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 .
DETAILED ACTION
Response to Arguments
Applicant's arguments filed 1/2/2026 have been fully considered but they are not persuasive. Applicant argues that the cited references, alone or in combination, do not disclose the amended features of independent claim 1, specifically: (1) executing a first AI model trained to evaluate a performance for a team of players, and (2) executing a second AI model trained to determine assistive measures to improve performance of the team of players. Applicant further references an examiner interview where it was allegedly agreed that the prior art does not show these features.
Examiner respectfully disagree. Please refer to updated rejection below addressing newly amended claim language.
The interview summary does not indicate agreement that the prior art fails to teach these features; rather, it noted potential amendments to overcome the rejection. The amendments do not distinguish over Osman, as the claimed "first AI model" and "second AI model" read on Osman's integrated AI components (as addressed below).
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 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, 5, 7, 9, 11, 13-17, 24, 26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Osman (US 2018/0001206 A1) or, in the alternative, under 35 U.S.C. 103 as obvious over Osman (US 2018/0001206 A1) in view of Bleasdale-Shepherd (US 2021/0146241 A1).
1. Osman discloses a system comprising: a memory comprising computer-executable instructions and a processor configured to access the memory and execute the computer- executable instructions to perform operations comprising (Fig. 1A-1C):
executing a first artificial intelligence (AI) model to evaluate a performance assessment parameter for a first team of players playing a video game, wherein the first Al model is trained to evaluate a performance for a team of players (profiler engine 145 implements AI to determine player proficiency scores for team members based on task types, trained on aggregated gameplay data from multiple players and teams), [0035], [0037]-[0038], [0047];
determining, based at least in part on the performance assessment parameter, that the team of players is struggling while playing the video game (detect inefficient/struggling play via low proficiency or success rates), [0035], [0044], [0047],
in response to determining that the team of players is struggling while playing the video game, executing a second Al model to determine an assistive measure for improving play of the first team of players within the video game, wherein the second Al model is trained to determine assistive measures to improve performance of the team of players (recommendation engine or deep learning engine, via AI personal assistant 120, determines assistive actions like task reassignments or suggestions based on predictive models), [0035], [0044], [0047], [0066]; and
implementing the assistive measure (assigns tasks to team members to maximize success), [0047], [0067].
Alternatively, Bleasdale-Shepherd discloses, in para. [0061], that trained machine learning model(s) may represent a single model or an ensemble of base-level machine learning models… An "ensemble" can comprise a collection of machine learning models whose outputs (predictions) are combined, such as by using weighted averaging or voting. The individual machine learning models of an ensemble can differ in their expertise, and the ensemble can operate as a committee of individual machine learning models that is collectively "smarter" than any individual machine learning model of the ensemble. It would have been obvious to a person of ordinary skilled in the art to incorporate Bleasdale-Shepherd’s use of multiple models into Osman and would have been motivated to do so as it can operate as a committee of individual machine learning models that is collectively "smarter".
5. Osman discloses the system as recited in claim 1, wherein the performance assessment parameter includes a decision score for the first team of players that indicates a degree of similarity between video game play decisions made by one or more players within the first team of players and video game play decisions within a set of training data reflective of successful team play within the video game [0020], [0038], [0047], [0051].
7. Osman discloses the system as recited in claim 1, but does not expressly disclose wherein the performance assessment parameter includes a location score for the first team of players that indicates a degree of similarity between locations of players within the first team of players and locations of players within a set of training data reflective of successful team play within the video game; however, such determination of the player struggle based on the player's location in relation to the team would have been obvious to a person of ordinary skilled in the art to implement and would yield predictable results. It is obvious that if the player is far behind from the team, the player is struggling and would benefit the team if the player is provided an assistance.
9. Osman discloses the system as recited in claim 1, wherein the performance assessment parameter includes a tactic score for the first team of players that indicates a degree of similarity between tactics used by one or more players within the first team of players and tactics of players within a set of training data reflective of successful team play within the video game, [0047], [0052], [0059].
11. Osman discloses the system as recited in claim 1, wherein the performance assessment parameter includes a coherency score for the first team of players that indicates a degree of similarity between shared strategy and style among players within the first team of players and shared strategy and style among players within a set of training data reflective of successful team play within the video game, [0047], [0052], [0059]-[0060].
13. Osman discloses the system as recited in claim 1, wherein the performance assessment parameter includes a sentiment score for the first team of players that indicates a degree of similarity between sentiments of players within the first team of players and sentiments of players within a set of training data reflective of successful team play within the video game [0063].
14. Osman discloses the system as recited in claim 1, wherein the performance assessment parameter corresponds to one or more of achieving a specified objective within the video game, achieving the specified objective within the video game within a set time period, achieving the specified objective within the video game in conjunction with having a particular status within the video game, or achieving the specified objective within the video game within the set time period in conjunction with having the particular status within the video game [0059].
15. Osman discloses the system as recited in claim 1, wherein the assistive measure includes one or more of removal of an existing player from the first team of players and addition of a new player to the first team of players [0020], [0104].
16. Osman discloses the system as recited in claim 1, wherein the assistive measure includes provision of information to the first team of players on how to progress within the video game [0097].
17. Osman discloses the system as recited in claim 1, wherein the assistive measure includes adjustment of tasks assigned to the players within the first team of players [0059].
24. Osman alone or in combination with Bleasdale-Shepherd discloses a computer-implemented method comprising: executing a first artificial intelligence (AI) model to evaluate a performance assessment parameter for a first team of players playing a video game, wherein the first AI model is trained to evaluate a performance for a team of players; determining, based at least in part on the performance assessment parameter, that the team of players is struggling while playing the video game; in response to determining that the team of players is struggling while playing the video game, executing a second AI model to determine an assistive measure for improving play of the first team of players within the video game, wherein the second AI model is trained to determine assistive measures to improve performance of the team of players; and implementing the assistive measure as similarly discussed above.
26. Osman alone or in combination with Bleasdale-Shepherd discloses one or more non-transitory computer-readable media comprising computer-executable instructions that, when executed by one or more processors of an electronic device, cause the electronic device to perform operations comprising: executing a first artificial intelligence (AI) model to evaluate a performance assessment parameter for a first team of players playing a video game, wherein the first AI model is trained to evaluate a performance for a team of players; determining, based at least in part on the performance assessment parameter, that the team of players is struggling while playing the video game; in response to determining that the team of players is struggling while playing the video game, executing a second AI model to determine an assistive measure for improving play of the first team of players within the video game, wherein the second AI model is trained to determine assistive measures to improve performance of the team of players; and implementing the assistive measure as similarly discussed above.
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over Osman (US 2018/0001206 A1) as applied above and further in view of Kaushik (US 2022/0067384 A1)
3. Osman discloses the system as recited in claim 1, wherein the players can communicate with each other [0048]-[0049], but does not expressly disclose the performance assessment parameter includes a communication score for the first team of players that indicates a degree of similarity between player-to-player communications within the first team of players and player-to-player communications within a set of training data reflective of successful team play within the video game. Kaushik teaches gauging communication in games via Al sentiment detection on chat/text, generating scores based on similarity to trained positive/negative patterns for team summaries [0049], [0054]. It would have been obvious to incorporate Kaushik's communication gauging and similarity-based scoring into the combination, as it quantifies team interactions for struggle detection, enhancing Osman's proficiency metrics with AI analysis of communications, a standard improvement for multiplayer dynamics.
Claim(s) 18-19, 21-23, 25, 27 are rejected under 35 U.S.C. 103 as being unpatentable over Osman (US 2018/0001206 A1) as applied above and further in view of Reiche (US 2019/0091577 A1).
18. Osman discloses the system as recited in claim 1, wherein the assistive measure includes adjustment of an aspect of the video game. Reiche disclose wherein the assistive measure includes adjustment of an aspect of the video game [0075], [0080], [0116]. It would have been obvious to a person of ordinary skilled in the art to modify Osman with Reiche and would have been motivated to do so keep player interested by adjusting the game to the appropriate player skill level.
19. Osman and Reiche discloses the system as recited in claim 1, wherein the memory comprises additional computer-executable instructions and the processor is further configured to access the memory and execute the additional computer-executable instructions to perform additional operations comprising: executing a third AI model trained to determine whether or not the first team of players is being sufficiently challenged with the video game; and in response to executing the third AI model, adjusting a difficulty level of the video game as similarly discussed above.
21-23. Osman discloses the system as recited in claim 1, but does not expressly disclose wherein the performance assessment parameter corresponds to a shared gameplay strategy of at least some players in the first team of players, wherein determining that the first team of players is struggling includes determining that the first team of players is struggling from a lack of teamwork, based at least in part on an inappropriate distribution of gameplay responsibilities to one or more players of the first team of players. Reiche teaches explicit cooperation metrics and team performance visualization by measuring degree of cooperation collectively, with low metrics indicating lack of teamwork leading to incentives (Claims 1-5). It would have been obvious to one of ordinary skill in the art to combine Osman's AI-driven team assistance with Reiche 's granular teamwork metrics to enable precise detection and remediation of coordination issues, as both aim to enhance multiplayer team engagement and success.
25. Osman and Reiche discloses the computer-implemented as recited in claim 24, wherein determining that the team of players is struggling includes determining that the team of players is struggling from a lack of teamwork as similarly discussed above.
27. Osman and Reiche discloses the one or more non-transitory computer-readable media as recited in claim 26, wherein determining that the first team of players is struggling includes determining that the first team of players is struggling from a lack of teamwork as similarly discussed above.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached USPTO form PTO-892.
Filing of New or Amended Claims
The examiner has the initial burden of presenting evidence or reasoning to explain why persons skilled in the art would not recognize in the original disclosure a description of the invention defined by the claims. See Wertheim, 541 F.2d at 263, 191 USPQ at 97 (“[T]he PTO has the initial burden of presenting evidence or reasons why persons skilled in the art would not recognize in the disclosure a description of the invention defined by the claims.”). However, when filing an amendment an applicant should show support in the original disclosure for new or amended claims. See MPEP § 714.02 and § 2163.06 (“Applicant should specifically point out the support for any amendments made to the disclosure.”). Please see MPEP 2163 (II) 3. (b)
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.
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SENG H LIM whose telephone number is (571)270-3301. The examiner can normally be reached Monday-Friday (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, David L. Lewis can be reached at (571) 272-7673. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Seng H Lim/Primary Examiner, Art Unit 3715