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 with respect to the pending claims have been considered but are moot because of the new ground of rejection below.
Claim Objections
Claim 25 is objected to because of the following informalities: missing the term “method” after “The computer-implemented”. 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 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.
Claim(s) 1, 5, 7, 9, 11, 13-19, 21-26, 28 are rejected under 35 U.S.C. 103 as being unpatentable over Osman (US 2018/0001206 A1) in view of Reiche (US 2019/0091577 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 (game cloud system 210, including profiler engine 145 and AI personal assistant 120, Fig. 1A-1C, paragraphs [0019] and [0020]): executing a first artificial intelligence (AI) model to evaluate a performance assessment parameter comprising 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 rates proficiency of team members from aggregated gameplay of multiple players and teams and uses those ratings to predict team success for task assignments, paragraphs [0020], [0035], [0037], [0038], [0047], and [0051]).
Osman does not expressly disclose that the team score indicates a degree of similarity among players of the first team regarding team gameplay interactions. Reiche teaches a cooperation metric for each player and an overall team metric that measures the degree of cooperation provided collectively by the players of the team, including items given between players, cooperation to defeat an enemy, and uncooperative actions such as killing a teammate or completing objectives alone (claims 1-5, paragraph [0009]-[0010],[0094]-[0097], Fig. 4A and Fig. 4B). That overall team metric is a team score indicating a degree of similarity, or lack of similarity, among players regarding team gameplay interactions. It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to use Reiche’s overall cooperation metric as the team score evaluated by Osman’s profiler. Osman already predicts team success from member profiles and assigns tasks to raise that success. Reiche’s metric supplies a direct measure of whether the members are actually cooperating. Combining them would let the profiler detect a team that is individually proficient but collectively uncooperative, which is the condition Osman is trying to correct.
Osman, as modified, discloses determining that the performance assessment parameter satisfies a condition indicative of one or more of social friction or a flawed gameplay approach among the first team of players (Osman detects inefficient or struggling play and deviation from successful patterns, paragraphs [0035], [0044], and [0047]. Reiche’s negative cooperation metric for uncooperative actions is the condition indicative of a flawed gameplay approach among the team, paragraph [0075]).
Osman discloses, in response to determining that the performance assessment parameter satisfies the condition, 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 generate remediating instructions for assistive measures for improving the play of the first team of players within the video game (recommendation engine 135 and AI personal assistant 120 generate task reassignments and guidance from predictive models, paragraphs [0035], [0044], [0047], and [0066]), and implementing the assistive measure (assigning tasks or providing guidance, paragraphs [0047] and [0067]).
Osman does not expressly disclose that the assistive measure and the remediating instructions are for reducing the social friction. Reiche teaches generating an in-game response to a low team cooperation metric, including rewards, penalties, and session modifications that discourage uncooperative play (paragraphs [0094]-[0097]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to direct Osman’s recommendation engine to generate the remediating instructions in response to Reiche’s low cooperation metric, for the purpose of reducing that uncooperative play. Both references assist a multiplayer team. Using the cooperation metric as the trigger, and generating instructions that reduce the uncooperative conduct, is the predictable use of Reiche’s metric in Osman’s assistance loop.
5. Osman and Reiche discloses the system as recited in claim 1, wherein the performance assessment parameter includes a decision score for the first team that indicates a degree of similarity between video game play decisions made by one or more players within the first team and video game play decisions within a set of training data reflective of successful team play within the video game, Osman [0020], [0038], [0047], [0051].
7. Osman and Reiche 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 that indicates a degree of similarity between locations of players within the first team 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 and Reiche discloses the system as recited in claim 1, wherein the performance assessment parameter includes a tactic score for the first team that indicates a degree of similarity between tactics used by one or more players within the first team and tactics of players within a set of training data reflective of successful team play within the video game, Osman [0047], [0052], [0059].
11. Osman and Reiche discloses the system as recited in claim 1, wherein the performance assessment parameter includes a coherency score for the first team that indicates a degree of similarity between shared strategy and style among players within the first team and shared strategy and style among players within a set of training data reflective of successful team play within the video game, Osman [0047], [0052], [0059]-[0060].
13. Osman and Reiche discloses the system as recited in claim 1, wherein the performance assessment parameter includes a sentiment score for the first team that indicates a degree of similarity between sentiments of players within the first team and sentiments of players within a set of training data reflective of successful team play within the video game, Osman [0063].
14. Osman and Reiche 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, Osman [0059].
15. Osman and Reiche 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 and addition of a new player to the first team, Osman [0020], [0104].
16. Osman and Reiche discloses the system as recited in claim 1, wherein the assistive measure includes provision of information to the first team regarding how to progress within the video game, Osman [0097].
17. Osman and Reiche discloses the system as recited in claim 1, wherein the assistive measure includes adjustment of tasks assigned to the players within the first team, Osman [0059].
18. Osman and Reiche discloses the system as recited in claim 1, 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 operations further comprising: executing a third AI model trained to determine whether or not the first team 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 and Reiche discloses the system as recited in claim 1, wherein Reiche further discloses the performance assessment parameter corresponds to a shared gameplay strategy of at least some players in the first team, wherein the condition includes determining that the first team 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 (Claims 1-5), [0094]-[0097].
24-25. Osman and Reiche discloses a computer-implemented method comprising: executing a first artificial intelligence (AI) model to evaluate a performance assessment parameter comprising 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 and the team score indicates a degree of similarity among players of the first team regarding team gameplay interactions; determining that the performance assessment parameter satisfies a condition indicative of one or more of social friction or a flawed gameplay approach among the first team of players; in response to determining that the performance assessment parameter satisfies the condition, executing a second AI model to determine an assistive measure for one or more of reducing the social friction or improving play of the first team of players within the video game, wherein the second AI model is trained to generate remediating instructions for assistive measures for one or more of reducing the social friction or improving the play of the first team of players within the video game; and implementing the assistive measure as similarly discussed above.
26. Osman and Reiche 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 comprising 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 and the team score indicates a degree of similarity among players of the first team regarding team gameplay interactions; determining that the performance assessment parameter satisfies a condition indicative of one or more of social friction or a flawed gameplay approach among the first team of players; in response to determining that the performance assessment parameter satisfies the condition, executing a second AI model to determine an assistive measure for one or more of reducing the social friction or improving play of the first team of players within the video game, wherein the second AI model is trained to generate remediating instructions for assistive measures for one or more of reducing the social friction or improving the play of the first team of players within the video game; and implementing the assistive measure as similarly discussed above.
28. Osman and Reiche discloses the system of claim 1, wherein the execution of the second Al model is automatically triggered in response to determining that the performance assessment parameter satisfies the condition (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), Osman [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) and Reiche (US 2019/0091577 A1) as applied above and further in view of Kaushik (US 2022/0067384 A1)
3. Osman and Reiche 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 that indicates a degree of similarity between player-to-player communications within the first team 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.
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
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.
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.
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/Seng H Lim/Primary Examiner, Art Unit 3715
