Patents by Inventor Navid Aghdaie

Navid Aghdaie has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10449440
    Abstract: Using voice recognition, a user can interact with a companion application to control a video game from a mobile device. Advantageously, the user can interact with the companion application when the video game is unavailable because, for example, of the user's location. Moreover, machine learning may be used to facilitate generating voice responses to user utterances that are predicted to improve or maintain a user's level of engagement with the companion application, or its corresponding video game.
    Type: Grant
    Filed: June 30, 2017
    Date of Patent: October 22, 2019
    Assignee: ELECTRONIC ARTS INC.
    Inventors: Mohsen Sardari, Kenneth Alan Moss, Kazi Atif-Uz Zaman, Navid Aghdaie, John Kolen, Mohamed Marwan Mattar
  • Publication number: 20190262718
    Abstract: Embodiments of systems presented herein may identify users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention. Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the video game.
    Type: Application
    Filed: March 27, 2019
    Publication date: August 29, 2019
    Inventors: Su Xue, Kazi Atif-Uz Zaman, Navid Aghdaie, John Kolen, Zhengxing Chen
  • Patent number: 10384133
    Abstract: Embodiments of the systems and methods described herein can automatically measure the difficulty metrics associated with various aspects of a video game using an artificial intelligence system. The artificial intelligence system may include multiple game agents. Telemetry data associated with the gameplay of each game agent may be recorded while the game application is automatically executed by the game agents. The telemetry data may be communicated to a data analysis system which can calculate game difficulty metrics for various aspects of the game. The data analysis system can determine game difficulty associated with the various aspects based on the game difficulty metrics. The results from the data analysis system may be visualized and communicated to a game developer for updating the operations of the video game.
    Type: Grant
    Filed: December 30, 2016
    Date of Patent: August 20, 2019
    Assignee: Electronic Arts Inc.
    Inventors: Navid Aghdaie, John Kolen, Mohamed Marwan Mattar, Mohsen Sardari, Su Xue, Kazi Atif-Uz Zaman, Meng Wu
  • Patent number: 10369472
    Abstract: The present disclosure provides embodiments of a virtual mapping system for using real world geographical locations to generate virtual environments within game applications. The virtual mapping system allows a game application to identify geographical information associated with real world locations. The geographical information can be used by the game application to recreate the selected geographical location using virtual assets from the game application.
    Type: Grant
    Filed: March 30, 2017
    Date of Patent: August 6, 2019
    Assignee: ELECTRONIC ARTS INC.
    Inventors: Mohamed Marwan Mattar, Meng Wu, John Kolen, Navid Aghdaie, Kazi Atif-Uz Zaman
  • Patent number: 10357718
    Abstract: Embodiments presented herein include systems and methods for performing dynamic difficulty adjustment using processes that may not be detectable or are more difficult to detect by users compared to static and/or existing difficulty adjustment processes. Historical user information may be utilized by a machine learning system to generate a prediction model that predicts an expected duration of game play. Before or during game play, the prediction model can be applied to information about the user to predict the user's expected duration of game play. Based on the expected duration, in some embodiments, the system may then utilize a mapping data repository to determine how to dynamically adjust the difficulty of the game, such as, for example, changing the values of one or more gameplay parameters to make portions of the game less difficult.
    Type: Grant
    Filed: February 28, 2017
    Date of Patent: July 23, 2019
    Assignee: Electronic Arts Inc.
    Inventors: Navid Aghdaie, John Kolen, Mohamed Marwan Mattar, Mohsen Sardari, Su Xue, Kazi Atif-Uz Zaman, Meng Wu
  • Patent number: 10286327
    Abstract: Embodiments of systems presented herein may identify users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention. Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the video game.
    Type: Grant
    Filed: January 26, 2017
    Date of Patent: May 14, 2019
    Assignee: Electronic Arts Inc.
    Inventors: Su Xue, Kazi Atif-Uz Zaman, Navid Aghdaie, John Kolen, Zhengxing Chen
  • Patent number: 10286323
    Abstract: Embodiments of systems presented herein may perform automatic granular difficulty adjustment. In some embodiments, the difficulty adjustment is undetectable by a user. Further, embodiments of systems disclosed herein can review historical user activity data with respect to one or more video games to generate a game retention prediction model that predicts an indication of an expected duration of game play. The game retention prediction model may be applied to a user's activity data to determine an indication of the user's expected duration of game play. Based on the determined expected duration of game play, the difficulty level of the video game may be automatically adjusted.
    Type: Grant
    Filed: February 14, 2018
    Date of Patent: May 14, 2019
    Assignee: Electronic Arts Inc.
    Inventors: Navid Aghdaie, John Kolen, Mohamed Marwan Mattar, Mohsen Sardari, Su Xue, Kazi Atif-Uz Zaman, Kenneth Alan Moss
  • Patent number: 10279264
    Abstract: Embodiments of the present disclosure provide a tutorial system that can aid a user in performing various game commands in response to different game states in a virtual game environment. As the user plays the game, various game states may be encountered. A tutorial engine may, based on a current game state, determine one or more game commands to be recommended to the user, based on historical information of the user and a game state model, wherein the game state model maintains associations between game states and different segments of users. The user is recommended relevant game commands during the normal course of gameplay, based on their own gameplay history and on game commands commonly performed by other users of the game application.
    Type: Grant
    Filed: March 22, 2016
    Date of Patent: May 7, 2019
    Assignee: ELECTRONIC ARTS INC.
    Inventors: Navid Aghdaie, John Kolen, Mohamed Marwan Mattar, Mohsen Sardari, Su Xue, Kazi Atif-Uz Zaman
  • Publication number: 20190001219
    Abstract: Using voice recognition, a user can interact with a companion application to control a video game from a mobile device. Advantageously, the user can interact with the companion application when the video game is unavailable because, for example, of the user's location. Moreover, machine learning may be used to facilitate generating voice responses to user utterances that are predicted to improve or maintain a user's level of engagement with the companion application, or its corresponding video game.
    Type: Application
    Filed: June 30, 2017
    Publication date: January 3, 2019
    Inventors: Mohsen Sardari, Kenneth Alan Moss, Kazi Atif-Uz Zaman, Navid Aghdaie, John Kolen, Mohamed Marwan Mattar
  • Publication number: 20180369696
    Abstract: Embodiments of systems presented herein may identify users to include in a match plan. A parameter model may be generated to predict the retention time of a set of users. A queue of potential users, a set of teammates, and/or opponents may be selected from a queue of waiting users. User information for the set of teammates and/or opponents may be provided to the parameter model to generate a predicted retention time. The set of teammates and/or opponents may be approved if the predicted retention time meets a predetermined threshold. Advantageously, by creating a match plan based on retention rates, the engagement and/or retention level for a number of users may be improved compared to existing multiplayer matching systems.
    Type: Application
    Filed: May 30, 2018
    Publication date: December 27, 2018
    Inventors: Navid Aghdaie, John Kolen, Mohamed Marwan Mattar, Mohsen Sardari, Su Xue, Kazi Atif-Uz Zaman
  • Publication number: 20180243656
    Abstract: Embodiments presented herein include systems and methods for performing dynamic difficulty adjustment. Further, embodiments disclosed herein perform dynamic difficulty adjustment using processes that may not be detectable or are more difficult to detect by users compared to static and/or existing difficulty adjustment processes. In some embodiments, historical user information utilized by a machine learning system to generate a prediction model that predicts an expected duration of game play, such as for example, an expected churn rate, a retention rate, the length of time a user is expected to play the game, or an indication of the user's expected game play time relative to a historical set of users who have previously played the game. Before or during game play, the prediction model can be applied to information about the user to predict the user's expected duration of game play.
    Type: Application
    Filed: February 28, 2017
    Publication date: August 30, 2018
    Inventors: Navid Aghdaie, John Kolen, Mohamed Marwan Mattar, Mohsen Sardari, Su Xue, Kazi Atif-Uz Zaman, Meng Wu
  • Publication number: 20180169526
    Abstract: Embodiments of systems presented herein may perform automatic granular difficulty adjustment. In some embodiments, the difficulty adjustment is undetectable by a user. Further, embodiments of systems disclosed herein can review historical user activity data with respect to one or more video games to generate a game retention prediction model that predicts an indication of an expected duration of game play. The game retention prediction model may be applied to a user's activity data to determine an indication of the user's expected duration of game play. Based on the determined expected duration of game play, the difficulty level of the video game may be automatically adjusted.
    Type: Application
    Filed: February 14, 2018
    Publication date: June 21, 2018
    Inventors: Navid Aghdaie, John Kolen, Mohamed Marwan Mattar, Mohsen Sardari, Su Xue, Kazi Atif-Uz Zaman, Kenneth Alan Moss
  • Patent number: 9993735
    Abstract: Embodiments of systems presented herein may identify users to include in a match plan. A parameter model may be generated to predict the retention time of a set of users. A queue of potential users, a set of teammates, and/or opponents may be selected from a queue of waiting users. User information for the set of teammates and/or opponents may be provided to the parameter model to generate a predicted retention time. The set of teammates and/or opponents may be approved if the predicted retention time meets a predetermined threshold. Advantageously, by creating a match plan based on retention rates, the engagement and/or retention level for a number of users may be improved compared to existing multiplayer matching systems.
    Type: Grant
    Filed: March 8, 2016
    Date of Patent: June 12, 2018
    Assignee: Electronic Arts Inc.
    Inventors: Navid Aghdaie, John Kolen, Mohamed Marwan Mattar, Mohsen Sardari, Su Xue, Kazi Atif-Uz Zaman
  • Publication number: 20180111051
    Abstract: Embodiments of systems presented herein may identify users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention. Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the video game.
    Type: Application
    Filed: January 26, 2017
    Publication date: April 26, 2018
    Inventors: Su Xue, Kazi Atif-Uz Zaman, Navid Aghdaie, John Kolen, Zhengxing Chen
  • Patent number: 9919217
    Abstract: Embodiments of systems presented herein may perform automatic granular difficulty adjustment. In some embodiments, the difficulty adjustment is undetectable by a user. Further, embodiments of systems disclosed herein can review historical user activity data with respect to one or more video games to generate a game retention prediction model that predicts an indication of an expected duration of game play. The game retention prediction model may be applied to a user's activity data to determine an indication of the user's expected duration of game play. Based on the determined expected duration of game play, the difficulty level of the video game may be automatically adjusted.
    Type: Grant
    Filed: March 8, 2016
    Date of Patent: March 20, 2018
    Assignee: Electronic Arts Inc.
    Inventors: Navid Aghdaie, John Kolen, Mohamed Marwan Mattar, Mohsen Sardari, Su Xue, Kazi Atif-Uz Zaman, Kenneth Alan Moss
  • Publication number: 20170259178
    Abstract: Embodiments of systems presented herein may identify users to include in a match plan. A parameter model may be generated to predict the retention time of a set of users. A queue of potential users, a set of teammates, and/or opponents may be selected from a queue of waiting users. User information for the set of teammates and/or opponents may be provided to the parameter model to generate a predicted retention time. The set of teammates and/or opponents may be approved if the predicted retention time meets a predetermined threshold. Advantageously, by creating a match plan based on retention rates, the engagement and/or retention level for a number of users may be improved compared to existing multiplayer matching systems.
    Type: Application
    Filed: March 8, 2016
    Publication date: September 14, 2017
    Inventors: Navid Aghdaie, John Kolen, Mohamed Marwan Mattar, Mohsen Sardari, Su Xue, Kazi Atif-Uz Zaman
  • Publication number: 20170259177
    Abstract: Embodiments of systems presented herein may perform automatic granular difficulty adjustment. In some embodiments, the difficulty adjustment is undetectable by a user. Further, embodiments of systems disclosed herein can review historical user activity data with respect to one or more video games to generate a game retention prediction model that predicts an indication of an expected duration of game play. The game retention prediction model may be applied to a user's activity data to determine an indication of the user's expected duration of game play. Based on the determined expected duration of game play, the difficulty level of the video game may be automatically adjusted.
    Type: Application
    Filed: March 8, 2016
    Publication date: September 14, 2017
    Inventors: Navid Aghdaie, John Kolen, Mohamed Marwan Mattar, Mohsen Sardari, Su Xue, Kazi Atif-Uz Zaman, Kenneth Alan Moss