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 .
Response to Amendment
The amendment filed April 2nd, 2026 has been entered. Claims 1-20 remain pending in the application. Applicant’s amendments to the Specification and Claims have overcome all objections, 112(b) rejections, and 101 rejections previously set forth in the Non-Final Office Action of February 2nd, 2026.
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 (i.e., changing from AIA to pre-AIA ) 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, 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, 2, 3, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lucas et al. (US 20170266568, Lucas hereinafter) in view Goossen et al. (US20090113303, Goossen hereinafter) and Kennett et al. (US 20220401843, Kennett hereinafter).
Regarding claim 1, Lucas discloses: A system, comprising: one or more processors (see Lucas et al., paragraph [0140]); and one or more non-transitory computer-readable media storing computer-executable instructions (see Lucas et al., paragraph [0140]); that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving video game execution data of a video game (see Lucas et al., paragraph [0003]: “…the game application analysis system configured to: …receive video data associated with the gameplay session…”); including:
a rendered output of the video game generated during the execution of the video game (see Lucas et al., paragraph [0004], “Another embodiment discloses a computer-implemented method comprising: …receiving gameplay data associated with the gameplay session, wherein the gameplay data comprises video data and telemetry data…”); and
telemetry data of the video game generated during the execution of the video game (see Lucas et al., paragraph [0003], “…the game application analysis system configured to: …receive telemetry data associated with the gameplay session…”); and
(see Lucas et al., paragraph [0017]: “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”).
Lucas does not appear to distinctly disclose:
textual data generated by instrumented code of the video game during execution of the video game, the textual data indicating at least one of:
where program execution is in video game code or code execution paths of the video game, and
configuring a machine learning (ML) model.
However, Goossen discloses:
textual data (time stamps) generated by instrumented code of the video game during execution of the video game (see Goossen, paragraph [0031], “…debugger software module 120 tracks each instrumented API call and stores information pertaining to the API call as well as time stamps associated with the API call. In other capture processes, debugger software module 120 captures and stores run-time game data associated with call stacks, timing of events, and user data.”), (see Goossen, paragraph [0019], “The following description generally provides details of systems and methods for analyzing the performance of a video game by non-intrusively capturing and storing run-time game data during execution of gaming application code…”), the textual data indicating at least one of:
where program execution is in video game code (time stamps, game data associated with call stacks) (see Goossen, paragraph [0031]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include receiving textual data indicating where program execution is in video game code as taught by Goossen for the result of more accurately detecting or predicting performance issues.
Lucas as modified does not appear to distinctly disclose:
configuring a machine learning (ML) model.
However, Kennett discloses:
configuring a machine learning (ML) model (see Kennett et al., paragraph [0015]: “…The event reporting system may identify a first event recognizer having been trained to detect instances of the event based on individual detection model(s) that have been trained to output one or more signals based on consumable gaming content (e.g., video content, audio content, controller inputs) associated with a given gaming session.…” and paragraph [0028]: “As used herein, a ‘recognizer” or ‘event recognizer” may refer to a component of the event reposting system having one or more models (e.g., machine learning models) that are trained ...”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include a configured ML model as taught by Kennett for the result of tracking gaming events.
Regarding claim 2, Lucas teaches: wherein at least a portion of the telemetry data is overlayed into the rendered output (see Lucas et al., paragraph [0003]: “Associating telemetry data with video data of the gameplay session can ameliorate this difficulty. During the gameplay session, both telemetry data and video data are recorded. The gameplay session can have a session identifier (session ID). The telemetry data and video data of the gameplay session can both be linked to the same session ID.”);
Regarding claim 3, Lucas teaches: wherein the instrumented code includes code for one or more of: (see Lucas et al., paragraph [0003]: “…In some embodiments, the telemetry data acquisition system 134 may also acquire system information associated with the game application. The system information may include performance data such as CPU or memory utilization rate, machine on which the game application is executed, and so on.”);
Regarding claim 6, Lucas teaches: wherein an event of the type of events is one of: (see Lucas et al., paragraph [0017]: “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”);
Regarding claim 7, Lucas teaches: an event that is at least one of (see Lucas et al., paragraph [0017]: “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”); and at least one of: a code execution path associated with the event(see Lucas et al., paragraph [0003]: “…the game application analysis system configured to: …receive telemetry data associated with the gameplay session; associate the telemetry data with the session identifier of the gameplay session, wherein the telemetry data comprises a plurality of gameplay events recorded during the gameplay session, wherein each event of the plurality of gameplay events is associated with at least one timestamp of the plurality of timestamps…”);
Lucas does not distinctly disclose: configuring the ML model to output data, and second video game execution data.
However, Kennett teaches: configuring the ML model to output data (see Kennett et al., paragraph [0015]: “…The event reporting system may identify a first event recognizer having been trained to detect instances of the event based on individual detection model(s) that have been trained to output one or more signals based on consumable gaming content (e.g., video content, audio content, controller inputs) associated with a given gaming session.…” and paragraph [0028]: “As used herein, a ‘recognizer” or ‘event recognizer” may refer to a component of the event reposting system having one or more models (e.g., machine learning models) that are trained ...”)); and second video game execution data (see Kennett et al., paragraph [0099], “As further shown, the series of acts 700 may include an act 740 of applying the event recognizer to the first gaming content and the second gaming content to identify multiple instances of the event within the first and second gaming content...”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include a configured ML model and data from a second gaming session as taught by Kennett for the result of tracking gaming events.
Regarding claim 8, Lucas teaches: detect or (see Lucas et al., paragraph [0017]: “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”);
Lucas does not distinctly disclose: configuring the ML model and second video game execution data of a second execution of the video game not including data generated by at least a portion of the instrumented code of the video game.
However, Kennett teaches: configuring the ML model (see Kennett et al., paragraph [0015]: “…The event reporting system may identify a first event recognizer having been trained to detect instances of the event based on individual detection model(s) that have been trained to output one or more signals based on consumable gaming content (e.g., video content, audio content, controller inputs) associated with a given gaming session…” and paragraph [0028]: “As used herein, a ‘recognizer” or ‘event recognizer” may refer to a component of the event reposting system having one or more models (e.g., machine learning models) that are trained ...”)); and second video game execution data of a second execution of the video game not including data generated by at least a portion of the instrumented code of the video game (see Kennett et al., paragraph [0099], “As further shown, the series of acts 700 may include an act 740 of applying the event recognizer to the first gaming content and the second gaming content to identify multiple instances of the event within the first and second gaming content...”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include a configured ML model and data from a second gaming session as taught by Kennett for the result of tracking gaming events.
Regarding claim 9, Lucas discloses: A computer-implemented method comprising:
receiving video game execution data of a video game (see Lucas et al., paragraph [0003]: “…the game application analysis system configured to: …receive video data associated with the gameplay session…”); including:
a rendered output of the video game generated during the execution of the video game (see Lucas et al., paragraph [0004], “Another embodiment discloses a computer-implemented method comprising: …receiving gameplay data associated with the gameplay session, wherein the gameplay data comprises video data and telemetry data…”); and
telemetry data of the video game generated during the execution of the video game (see Lucas et al., paragraph [0003], “…the game application analysis system configured to: …receive telemetry data associated with the gameplay session…”); and
(see Lucas et al., paragraph [0017]: “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”).
Lucas does not appear to distinctly disclose:
textual data generated by instrumented code of the video game during execution of the video game, the textual data indicating at least one of:
where program execution is in video game code or code execution paths of the video game, and
configuring a machine learning (ML) model.
However, Goossen discloses:
textual data (time stamps) generated by instrumented code of the video game during execution of the video game (see Goossen, paragraph [0031], “…debugger software module 120 tracks each instrumented API call and stores information pertaining to the API call as well as time stamps associated with the API call. In other capture processes, debugger software module 120 captures and stores run-time game data associated with call stacks, timing of events, and user data.”), (see Goossen, paragraph [0019], “The following description generally provides details of systems and methods for analyzing the performance of a video game by non-intrusively capturing and storing run-time game data during execution of gaming application code…”), the textual data indicating at least one of:
where program execution is in video game code (time stamps, game data associated with call stacks) (see Goossen, paragraph [0031]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include receiving textual data indicating where program execution is in video game code as taught by Goossen for the result of more accurately detecting or predicting performance issues.
Lucas as modified does not appear to distinctly disclose:
configuring a machine learning (ML) model.
However, Kennett discloses:
configuring a machine learning (ML) model (see Kennett et al., paragraph [0015]: “…The event reporting system may identify a first event recognizer having been trained to detect instances of the event based on individual detection model(s) that have been trained to output one or more signals based on consumable gaming content (e.g., video content, audio content, controller inputs) associated with a given gaming session.…” and paragraph [0028]: “As used herein, a ‘recognizer” or ‘event recognizer” may refer to a component of the event reposting system having one or more models (e.g., machine learning models) that are trained ...”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include a configured ML model as taught by Kennett for the result of tracking gaming events.
Regarding claim 10, Lucas teaches: wherein at least a portion of the telemetry data is overlayed into the rendered output (see Lucas et al., paragraph [0003]: “Associating telemetry data with video data of the gameplay session can ameliorate this difficulty. During the gameplay session, both telemetry data and video data are recorded. The gameplay session can have a session identifier (session ID). The telemetry data and video data of the gameplay session can both be linked to the same session ID.”);
Regarding claim 11, Lucas teaches: wherein the instrumented code includes code for one or more of: (see Lucas et al., paragraph [0003]: “…In some embodiments, the telemetry data acquisition system 134 may also acquire system information associated with the game application. The system information may include performance data such as CPU or memory utilization rate, machine on which the game application is executed, and so on.”);
Regarding claim 14, Lucas teaches: wherein an event of the type of events is one of: (see Lucas et al., paragraph [0017]: “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”);
Regarding claim 15, Lucas discloses: One or more non-transitory computer-readable media storing computer-executable instructions (see Lucas et al., paragraph [0140]); that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving video game execution data of a video game (see Lucas et al., paragraph [0003]: “…the game application analysis system configured to: …receive video data associated with the gameplay session…”); including:
a rendered output of the video game generated during the execution of the video game (see Lucas et al., paragraph [0004], “Another embodiment discloses a computer-implemented method comprising: …receiving gameplay data associated with the gameplay session, wherein the gameplay data comprises video data and telemetry data…”); and
telemetry data of the video game generated during the execution of the video game (see Lucas et al., paragraph [0003], “…the game application analysis system configured to: …receive telemetry data associated with the gameplay session…”); and
(see Lucas et al., paragraph [0017]: “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”).
Lucas does not appear to distinctly disclose:
textual data generated by instrumented code of the video game during execution of the video game, the textual data indicating at least one of:
where program execution is in video game code or code execution paths of the video game, and
configuring a machine learning (ML) model.
However, Goossen discloses:
textual data (time stamps) generated by instrumented code of the video game during execution of the video game (see Goossen, paragraph [0031], “…debugger software module 120 tracks each instrumented API call and stores information pertaining to the API call as well as time stamps associated with the API call. In other capture processes, debugger software module 120 captures and stores run-time game data associated with call stacks, timing of events, and user data.”), (see Goossen, paragraph [0019], “The following description generally provides details of systems and methods for analyzing the performance of a video game by non-intrusively capturing and storing run-time game data during execution of gaming application code…”), the textual data indicating at least one of:
where program execution is in video game code (time stamps, game data associated with call stacks) (see Goossen, paragraph [0031]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include receiving textual data indicating where program execution is in video game code as taught by Goossen for the result of more accurately detecting or predicting performance issues.
Lucas as modified does not appear to distinctly disclose:
configuring a machine learning (ML) model.
However, Kennett discloses:
configuring a machine learning (ML) model (see Kennett et al., paragraph [0015]: “…The event reporting system may identify a first event recognizer having been trained to detect instances of the event based on individual detection model(s) that have been trained to output one or more signals based on consumable gaming content (e.g., video content, audio content, controller inputs) associated with a given gaming session.…” and paragraph [0028]: “As used herein, a ‘recognizer” or ‘event recognizer” may refer to a component of the event reposting system having one or more models (e.g., machine learning models) that are trained ...”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include a configured ML model as taught by Kennett for the result of tracking gaming events.
Regarding claim 16, Lucas teaches: wherein the data associated with the event includes at least one of: a code execution path associated with the event; (see Lucas et al., paragraph [0017]: “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event. Accordingly, when the user clicks on a timestamp, the system can show the user what is visually and audibly happening in the game application. The system can also provide the user with telemetry data associated with that timestamp…”);
Regarding claim 17, Lucas teaches: wherein an event of the type of events is one of: (see Lucas et al., paragraph [0017]: “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”);
Regarding claim 20, Lucas teaches: wherein at least a portion of the telemetry data is overlayed into the rendered output (see Lucas et al., paragraph [0003]: “Associating telemetry data with video data of the gameplay session can ameliorate this difficulty. During the gameplay session, both telemetry data and video data are recorded. The gameplay session can have a session identifier (session ID). The telemetry data and video data of the gameplay session can both be linked to the same session ID.”);
Claims 4, 5, 12, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lucas, Goossen, and Kennett as applied to claims 1, 9 and 15 above, and further in view of Xu et al., (US 20230028898)
Regarding claim 4, Lucas teaches: at least one of (see Lucas et al., paragraph [0017]: “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”);
Lucas does not distinctly disclose: configuring the ML model to output changes to one or more settings of the video game, and second video game execution data.
However, Kennett teaches: configuring the ML model to output (see Kennett et al., paragraph [0015]: “…The event reporting system may identify a first event recognizer having been trained to detect instances of the event based on individual detection model(s) that have been trained to output one or more signals based on consumable gaming content (e.g., video content, audio content, controller inputs) associated with a given gaming session…”); and second video game execution data (see Kennett et al., paragraph [0099], “As further shown, the series of acts 700 may include an act 740 of applying the event recognizer to the first gaming content and the second gaming content to identify multiple instances of the event within the first and second gaming content...”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include a configured ML model and data from a second gaming session as taught by Kennett for the result of tracking gaming events.
Lucas as modified does not appear to distinctly disclose: changes to one or more settings of the video game.
However, Xu teaches: changes to one or more settings of the video game (see Xu et al., paragraph [0207]: “According to the frame rate adjustment method provided in this embodiment of this application, the game client can dynamically adjust the running frame rate of the game, and the dynamic frame rate adjustment can be notified to the terminal device in real time, allowing the terminal device to perform real-time adjustment with changes of the game…”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include the adjustment of settings as taught by Xu for the result of handling performance related events.
Regarding claim 5, Lucas as modified teaches: wherein the execution is a first execution, and wherein the second video game execution data (see Kennett et al., paragraph [0099], “As further shown, the series of acts 700 may include an act 740 of applying the event recognizer to the first gaming content and the second gaming content to identify multiple instances of the event within the first and second gaming content...”); is output during a second execution of the video game (see Kennett et al., paragraph [0061], “In addition, in one or more embodiments, one or more event recognizers may be used to detect events in real-time as the original gaming content is delivered to the client system 106 while one or more additional event recognizers may be used to detect events after the fact for other reporting purposes…”).
Lucas does not appear to distinctly disclose: the ML model is configured to output.
However, Kennett discloses: the ML model is configured to output (see Kennett et al., paragraph [0015]: “…The event reporting system may identify a first event recognizer having been trained to detect instances of the event based on individual detection model(s) that have been trained to output one or more signals based on consumable gaming content (e.g., video content, audio content, controller inputs) associated with a given gaming session.…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include a configured ML model and data from a second gaming session as taught by Kennett for the result of tracking gaming events.
Lucas as modified does not distinctly disclose: changes to the one or more settings of the video game during the second execution of the video game.
However, Xu teaches: changes to the one or more settings of the video game during the second execution of the video game (see Xu et al., paragraph [0207]: “According to the frame rate adjustment method provided in this embodiment of this application, the game client can dynamically adjust the running frame rate of the game, and the dynamic frame rate adjustment can be notified to the terminal device in real time, allowing the terminal device to perform real-time adjustment with changes of the game…”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include the adjustment of settings as taught by Xu for the result of handling performance related events.
Regarding claim 12, Lucas teaches: at least one of (see Lucas et al., paragraph [0017]: “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”);
Lucas does not distinctly disclose: configuring the ML model to output changes to one or more settings of the video game, and second video game execution data.
However, Kennett teaches: configuring the ML model to output (see Kennett et al., paragraph [0015]: “…The event reporting system may identify a first event recognizer having been trained to detect instances of the event based on individual detection model(s) that have been trained to output one or more signals based on consumable gaming content (e.g., video content, audio content, controller inputs) associated with a given gaming session…”); and second video game execution data (see Kennett et al., paragraph [0099], “As further shown, the series of acts 700 may include an act 740 of applying the event recognizer to the first gaming content and the second gaming content to identify multiple instances of the event within the first and second gaming content...”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include a configured ML model and data from a second gaming session as taught by Kennett for the result of tracking gaming events.
Lucas as modified does not appear to distinctly disclose: changes to one or more settings of the video game.
However, Xu teaches: changes to one or more settings of the video game (see Xu et al., paragraph [0207]: “According to the frame rate adjustment method provided in this embodiment of this application, the game client can dynamically adjust the running frame rate of the game, and the dynamic frame rate adjustment can be notified to the terminal device in real time, allowing the terminal device to perform real-time adjustment with changes of the game…”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include the adjustment of settings as taught by Xu for the result of handling performance related events.
Regarding claim 13, Lucas as modified teaches: wherein the execution is a first execution and wherein the second video game execution data (see Kennett et al., paragraph [0099], “As further shown, the series of acts 700 may include an act 740 of applying the event recognizer to the first gaming content and the second gaming content to identify multiple instances of the event within the first and second gaming content...”); is output during a second execution of the video game (see Kennett et al., paragraph [0061], “In addition, in one or more embodiments, one or more event recognizers may be used to detect events in real-time as the original gaming content is delivered to the client system 106 while one or more additional event recognizers may be used to detect events after the fact for other reporting purposes…”).
Lucas does not appear to distinctly disclose: the ML model is configured to output.
However, Kennett discloses: the ML model is configured to output (see Kennett et al., paragraph [0015]: “…The event reporting system may identify a first event recognizer having been trained to detect instances of the event based on individual detection model(s) that have been trained to output one or more signals based on consumable gaming content (e.g., video content, audio content, controller inputs) associated with a given gaming session.…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include a configured ML model and data from a second gaming session as taught by Kennett for the result of tracking gaming events.
Lucas as modified does not distinctly disclose: changes to the one or more settings of the video game.
However, Xu teaches: changes to the one or more settings of the video game during the execution of the video game (see Xu et al., paragraph [0207]: “According to the frame rate adjustment method provided in this embodiment of this application, the game client can dynamically adjust the running frame rate of the game, and the dynamic frame rate adjustment can be notified to the terminal device in real time, allowing the terminal device to perform real-time adjustment with changes of the game…”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include the adjustment of settings as taught by Xu for the result of handling performance related events.
Regarding claim 18, Lucas teaches: wherein the data associated with the event of the type of events in the execution of the video game includes (see Lucas et al., paragraph [0017], “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”);
Lucas does not distinctly disclose: changes to one or more settings of the video game.
However, Xu teaches: changes to one or more settings of the video game (see Xu et al., paragraph [0207]: “According to the frame rate adjustment method provided in this embodiment of this application, the game client can dynamically adjust the running frame rate of the game, and the dynamic frame rate adjustment can be notified to the terminal device in real time, allowing the terminal device to perform real-time adjustment with changes of the game…”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include the adjustment of settings as taught by Xu for the result of handling performance related events.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Lucas, Goossen, Kennett, and Xu as applied to claim 18 above, further in view of Walters et al. ( US20220215466, Walter hereinafter).
Regarding claim 19, Lucas teaches: wherein the event is a (see Lucas et al., paragraph [0017], “…The system described herein can identify an event (for example, a bug or a crash) automatically or based on the user's input (for example, a bug report). The system can associate the telemetry data and the video data with the event based on the session ID and the timestamp of the event…”);
Lucas does not distinctly disclose:
However, Xu teaches: changing the settings of the video game (see Xu et al., paragraph [0207]: “According to the frame rate adjustment method provided in this embodiment of this application, the game client can dynamically adjust the running frame rate of the game, and the dynamic frame rate adjustment can be notified to the terminal device in real time, allowing the terminal device to perform real-time adjustment with changes of the game…”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include the adjustment of settings as taught by Xu for the result of handling performance-related events.
Lucas as modified further does not distinctly disclose:
However, Walters teaches: (see Walters et al., paragraph [0052], “At 208, automatic account modification system 100 may perform automatic account setting modification. Based on the nature of the expected trigger event, one or more account modifications may be available to mitigate harm from the expected trigger event and/or prevent the expected trigger event from occurring altogether…”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a system for analyzing game applications as taught by Lucas to include the adjustment of settings prior to a predicted event as taught by Walters for the result of avoiding performance-related issues.
Response to Arguments
Applicant’s argument: Prior art does not teach the newly amended claims.
Examiner’s response: Applicant’s argument is moot as the newly amended claim is responded to in the above rejection. New art is used to teach the newly amended claim. Goossen teaches storing run-time game data including stack information, timing of events (timestamps of API calls), and user data. Considering broadest reasonable interpretation, timing of events and user data are representable in a textual format. Furthermore, the combination of stack information and timing of events indicate where program execution is in video game code. Therefore, the prior art of reference teaches the newly amended claim.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joshua Tran whose telephone number is (571)272-5460. The examiner can normally be reached on M-F 9-5.
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/JOSHUA TRAN/Examiner, Art Unit 2192
/S. Sough/SPE, Art Unit 2192
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