GAME ANALYTICS USING NATURAL LANGUAGE PROCESSING

    公开(公告)号:US20220370913A1

    公开(公告)日:2022-11-24

    申请号:US17769977

    申请日:2020-05-15

    Applicant: GOOGLE LLC

    Abstract: A processor executes program code that represents a portion of a video game and adds a sequence of text strings that represent game events to a text log during execution of the program code. The processor (or another processor that has access to the text log) performs a natural language processing (NLP) analysis of the text log to determine one or more characteristics of the portion of the video game. In some cases, the NLP analysis includes a sentiment analysis that attempts to determine characteristics of a player's experience while playing the video game, summarization technology that creates a human-readable summary of an aspect of the game or a portion of the video game, a semantic NLP ML algorithm in the semantic similarity modality to answer questions regarding the player's experience during the video game, or grouping players in a multiplayer game based on in-game behavior.

    Using semantic natural language processing machine learning algorithms for a video game application

    公开(公告)号:US12138541B2

    公开(公告)日:2024-11-12

    申请号:US17770946

    申请日:2020-05-15

    Applicant: GOOGLE LLC

    Inventor: Anna Kipnis

    Abstract: Game decisions are coordinated using a semantic natural language processing (NLP) machine learning (ML) algorithm, which is stored in a memory in some cases. In response to a game event, a processor records a text string that represents the game event in a text log that includes a sequence of text strings that represent game events that have transpired during a portion of the game. The processor also generates, using the semantic NLP ML algorithm, scores for labeled actions or content based on the text log and a curve that represents a target player experience as a function of progress through the game. The processor further serves one or more of the labeled actions or content that is selected based on the scores. The labeled actions or content are served to a display associated with the processor.

    USING SEMANTIC NATURAL LANGUAGE PROCESSING MACHINE LEARNING ALGORITHMS FOR A VIDEO GAME APPLICATION

    公开(公告)号:US20220370908A1

    公开(公告)日:2022-11-24

    申请号:US17770946

    申请日:2020-05-15

    Applicant: GOOGLE LLC

    Inventor: Anna Kipnis

    Abstract: Game decisions are coordinated using a semantic natural language processing (NLP) machine learning (ML) algorithm, which is stored in a memory in some cases. In response to a game event, a processor records a text string that represents the game event in a text log that includes a sequence of text strings that represent game events that have transpired during a portion of the game. The processor also generates, using the semantic NLP ML algorithm, scores for labeled actions or content based on the text log and a curve that represents a target player experience as a function of progress through the game. The processor further serves one or more of the labeled actions or content that is selected based on the scores. The labeled actions or content are served to a display associated with the processor.

    CONTROLLING AGENTS IN A VIDEO GAME USING SEMANTIC MACHINE LEARNING AND A NATURAL LANGUAGE ACTION GRAMMAR

    公开(公告)号:US20230330526A1

    公开(公告)日:2023-10-19

    申请号:US17788229

    申请日:2020-04-30

    Applicant: GOOGLE LLC

    Inventor: Anna Kipnis

    CPC classification number: A63F13/422 G06N5/025

    Abstract: A semantic natural language processing (NLP) machine learning (ML) model accesses an expression space that includes first natural language phrases that are mapped to actions that are available to an agent in a video game. The semantic NLP ML model receives a second natural language phrase that represents a stimulus for the agent in the video game. One of the actions for the agent is selected based on comparisons of the first natural language phrases and the second natural language phrase. The agent in the video game is then caused to perform the selected one of the actions. In some cases, the expression space is constructed based on an action grammar that defines one or more sentence structures that include one or more tokens that are replaced by natural language phrases to form natural language sentences in the expression space.

    USING SEMANTIC NATURAL LANGUAGE PROCESSING MACHINE LEARNING ALGORITHMS FOR A VIDEO GAME APPLICATION

    公开(公告)号:US20250114705A1

    公开(公告)日:2025-04-10

    申请号:US18911577

    申请日:2024-10-10

    Applicant: GOOGLE LLC

    Inventor: Anna Kipnis

    Abstract: Game decisions are coordinated using a semantic natural language processing (NLP) machine learning (ML) algorithm, which is stored in a memory in some cases. In response to a game event, a processor records a text string that represents the game event in a text log that includes a sequence of text strings that represent game events that have transpired during a portion of the game. The processor also generates, using the semantic NLP ML algorithm, scores for labeled actions or content based on the text log and a curve that represents a target player experience as a function of progress through the game. The processor further serves one or more of the labeled actions or content that is selected based on the scores. The labeled actions or content are served to a display associated with the processor.

    Game analytics using natural language processing

    公开(公告)号:US12226701B2

    公开(公告)日:2025-02-18

    申请号:US17769977

    申请日:2020-05-15

    Applicant: GOOGLE LLC

    Abstract: A processor executes program code that represents a portion of a video game and adds a sequence of text strings that represent game events to a text log during execution of the program code. The processor (or another processor that has access to the text log) performs a natural language processing (NLP) analysis of the text log to determine one or more characteristics of the portion of the video game. In some cases, the NLP analysis includes a sentiment analysis that attempts to determine characteristics of a player's experience while playing the video game, summarization technology that creates a human-readable summary of an aspect of the game or a portion of the video game, a semantic NLP ML algorithm in the semantic similarity modality to answer questions regarding the player's experience during the video game, or grouping players in a multiplayer game based on in-game behavior.

    RE-RANKING RESULTS FROM SEMANTIC NATURAL LANGUAGE PROCESSING MACHINE LEARNING ALGORITHMS FOR IMPLEMENTATION IN VIDEO GAMES

    公开(公告)号:US20240050848A1

    公开(公告)日:2024-02-15

    申请号:US17766616

    申请日:2020-04-30

    Applicant: GOOGLE LLC

    CPC classification number: A63F13/424 G06F40/30 A63F13/422

    Abstract: Program code representing a semantic natural language processing (NLP) machine learning (ML) algorithm is stored in a memory. A processor executes the semantic NLP ML algorithm to generate initial scores that represent a degree of matching between candidate responses and an input phrase provided by a user during execution of program code. The processor also modifies one or more of the initial scores using one or more rules that associate a first phrase with a second phrase. The one or more rules are selected to modify the initial scores based on semantic similarity of the user input phrase and the first phrase determined by the semantic NLP ML algorithm and the semantic similarity of the response phrase with a corresponding candidate response. Execution of the program code is modified based on the modified initial scores. In some cases, the semantic NLP ML algorithm is used to implement a video game.

    USING CANONICAL UTTERANCES FOR TEXT OR VOICE COMMUNICATION

    公开(公告)号:US20230245650A1

    公开(公告)日:2023-08-03

    申请号:US18009488

    申请日:2020-06-11

    Applicant: GOOGLE LLC

    CPC classification number: G10L15/1815 G10L15/183 G10L25/54 G10L2015/088

    Abstract: A memory stores information representing a set of canonical utterances. A processor receives information representing an utterance from a first user of an application and selects a canonical utterance from the set of canonical utterances based on semantic comparisons of the utterance from the first user and the set of canonical utterances. The semantic comparisons include semantic retrieval and semantic similarity operations that can be performed by a semantic natural language processing machine learning model. The processor presents the canonical utterance to a second user of the application instead of presenting the utterance from the first user. In some cases, the processor replaces the utterances from the user in a text stream or a voice chat with the canonical utterances in the set of canonical utterances.

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