DETAILED ACTION
Applicant's Submission of a Response
Applicant’s submission of a response on 7/22/2026 has been received and fully considered. In the response, claims 1-15 have been amended. Therefore, claims 1-15 are pending.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1-6 and 8-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 2017/0259177 to Aghdaie.
With regard to claim 1, Aghdaie discloses a computer system (e.g., see system diagram in Fig. 1A) comprising: one or more processors; and one or more memory storing computer-readable instructions that, upon execution by the one or more processors, configured the computer system to: (e.g., see Fig. 1A that shows an interactive computing system 130) to receive user information indicative of an inactivity period for one or more video games previously played by a user (e.g., see at least paragraphs 30 and 41 that discuss activity level as a factor in adjusting mitigation actions; see also Fig. 1B that shows a diagram including Historical data 151 feeding the model); to predict a mitigation action associated with a respective video game of the one or more video games previously played by the user, the mitigation action predicted by using a machine learning model based at least in part on an inactivity period for the respective video game, wherein the machine learning model has been trained based on mapping between a plurality of historical inactivity periods and subsequent user inaction profiles (e.g., see at least paragraph 48 for discussion of using machine learning to generate predication models); generate video game mitigation information for the mitigation action associated with the respective video game (e.g., see at least paragraphs 46 and 47 that discuss configuring the difficulty level based on various factors, including inactivity that was disclosed in paragraphs 30 and 41; see also Fig. 2 that shows a process of predicting model information); and output the video game mitigation information (e.g., see at least Fig. 1C that shows a diagram including Output Data 174; see also paragraph 64 for discussion of outputting data);
[claim 2] wherein the mitigation action comprises a reduction in difficulty associated with the respective video game (e.g., see at list Fig. 6 that shows a Difficulty Setting Process; see also paragraphs 107+ for discussion of a difficulty setting process 100);
[claim 3] further configured to generate the video game mitigation information to comprise video game difficulty setting information (e.g., see at list Fig. 6 that shows a Difficulty Setting Process; see also paragraphs 107+ for discussion of a difficulty setting process 100), the video game difficulty setting information comprising one or more parameters specifying at least one of a relative reduction in difficulty associated with the respective video game and a difficulty setting to be used for the respective video game (e.g., see at least paragraph 47 for discussion of that the system “may reduce the difficulty of a particular portion of the video game 112”);
[claim 4] further configured to output the video game mitigation information for use by a video game processing device for execution of a next session of the respective video game according to a difficulty that is dependent on the video game difficulty setting information (e.g., see at list Fig. 6 that shows a Difficulty Setting Process; see also paragraphs 107+ for discussion of a difficulty setting process 100);
[claim 5] further is configured to associate control information with the video game mitigation information for modifying the difficulty during a next session of the respective video game to increase the difficulty in response to one or more of a predetermined input by the user and an elapse of a predetermined period of time during the next session (e.g., see at least paragraph 115 that discusses that adjustments can be “more challenging for the user”);
[claim 6] wherein the control information comprises time information indicative of a plurality of times during the next session at which the difficulty is to be automatically increased (e.g., see at least paragraphs and 118 that discusses adjustments to be more difficult; these adjustments are automatic in the sense that the system makes the adjustment and are not made directly by the user);
[claim 8] wherein the user information is indicative of a previous difficulty associated with a previous game session played by the user for the respective video game (e.g., see at least paragraph 47 for discussion of that the system “may reduce the difficulty of a particular portion of the video game 112”), and the prediction circuitry is configured to predict the mitigation action associated with the respective video game in dependence on the inactivity period for the respective video game and the previous difficulty associated with the previous game session played by the user for the respective video game (e.g., see at least paragraphs 46 and 47 that discuss configuring the difficulty level based on various factors, including inactivity that was disclosed in paragraphs 30 and 41; see also Fig. 2 that shows a process of predicting model information);
[claim 9] further configured to predict the mitigation action associated with the respective video game in dependence on whether one or more of the video games previously played by the user have been played during the inactivity period for the respective video game (e.g., see at least paragraph 30 that discusses that the “systems herein monitor user activity with respect to one or more video games”; see also paragraph 89 that discusses implementing the prediction modelling process across different genres/types of games);
[claim 10] further configured to predict the mitigation action associated with the respective video game in dependence on one or more from a list consisting of: a duration of one or more previous game sessions for one or more of the video games during the inactivity period for the respective video game (e.g., see at least paragraphs 29, 30, and 41 that discuss activity level as a factor in adjusting mitigation actions; a genre of one or more of the video games previously played by the user during the inactivity period for the respective video game (e.g., see at least paragraph 89 that discusses implementing the prediction modelling process across different genres/types of games); and a total duration of one or more previous game sessions for the respective video game within a predetermined period of time (e.g., see at least paragraphs 30 and 41 that discuss activity level as a factor in adjusting mitigation actions);
[claim 11] wherein the machine learning model has been trained using training data comprising activity information for a number of users, wherein for each user the activity information is indicative of an inactivity period for at least one video game previously played that user and one or more corresponding difficulty settings associated with a subsequent game session of the at least one video game following the inactivity period (e.g., see at least paragraphs 46 and 47 that discuss configuring the difficulty level based on various factors, including inactivity that was disclosed in paragraphs 30 and 41; see also Fig. 2 that shows a process of predicting model information);
[claim 12] wherein the activity information for each of the number of users relates to one or more from a list consisting of: a plurality of video games each of a same video game genre (e.g., see at least paragraph 89 that discusses implementing the prediction modelling process across different genres/types of games); a plurality of video games each of a same video game series; and a same respective video game; and
[claim 13] wherein the one or more trained machine learning models comprise one or more from the list consisting of: a first trained machine learning model having been trained using training data for a same respective video game (e.g., see at least paragraph 89 that discusses implementing the prediction modelling process across different genres/types of games); a second trained machine learning model having been trained using training data for a plurality of video games associated with a same respective video game series; and a third trained machine learning model having been trained using training data for a plurality of video games associated with a same video game genre (e.g., see at least paragraph 89 that discusses implementing the prediction modelling process across different genres/types of games).
With regard to claim 14, Aghdaie also discloses a computer-implemented method, which is similar in claim scope to the apparatus analyzed above for claim 1. The same analysis applied above for claim 1 also applies to claim 14.
With regard to claim 15, Aghdaie also discloses a non-transitory computer-readable storage medium, which is similar in claim scope to the apparatus analyzed above for claim 1. The same analysis applied above for claim 1 also applies to claim 15.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-15 rejected under 35 U.S.C. 103 as being unpatentable over Aghdaie in view of U.S. Patent Application Publication No. 2011/0086686 to Avent.
With regard to claim 7, Aghdaie discloses all of the recited features but is silent regarding the prediction circuitry is configured to generate the video game mitigation information to comprise one or more from the list consisting of: a notification indicative of a controller mapping associated with the respective video game; a tutorial associated with the respective video game; and an auto-complete function associated with the respective video game.
In the same field of endeavor, Avent teaches an apparatus including video game mitigation information to comprise a tutorial associated with the respective video game (e.g., see at least paragraphs 72-81 for discussion of implementing a game tutorial).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the current invention to modify Aghdaie with the tutorial tool as taught by Avent in order to use a known technique to improve similar devices (methods, or products) in the same way. In this case, providing an in-game tutorial based provides proactive assistance to users that require game action mitigation.
Alternative Rejection of claims 1-6 and 8-15
Additionally, with regard to claims 1-6, and 8-15, if a reviewing authority holds that Aghdaie fails to mitigate game actions based on “inactivity” then Avent is also relied upon to teach implementing a gaming mitigation activity based on inactivity (e.g., see paragraph 46 for discussion of inactivity “long period of time”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the current invention to modify Aghdaie with inactivity-based mitigation as taught by Avent in order to use a known technique to improve similar devices (methods, or products) in the same way. In this case, providing inactivity-based mitigation allows users that have skill atrophy to slowly reestablish their skills due to time away from the game, which will make restarting a game more enjoyable.
Response to Arguments
Applicant's arguments filed 7/22/2026 have been fully considered but they are not persuasive.
On page 8, Applicant’s remarks and associated claim amendments are persuasive in overcoming the 101 rejection.
On page, 10 (continued on page 11), Applicant asserts that Aghdaie fails to disclose “predict a mitigation action associated with a respective video game of the one or more video games previously played by the user, the mitigation action predicted by using machine learning based at least in part on an inactivity period for the respective video game, wherein the machine learning has been trained on mapping between a plurality of historical inactivity periods and subsequent user interaction profiles.” The Examiner respectfully disagrees. The above quoted portion of claim 1 includes numerous recited features. For example, Aghdaie discloses in at least paragraph 48 the use of machine learning to generate predication models and in at least paragraphs 46 and 47 configuring the difficulty level based on various factors, including inactivity (e.g., see paragraphs 30 and 41).
On page 11, Applicant argues that’s that Avent “contains no machine learning algorithm, trained models, or historical multi-user profile mappings, it simply relies on a standard local CPU architecture to pull static text or audio files from a database when a time threshold is reached.” Applicant then states that a “person of ordinary skill in the art would have no technical rationale or motivation to substitute Avent’s low-overhead lookup table architecture with a complex machine learning engine”. As set forth above, Avent is also relied upon to teach implementing a gaming mitigation activity based on inactivity (e.g., see paragraph 46 for discussion of inactivity “long period of time”). The Examiner’s rejection doesn’t seek to replace every feature of Avent into Aghdaie, but merely teach the use of game play inactivity as a factor for mitigation purposes. It would have been obvious to a person of ordinary skill in the art to modify Aghdaie with inactivity-based mitigation as taught by Avent in order to use a known technique to improve similar devices (methods, or products) in the same way. In this case, providing inactivity-based mitigation allows users that have skill atrophy to slowly reestablish their skills due to time away from the game, which will make restarting a game more enjoyable.
For at least these reasons, claims 1-15 remain rejected.
Conclusion
THIS ACTION IS MADE FINAL. 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 JAMES S MCCLELLAN whose telephone number is (571)272-7167. The examiner can normally be reached Monday-Friday (8:30AM-5:00PM).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kang Hu can be reached at 571-270-1344. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James S. McClellan/Primary Examiner, Art Unit 3715
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