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 .
DETAILED ACTION
This action is responsive to the application filed February 10, 2025, claims 1-20 are presented for examination. Claims 1, 11 and 19 are independent claims.
Oath/Declaration
The Office acknowledges receipt of a properly signed Oath/Declaration submitted March 26, 2025.
Information Disclosure Statement
The Applicant’s Information Disclosure Statement filed (June 11, 2025) has been received, entered into the record, and considered.
Drawings
The drawings filed February 10, 2025 are accepted by the examiner.
Abstract
The abstract filed February 10, 2025 is accepted by the examiner.
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 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 of this title, 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 1-7, 9-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Beltran et al (US 20240382856 A1) in view of Varanese (US 20260151707 A1).
As to Claim 1:
Beltran et al. discloses a method, at a processing system (Beltran, see Abstract, where Beltran discloses a method for processing an artificial intelligence (AI) model for a gaming application. The method includes training the AI model from a plurality of game plays of a scenario of the gaming application using training state data collected from the plurality of game plays of the scenario and associated success criteria of each of the plurality of game plays. The method includes receiving first input state data during a first game play of the scenario. The method includes applying the first input state data to the AI model to generate an output indicating a degree of success for the scenario for the first game play. The method includes performing an analysis of the output based on a predefined objective. The method includes performing an action to achieve the predefined
objective based on the output that is analyzed), comprising: extracting, independent of one or more application programming interfaces associated with a computational environment, application state data representing a current state of the computational environment (Beltran, see paragraph [0086], where Beltran discloses that the plurality of game plays 310 is controlled by a plurality of players P-1 through P-n, through respective client devices. In another embodiment, the plurality of game plays 310 may be automatically controlled, such as for purposes of self-training the AI model using the plurality of back-end servers. As shown, the game plays provide various game play data 320a through 320n. The game play data may include metadata, including game state data, as previously described. For example, game state data describes the state of the game at a particular point, and
may include controller input data. In addition, the game play data 320a through 320n may include recordings of the game plays 310a through 310n for purposes of extracting the metadata and/or training state data); identifying one or more objectives based on the application state data (Beltran, see paragraph [0097], where Beltran discloses that FIGS. 5A-5F provide various illustrations of different actions or responses that can be performed depending on the objective predefined. In particular, the AI model, as implemented through the deep learning engine 190, matches a
given input state data to one or more rules (each rule providing linked or interconnected nodes and/or features) defined within the trained AI model. Each rule is associated with an output. A success criteria may be applied to generate the rule. In addition, an analyzer 140 takes the output and performs additional analysis to determine the appropriate action in relation to the corresponding input data. For example, when the rule is satisfied with respect to the
success criteria for a given set of input state data, a corresponding action may be identified and/or performed); analyzing the one or more objectives into a plurality of sub-objectives (Beltran, see paragraph [0107], where Beltran discloses at 440, the method includes performing an analysis
of the output based on a predefined objective. In addition, the set of inputs (e.g., current and past sets of inputs) may also be analyzed. Depending on the predefined objective, the analysis may produce an action to be performed for a particular point in the corresponding game play of the
scenario (as determined by the condition or game state of the gaming application indicated by the input set of data). For example, if the predefined objective is to provide assistance, the analysis may produce a recommendation or advice on how to progress through the encountered condition during
game play of the scenario of the gaming application. If the predefined objective is to provide coaching, the analysis may determine a weakness of the player, and provide tutorial sessions for the player to address the weakness. Other predefined objectives are supported, such as to provide
gaming support, provide parity in game plays, to automatically train the AI model, to find flaws in the gaming application, to automatically test the gaming application, etc); generating executable code corresponding to the plurality of sub-objectives; and executing the executable code to perform one or more actions in the computational environment (Beltran, see paragraph [0041], where Beltran discloses that analyzer 140 is configured to utilize the AI model 160 that is trained to provide various functionalities in relation to a game play of the gaming application. In particular, an input data stream 405 is provided as input to the deep learning engine 190 that is configured to implement the trained AI model 160. The trained AI model 160 provides an output in response to the input, wherein the output is dependent on the predefined functionality and/or predefined
objective of the trained AI model 160. For example, the trained AI model 160 may be used by the analyzer 140 to determine what actions need to be taken during the game play-either by the player, or by the corresponding executing instance of the gaming application. The analyzer 140 includes an action generator 170 that is configured to perform an action responsive to the input state data 405 and in consideration of the predefined objective of the trained AI model 160. In that manner, the analyzer through the use of the AI model 160 can provide various functionalities, including providing services to the player playing the gaming application ( e.g., providing recommendations, finding weaknesses of the player, training the player, providing an opponent to the player, finding flaws in the gaming application, etc.)).
Beltran differs from the claimed subject matter in that Beltran does not explicitly disclose decompose.
However in an analogous art, Varanese discloses decompose (Varanese, see paragraph [0039], where Varanese discloses that the game building model 206 can include multiple sub-models for entities, properties, events, and implementations, as shown in and described in relation to FIG. 3 below. In these embodiments, the game building model 206 generates primitive code at each sub-model that is used to generate further primitive code at subsequent sub-models.
It would have been obvious to one of ordinary skill in the art to modify the invention of Beltran with Varanese. One would be motivated to modify Beltran by disclosing decompose as taught by Varanese thereby using game development tools to swiftly and simply convert ideas into playable video games by saving game developers the time and effort of writing code for several monotonous jobs (Varanese, paragraph [0004]).
As to Claim 2:
Beltran in view of Varanese et al. discloses the method of claim 1, wherein the computational environment is a video gaming environment, and the application state data comprises at least one or more in-game variables (Beltran, see paragraph [0089], where Beltran discloses that as shown in FIGS. 3B-1 and 3B-2, modeler 120 of deep learning engine 190 includes a feature identification engine 350 that is configured for identifying a plurality of features of the training state data. For each game play of a corresponding scenario, the training state data includes features. For example, at a particular point in the game play, an instance of training state data may be collected, wherein the training instance includes one or more features ( e.g., a set of features for the training instance), wherein features may include variables, parameters, controller inputs, game state metadata, etc.).
As to Claim 3:
Beltran in view of Varanese et al. discloses that the method of claim 1, further comprising: validating the one or more objectives based on constraints within the computational environment before decomposing the one or more objectives into the plurality of sub-objectives (Beltran, see paragraph [0041], where Beltran discloses that analyzer 140 is configured to utilize the AI model 160 that is trained to provide various functionalities in relation to a game play of the gaming application. In particular, an input data stream 405 is provided as input to the deep learning engine 190 that is configured to implement the trained AI model 160. The trained AI model 160 provides an output in response to the input, wherein the output is dependent on the predefined functionality and/or predefined objective of the trained AI model 160. For example, the trained AI model 160 may be used by the analyzer 140 to determine what actions need to be taken during the game play-either by the player, or by the corresponding executing instance of the gaming application. The analyzer 140 includes an action generator 170 that is configured to perform an action responsive to the input state data 405 and in consideration of the predefined objective of the trained AI model 160. In that manner, the analyzer through the use of the AI model 160 can provide various functionalities, including providing services to the player playing the gaming application ( e.g., providing recommendations, finding weaknesses of the player, training the player, providing an opponent to the player, finding flaws in the gaming application, etc.)).
As to Claim 4:
Beltran in view of Varanese et al. discloses that the method of claim 1, wherein generating the one or more objectives comprises: analyzing, with one or more machine learning models, the application state data; and generating, with the one or more machine learning models, the one or more objectives based on the analyzed application state data (Beltran, see paragraph [0096], where Beltran discloses that as shown in FIGS. 3B-1 and 3B-2, the modeler 120 builds and/or outputs the trained AI model 160, which links the learned paths and/or learned patterns ( e.g., linking labels of the AI model) to a given set of inputs and/or input data relating to game play of a scenario of a gaming application. The AI model 160 can be later used to provide one or more functionalities related to the gaming application and/or game play of the gaming application. That is, given a set of inputs that may indicate a condition of a subsequent game play by a player, the resulting output of the trained AI model 160 can be used (e.g., via the analyzer) to predict and/or determine the best course of action to be taken for that particular point in the game play of the scenario as defined by the corresponding set of input data. For example, a player may be playing the scenario of the gaming application after the AI model 160 has been trained. The player is also encountering difficulty in progressing through the scenario, which may be reflected in the output of the AI model. New and subsequent input state data (e.g., game state) may be related to any data related to that particular
point in a game play of that player (where difficulty is experienced). That input state data for the scenario is received and provided to the AI model via the deep learning engine 190, wherein the AI model may predict as an output how successful the game play will be when playing the
scenario given the given condition of the gaming application. The output from the AI model can be analyzed and used to perform various functionalities related to the gaming application and/or the game play of the gaming application. For example, the output may be analyzed to determine the
best course of action to be taken for that particular point in the game play of the scenario. An action may be performed based on the output. For example, the trained AI model 160 may provide a recommendation to the player to advance his or her game play).
As to Claim 5:
Beltran in view of Varanese et al. discloses that the method of claim 1, further comprising: dynamically adapting the plurality of sub-objectives based on real-time changes in the application state data during execution of the executable code (Beltran, see paragraph [0096], where Beltran discloses that as shown in FIGS. 3B-1 and 3B-2, the modeler 120 builds and/or outputs the trained AI model 160, which links the learned paths and/or learned patterns ( e.g., linking labels of the AI model) to a given set of inputs and/or input data relating to game play of a scenario of a gaming application. The AI model 160 can be later used to provide one or more functionalities related to the gaming application and/or game play of the gaming application. That is, given a set of inputs that may indicate a condition of a subsequent game play by a player, the resulting output of the trained AI model 160 can be used (e.g., via the analyzer) to predict and/or determine the best course of action to be taken for that particular point in the game play of the scenario as defined by the corresponding set of input data. For example, a player may be playing the scenario of the gaming application after the AI model 160 has been trained. The player is also encountering difficulty in progressing through the scenario, which may be reflected in the output of the AI model. New and subsequent input state data (e.g., game state) may be related to any data related to that particular point in a game play of that player (where difficulty is experienced). That input state data for the scenario is received and provided to the AI model via the deep learning engine 190, wherein the AI model may predict as an output how successful the game play will be when playing the scenario given the given condition of the gaming application. The output from the AI model can be analyzed and used to perform various functionalities related to the gaming application and/or the game play of the gaming application. For example, the output may be analyzed to determine the best course of action to be taken for that particular point in the game play of the scenario. An action may be performed based on the output. For example, the trained AI model 160 may provide a recommendation to the player to advance his or her game play).
As to Claim 6:
Beltran in view of Varanese et al. discloses that the method of claim 1, wherein generating the executable code comprises: retrieving functional code from a stored library of previously validated functional code (Beltran, see paragraph [0130], where Beltran discloses that the analyzer 140 is configured to perform quality analysis on the gaming application, such as for purposes of discovering weak points in the gaming application ( e.g., excessively long and boring sequences, difficult sections, etc.), or flaws (e.g., glitches, loops, etc.). For example, the ma/route analyzer 441 is configured to analyze the output ( e.g., game states) of the different permutations of the gaming application to discover the weak points in the gaming application. In one implementation, the game code identifier 443 is configured to discover a problem in the coding of the gaming application, wherein the code location 447 is provided as an output).
As to Claim 7:
Beltran in view of Varanese et al. discloses that the method of claim 1, further comprising: evaluating one or more outcomes of executing the executable code by comparing pre-execution (Beltran, see 310a-310n in figure 3B-1) and post-execution application state data (Beltran, see 320a-320n in figure 3B-1) to determine task completion (Beltran, see success criteria 330 in figure 3B-1).
As to Claim 9:
Beltran in view of Varanese et al. discloses that the method of claim 1, further comprising: generating one or more annotations describing execution context associated with the executable code; and storing executable code with the annotations in a skill library for reuse (Varanese, see paragraph [0086], where Varanese discloses that to train an AI model 880 that is intended to model human language (also referred to as a language model), the data layer 802 is a collection of text documents referred to as a text corpus ( or simply referred to as a corpus). The corpus represents a language domain (e.g., a single language), a subject domain (e.g., scientific papers), and/or encompasses another domain or domains, be they larger or smaller than a single language or subject domain. For example, a relatively large, multilingual, and nonsubject- specific corpus is created by extracting text from online web pages and/or publicly available social media posts. In some embodiments, data layer 802 is annotated with ground truth labels (e.g., each data entry in the training dataset is paired with a label) or unlabeled).
As to Claim 10:
Beltran in view of Varanese et al. discloses that the method of claim 1, wherein executing the executable code comprises: interacting with the computational environment through an action application programming interface (API) to perform one or more actions within the computational environment (Varanese, see paragraph [0083], where Varanese discloses that the ML framework 814 includes
an open-source library, an application programming interface (API), a gradient-boosting library, an ensemble method, and/or a deep learning toolkit that works with the layers of the AI system to facilitate development of the AI model 880).
As to Claim 11:
Beltran et al. discloses a processing system (Beltran, see Abstract, where Beltran discloses a method for processing an artificial intelligence (AI) model for a gaming application. The method includes training the AI model from a plurality of game plays of a scenario of the gaming application using training state data collected from the plurality of game plays of the scenario and associated success criteria of each of the plurality of game plays. The method includes receiving first input state data during a first game play of the scenario. The method includes applying the first input state data to the AI model to generate an output indicating a degree of success for the scenario for the first game play. The method includes performing an analysis of the output based on a predefined objective. The method includes performing an action to achieve the predefined
objective based on the output that is analyzed), comprising: a processing unit (Beltran, AI processor (engine) in figure 2B); and an automation circuit (Beltran, see 190 in figure 2B) coupled to the processing unit (Beltran, AI processor (engine) in figure 2B) and configured to: extract, independent of one or more application programming interfaces associated with a computational environment executing at the processing system, application state data representing a current state of the computational environment (Beltran, see paragraph [0086], where Beltran discloses that the plurality of game plays 310 is controlled by a plurality of players P-1 through P-n, through respective client devices. In another embodiment, the plurality of game plays 310 may be automatically controlled, such as for purposes of self-training the AI model using the plurality of back-end servers. As shown, the game plays provide various game play data 320a through 320n. The game play data may include metadata, including game state data, as previously described. For example, game state data describes the state of the game at a particular point, and may include controller input data. In addition, the game play data 320a through 320n may include recordings of the game plays 310a through 310n for purposes of extracting the metadata and/or training state data); generate one or more objectives based on the application state data Beltran, see paragraph [0097], where Beltran discloses that FIGS. 5A-5F provide various illustrations of different actions or responses that can be performed depending on the objective predefined. In particular, the AI model, as implemented through the deep learning engine 190, matches a given input state data to one or more rules (each rule providing linked or interconnected nodes and/or features) defined within the trained AI model. Each rule is associated with an output. A success criteria may be applied to generate the rule. In addition, an analyzer 140 takes the output and performs additional analysis to determine the appropriate action in relation to the corresponding input data. For example, when the rule is satisfied with respect to the success criteria for a given set of input state data, a corresponding action may be identified and/or performed); analyze the one or more objectives into a plurality of sub-objectives (Beltran, see paragraph [0107], where Beltran discloses at 440, the method includes performing an analysis of the output based on a predefined objective. In addition, the set of inputs (e.g., current and past sets of inputs) may also be analyzed. Depending on the predefined objective, the analysis may produce an action to be performed for a particular point in the corresponding game play of the scenario (as determined by the condition or game state of the gaming application indicated by the input set of data). For example, if the predefined objective is to provide assistance, the analysis may produce a recommendation or advice on how to progress through the encountered condition during game play of the scenario of the gaming application. If the predefined objective is to provide coaching, the analysis may determine a weakness of the player, and provide tutorial sessions for the player to address the weakness. Other predefined objectives are supported, such as to provide gaming support, provide parity in game plays, to automatically train the AI model, to find flaws in the gaming application, to automatically test the gaming application, etc); generate executable code corresponding to the plurality of sub-objectives; and execute the executable code to perform one or more actions in the computational environment (Beltran, see paragraph [0041], where Beltran discloses that analyzer 140 is configured to utilize the AI model 160 that is trained to provide various functionalities in relation to a game play of the gaming application. In particular, an input data stream 405 is provided as input to the deep learning engine 190 that is configured to implement the trained AI model 160. The trained AI model 160 provides an output in response to the input, wherein the output is dependent on the predefined functionality and/or predefined
objective of the trained AI model 160. For example, the trained AI model 160 may be used by the analyzer 140 to determine what actions need to be taken during the game play-either by the player, or by the corresponding executing instance of the gaming application. The analyzer 140 includes an action generator 170 that is configured to perform an action responsive to the input state data 405 and in consideration of the predefined objective of the trained AI model 160. In that manner, the analyzer through the use of the AI model 160 can provide various functionalities, including providing services to the player playing the gaming application ( e.g., providing recommendations, finding weaknesses of the player, training the player, providing an opponent to the player, finding flaws in the gaming application, etc.)).
Beltran differs from the claimed subject matter in that Beltran does not explicitly disclose decompose.
However in an analogous art, Varanese discloses decompose (Varanese, see paragraph [0039], where Varanese discloses that the game building model 206 can include multiple sub-models for entities, properties, events, and implementations, as shown in and described in relation to FIG. 3 below. In these embodiments, the game building model 206 generates primitive code at each sub-model that is used to generate further primitive code at subsequent sub-models.
It would have been obvious to one of ordinary skill in the art to modify the invention of Beltran with Varanese. One would be motivated to modify Beltran by disclosing decompose as taught by Varanese thereby using game development tools to swiftly and simply convert ideas into playable video games by saving game developers the time and effort of writing code for several monotonous jobs (Varanese, paragraph [0004]).
As to Claim 12:
Beltran in view of Varanese et al. discloses that the processing system of claim 11, wherein the computational environment is a video gaming environment, and the application state data comprises at least one or more in-game variables (Beltran, see paragraph [0089], where Beltran discloses that as shown in FIGS. 3B-1 and 3B-2, modeler 120 of deep learning engine 190 includes a feature identification engine 350 that is configured for identifying a plurality of features of the training state data. For each game play of a corresponding scenario, the training state data includes features. For example, at a particular point in the game play, an instance of training state data may be collected, wherein the training instance includes one or more features (e.g., a set of features for the training instance), wherein features may include variables, parameters, controller inputs, game state metadata, etc.).
As to Claim 13:
Beltran in view of Varanese et al. discloses that the processing system of claim 11, wherein the automation circuit is further configured to: validate the one or more objectives based on constraints within the computational environment before the one or more objectives are decomposed into the plurality of sub-objectives (Beltran, see paragraph [0041], where Beltran discloses that analyzer 140 is configured to utilize the AI model 160 that is trained to provide various functionalities in relation to a game play of the gaming application. In particular, an input data stream 405 is provided as input to the deep learning engine 190 that is configured to implement the trained AI model 160. The trained AI model 160 provides an output in response to the input, wherein the output is dependent on the predefined functionality and/or predefined objective of the trained AI model 160. For example, the trained AI model 160 may be used by the analyzer 140 to determine what actions need to be taken during the game play-either by the player, or by the corresponding executing instance of the gaming application. The analyzer 140 includes an action generator 170 that is configured to perform an action responsive to the input state data 405 and in consideration of the predefined objective of the trained AI model 160. In that manner, the analyzer through the use of the AI model 160 can provide various functionalities, including providing services to the player playing the gaming application ( e.g., providing recommendations, finding weaknesses of the player, training the player, providing an opponent to the player, finding flaws in the gaming application, etc.)).
As to Claim 14:
Beltran in view of Varanese et al. discloses that the processing system of claim 11, wherein the automation circuit is configured to generate the one or more objectives by: analyzing, with one or more machine learning models, the application state data; and generating, with the one or more machine learning models, the one or more objectives based on the analyzed application state data (Beltran, see paragraph [0096], where Beltran discloses that as shown in FIGS. 3B-1 and 3B-2, the modeler 120 builds and/or outputs the trained AI model 160, which links the learned paths and/or learned patterns ( e.g., linking labels of the AI model) to a given set of inputs and/or input data relating to game play of a scenario of a gaming application. The AI model 160 can be later used to provide one or more functionalities related to the gaming application and/or game play of the gaming application. That is, given a set of inputs that may indicate a condition of a subsequent game play by a player, the resulting output of the trained AI model 160 can be used (e.g., via the analyzer) to predict and/or determine the best course of action to be taken for that particular point in the game play of the scenario as defined by the corresponding set of input data. For example, a player may be playing the scenario of the gaming application after the AI model 160 has been trained. The player is also encountering difficulty in progressing through the scenario, which may be reflected in the output of the AI model. New and subsequent input state data (e.g., game state) may be related to any data related to that particular point in a game play of that player (where difficulty is experienced). That input state data for the scenario is received and provided to the AI model via the deep learning engine 190, wherein the AI model may predict as an output how successful the game play will be when playing the scenario given the given condition of the gaming application. The output from the AI model can be analyzed and used to perform various functionalities related to the gaming application and/or the game play of the gaming application. For example, the output may be analyzed to determine the best course of action to be taken for that particular point in the game play of the scenario. An action may be performed based on the output. For example, the trained AI model 160 may provide a recommendation to the player to advance his or her game play).
As to Claim 15:
Beltran in view of Varanese et al. discloses that the processing system of claim 11, wherein the automation circuit is further configured: dynamically adapt the plurality of sub-objectives based on real-time changes in the application state data during execution of the executable code (Beltran, see paragraph [0096], where Beltran discloses that as shown in FIGS. 3B-1 and 3B-2, the modeler 120 builds and/or outputs the trained AI model 160, which links the learned paths and/or learned patterns ( e.g., linking labels of the AI model) to a given set of inputs and/or input data relating to game play of a scenario of a gaming application. The AI model 160 can be later used to provide one or more functionalities related to the gaming application and/or game play of the gaming application. That is, given a set of inputs that may indicate a condition of a subsequent game play by a player, the resulting output of the trained AI model 160 can be used (e.g., via the analyzer) to predict and/or determine the best course of action to be taken for that particular point in the game play of the scenario as defined by the corresponding set of input data. For example, a player may be playing the scenario of the gaming application after the AI model 160 has been trained. The player is also encountering difficulty in progressing through the scenario, which may be reflected in the output of the AI model. New and subsequent input state data (e.g., game state) may be related to any data related to that particular point in a game play of that player (where difficulty is experienced). That input state data for the scenario is received and provided to the AI model via the deep learning engine 190, wherein the AI model may predict as an output how successful the game play will be when playing the scenario given the given condition of the gaming application. The output from the AI model can be analyzed and used to perform various functionalities related to the gaming application and/or the game play of the gaming application. For example, the output may be analyzed to determine the best course of action to be taken for that particular point in the game play of the scenario. An action may be performed based on the output. For example, the trained AI model 160 may provide a recommendation to the player to advance his or her game play).
As to Claim 16:
Beltran in view of Varanese et al. discloses that the processing system of claim 11, wherein the automation circuit is configured to generate the executable code by: retrieving functional code from a stored library of previously validated functional code (Beltran, see paragraph [0130], where Beltran discloses that the analyzer 140 is configured to perform quality analysis on the gaming application, such as for purposes of discovering weak points in the gaming application ( e.g., excessively long and boring sequences, difficult sections, etc.), or flaws (e.g., glitches, loops, etc.). For example, the ma/route analyzer 441 is configured to analyze the output ( e.g., game states) of the different permutations of the gaming application to discover the weak points in the gaming application. In one implementation, the game code identifier 443 is configured to discover a problem in the coding of the gaming application, wherein the code location 447 is provided as an output).
As to Claim 18:
Beltran in view of Varanese et al. discloses that the processing system of claim 11, wherein the automation circuit is further configured to: generate one or more annotations describing execution context associated with the executable code; and storing executable code with the one or more annotations in a skill library for reuse (Varanese, see paragraph [0086], where Varanese discloses that to train an AI model 880 that is intended to model human language (also referred to as a language model), the data layer 802 is a collection of text documents referred to as a text corpus ( or simply referred to as a corpus). The corpus represents a language domain (e.g., a single language), a subject domain (e.g., scientific papers), and/or encompasses another domain or domains, be they larger or smaller than a single language or subject domain. For example, a relatively large, multilingual, and nonsubject- specific corpus is created by extracting text from online web pages and/or publicly available social media posts. In some embodiments, data layer 802 is annotated with ground truth labels (e.g., each data entry in the training dataset is paired with a label) or unlabeled).
As to Claim 19:
Beltran et al. discloses a method (Beltran, see Abstract, where Beltran discloses a method for processing an artificial intelligence (AI) model for a gaming application. The method includes training the AI model from a plurality of game plays of a scenario of the gaming application using training state data collected from the plurality of game plays of the scenario and associated success criteria of each of the plurality of game plays. The method includes receiving first input state data during a first game play of the scenario. The method includes applying the first input state data to the AI model to generate an output indicating a degree of success for the scenario for the first game play. The method includes performing an analysis of the output based on a predefined objective. The method includes performing an action to achieve the predefined objective based on the output that is analyzed), comprising: generating one or more objectives based on an analysis of application state data representing a current state of a computational environment (Beltran, see paragraph [0086], where Beltran discloses that the plurality of game plays 310 is controlled by a plurality of players P-1 through P-n, through respective client devices. In another embodiment, the plurality of game plays 310 may be automatically controlled, such as for purposes of self-training the AI model using the plurality of back-end servers. As shown, the game plays provide various game play data 320a through 320n. The game play data may include metadata, including game state data, as previously described. For example, game state data describes the state of the game at a particular point, and
may include controller input data. In addition, the game play data 320a through 320n may include recordings of the game plays 310a through 310n for purposes of extracting the metadata and/or training state data); generating programming code based on sub-objectives derived from contextual data associated with the computational environment(Beltran, see paragraph [0041], where Beltran discloses that analyzer 140 is configured to utilize the AI model 160 that is trained to provide various functionalities in relation to a game play of the gaming application. In particular, an input data stream 405 is provided as input to the deep learning engine 190 that is configured to implement the trained AI model 160. The trained AI model 160 provides an output in response to the input, wherein the output is dependent on the predefined functionality and/or predefined
objective of the trained AI model 160. For example, the trained AI model 160 may be used by the analyzer 140 to determine what actions need to be taken during the game play-either by the player, or by the corresponding executing instance of the gaming application. The analyzer 140 includes an action generator 170 that is configured to perform an action responsive to the input state data 405 and in consideration of the predefined objective of the trained AI model 160. In that manner, the analyzer through the use of the AI model 160 can provide various functionalities, including providing services to the player playing the gaming application ( e.g., providing recommendations, finding weaknesses of the player, training the player, providing an opponent to the player, finding flaws in the gaming application, etc.)); obtaining at least one reusable code component associated with prior validated functional code and that is relevant to the one or more objectives (Beltran, see paragraph [0130], where Beltran discloses that the analyzer 140 is configured to perform quality analysis on the gaming application, such as for purposes of discovering weak points in the gaming application ( e.g., excessively long and boring sequences, difficult sections, etc.), or flaws (e.g., glitches, loops, etc.). For example, the ma/route analyzer 441 is configured to analyze the output ( e.g., game states) of the different permutations of the gaming application to discover the weak points in the gaming application. In one implementation, the game code identifier 443 is configured to discover a problem in the coding of the gaming application, wherein the code location 447 is provided as an output); generating executable code by combining the at least one reusable code component with the dynamically generated programming code; executing the executable code to perform one or more actions within the computational environment (Beltran, see paragraph [0041], where Beltran discloses that analyzer 140 is configured to utilize the AI model 160 that is trained to provide various functionalities in relation to a game play of the gaming application. In particular, an input data stream 405 is provided as input to the deep learning engine 190 that is configured to implement the trained AI model 160. The trained AI model 160 provides an output in response to the input, wherein the output is dependent on the predefined functionality and/or predefined
objective of the trained AI model 160. For example, the trained AI model 160 may be used by the analyzer 140 to determine what actions need to be taken during the game play-either by the player, or by the corresponding executing instance of the gaming application. The analyzer 140 includes an action generator 170 that is configured to perform an action responsive to the input state data 405 and in consideration of the predefined objective of the trained AI model 160. In that manner, the analyzer through the use of the AI model 160 can provide various functionalities, including providing services to the player playing the gaming application ( e.g., providing recommendations, finding weaknesses of the player, training the player, providing an opponent to the player, finding flaws in the gaming application, etc.)); and responsive to evaluating at least one outcome of the one or more actions (Beltran, see 310a-310n in figure 3B-1), refining the one or more objectives or the executable code (Beltran, see 320a-320n in figure 3B-1).
Beltran differs from the claimed subject matter in that Beltran does not explicitly disclose dynamically.
However in an analogous art, Varanese discloses dynamically (Varanese, see paragraph [0081], where Varanese discloses that the servers can perform back-end operations such as matrix calculations, parallel calculations, machine learning (ML) training, and the like).
It would have been obvious to one of ordinary skill in the art to modify the invention of Beltran with Varanese. One would be motivated to modify Beltran by disclosing dynamically as taught by Varanese thereby using game development tools to swiftly and simply convert ideas into playable video games by saving game developers the time and effort of writing code for several monotonous jobs (Varanese, paragraph [0004]).
As to Claim 20:
Beltran in view of Varanese et al. discloses that the method of claim 19, wherein evaluating at least one outcome comprises: detecting whether the one or more objectives have been achieved based on analyzing at least one change in the application state data resulting from executing the executable code (Beltran, see paragraph [0096], where Beltran discloses that as shown in FIGS. 3B-1 and 3B-2, the modeler 120 builds and/or outputs the trained AI model 160, which links the learned paths and/or learned patterns ( e.g., linking labels of the AI model) to a given set of inputs and/or input data relating to game play of a scenario of a gaming application. The AI model 160 can be later used to provide one or more functionalities related to the gaming application and/or game play of the gaming application. That is, given a set of inputs that may indicate a condition of a subsequent game play by a player, the resulting output of the trained AI model 160 can be used (e.g., via the analyzer) to predict and/or determine the best course of action to be taken for that particular point in the game play of the scenario as defined by the corresponding set of input data. For example, a player may be playing the scenario of the gaming application after the AI model 160 has been trained. The player is also encountering difficulty in progressing through the scenario, which may be reflected in the output of the AI model. New and subsequent input state data (e.g., game state) may be related to any data related to that particular point in a game play of that player (where difficulty is experienced). That input state data for the scenario is received and provided to the AI model via the deep learning engine 190, wherein the AI model may predict as an output how successful the game play will be when playing the scenario given the given condition of the gaming application. The output from the AI model can be analyzed and used to perform various functionalities related to the gaming application and/or the game play of the gaming application. For example, the output may be analyzed to determine the best course of action to be taken for that particular point in the game play of the scenario. An action may be performed based on the output. For example, the trained AI model 160 may provide a recommendation to the player to advance his or her game play).
Allowable Subject Matter
Claims 8 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Referring to claims 8 and 17, the following is a statement of reasons for the indication of allowable subject matter: the prior art fail to suggest limitations “responsive to evaluating the one or more outcomes, generating feedback to at least one of refine future executable code or to modify the plurality of sub-objectives based on the one or more outcomes”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kovacs (US 11763143 B2) discloses an encoded artificial intelligence (AI) behavior specification is received. A data generation configuration specification is received. And a deep neural network configuration specification is received. A training data set based on the data generation configuration specification is generated. An AI behavior deep neural network that conforms to the deep neural network configuration specification is trained using at least a subset of the generated training data. The trained AI behavior deep neural network is provided from a remote AI add-in service to a development environment.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NELSON ROSARIO whose telephone number is (571)270-1866. The examiner can normally be reached on Monday through Friday, 7:30am- 5:00pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Eason can be reached on (571) 270-7230. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NELSON M ROSARIO/Primary Examiner, Art Unit 2624
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