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
This is in response to the amendments filed on 3/18/26. Claims 1, 8, and 15 have been amended. Claims 1 – 20 are pending in the current application.
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.
Step 1: It must be determined whether the invention falls in one of the four statutory categories of invention. Claims 1 – 7 are directed towards a method, (process) and claims 8 – 20 are directed towards a system, (machine), which are a statutory categories of invention.
Step 2a:
Prong 1: It must be determined whether the invention is directed to judicially recognized exception. Claim 1 is analyzed below with limitations indicating recitations of an abstract idea.
A method for operating an electronic game, the method comprising: maintaining, by a game complexity assessment system, a trained player experience model, the player experience model defining a plurality of factors indicating player engagement with the electronic game; receiving, by the game complexity assessment system, from a gaming system, electronic information related to activity of a player of the electronic game during execution of the electronic game on the gaming system; determining, by the game complexity assessment system, a player engagement level for the player while the player is playing the electronic game based on the received information related to the activity of the player and the player experience model; and in response to the determined player engagement level being less than a predetermined threshold, providing, by the game complexity assessment system, to the gaming system, an electronic instruction to enable an additional, new feature in the electronic game.
The abstract idea is defined by the underlined portions exemplary claim 1, with substantially similar features found in claims 8 and 15. Dependent claims 2 – 7, 9 – 14, and 16 - 20 further define the abstract idea or relate to the implementation of the abstract idea. The abstract idea is defined in at least the following grouping below:
Certain methods of organizing human activity (managing personal behavior)
Mental processes (observation, evaluation, judgment)
The claims are directed towards an abstract idea of managing personal behavior which falls into the category of organizing human activity, (See MPEP 2106/04(a)(2)(II)(C)). More specifically, the claimed invention recites a gaming system comprising a trained player experience model, (artificial intelligence model), that monitors game activity of a player playing the game, and based on said player activity, provide an additional or new game feature. Providing new game features based on a player’s gaming activity by invoking an AI model, represents managing personal behavior. (use of machine learning machine in a given environment, see Recentive Analytics v. Fox Corp., 134 F.4th 1205 (Fed Cir. 2025). This also represents following rules/instructions that define how the game is conducted.
The claims are also directed towards a series of steps which can practically be performed by one or more human, which fall into the category of mental processes, (See MPEP 2106.04(a)(2)(III)). More specifically, the claimed invention is drawn towards monitoring a player’s gaming activity and determining whether an additional or new feature is enabled and then providing said additional or new feature. The claims recite instructions for controlling a game with these features. Here, a human can observe and determine that an additional or new feature has been enabled. Therefore, since the claimed invention can practically be performed in the human mind, it represents an ineligible abstract mental process.
Prong 2: Does the Claim recite additional elements that integrate the exception in to a practical application of the exception?
The claims recite a generic processor, interface, and memory along with instructions that generate and present a video game to a player, wherein a player’s gaming activity is monitored to determine whether additional features are enabled, which is viewed as no more than instructions to implement a judicial exception.
These additional limitations do not represent an improvement to the functioning of a computer, or to any other technology or technical field, (MPEP 2106.05(a)). Nor do they apply the exception using a particular machine, (MPEP 2106.05(b)). Furthermore, they do not effect a transformation. (MPEP 2106.05(c)). Rather, these additional limitations amount to an instruction to “apply” the judicial exception using a computer as a tool to perform the abstract idea.
Step 2b: It must be determined whether the claimed invention recites additional elements that amount to significantly more than the judicial exception.
The claim language does recite a processor, memory, and an interface, however, viewed as a whole, these additional elements are indistinguishable from conventional computing elements known in the art. The claims further recite the use of AI models arranged in conventional ways. Nothing in the claims provide details about specific or improved learning models, rather they apply particular game information to existing machine learning models to process game information. In light of Recentive, the courts determined that claims are not made patent-eligible merely because they execute tasks with greater speed or efficiency. Therefore, the additional elements fail to supply additional elements that yield significantly more than the underlying abstract idea. Viewing the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 20 are rejected under 35 U.S.C. 102(a) as being anticipated by Beltran et al. (U.S. 2020/0197815).
Regarding claims 1, 8, and 15, Nelson discloses a method and system for operating an electronic game, (“game play of a gaming application”, par. 0006), the method comprising, maintaining by a game complexity assessment system, (“ The deep learning engine 190 may be configured to continually refine the trained AI model given any updated training data”, par. 0045), a trained player experience model, (“The trained AI model 160 is trained to learn the intricacies of the gaming application”, par. 0033), wherein the Examiner views the deep learning engine continually refining the trained AI model as being equivalent to a game complexity assessment system maintaining a trained player experience model. Beltran further discloses a player experience model defining a plurality of factors indicating player engagement with the electronic game, (“The training state data may include metadata associated with the game plays, to include controller inputs, game state, progress through the game play, results (e.g., success or failure) of the scenario, user profile information”, par. 0076), wherein the Examiner views the data including controller inputs, game, progress through the game play, etc. as being equivalent to factors indicating player engagement. Beltran further discloses receiving by the game complexity assessment system, from a gaming server, electronic information related to activity of a player of the electronic game during execution of the electronic game on the gaming system, (“The method includes training the AI model from a plurality of game plays of a scenario of the gaming application using training state data collected from the plurality of game plays of the scenario and associated success criteria of each of the plurality of game plays”, par. 0007), determining, by the game complexity assessment system, a player engagement level for the player while the player is playing the electronic game based on the received information related to the activity of the player and the player experience model, (“In that manner, success criteria may be defined to determine skill level of the player, to include how quick is the players' response time, how accurate is the player in targeting one or more targets (e.g., generally a skilled player has a fast trigger and moves from one target to another quickly, decisively, and accurately), how quick is the period between controller inputs”, par. 0095), wherein the Examiner views determining the skill level of the player based on the player’s response time and accuracy targeting targets as being equivalent to determining a player’s engagement level while the player is playing the electronic game. Beltran further discloses in response to the determined player engagement level being less than a predetermined threshold, providing, by the game complexity assessment system, to the gaming system, an electronic instruction to enable an additional, new feature in the electronic game, (“The analyzer 140 is further configured to determine and perform an action by the action generator 170 in response to the analysis of the output determined in response to the given input state data 405. The action is determined and performed depending on the predefined objective. For example, the action may provide services to the player playing the gaming application (e.g., provide a profile of a player playing the gaming application, providing recommendations to a player during game play of a gaming application by the player wherein the recommendation may be structured in consideration of the user profile, finding weaknesses of the player, provide services to address those weaknesses, training the player, providing a bot opponent to the player, take over game play for the player, etc.)”, par. 0112), wherein the Examiner views finding weaknesses of the player and providing services to address those weaknesses as being equivalent to determining that a player’s engagement being less than a predetermined threshold, (finding player weaknesses), and providing an electronic instruction, (provide services to address the weaknesses), to enable an additional new feature in the electronic game, (e.g. training the player, providing a bot opponent to the player).
Regarding claim 2, Beltran discloses determining, by the game complexity assessment system, a player skill level based on the received information related to the activity of the player and the game complexity model, (“In that manner, success criteria may be defined to determine skill level of the player”, par. 0095), and in response to the determined player skill level being less than a predetermined threshold for a current level of the electronic game, providing, by the game complexity assessment system, to the gaming system, an instruction to provide a tutorial to the player of the electronic game, (“the weakness trainer 141b may provide one or more tutorials 561 (e.g., videos, gaming sessions, etc.) that are targeted at improving the skills of the player in relation to the player's weakness”, par. 0144).
Regarding claims 3, 10, and 17, Beltran discloses training, by the game complexity assessment system, the player experience model based on the received information related to the activity of the player, (“The method includes training the AI model from a plurality of game plays of a scenario of the gaming application using training state data collected from the plurality of game plays of the scenario and associated success criteria of each of the plurality of game plays”, par. 0007).
Regarding claims 4 and 18, Nelson discloses training, by the game complexity assessment system, the game complexity model based on the received information related to the activity of the player, (“The method includes training the AI model from a plurality of game plays of a scenario of the gaming application using training state data collected from the plurality of game plays of the scenario and associated success criteria of each of the plurality of game plays”, par. 0007).
Regarding claims 5, 12, and 19, Beltran discloses information related to activity of the player of the electronic game related to the additional, new feature of the electronic game during execution of the electronic game on the gaming system; and training, by the game complexity assessment system, the player experience model based on the received information related to activity of the player of the electronic game related to the additional, new feature of the electronic game, (“The analyzer 140 is further configured to determine and perform an action by the action generator 170 in response to the analysis of the output determined in response to the given input state data 405. The action is determined and performed depending on the predefined objective. For example, the action may provide services to the player playing the gaming application (e.g., provide a profile of a player playing the gaming application, providing recommendations to a player during game play of a gaming application by the player wherein the recommendation may be structured in consideration of the user profile, finding weaknesses of the player, provide services to address those weaknesses, training the player, providing a bot opponent to the player, take over game play for the player, etc.)”, par. 0112).
Regarding claims 7 and 14, Beltran discloses comprising saving, by the game complexity assessment system, the determined player engagement level and the determined player skill level in an electronic record associated with the player, (“In one embodiment, the recommendation may take into account the user/player profile 510x of the player P-x (e.g., consider the skill level of the player)”, par. 0116), wherein the Examiner views the user/player profile comprising the player’s skill level as being equivalent to saving the engagement level of the player in an electronic record.
Regarding claims 9 and 16, Beltran discloses maintain a game complexity model, the game complexity model defining a plurality of factors indicating player proficiency with the electronic game, (“The training state data may include metadata associated with the game plays, to include controller inputs, game state, progress through the game play, results (e.g., success or failure) of the scenario, user profile information”, par. 0076), determine a player skill level based on the received information related to the activity of the player and the game complexity model, (“In that manner, success criteria may be defined to determine skill level of the player”, par. 0095), and in response to the determined player skill level being less than a predetermined threshold for a current level of the electronic game, provide, to the gaming system, an instruction to provide a tutorial to the player of the electronic game,(“the weakness trainer 141b may provide one or more tutorials 561 (e.g., videos, gaming sessions, etc.) that are targeted at improving the skills of the player in relation to the player's weakness”, par. 0144).
Regarding claims 11 and 18, Beltran discloses instructions further cause the processor to train the game complexity model based on the received information related to the activity of the player, (“the model may be trained using the gameplay data, player information”, par. 0048).
Regarding claims 13 and 20, Beltran discloses an instruction to collect information indicating a response by the player to the provided tutorial; receive, from the gaming system, the information indicating the response by the player to the tutorial; and train the game complexity model based on the received information indicating the response by the player to the tutorial, (“For example, the trained AI model may be used to train a pro-gamer to be the best gamer in the world, or to provide various tutorial sessions (e.g., video instruction, game play challenges, etc.) that are designed to address the weaknesses of the player”, par. 0045).
Response to Arguments
Applicant’s arguments with respect to the 102 rejection of claims 1 – 20 have been considered but are moot based on new grounds of rejection.
Applicant's arguments/statements with respect to the 101 rejection of claims 1 – 20 have been fully considered but they are not persuasive. Regarding claims 1 – 20, Applicants argue that disagrees with the 101 rejection and “requests withdrawal of the rejection”. The Examiner respectfully disagrees. The claims, as amended, are directed towards a gaming system that monitors game activity of a player playing the game, and based on said player activity, provide an additional or new game feature. The claims are clearly directed towards managing personal behavior which has been identified by the courts as abstract ideas. While the Examiner acknowledges the recent claim amendments, the current claim language still does not reflect an improvement to gaming technology nor does it provide a technical solution to a problem. Therefore, the Examiner maintains that claims 1 - 20 stand rejected under 35 U.S.C. 101.
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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC M THOMAS whose telephone number is (571)272-1699. The examiner can normally be reached 9:00am - 5:00pm.
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/E.M.T/ Examiner, Art Unit 3715
/JUSTIN L MYHR/ Primary Examiner, Art Unit 3715
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