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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/18/2026 has been entered.
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.
Claim(s) 1-2, 4-10, 12-17, and 21-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Josiah Eatedali et al., (US 20200197811 A1; hereinafter Eatedali).
Regarding claim 1, Eatedali discloses a system comprising: at least one processor; and memory encoding computer-executable instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising (“computer system 110 may include one or more processors 112 (also interchangeably referred to herein as processors 112, processor(s) 112, or processor 112 for convenience), one or more storage devices 114, one or more peripherals 140, and/or other components. Processors 112 may be programmed by one or more computer program instructions” (recited in at least: Eatedali paragraph [0049])): receiving agent personalization data that is associated with a user and includes machine learning model instructions (“tracking a player's profile, wherein the profile comprises at least one of information about a method of playing by the player, playing skill of the player, playing actions performed by the player, and appearance of the player; monitoring changes in the profile based on at least one factor; and modifying the existing non-player character according to the tracking and the monitoring to create a modified non-player character corresponding to the player” (recited in at least: Eatedali paragraph [0030]); “a process of applying a machine learning process to game data and/or human player data” (recited in at least: Eatedali paragraph [0042])); instantiating a first agent in a first gameplay session of a first game title with the user, wherein behavior of the first agent is defined by the first game title as a non-playable character (NPC) of the first game title (“at 206, one or more NPCs corresponding to the player's play profile are modified in real time or generated by AI engine 128. Initially, in some embodiments, a baseline NPC assigned to the player is developed to mimic the play profile of the user. 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” (recited in at least: Eatedali paragraph [0086])). In paragraphs 13-14 of Eatedali, it is described that there can be different video games with different genres used in this system; receiving an indication for control of the first agent according to a personalized agent of the user; controlling the first agent based upon one or more machine learning models comprising a generative machine learning model configured to process user input and the machine learning model instructions included in the agent personalization data to generate model output based thereon, the controlling comprising (“During gameplay, AI engine 128 may control an NPC's behavior (including gameplay actions) such that the NPC's gameplay more closely mimics the gameplay of the corresponding player. In one implementation, AI engine 128 may analyze an NPC player play profile (as selected or generated in the manner described above) to determine an appropriate skill level of play of the NPC” (recited in at least: Eatedali paragraph [0079])): receiving, by the first agent, a user interaction during gameplay; generating, via the one or more machine learning models, a response to the user interaction for the first agent; and instructing the first agent to perform the generated response, thereby controlling the non-playable character via the personalized agent of the user updating the machine learning model instructions included in the agent personalization data based at least in part on the gameplay of the first game title (“an NPC may be directed (or trained) by AI engine 128 to modify or improve its gameplay behavior corresponding to the changes in the player's gameplay behavior. AI engine 128 may monitor player and NPC gameplay performance in an effort to continually improve NPC performance, logic, strategy, and/or other NPC characteristics. In some implementations, the player's play profile is monitored over and over in multiple gameplay sessions, and AI engine 128 may fine tune the performance of the NPC each time so that it behaves in a manner more and more consistent with that of the player in its gameplay sessions. In this regard, NPC management engine 124 may, over time, have access to a plurality of NPCs (e.g., stored in database 160) that have been fine-tuned according to the play profile of each player. In implementations wherein a NPC is not saved or stored for later gameplay sessions, AI engine 128 may nonetheless fine tune one or more of the NPC's player profile attributes and save them in a template or model for later use by NPC management engine 124 and or NPC profile engine 126” (recited in at least: Eatedali paragraph [0080])); instantiating a second agent in a second gameplay session of a second game title with the user, wherein behavior of the second agent is defined by the second game title as an NPC of the second game title; and at least in part at the one or more machine learning models, controlling the second agent based at least in part on the updated machine learning model instructions (“In this regard, NPC management engine 124 may, over time, have access to a plurality of NPCs (e.g., stored in database 160) that have been fine-tuned according to the play profile of each player” (recited in at least: Eatedali paragraph [0080]); “A game profile may be specific for a given gameplay session (e.g., different game profiles may be associated with different gameplay sessions) and/or may be used to generate a broader game profile for a particular game (e.g., different games may be associated with different game profiles). In this manner, a given game or gameplay session may be characterized using a game profile” (recited in at least: Eatedali paragraph [0071])).
Regarding claim 9, Eatedali discloses a method comprising: steps similar in scope to independent claim 1.
Regarding claim 2, and similarly claim 10, Eatedali discloses the claimed subject matter as stated above, and Eatedali further discloses 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 title (“the neural network or machine learning process comprises at least one… a reinforcement learning process” (recited in at least: Eatedali paragraph [0014])).
Regarding claim 4, and similarly claim 12, Eatedali discloses the claimed subject matter as stated above, and Eatedali further discloses wherein the first agent is controlled based on one or more of: information about the first game title; information about the non-player character; identification of a specific agent; or identification of specific agent characteristics (“game outcomes may be used to modify one or more NPC behavioral or decision traits. In particular, AI engine 128 may be configured to associate, in real-time, specific game outcome data, such as an amount of time a game is played, an amount of virtual resources earned, lost, or exchanged, a score, a time of completion, a number of levels achieved, or other metrics indicative of engagement, with a reaction time of the NPCs, how the NPC reacts to one or more specific events, or the choice the NPC may make in response to one or more game options” (recited in at least: Eatedali paragraph [0070])).
Regarding claim 5, and similarly claim 13, Eatedali discloses the claimed subject matter as stated above, and Eatedali further discloses determining a genre for the first game title; and identifying the agent personalization data related to the genre (“NPC modification engine 122 and AI engine 128 may also correlate player's play profile and game profile information in order to evolve the NPC(s). The game profile information for all the types of games played by the player, such as games provided by different game engines, may be used by NPC modification engine 122 for its purpose. In one implementation, a game profile may be generated for a gameplay session based on gameplay information. Gameplay information may describe various game characteristics of a gameplay session that may influence the quality of gameplay. For example, gameplay information may include, without limitation, a number of players, types of roles (e.g., snipers), types of in-game items used or purchased (e.g., weapons, vehicles, armor, custom suits, custom paint, tires, engine modifications, etc.), composition of teams (e.g., number and/or types of roles in each team), maps or game levels played (e.g., battle zones, racetracks, sporting arenas, etc.), duration of gameplay (e.g., how duration of a given gameplay session), player skill levels, player styles (e.g., aggressive, prefers to be a sniper, etc.), types of matches (e.g., team death match, capture the flag, etc.), and/or other information related to a gameplay session” (recited in at least: Eatedali paragraph [0069])).
Regarding claim 6, and similarly claim 14, Eatedali discloses the claimed subject matter as stated above, and Eatedali further discloses determining one or more NPC characteristics, wherein NPC characteristic comprise one or more of: NPC abilities, NPC party role; or NPC characteristics; and identifying the agent personalization data related to the one or more NPC characteristics (“player information may comprise a variety of player attributes including, without limitation, screen name (or gamer tag), style of gameplay (e.g., aggressive), a role preference (e.g., an explicit indication by the player of such preference), a role actually played, a duration of gameplay sessions, a number of gameplay sessions played by in a given login session, in-game items used or purchased by the player, membership in a clan or team, preference to play with clan mates or friends, demographic information of the player (e.g., geographic location, gender, income level, etc.), win/loss records, scores, and/or other attributes or information without limitation that may be used to determine the play profile of the player in a given gameplay session, a match, and/or a game” (recited in at least: Eatedali paragraph [0073])).
Regarding claim 7, and similarly claim 15, Eatedali discloses the claimed subject matter as stated above, and Eatedali further discloses identifying the specific agent and identifying the agent personalization data comprises identifying the agent personalization data associated with the specific agent (“the modified and improved NPC(s) are stored in database 160. NPC management engine 124 may select and retrieve one or more NPCs from database 160” (recited in at least: Eatedali paragraph [0076])).
Regarding claim 8, and similarly claim 16, Eatedali discloses the claimed subject matter as stated above, and Eatedali further discloses wherein the controlling further comprises identifying the specific agent characteristics and identifying of the agent personalization data comprises identifying the specific agent characteristics from a plurality of agent characteristics (“NPCs created in NPC application 120 are styled by NPC profile engine 126 to resemble their corresponding human players in terms of both player profile attributes and gameplay actions such that players may not recognize NPCs as non-human, computer-controlled players. A variety of known computational and/or statistical methods may be used to ensure that an NPC is generated (for current gameplay or later selection) having attributes and attribute values typical of other human players in a gameplay session” (recited in at least: Eatedali paragraph [0078])).
Regarding claim 17, Eatedali discloses a method comprising: receiving agent personalization data that is associated with a user and includes machine learning model instructions (“tracking a player's profile, wherein the profile comprises at least one of information about a method of playing by the player, playing skill of the player, playing actions performed by the player, and appearance of the player; monitoring changes in the profile based on at least one factor; and modifying the existing non-player character according to the tracking and the monitoring to create a modified non-player character corresponding to the player” (recited in at least: Eatedali paragraph [0030]); “a process of applying a machine learning process to game data and/or human player data” (recited in at least: Eatedali paragraph [0042])); instantiating a first non-playable character (NPC) for a first video game title, wherein the first NPC is operable to interact with the user of the first video game title based on a generative machine learning model (“at 206, one or more NPCs corresponding to the player's play profile are modified in real time or generated by AI engine 128” (recited in at least: Eatedali paragraph [0086])) configured to process first user input and first prompt input to generate first model output based thereon, wherein the first prompt corresponds to the agent personalization data for the user (“AI engine 128, generates an NPC to mimic the tracked human player. In some embodiments, the tracked human player is a high performing human player, or ‘star’ player” (recited in at least: Eatedali paragraph [00067])); receiving, for the first NPC, a user interaction during gameplay; generating, via the generative machine learning model and using a prompt corresponding to the user, a response to the user interaction; instructing the first NPC to perform the generated response; updating the machine learning model instructions included in the agent personalization data based at least in part on the gameplay of the first video game title (“game outcomes may be used to modify one or more NPC behavioral or decision traits. In particular, AI engine 128 may be configured to associate, in real-time, specific game outcome data, such as an amount of time a game is played, an amount of virtual resources earned, lost, or exchanged, a score, a time of completion, a number of levels achieved, or other metrics indicative of engagement, with a reaction time of the NPCs, how the NPC reacts to one or more specific events, or the choice the NPC may make in response to one or more game options. To do so, AI engine 128 may comprise one or more nodes, virtually interlinked in a form of one or more layers, that is trained by providing one or more datasets having the game data or human profile data described above, wherein, as a result of the training process, one or more of the aforementioned nodes is associated with a coefficient, weight, or other value indicator used to weight the importance of the node and to associate inputted data, such as a game event, with a preferred behavior, such as how the NPC should react” (recited in at least: Eatedali paragraph [0070])); instantiating a second NPC for a second video game title, wherein the second NPC is operable to interact with the user of the second video game title based on the generative machine learning model to process second user input and second prompt input to generate second model output based thereon, wherein the second prompt corresponds to the agent personalization data for the user; and at least in part at the one or more machine learning models, controlling the second agent based at least in part on the second user input and the updated machine learning model instructions (“In this regard, NPC management engine 124 may, over time, have access to a plurality of NPCs (e.g., stored in database 160) that have been fine-tuned according to the play profile of each player” (recited in at least: Eatedali paragraph [0080]); “A game profile may be specific for a given gameplay session (e.g., different game profiles may be associated with different gameplay sessions) and/or may be used to generate a broader game profile for a particular game (e.g., different games may be associated with different game profiles). In this manner, a given game or gameplay session may be characterized using a game profile” (recited in at least: Eatedali paragraph [0071])).
Regarding claim 21, Eatedali discloses the claimed subject matter as stated above, and Eatedali further discloses 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 (“FIG. 2 illustrates an exemplary flow chart of processing operations for modifying existing NPCs or generating new NPCs in multiplayer video games, in accordance with some embodiments of the present specification” (recited in at least: Eatedali paragraph [0082])).
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 as applied to claims 1 and 9 above, and further in view of Ilya Gelfenbeyn, et al., (US 20230351661 A1; hereinafter Gelfenbeyn).
Regarding claim 3, and similarly claim 11, Eatedali discloses the claimed subject matter as stated above; however, Eatedali does not explicitly disclose wherein the one or more machine learning models comprise: a language model.
Gelfenbeyn discloses using a language model (“based on the message, an input to a language model configured to predict a first response to the message. The method may proceed with providing the input to the language model to obtain the first response and modifying, based on the goal, the first response to obtain a second response. The second response may be configured to prompt the user to provide a response to achieve the goal” (recited in at least: Gelfenbeyn paragraph [0007])).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have used a language model as taught by Gelfenbeyn into the system of Eatedali to generate dialogue for NPCs based on the data pulled to create the NPCs. A person who has ordinary skill in the art would further use other forms of machine learning models to further personalize NPCs as machine learning models are often used to find patterns in data, make predictions, add personalization, and adapt as new information is provided.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Eatedali as applied to claim 17 above, and further in view of Timothy Stamper et al., (US 20080045283 A1; hereinafter Stamper).
Regarding claim 20, Eatedali discloses the claimed subject matter as stated above; however, Eatedali does not explicitly disclose modifying the agent personalization data, by at least one of: deleting agent personalization data that does not comply with NPC characteristics; or modifying the agent personalization data to comply with the NPC characteristics.
Stamper teaches modifying the agent personalization data, by at least one of: deleting agent personalization data that does not comply with NPC characteristics; or modifying the agent personalization data to comply with the NPC characteristics (“the object may be a laser sword and the source game may be a fantasy game. The target game may, however, be a medieval game scenario in which laser swords do not exist and the powers of which would provide an unfair advantage in that game world (e.g. over a conventional sword or other period weapon)” (recited in at least: Stamper paragraph [0030])).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have changed the NPC data dependent on the game data as taught by Stamper into the system of Eatedali to ensure that game data collected and applied in different scenarios do not break the immersion of the player.
Response to Arguments
35 U.S.C. § 102:
Applicant’s arguments filed 03/18/2026 have been fully considered; however, they are not persuasive. Applicant argues that the prior art does not disclose nor suggest the claimed features in claim 1 as currently amended, the Examiner respectfully disagrees. Eatedali discloses that the system can be used in different games (“A game profile may be specific for a given gameplay session (e.g., different game profiles may be associated with different gameplay sessions) and/or may be used to generate a broader game profile for a particular game (e.g., different games may be associated with different game profiles” (recited in at least: Eatedali paragraph [0071])). Applicant amended the claims to claim two different game titles; however this does not have the specificity needed to make it clear that is distinguishable from Eatedali. Furthermore, the Applicant states that Gelfenbeyn and Stamper do not teach the limitations that are not disclosed in Eatedali. The Examiner respectfully disagrees. Gelfenbeyn teaches the use of Artificial Intelligence (AI), a language model, that can be used in game applications (“The platform may include integration interfaces, such as application programming interfaces (APIs), allowing external applications to use the AI character model. The AI character models generated by the platform can be used in game applications, virtual events and conversations, corporate training, and so on” (recited in at least: Gelfenbeyn paragraph [0029])). The instant application claims the use of a language model, there are no particulars and the language model is used the same way a language model is used in Gelfenbeyn. According to the claims, it does not appear that that the language model that Applicant is using is any different from that used by Gelfenbeyn. If Applicant believes that their language model is somehow different such differences need to be conveyed in the claims with support from the originally filed specification. Furthermore, Gelfenbeyn is used to teach that language models can be used in game applications, not to teach what Eatedali discloses. Stamper teaches moving character data from one game to another game as shown in the rejection above; the prior art is not relied on to teach the claim limitations disclosed in Eatedali just to show that the features not disclosed in Eatedali are used in game applications and a person having ordinary skill in the art would be able to combine the two in a game environment before the effective filing date of the claimed invention as explained above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US 20220309364 A1: Thomas Daniel Perry
Systems for an AI creating NPC that has movements when accompanying a player controlled by a user who is playing a video game. The AI engine teaches the NPC created by the machine learning to stay close to the player but not get in the player's way while acting in a human-like manner. Also, the AI engine is RL-trained to evaluate the quality of information that is received over time from other AI engines and then to act on the evaluated information quality.
US 10839215 B2: Caedmon Somers
Using Artificial Intelligence to emulate human behavior in a video game. The AI model is used by receiving gameplay logs of a video gameplay session, generating, based on the gameplay data, first situational data indicating first states of the video game, generating first control inputs provided by a human, the first control inputs corresponding to the first states of the video game, training a first machine learning system using the first situational data and corresponding first control inputs, and generating, using the first machine learning system, a first artificial intelligence model.
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/SELWA A ALSOMAIRY/Examiner, Art Unit 3715
/Jay Trent Liddle/Primary Examiner, Art Unit 3715
