DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/12/2026 has been entered.
Applicant’s Submission of a Response
Applicant’s submission of a response was received on 02/12/2026. Presently, claims 1-19 are pending.
Claim Objections
Claims 1, 17, and 18 are objected to because of the following informalities: using claim 1 as an example, in line 10 it states, “the respective activity,” however there is no antecedent basis for this. While it is generally understood that this relates to “a plurality of activities” it is not directed towards all of them and therefore the antecedent basis would not be fulfilled. Thus, it appears that it should be “a respective activity.” If Applicant believes this is incorrect, Applicant is invited to explain reasoning as to why the article “the” is most appropriate. Appropriate correction is required.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication No. 2020/0269136 to Gurumurthy in view of US Patent Application Publication No. 2015/0119120 to Spagnola.
With regard to claim 1¸ Gurumurthy discloses a method comprising: storing historical game data associated with gameplay of a plurality of users of a game title in memory, wherein the historical game data includes a plurality of activities available in the game title (0022; 0029; 0032; 00378); receiving game data of a current session over a communication network from a user device of a user (0032); constructing a learning model for each activity based on a set of historical game data associated with the user and one or more other users from the plurality of users based on a shared attribute, wherein the learning model is trained to identify one or more activity characteristics associated with the respective activity (0032; 0038); generating a customized recommendation for an identified activity by applying the learning model to the game data of the current session in which the user device of the user is playing the game title, wherein the recommendation includes one or more steps for achieving an in-game objective associated with the identified activity (0020-0023; 0032); and updating the learning model based on the outcome, wherein the updated learning model is used to generate a subsequent recommendation in relation to a subsequent performance of the identified activity (0022; 0034).
Gurumurthy does not appear to explicitly discuss learning styles. However, the combination of Gurumurthy and Spagnola teaches that the shared attribute as being indicative of a learning style (Spagnola at 0131-0135; 0149-0166); wherein the recommendation includes one or more steps in a form associated with the learning style (Spagnola at 0131-0135; 0149-0166) and tracking an outcome of the recommendation after the steps are determined to have been performed, wherein the outcome based at least in part on the form associated with the learning style is included in updated game data of the current session (Gurumurthy at 0022; 0034; Spagnola at 0131-0135; 0149-0166)
With regard to claim 2, Gurumurthy discloses wherein the historical game data further includes metadata regarding one or more associated training content files, and wherein generating the customized recommendation includes identifying one or more of the training content files associated with the identified activity (0052).
With regard to claim 3, Gurumurthy discloses wherein the historical game data further include includes a sequence of user inputs associated with the activities (0020).
With regard to claim 4, the combination of Gurumurthy and Spagnola teaches wherein constructing the learning model of outcomes include includes analyzing the historical game data associated with similar activities, and identifying that the similar activities share a patter of in-game behaviors among the user and the one or more other users (Gurumurthy at 0022; Spagnola at 0131-0135; 0149-0166).
With regard to claim 5, Gurumurthy discloses wherein generating the customized recommendation is further based on a cumulative failure rate associated with one or more users engaged in the identified activity, the cumulative failure rate exceeding a threshold level (0023).
With regard to claim 6, the combination of Gurumurthy and Spagnola teaches wherein generating the customized recommendation is further based on respective output of the steps having been executed by the other users that have engaged in the identified activity, wherein the other users share a pattern of positive actions or reactions towards a type of training content associated with the learning style of the user (Gurumurthy at 0022; Spagnola at 0131-0135; 0149-0166).
With regard to claim 7, Gurumurthy discloses wherein generating the customized recommendation is further based on a pattern of activities in which the user has been engaged (0020).
With regard to claim 8, Gurumurthy discloses further comprising weighting the pattern of activities based on recency, wherein generating the customized recommendation is further based on the weighted pattern of activities (0039-0041; 0044).
With regard to claim 9, Gurumurthy discloses wherein generating the customized recommendation is further based on a difference between a skill level of the user and a required skill level for the identified activity (0012; 0044).
With regard to claim 10, Gurumurthy discloses wherein the customized recommendation corresponds to one task of a plurality of different tasks associated with the identified activity (0020).
With regard to claim 11, Gurumurthy discloses further comprising providing the customized recommendation to the user device as an overlay to present over a display of the current session (0020-0021).
With regard to claim 12, Gurumurthy discloses further comprising detecting a trigger event associated with the identified activity based on the game data of the current session, wherein the customized recommendation is generated upon detection of the trigger event (0023).
With regard to claim 13, Gurumurthy discloses wherein detecting the trigger event includes identifying that a threshold level associated with the trigger event has been met (0020-0021).
With regard to claim 14, Gurumurthy discloses further comprising generating a different customized recommendation based on detecting a different trigger event (0023).
With regard to claim 15, Gurumurthy discloses wherein detecting the trigger event includes identifying that the user failed to execute a skill associated with the identified activity (0023).
With regard to claim 16, Gurumurthy discloses further comprising providing a second user with a different customized recommendation based on application of the updated learning model to game data of the second user (0012).
Claims 17 and 18 are mirrored claims to claim 1 and are rejected in like manner.
With regard to claim 19, Gurumurthy discloses wherein the trigger event is identifiable as at least one of: increase or decrease in a skill of the user in a particular game skill, lack of improvement in success rate of performing the skill, increase or decrease in a player rank, and previous disengagement with gameplay after failure to perform the identified activity (0023).
Response to Arguments
The rejection based upon non-statutory double patenting has been withdrawn as the claims have diverged enough from US Patent No. 12,145,064 that it no longer seems appropriate.
Applicant’s arguments have been considered, but are moot in view of the new grounds of rejection that has been presented above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jay Liddle whose telephone number is (571)270-1226. The examiner can normally be reached M-F 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dmitry Suhol can be reached at (571)272-4430. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jay Trent Liddle/Primary Examiner, Art Unit 3715
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