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 .
Response to Amendment
The examiner acknowledges applicant’s arguments in the Response dated May 15, 2026 directed to the Non-Final Office Action dated February 24, 2026. Claims 1-20 are pending in the application and subject to examination as part of this office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. Each of claims 1-20 has been analyzed to determine whether it is directed to any judicial exceptions.
The determination of subject matter eligibility under 35 USC 101, relies on the Mayo/Alice two-step analysis.
In step 1 of the analysis, the claims are evaluated to determine whether they fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). In the present case, claims 1-10 are directed to a method (i.e., a process), claims 10-19 are directed to a non-transitory computer readable medium (i.e., a manufacture), and claim 20 is directed to a system (i.e., a machine). The claims are, therefore directed to one of the four statutory categories.
Under prong 1 of step 2A, the examiner is directed to determine whether the claim recites a judicial exception. The claims are compared to groupings of subject matter that have been found by courts as abstract ideas. These groupings include
(a) Mathematical concepts—mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity—fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
(c) Mental processes—concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Claim 1 is considered representative and recite (the abstract idea is underlined) a method for matchmaking of game players, comprising:
receiving player data associated with a user account;
receiving match state data associated with a match of an online multiplayer game, the match state data defining a current state of the match;
based on the player data and the match state data, extracting a plurality of engagement prediction features;
providing, as an input to an engagement prediction model, the plurality of engagement prediction features;
receiving, as an output from the engagement prediction model, a predicted engagement metric, the predicted engagement metric being based on the plurality of engagement prediction features;
providing the predicted engagement metric as an input to a matchmaking system; and
receiving from the matchmaking system a decision whether to match the user account to the match of the online multiplayer game for gameplay.
The present claims are directed to “improving the experience of players who are playing games, and more particularly to improving matchmaking between game players” (Specification [0001]). These steps fall under the category of certain methods of organizing human activity”. Specifically, they are directed to the sub-category of managing personal behavior or relationships or interactions between people. Accordingly, the claim recites an abstract idea.
Under prong 2 of Step 2A, the examiner considers whether additional elements integrate the abstract idea into a practical application. To do so, the examiner looks to the following exemplary considerations, looking at the elements individually and in combination:
• an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
• an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (not considered relevant to the present claims);
• an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
• an additional element effects a transformation or reduction of a particular article to a different state or thing; and
• an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The additional elements in the present claims are a computer, a processor, a non-transitory computer readable medium, and a matchmaking system. The additional elements do no integrate the judicial exception into a practical application. In particular, the additional elements do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field. The additional elements do not implement a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim. The additional elements do not effect a transformation or reduction of a particular article to a different state or thing. The additional elements do not apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea.
Under step 2B, the examiner evaluates whether the additional elements amount to significantly more than the judicial exception itself. The examiner considers if the additional elements:
• add a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
• simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
The present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are well-understood, routine, or conventional, as shown:
a computer, a processor, and a non-transitory computer readable medium, and a matchmaking system (Vagner, US 2015/0302482 A1, a general computer can include a memory, a processor, input/out components, and other components that are common for general computers, all of which are well known in the art [0099]).
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As a result, the claims are not directed to patent eligible subject matter.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over AMER et al., US 2021/0275908 A1 (hereinafter Amer) in view of Xue et al., US 2018/0111051 A1 (hereinafter Xue).
Regarding Claim 1 (Currently Amended): Ameri discloses a method for matchmaking of game players, comprising:
receiving player data associated with a user account (Amer, one or more of player activity data indicative of levels of engagement associated with a player's voice, presence manual inputs, or body language [0018]);
receiving match state data associated with a match (Amer, gameplay status data indicative of a game state of a game being streamed by the player or of the player being engaged in a side task [0018]) of an online multiplayer game, the match state data defining a current state of the match (Amer, the engagement analytics engine 112 applies one or more trained machine learning models, convolutional filters, object identification algorithms, or temporal analysis algorithms to one or both of the rendered scenes or the player command inputs 314 to determine that the game state corresponds to a “matchmaking” state in which the player is waiting for completion of a multiplayer matchmaking process; the engagement analytics engine receives an indication of the games state from the game application 120, the indication defining whether the game application 120 is in the matchmaking state or in an “active” state in which the corresponding game is actively being played by the player [0051]);
based on the player data and the match state data, extracting a plurality of engagement prediction features (Amer, the engagement data 114 includes one or more of player activity data indicative of levels of engagement associated with a player's voice, presence manual inputs, or body language, ... gameplay status data indicative of a game state of a game being streamed by the player or of the player being engaged in a side task [0018]);
providing, as an input to an engagement prediction model, the plurality of engagement prediction features (Amer, the engagement analytics engine 114 includes player activity data, color anomaly data, gameplay status data, UI element data 408, motion characterization data 410, audio source data 412, and aggregate engagement data 414 [0042] and [Fig. 4]);
receiving, as an output from the engagement prediction model, a predicted engagement metric, the predicted engagement metric being based on the plurality of engagement prediction features (Amer, the engagement analytics engine 112 generates the player engagement score 452 based on one or both of the player activity data 402 and the gameplay status data 406 [0061]).
Xue fails to explicitly disclose
providing the predicted engagement metric as an input to a matchmaking system; and
receiving from the matchmaking system a decision whether to match the user account to the match of the online multiplayer game for gameplay.
Xue teaches
providing the predicted engagement metric as an input to a matchmaking system (Xue, matchmaking may be applied to a pool of users or players, P={p.sub.1, . . . , p.sub.N}, who are waiting to start or play 1-vs-1 matches; one such example of a pool of users P is illustrated in FIG. 3 as the pool of users 302; a graph G can be constructed to model the set of players waiting to play the multiplayer video game; one such example of the graph G is illustrated in FIG. 3 as the graph 304; each player p.sub.i may be represented by a vertex or a node of the graph, which has a current player state s.sub.i.; the player state s.sub.i may represent any data specific to the user and the user's interaction with the video game 112; the edge between two players p.sub.i and p.sub.i may be associated with the expected sum objective or engagement metric (for example, sum churn risk) if the users are paired; this metric relies on both users' states and may be denoted as a function f(s.sub.i, s.sub.j) [0062]); and
receiving from the matchmaking system a decision whether to match the user account to the match of the online multiplayer game for gameplay (Xue, a list of user or player tuples, M={(p.sub.i, p.sub.j)}, may be used to denote a matchmaking result, or a pair assignment, in which all players in P are paired and are only paired once [0062]).
Amer discloses an engagement analytics engine of a computing device analyzes one or more of scene representations, player inputs, or player meta information and generates corresponding engagement data indicative of a level of engagement corresponding to the represented scene (Amer [Abstract]). The engagement analytics engine generates encoding parameters based on the engagement data to cause scenes or regions within scenes to be encoded with a level of quality based on the indicated level of engagement (Amer [Abstract]). In some examples, the engagement analytics engine generates rendering parameters based on the engagement data to cause scenes to be rendered with a frame rate or quality parameters based on the indicated level of engagement (Amer [Abstract]). In some examples, the engagement analytics engine causes a load balancer to shift workloads associated with one or more scenes to higher or lower performance servers based on the engagement data (Amer [Abstract]).
Xue teaches identifying users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention (Xue [Abstract]). Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the video game (Xue [Abstract]). Software developers typically desire for their software to engage users for as long as possible (Xue [0002]). The longer a user is engaged with the software, the more likely that the software will be successful (Xue [0002]). The relationship between the length of engagement of the user and the success of the software is particularly true with respect to video games (Xue [0002]). The longer a user plays a particular video game, the more likely that the user enjoys the game and thus, the more likely the user will continue to play the game (Xue [0002]). The principle of engagement is not limited to single player games and can also be applied to multiplayer video games (Xue [0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the engagement analytics engine as disclosed by Amer with the matchmaking system as taught by Xue in order to ensure users enjoy the game.
Regarding Claim 2 (Currently Amended): Amer further discloses wherein the match state data comprises at least one of a game map associated with the match, a match capacity of the match, a number of players currently playing in the match, current scores in the match, progress of the match, and a number of objectives completed in the match (Amer, the engagement analytics engine 112 generates the game state data 430 based on at least one of analysis of rendered scenes associated with the game application 120, analysis of player command inputs 134, or analysis of game state information received from the game application 120 [0051]).
Regarding Claim 3 (Original): Amer further discloses wherein the player data comprises gameplay history data, player engagement history data, and player-specific attribute data (Amer, a prediction model can be used to determine an expected churn rate or a probability that a user will cease playing the video game 112 based on one or more inputs to the prediction model, such as, for example, historical user interaction information for a user [0032]; the player activity data includes player voice data 416, player visual presence data 418, player manual input data 420, and player body language data 422 [0043]).
Regarding Claim 4 (Original): Xue further teaches the player-specific attribute data comprises at least one of player preferences, skill, playstyle, toxicity level, average kills and deaths in a session, winning streak, hours played in current session, hours played total, joining as a group, playing as a group, and friend connections (Xue, these play characteristics may include characteristics relating to skill level, play style (for example, a user who plays defensively, plays offensively, plays a support role, prefers stealth attacks, prefers to use magic abilities, or prefers to use melee abilities, and the like), and/or sportsmanship (for example, a user who is a gracious winner or loser, is or is not gregarious, or does not insult other users, and the like) [0027]).
Regarding Claim 5 (Original): Xue further teaches wherein the player preferences comprise at least one of game map preferences, weapon preferences, vehicle preferences, and character class preferences (Xue, the data may include in-game character selection preferences, role preferences, and other information [0057]).
Regarding Claim 6 (Currently Amended): Xue further teaches wherein the user account is matched to the match of the online multiplayer game based on the decision, the match including one or more other matched accounts (Xue, matchmaking may be applied to a pool of users or players, P={p.sub.1, . . . , p.sub.N}, who are waiting to start or play 1-vs-1 matches [0062]).
Regarding Claim 7 (Currently Amended): Xue further teaches wherein the predicted engagement comprises an engagement score that represents a likelihood that the user account will engage in the match in the current state defined by the match state data (Xue, multiplayer games with poor matchmaking algorithms can result in lower engagement by users; in other words, poorly matched opponents and/or teammates may result in users ceasing to play a video game or playing the video game less often than if the multiplayer game has better matchmaking algorithms [0019]).
Regarding Claim 8 (Original): Amer in view of Xue discloses the invention as recited above. Amer in view of Xue fails to explicitly disclose wherein the engagement score is a value between zero and one, inclusive, with a minimum value of zero representing a first prediction that the user account will not engage, and a maximum value of one representing a second prediction that the user account will engage.
Amer discloses wherein aggregate engagement data includes a temporal importance score, high engagement regions, low engagement regions, a player engagement score, and an aggregate engagement score (Amer [0058]).
Xue teaches finding an optimal pair assignment M*, which maximizes the overall player engagement by using an equation (i.e., M*=arg max.sub.M Σ.sub.(p.sub.i.sub.,p.sub.j)ϵMf(s.sub.i, s.sub.j)) (Xue [0058]).
In both instances, it is clear that engagement is a value that is used in an equation. Engagement has a range of no engagement to full engagement and could be described by any range of values. The ultimate decision to select the numerical range of engagement is a matter of obvious design choice that would work equally well.
Therefore, it would have been prima facie obvious to modify Amer to define engagement as ranging from zero to one because such a modification would have been considered a mere design consideration which fails to patentably distinguish over the prior art of Amer.
Regarding Claim 9 (Currently Amended): Xue further teaches wherein providing the predicted engagement metric as an input to the matchmaking system is based on a determination that the engagement score is greater than a pre-defined engagement threshold associated with the match state data, wherein proposed matches with engagement scores below the pre-defined engagement threshold are not provided to the matchmaking system (Xue, the prediction model 160 may be use to confirm whether a particular match plan satisfies a set of conditions, such as, for example, a particular threshold retention rate [0049]).
Regarding Claim 10 (Original): Amer further discloses providing the match state data as a further input to the matchmaking system (Amer, the engagement data 114 includes one or more of player activity data indicative of levels of engagement associated with a player's voice, presence manual inputs, or body language, ... gameplay status data indicative of a game state of a game being streamed by the player or of the player being engaged in a side task [0018]).
Regarding Claim 11 (Currently Amended): Amer discloses a non-transitory computer readable medium storing a program for matchmaking of game players, which when executed by a computer, configures the computer to:
receive player data associated with a user account (Amer, one or more of player activity data indicative of levels of engagement associated with a player's voice, presence manual inputs, or body language [0018]);
receive match state data associated with a match (Amer, gameplay status data indicative of a game state of a game being streamed by the player or of the player being engaged in a side task [0018]) of an online multiplayer game, the match state data defining a current state of the match (Amer, the engagement analytics engine 112 applies one or more trained machine learning models, convolutional filters, object identification algorithms, or temporal analysis algorithms to one or both of the rendered scenes or the player command inputs 314 to determine that the game state corresponds to a “matchmaking” state in which the player is waiting for completion of a multiplayer matchmaking process; the engagement analytics engine receives an indication of the games state from the game application 120, the indication defining whether the game application 120 is in the matchmaking state or in an “active” state in which the corresponding game is actively being played by the player [0051]);
based on the player data and the match state data, extract a plurality of engagement prediction features (Amer, the engagement data 114 includes one or more of player activity data indicative of levels of engagement associated with a player's voice, presence manual inputs, or body language, ... gameplay status data indicative of a game state of a game being streamed by the player or of the player being engaged in a side task [0018]);
provide, as an input to an engagement prediction model, the plurality of engagement prediction features (Amer, the engagement analytics engine 114 includes player activity data, color anomaly data, gameplay status data, UI element data 408, motion characterization data 410, audio source data 412, and aggregate engagement data 414 [0042] and [Fig. 4]);
receive, as an output from the engagement prediction model, a predicted engagement metric, the predicted engagement metric being based on the plurality of engagement prediction features (Amer, the engagement analytics engine 112 generates the player engagement score 452 based on one or both of the player activity data 402 and the gameplay status data 406 [0061]);
wherein the player data comprises gameplay history data, player engagement history data, and player-specific attribute data (Amer, a prediction model can be used to determine an expected churn rate or a probability that a user will cease playing the video game 112 based on one or more inputs to the prediction model, such as, for example, historical user interaction information for a user [0032]; the player activity data includes player voice data 416, player visual presence data 418, player manual input data 420, and player body language data 422 [0043]).
Amer fails to explicitly disclose
provide the predicted engagement metric as an input to a matchmaking system; and
receive from the matchmaking system a decision whether to match the user account to the match of the online multiplayer game for gameplay.
Xue teaches
provide the predicted engagement metric as an input to a matchmaking system (Xue, matchmaking may be applied to a pool of users or players, P={p.sub.1, . . . , p.sub.N}, who are waiting to start or play 1-vs-1 matches; one such example of a pool of users P is illustrated in FIG. 3 as the pool of users 302; agraph G can be constructed to model the set of players waiting to play the multiplayer video game; one such example of the graph G is illustrated in FIG. 3 as the graph 304; each player p.sub.i may be represented by a vertex or a node of the graph, which has a current player state s.sub.i.; the player state s.sub.i may represent any data specific to the user and the user's interaction with the video game 112; the edge between two players p.sub.i and p.sub.i may be associated with the expected sum objective or engagement metric (for example, sum churn risk) if the users are paired; this metric relies on both users' states and may be denoted as a function f(s.sub.i, s.sub.j) [0062]); and
receive from the matchmaking system a decision whether to match the user account to the match of the online multiplayer game for gameplay (Xue, a list of user or player tuples, M={(p.sub.i, p.sub.j)}, may be used to denote a matchmaking result, or a pair assignment, in which all players in P are paired and are only paired once [0062]).
As stated with respect to claim 1, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the engagement analytics engine as disclosed by Amer with the matchmaking system as taught by Xue in order to ensure users enjoy the game.
Regarding Claim 12 (Currently Amended): Amer further discloses wherein the match state data comprises at least one of a game map associated with the match, a match capacity of the match, a number of players currently playing in the match, a number of artificial intelligence bots playing in the match, current scores in the match, progress of the match, and a number of objectives completed in the match (Amer, the engagement analytics engine 112 generates the game state data 430 based on at least one of analysis of rendered scenes associated with the game application 120, analysis of player command inputs 134, or analysis of game state information received from the game application 120 [0051]).
Regarding Claim 13 (Original): Xue further teaches wherein the player-specific attribute data comprises at least one of player preferences, skill, playstyle, toxicity level, average kills and deaths in a session, winning streak, hours played in current session, hours played total, joining as a group, playing as a group, and friend connections (Xue, these play characteristics may include characteristics relating to skill level, play style (for example, a user who plays defensively, plays offensively, plays a support role, prefers stealth attacks, prefers to use magic abilities, or prefers to use melee abilities, and the like), and/or sportsmanship (for example, a user who is a gracious winner or loser, is or is not gregarious, or does not insult other users, and the like) [0027]).
Regarding Claim 14 (Original): Xue further teaches wherein the player preferences comprise at least one of game map preferences, weapon preferences, vehicle preferences, and character class preferences (Xue, the data may include in-game character selection preferences, role preferences, and other information [0057]).
Regarding Claim 15 (Currently Amended): Xue further teaches wherein the user account is matched to the match of the online multiplayer game based on the decision, the match including one or more other matched accounts (Xue, matchmaking may be applied to a pool of users or players, P={p.sub.1, . . . , p.sub.N}, who are waiting to start or play 1-vs-1 matches [0062]).
Regarding Claim 16 (Currently Amended): Xue further teaches wherein the predicted engagement comprises an engagement score that represents a likelihood that the user account will engage in the match in the current state defined by the match state data (Xue, multiplayer games with poor matchmaking algorithms can result in lower engagement by users; in other words, poorly matched opponents and/or teammates may result in users ceasing to play a video game or playing the video game less often than if the multiplayer game has better matchmaking algorithms [0019]).
Regarding Claim 17 (Original): Amer in view of Xue discloses the invention as recited above. Amer in view of Xue fails to explicitly disclose wherein the engagement score is a value between zero and one, inclusive, with a minimum value of zero representing a first prediction that the user account will not engage, and a maximum value of one representing a second prediction that the user account will engage.
Amer discloses wherein aggregate engagement data includes a temporal importance score, high engagement regions, low engagement regions, a player engagement score, and an aggregate engagement score (Amer [0058]).
Xue teaches finding an optimal pair assignment M*, which maximizes the overall player engagement by using an equation (i.e., M*=arg max.sub.M Σ.sub.(p.sub.i.sub.,p.sub.j)ϵMf(s.sub.i, s.sub.j)) (Xue [0058]).
In both instances, it is clear that engagement is a value that is used in an equation. Engagement has a range of no engagement to full engagement and could be described by any range of values. The ultimate decision to select the numerical range of engagement is a matter of obvious design choice that would work equally well.
Therefore, it would have been prima facie obvious to modify Amer to define engagement as ranging from zero to one because such a modification would have been considered a mere design consideration which fails to patentably distinguish over the prior art of Amer.
Regarding Claim 18 (Currently Amended): Xue further teaches wherein providing the predicted engagement metric as an input to the matchmaking system is based on a determination that the engagement score is greater than a pre-defined engagement threshold associated with the match state data, wherein proposed matches with engagement scores below the pre-defined engagement threshold are not provided to the matchmaking system (Xue, the prediction model 160 may be use to confirm whether a particular match plan satisfies a set of conditions, such as, for example, a particular threshold retention rate [0049]).
Regarding Claim 19 (Original): Amer further discloses wherein the program, when executed by the computer, further configures the computer to provide the match state data as a further input to the matchmaking system (Amer, the engagement data 114 includes one or more of player activity data indicative of levels of engagement associated with a player's voice, presence manual inputs, or body language, ... gameplay status data indicative of a game state of a game being streamed by the player or of the player being engaged in a side task [0018]).
Regarding Claim 20 (Currently Amended): Amer discloses a system for matchmaking of game players, comprising:
a matchmaking system (Amer, FIG. 3 is a block diagram of an illustrative computing device having an engagement analytics engine configured to generate engagement data indicative of a level of engagement of one or more scenes corresponding to a game application, and to allocate encoding resources based on the level of engagement, in accordance with some embodiments [0005]);
a processor (Amer, a hardware processor [0006]); and
a non-transitory computer readable medium (Amer, a hardware processor in communication with the electronic data store [0006]) storing a set of instructions, which when executed by the processor, configure the processor to:
receive player data associated with a user account (Amer, one or more of player activity data indicative of levels of engagement associated with a player's voice, presence manual inputs, or body language [0018]);
receive match state data associated with a match (Amer, gameplay status data indicative of a game state of a game being streamed by the player or of the player being engaged in a side task [0018]) of an online multiplayer game, the match state data defining a current state of the match (Amer, the engagement analytics engine 112 applies one or more trained machine learning models, convolutional filters, object identification algorithms, or temporal analysis algorithms to one or both of the rendered scenes or the player command inputs 314 to determine that the game state corresponds to a “matchmaking” state in which the player is waiting for completion of a multiplayer matchmaking process; the engagement analytics engine receives an indication of the games state from the game application 120, the indication defining whether the game application 120 is in the matchmaking state or in an “active” state in which the corresponding game is actively being played by the player [0051]);
based on the player data and the match state data, extract a plurality of engagement prediction features (Amer, the engagement data 114 includes one or more of player activity data indicative of levels of engagement associated with a player's voice, presence manual inputs, or body language, ... gameplay status data indicative of a game state of a game being streamed by the player or of the player being engaged in a side task [0018]);
provide, as an input to an engagement prediction model, the plurality of engagement prediction features (Amer, the engagement analytics engine 114 includes player activity data, color anomaly data, gameplay status data, UI element data 408, motion characterization data 410, audio source data 412, and aggregate engagement data 414 [0042] and [Fig. 4]);
receive, as an output from the engagement prediction model, a predicted engagement metric, the predicted engagement metric being based on the plurality of engagement prediction features (Amer, the engagement analytics engine 112 generates the player engagement score 452 based on one or both of the player activity data 402 and the gameplay status data 406 [0061]);
Amer fails to explicitly disclose
provide the predicted engagement metric as an input to the matchmaking system; and
receive from the matchmaking system a decision whether to match the user account to the match of the online multiplayer game for gameplay.
Xue teaches
provide the predicted engagement metric as an input to the matchmaking system (Xue, matchmaking may be applied to a pool of users or players, P={p.sub.1, . . . , p.sub.N}, who are waiting to start or play 1-vs-1 matches; one such example of a pool of users P is illustrated in FIG. 3 as the pool of users 302; agraph G can be constructed to model the set of players waiting to play the multiplayer video game; one such example of the graph G is illustrated in FIG. 3 as the graph 304; each player p.sub.i may be represented by a vertex or a node of the graph, which has a current player state s.sub.i.; the player state s.sub.i may represent any data specific to the user and the user's interaction with the video game 112; the edge between two players p.sub.i and p.sub.i may be associated with the expected sum objective or engagement metric (for example, sum churn risk) if the users are paired; this metric relies on both users' states and may be denoted as a function f(s.sub.i, s.sub.j) [0062]); and
receive from the matchmaking system a decision whether to match the user account to the match of the online multiplayer game for gameplay (Xue, a list of user or player tuples, M={(p.sub.i, p.sub.j)}, may be used to denote a matchmaking result, or a pair assignment, in which all players in P are paired and are only paired once [0062]).
As stated with respect to claim 1, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the engagement analytics engine as disclosed by Amer with the matchmaking system as taught by Xue in order to ensure users enjoy the game.
Response to Arguments
Applicant's arguments filed May 15, 2026 have been fully considered but they are not persuasive.
With respect to the rejections under 35 USC 101, applicant argues
The rejection characterizes claims 1, 11, and 20 as "matchmaking" and treats the claims as a method of organizing human activity. That characterization overgeneralizes the amended claims and does not account for the specific limitations actually recited. Claims 1, 11, and 20 are not directed merely to the result of matching game players. As amended, these claims recite a specific game matchmaking pipeline in which player data is received, match state data defining a current state of a match of an online multiplayer game is received, engagement prediction features are extracted based on both the player data and the current match state data, those extracted features are provided to an engagement prediction model, a predicted engagement metric is generated, and the predicted engagement metric is provided as an input to a matchmaking system that returns a gameplay match decision. (Response [pp. 9-10])
The examiner disagrees. Applicant’s specification explicitly states:
The present disclosure generally relates to improving the experience of players who are playing games, and more particularly to improving matchmaking between game players. (Specification [0001])
The examiner maintains that the claims are directed to matchmaking. Matching players with each other clearly falls into managing personal behavior or relationships or interactions between people, a subcategory of certain methods of organizing human activity.
Applicant goes on to state:
The Examiner's statement that the claims are just "matchmaking" does not identify which specific limitations allegedly fall within the asserted organizing-human-activity grouping. Nor does it explain why limitations directed to current match state data, feature extraction based on both player data and match state data, model-based generation of a predicted engagement metric, and use of that metric as an input to a matchmaking system are themselves "managing personal behavior or relationships or interactions between people." USPTO guidance requires identifying the specific limitations believed to recite an abstract idea and evaluating the claim as a whole, including the ordered combination and how the limitations interact. See MPEP §§ 2106.04(a), 2106.07(a). (Response [p. 10])
The examiner encourages applicant to look at the subject matter eligibility analysis under 35 USC 101, as recited above. Specifically, under prong 1 of step 2A, the entirety of claim 1 is recited with the abstract idea underlined.
The underlined steps are used to match users in an online multiplayer game. As previously stated, matchmaking of the players involves the underlined steps that have been identified as the abstract idea. The examiner maintains that the analysis is proper.
With respect to prong 2 of step 2A, applicant argues
Even assuming, arguendo, that a gameplay matchmaking decision could be characterized as an abstract idea, claims 1, 11, and 20 integrate any such idea into a practical application. The claim does not merely collect information, generate a score, or display a recommendation. Rather, the claims recite how a computerized game matchmaking process is modified: current match state data for an online multiplayer match is combined with player data to extract engagement prediction features, the extracted features are processed by an engagement prediction model to generate a predicted engagement metric, and the predicted engagement metric is supplied as an input to a matchmaking system for a gameplay match decision. See Spec., 11 [0002]-[0004], [0015], [0033]-[0040]. (Response [p. 10])
Applicant appears to argue that the improved matchmaking process justifies being considered as integrating the abstract idea into a practical application. This reasoning seems circular. While applicant focuses on the limitations making up the abstract idea, Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application. That is, the portions of claim 1 that are not underlined are considered the additional elements (i.e., a computer, a processor, and a non-transitory computer readable medium, a matchmaking system). In the present case, the additional elements to do not integrate the abstract idea into a practical application, as stated above.
Applicant states “The ‘match’ and ‘current state’ limitations are meaningful” (Response [p. 10]). The examiner has considered the “match” and the “current state”, however in term of determining subject matter eligibility, these limitations are part of the abstract idea rather than additional elements. Applicant’s argument that an improvement can improve the computerized process to which the claims are directed (Response [p. 11]) does not necessarily integrate an abstract idea into a practical application.
Applicant identifies claims 9 and 18 as “recit[ing] meaningful eligibility-supporting limitations that require separate consideration” (Response [p. 11]). The examiner maintains that the recited limitations in claims 9 and 18 are still just part of the matchmaking process.
In the present instance, applicant has not provided valid reasons that the additional elements integrate the abstract idea into a practical application. The examiner maintains that the additional elements do not integrate the abstract idea into a practical application under prong two of step 2A.
With respect to step 2B, applicant states “the rejection does not establish that the threshold-based gating limitations of claims 9 and 18 were well-understood, routine, and conventional.” (Response [p. 12]). Step 2B asks whether the claim recites additional elements that amount to significantly more than the judicial exception. The examiner considers if the additional elements:
• add a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
• simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
As stated above, the examiner considers the “threshold-based gating limitations” as part of the abstract idea rather than an additional element. The additional elements are merely generic computer components that are used in their normal manner.\
The examiner maintains that the present claims are not directed to patent eligible subject matter.
With respect to the rejections under 35 USC 103, applicant states:
Amer is directed to adapting encoder, rendering, and server-resource allocation based on engagement information for rendered scenes. See Amer, Abstract, 11 [0012]-[0014], [0018]- [0020]. Amer's engagement analytics engine analyzes rendered scenes, player inputs, or player meta information to generate engagement data, and then uses that engagement data to generate encoding parameters, rendering parameters, or load-balancing decisions. Amer expressly defines the player engagement score as a level of engagement with a rendered scene or sequence of rendered scenes, and uses it for encoding quality/bitrate, not match selection. See Amer,1 [0061]. Even where Amer refers to "game state data" indicating a "matchmaking game state," that information is used to adjust quality, bitrate, rendering parameters, or server workload allocation for streamed scenes. See Amer, 11 [0051], [0061]-[0062], [0086]-[0088]. Amer does not disclose match state data associated with a match to which a user account may be matched and does not disclose extracting engagement prediction features from such match state data for a matchmaking decision. Amer's disclosure that a "matchmaking" state may correspond to lower engagement while a player waits for matchmaking underscores the distinction: Amer evaluates the engagement level of streamed/rendered scenes during a waiting state, not the current state of a match to which the user account may be matched. (Response [p. 13])
The examiner disagrees with applicant’s arguments. Amer discloses
The gameplay status data 406 includes a game state data 430 and a side task indicator 432. In some embodiments, the engagement analytics engine 112 generates the game state data 430 based on at least one of analysis of rendered scenes associated with the game application 120, analysis of player command inputs 134, or analysis of game state information received from the game application 120. In an example, the engagement analytics engine 112 applies one or more trained machine learning models, convolutional filters, object identification algorithms, or temporal analysis algorithms to one or both of the rendered scenes or the player command inputs 314 to determine that the game state corresponds to a “matchmaking” state in which the player is waiting for completion of a multiplayer matchmaking process. Such matchmaking states are typically identifiable based on a sequence of rendered scenes remaining substantially static (e.g., with little to no motion occurring in the scenes) or based on features typical of matchmaking lobbies. In another example, the engagement analytics engine receives an indication of the games state from the game application 120, the indication defining whether the game application 120 is in the matchmaking state or in an “active” state in which the corresponding game is actively being played by the player. For example, in response to determining that the game state data 430 indicates that the game application 120 is in the matchmaking state, the engagement analytics engine 112 generates encoding parameters 118 that cause rendered scenes to be encoded with lower quality and lower bitrate during the matchmaking state. For example, in response to determining that the game state data 430 indicates that the game application 120 is in the active state, the engagement analytics engine 112 generates encoding parameters 118 that cause rendered scenes to be encoded with higher quality and higher bitrate during the active state. (Amer [0051])
The examiner maintains that Amer in view of Xue reads on the limitations of claim 1. Whatever reasons the engagement analytics engine generates game state data, the examiner maintains that the engagement analytics engine is used in the multiplayer matchmaking process.
With respect to claims 2 and 12, the examiner maintains that the Amer discloses progress of the match, at least to the level of detail claimed (Amer, the engagement analytics engine 112 generates the game state data 430 based on at least one of analysis of rendered scenes associated with the game application 120, analysis of player command inputs 134, or analysis of game state information received from the game application 120 [0051]).
With respect to claims 7 and 16, applicant argues that Xue does not disclose “the predicted engagement comprises an engagement score that represents a likelihood that the user account will engage in the match in the current state defined by the match state data” (Response [pp. 14-15]). Xue teaches “Each player p.sub.i may be represented by a vertex or a node of the graph, which has a current player state s.sub.i. The player state s.sub.i may represent any data specific to the user and the user's interaction with the video game 112. (Xue [0062]).
With respect to claims 8 and 17, applicant argues
As yet another example, dependent claims 8 and 17 are also patentable. The Office Action acknowledges that Amer and Xue do not expressly disclose the claimed engagement score as "a value between zero and one, inclusive," with zero representing a prediction that the user account will not engage and one representing a prediction that the user account will engage and instead relies only on a conclusory "design choice" rationale. That is insufficient because the claimed limitation is not an arbitrary numerical presentation but defines the meaning of the model output used by the claimed matchmaking framework: a score representing the likelihood that a particular user account will engage in a particular match in its current state. Amer concerns engagement scoring for rendering, encoding, and load-balancing decisions, not matchmaking prediction for a match, and Xue concerns graph-based churn/retention optimization, not the claimed normalized engagement score with the expressly recited zero and one endpoint semantics. See Xue, [0054]. Because the Office Action does not identify any teaching or articulated reason why a person of ordinary skill would have selected this specific score range and endpoint definition in the claimed context, the rejection does not establish that claims 8 and 17 would have been obvious. See MPEP §§ 2143, 2144.04. (Response [p. 15])
The examiner thanks applicant for his opinion. The examiner does not agree.
Amer in view of Xue discloses the invention as recited above. Amer in view of Xue fails to explicitly disclose wherein the engagement score is a value between zero and one, inclusive, with a minimum value of zero representing a first prediction that the user account will not engage, and a maximum value of one representing a second prediction that the user account will engage.
Amer discloses wherein aggregate engagement data includes a temporal importance score, high engagement regions, low engagement regions, a player engagement score, and an aggregate engagement score (Amer [0058]).
Xue teaches finding an optimal pair assignment M*, which maximizes the overall player engagement by using an equation (i.e., M*=arg max.sub.M Σ.sub.(p.sub.i.sub.,p.sub.j)ϵMf(s.sub.i, s.sub.j)) (Xue [0058]).
In both instances, it is clear that engagement is a value that is used in an equation. Engagement has a range of no engagement to full engagement and could be described by any range of values. Engagement can range from absolutely no engagement to complete engagement. The value assigned to this range could be any values. Applicant has provided no reasoning within the specification why the range of zero to one is particularly significant. The ultimate decision to select the numerical range of engagement is a matter of obvious design choice that would work equally well.
Therefore, it would have been prima facie obvious to modify Amer to define engagement as ranging from zero to one because such a modification would have been considered a mere design consideration which fails to patentably distinguish over the prior art of Amer.
With respect to claims 9 and 18, applicant states “proposed matches with engagement scores below the pre-defined engagement threshold associated with the match state data are not provided to the matchmaking system” (Response [pp. 15-16]). Applicant states “These claims require proposed matches with engagement scores below the pre-defined engagement threshold associated with the match state data are not provided to the matchmaking system” (Response [p. 15]).
Xue's threshold retention-rate disclosure concerns approving, rejecting, or selecting a match plan within Xue's user-pairing system. This sounds to the examiner that a match plan is rejected (i.e., withheld) based on a threshold retention rate.
Finally, applicant states that “the rejection also lacks an adequate reason why a person of ordinary skill would have modified Xue using Amer in the manner required by claim 1” (Response [p. 16]).
The examiner disagrees.
As stated above,
Amer discloses an engagement analytics engine of a computing device analyzes one or more of scene representations, player inputs, or player meta information and generates corresponding engagement data indicative of a level of engagement corresponding to the represented scene (Amer [Abstract]). The engagement analytics engine generates encoding parameters based on the engagement data to cause scenes or regions within scenes to be encoded with a level of quality based on the indicated level of engagement (Amer [Abstract]). In some examples, the engagement analytics engine generates rendering parameters based on the engagement data to cause scenes to be rendered with a frame rate or quality parameters based on the indicated level of engagement (Amer [Abstract]). In some examples, the engagement analytics engine causes a load balancer to shift workloads associated with one or more scenes to higher or lower performance servers based on the engagement data (Amer [Abstract]).
Xue teaches identifying users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention (Xue [Abstract]). Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the video game (Xue [Abstract]). Software developers typically desire for their software to engage users for as long as possible (Xue [0002]). The longer a user is engaged with the software, the more likely that the software will be successful (Xue [0002]). The relationship between the length of engagement of the user and the success of the software is particularly true with respect to video games (Xue [0002]). The longer a user plays a particular video game, the more likely that the user enjoys the game and thus, the more likely the user will continue to play the game (Xue [0002]). The principle of engagement is not limited to single player games and can also be applied to multiplayer video games (Xue [0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the engagement analytics engine as disclosed by Amer with the matchmaking system as taught by Xue in order to ensure users enjoy the game.
The examiner maintains that this is sufficient motivation to combine Amer and Xue.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WERNER G GARNER whose telephone number is (571)270-7147. The examiner can normally be reached M-F 7:30-15:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DAVID 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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/WERNER G GARNER/ Primary Examiner, Art Unit 3715
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