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 5/26/2026 have been fully considered but they are not persuasive.
Applicant argues that Osman fails to disclose or suggest the amended features of independent claim 1, specifically: (1) executing a first AI model to evaluate a team score for a first team of players, wherein the first AI model is trained to evaluate team strategies relative to success team play training data; and (2) executing a second AI model trained to generate a remediating instructions for assistive measures to improve performance of the first team of players. Applicant further contends that Osman is directed to individual user proficiency rather than team-level evaluation, and references sensor data/biometric inputs (e.g., blood pressure, heart rate) from the specification that are allegedly not taught by Osman.
Examiner respectfully disagrees. The amendments do not distinguish the claims over Osman. As set forth in the updated rejection below, Osman explicitly discloses AI-driven evaluation of team member performance and team-level metrics using aggregated gameplay data from multiple players and teams. The 'team score' recited in amended claim 1 reads directly on Osman's player proficiency scores determined for team members in the context of team play and task types (see Osman [0035], [0037]-[0038], [0047]). These scores are generated based on comparison to successful play patterns derived from aggregated historical and multi-player/team data, which constitutes training data reflective of successful team play strategies.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., receiving or processing sensor data, biometric inputs, bodily functions (blood pressure, heart rate, body temperature, etc.), or any client-machine sensor inputs) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Osman's recommendation engine / deep learning engine / personal assistant 120 generates assistive actions (task reassignments, suggestions for improving play) in direct response to detected struggling or inefficient team play. These outputs constitute 'remediating instructions' for assistive measures within the broadest reasonable interpretation of the claim language. The system monitors gameplay in real time and automatically triggers recommendations upon detection of low proficiency/success rates, thus meeting new claim 28 as well.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3, 5, 7, 9, 11, 13-19, 21-26, 28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 3, 5, 7, 9, 11, 13-19, 21-26, 28 recites the limitation "the performance assessment parameter". There is insufficient antecedent basis for this limitation in the claim.
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, 28 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 team score for a first team of players playing a video game, wherein the first AI model is trained to evaluate team strategies relative to success team play training data (profiler engine 145 implements AI to determine player proficiency scores for team members based on task types; the scores reflect team performance and are trained on aggregated gameplay data from multiple players and teams, which data is reflective of successful team play strategies and patterns [0035], [0037]-[0038], [0047], [0051]);
determining, based at least in part on the performance assessment parameter (team score), that the first team of players is struggling while playing the video game (detect inefficient/struggling play via low proficiency scores, low success rates, or deviation from successful team patterns [0035], [0044], [0047]);
in response to determining that the first 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 generate a remediating instructions for assistive measures to improve performance of the first team of players (recommendation engine, deep learning engine, or AI personal assistant 120 determines and generates assistive actions/recommendations such as task reassignments, strategy suggestions, or other remediating instructions based on predictive models trained to improve team outcomes), [0035], [0044], [0047], [0066]; and
implementing the assistive measure (assigns tasks to team members or provides guidance to maximize team 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 (Al) model to evaluate a team score for a first team of players playing a video game, wherein the first Al model is trained to evaluate team strategies relative to success team play training data; determining, based at least in part on the performance assessment parameter, that the first team of players is struggling while playing the video game; in response to determining that the first 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 generate a remediating instructions for assistive measures to improve performance of the first 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 (Al) model to evaluate a team score for a first team of players playing a video game, wherein the first Al model is trained to evaluate team strategies relative to success team play training data; determining, based at least in part on the performance assessment parameter, that the first team of players is struggling while playing the video game; in response to determining that the first 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 generate a remediating instructions for assistive measures to improve performance of the first team of players; and implementing the assistive measure as similarly discussed above.
28. Osman discloses the system of claim 1, wherein the execution of the second Al model is automatically triggered in response to the determination that the first team of players is struggling (the system continuously monitors gameplay metrics via the profiler; upon detecting low proficiency or struggling indicators, the recommendation/personal assistant engine automatically generates and provides assistive measures without requiring manual user invocation) [0035], [0044], [0047], [0066]-[0067].
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, but does not expressly disclose 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.
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)
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, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Seng H Lim/Primary Examiner, Art Unit 3715
Prosecution Insights