedali discloses a method comprising:
instantiating a non-player character (NPC) for a first video game An “existing on-player character” is modified in video game. The NPC is modified based on tracked and monitored player’s profile. The NPC is modifies “on the fly” and continuously, and therefore the NPC is instantiated prior to being modified. The existing NPC can be the original or default NPC or also referred to as a baseline NPC. See paragraphs 30-31, 35, 66, 68, 72, 86. Eatedali explicitly discloses, “In some embodiments, the initial attributes of the NPC are not based on any tracked human player attributes and are rather default attributes provided by the game; paragraph 86.), wherein the NPC is operable to interact with a user of the first video game based on a generative machine learning model (AI engine or machine learning process is trained and used in the NPC application to control the NPC: paragraphs 20, 53, 70, 81. Generate data representative of a behavior of the NPCs by applying a neural network or machine learning process to the human player data and game data; paragraphs 20-21) configured to process user input and prompt input to generate model output based thereon, wherein the prompt corresponds to agent personalization data for the user (process user input and prompt input to generate model output; paragraphs 28, 33, 56, 65, 70-73);
receiving, for the NPC, a user interaction during gameplay (paragraphs 28, 33, 56, 65, 70-73)l
generating, via the generative machine learning model and using a prompt corresponding to the user, a response to the user interaction Generate data representative of a behavior of the NPCs by applying a neural network or machine learning process to the human player data and game data; paragraphs 20-21); and
instructing the NPC to perform the generated response (generate data representative of a behavior of the NPC by applying a neural network or machine learning process and updating the NPC in real time or in the next game; paragraphs 20, 70, 77, 86, 79-90).
Claim 21. Eatedali discloses the method of claim 17, further comprising: receiving feedback for the generated response to the user interaction; and updating the agent personalization data based on the feedback, thereby improving a subsequent prompt corresponding to the user (continuously tracking monitoring and continually modifying the modified NPC and continually improve NPC performance; paragraphs 31, 65, 68, 75, 80).
Claim 22. Eatedali discloses the system of claim 1, wherein the model output is generated further based on prompt input associated with agent personalization data for the user (based on prompt input to generate model output; paragraphs 28, 33, 56, 65, 70-73).
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.
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Eatedali (US 2020/0197811) as applied to claims 1 and 9 above, and further in view of Gelfenbeyn (US 2023/0351661)
Claims 3, 11. Eatedali discloses the claimed invention but fails to teach that the one or more machine learning models comprise: a foundation model; a language model; a computer vision models; or a speech model. Nevertheless, such modification would have been obvious to one of ordinary skilled in the art. In an analogous art to creating characters for video games Gelfenbeyn discloses that a machine learning algorithm includes a language model to recognize, predict, and generate responses for the character (paragraphs 28-34 48, 52, 56, 88-89). It would have been obvious to one of ordinary skilled in the art before the effect filling date to modify Eatedali’s invention and incorporate a language model in order to provide the predictable result of recognize, predict, and generate responses for the character.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Eatedali (US 2020/0197811) as applied to claim 17 above, and further in view of Stamper (US 2008/0045283).
Claim 20. Eatedali discloses the claimed invention as discussed above but fails to teach that modifying the agent personalization data, by at least one of: deleting agent personalization data that does not comply with the NPC characteristics; or modifying the agent personalization data to comply with the NPC characteristics. Nevertheless, such modification would have been obvious to one of ordinary skilled in the art. In an analogous art to creating characters for video games, Stamper discloses that game objects and game characters comprises have limited parameters or attributes (paragraph 29). Stamper discloses character data identifies the information type associated with the character or object (paragraph 29). The system ensures based on the filter data (or constraint data) that based on the identified type and target game; the data used to define the character/object are acceptable. The data with parameters that fall outside the defined boundaries are ignore such that the resultant character/object does not contain overpowering abilities (paragraph 29). This ensures that the game objects and characters are within the game design constraints and provides fairness to the game. It would have been obvious to one of ordinary skilled in the art before the effective filing date to modify Eatedali’s invention and delete agent personalization data that does not comply with the NPC characteristics; or modify the agent personalization data to comply with the NPC characteristics in order to provide the predictable result of ensuring that the NPC is within the game design constraints and provide fairness to the game.
Response to Arguments
Applicant's arguments filed 11/21/25 have been fully considered but they are not persuasive.
Applicant argues that the prior art fails to teach the amended limitations.
New grounds of rejection have been made using previously cited art to address the amended limitations.
As indicated in the rejection above. Eatedali discloses instantiating a first agent in a gameplay session with a user for a first game wherein behavior of the first agent is defined by the first game as a non-playable character (NPC) of the first game. Eatedali discloses an “existing on-player character” is modified in video game. The NPC is modified based on tracked and monitored player’s profile. The NPC is modifies “on the fly” and continuously, and therefore the NPC is instantiated prior to being modified. The existing NPC can be the original or default NPC or also referred to as a baseline NPC. (See paragraphs 30-31, 35, 66, 68, 72, 86). Eatedali explicitly discloses, “In some embodiments, the initial attributes of the NPC are not based on any tracked human player attributes and are rather default attributes provided by the game (paragraph 86).
Eatedali discloses a generative machine learning model (paragraphs 20-21) configured to process user input to generate model output based thereon (paragraphs 20, 70, 77, 86, 79-90), thereby overriding the behavior of the first agent (modify the existing NPC; paragraphs 30-35, 65-81 ).
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 Jasson H Yoo whose telephone number is (571)272-5563. The examiner can normally be reached M-F 9am-5pm.
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/JASSON H YOO/ Primary Examiner, Art Unit 3715