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 2/27/26. Claims 1 – 18 and 20 have been cancelled, claim 19 has been amended, and claims 21 – 39 have been added. Claims 19 and 21 – 39 are now 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 19 and 21 - 39 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 19 and 21 - 30 are directed towards a method, (process) and claims 31 - 39 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.
19. A computer-implemented method comprising: evaluating, in real-time as game data is being generated, the game data representing a user behavior in a computer simulation, using a machine learning (ML) model; and determining, based on an output of the ML model, to apply a corrective action to an account associated with the game data, in real-time as the game data is being generated.
The abstract idea is defined by the underlined portions exemplary claim 1, with substantially similar features found in claims 31 and 38. Dependent claims 21 – 30, 32 – 37, and 39 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 machine learning, that evaluates player behavior of a player playing the game, and based on said player behavior, determine a corrective action based on the player’s behavior. Determining a corrective action based on a player’s behavior playing a game, 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 evaluating player behavior and determining whether a correction action is applied. The claims recite instructions for controlling a game with these features. Here, a human can observe and determine that a correction action has been applied. 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 computer and storage, (see claim 31), along with instructions that generate and present a video game to a player, wherein a player’s behavior is monitored to determine whether a correction is applied, 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 computer and a storage, 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 machine learning 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 19 and 21 – 39 are rejected under 35 U.S.C. 102(a) as being anticipated by Panattoni et al. (U.S. 2019/0052471).
Regarding claims 19, 31, and 38, Pannattoni discloses a computer implemented method and system, (fig. 1), comprising evaluating, in real-time as game data is being generated, the game data representing user behavior, in a computer simulation, using a machine learning model, (“the virtual environment service 106 includes a machine learning engine 150 to analyze data sources associated with the multiuser virtual environment 104 and/or the communications session 144 to identify “indicators” that have a strong correlation with an instance of a participant's behavior”, par. 0050), wherein Pannattoni discloses that the multiuser virtual environment is a multiplayer gaming session, (“the multiuser virtual environment 104 being a multiplayer gaming session”, par. 0024), wherein the Examiner views the data sources that are analyzed being associated with virtual environment as being equivalent to evaluating game data in a computer simulation. Pannattoni further discloses determining, based on an output of the ML model, to apply a corrective action to an account associated with game data, (“FIG. 5 is a flow diagram of an example method for selectively suspending communications functionality of a participant's user account in response to that participant exhibiting a predetermined toxic behavior within a multiuser virtual environment”, par. 0083, wherein the Examiner views suspending a user account as being equivalent to applying a corrective action. Pannattoni further discloses a corrective action being applied in real-time as the game data is being generated, (“the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time”, par. 0021), wherein the Examiner views the system preventing participants from being exposed to toxic behavior as being equivalent to a corrective action.
Regarding claim 21, Pannattoni discloses wherein evaluating the game data using the ML model comprises generating a quantitative risk score, and wherein determining to apply the corrective action comprises comparing the quantitative risk score to a predefined risk threshold, (“if another participant's reputation score is below a threshold level the machine learning engine 150 may determine that the other participant should be kept away from the participant”, par. 0065).
Regarding claims 22, 32, and 39, Pannattoni discloses wherein the game data comprises message data, (“ a system may monitor voice-based and/or text-based “chat” sessions between participants of the multiuser virtual environment to identify and ultimately shield the individual participant from instances of the predetermined toxic behavior”, par. 0020), and gameplay behavior data, and wherein evaluating the game data comprises extracting first feature data from the message data and second feature data from the gameplay behavior data and performing inference using the ML model on the first feature data and the second feature data to generate a quantitative risk score, (“When analyzing the first factor-category, the machine learning engine 150 may analyze the participant's gaming activities and communications activities to generate a “Behavior/Skill” score for the participant”, par. 0065).
Regarding claims 23 and 33, Pannattoni discloses wherein the ML model is configured to generate a likelihood that a message associated with the account falls within one or more predefined risk moderation categories, (“The individual analytical services may analyze specific factor-categories with respect to an individual participant”, par. 0065).
Regarding claims 24 and 34, Pannattoni discloses wherein the one or more predefined risk moderation categories comprise at least one of spam, offensive content, abusive content, child protection content, hate speech, pornographic content, or obscene content, (“a player using the in-session voice “chat” service during a multiplayer gaming session may use constant profanity without malicious intent but while being ignorant of the fact that such profanity is highly offensive to another player within that multiplayer gaming session”, par. 0022).
Regarding claims 25 and 35, Pannattoni discloses aggregating outputs of the ML model across multiple messages associated with the account prior to determining to apply the corrective action, (“the toxicity-tolerance data may indicate that the first participant is intolerant of the particular expletive being used in excess of a usage threshold”, par. 0005).
Regarding claim 26, Pannattoni discloses wherein the evaluating and determining are performed by a server separate from a client device executing the computer simulation, (fig. 1).
Regarding claims 27 and 36, Pannattoni discloses further comprising training the ML model using historical labeled data corresponding to prior risk moderation categories, (“ the toxicity prediction model 206 may be created by employing supervised learning wherein one or more humans assists in generating labeled training data. For example, a human such as a game developer that authors a title 108, toxicity level officer associated with the virtual environment service 106, a participant of the multiuser virtual environment 104, or any other type of human reviewer may label instances of behavior within historical communications data 142 to be used as training data for the machine learning engine”, par. 0053).
Regarding claims 28 and 37, Pannattoni discloses wherein the corrective action increases in severity based on prior corrective actions associated with the account, (“if the toxicity report 146 indicates that the offending participant's behavior is highly toxic (e.g., use of an expletive to trash talk or insult another participant) and the offending participant has a long history of toxic behavior, then the one or more repercussions may be relatively harsh”, par. 0049).
Regarding claim 29, Pannattoni discloses wherein increasing in severity comprises escalating from issuing a warning to suspending the account, (“the system 100 may transmit a consequence instruction 148 that causes the client device 102(2) to audibly recite to the second participant “That type of language is not tolerated within this gaming session. Also, even after two warnings you're still using that language. Your user account is now being suspended for a predetermined period of time during which time you will not be able to initiate and/or join any gaming sessions”, par. 0049).
Regarding claim 30, Pannattoni discloses updating the ML model using historical data including previously recorded corrective actions associated with the account, (“the machine learning engine 150 may build a toxicity prediction model 206 and update and/or revise the toxicity prediction model 206 as data evolves over time”, par. 0064).
Response to Arguments
Applicant’s arguments with respect to the double-patenting rejection have been fully considered and are persuasive. The double patenting rejection of claims 19 and 21 - 29 has been withdrawn.
Applicant's arguments filed on 2/27/26 have been fully considered but they are not persuasive. Regarding claims 19 and 21 – 39, there no arguments with respect to the 101 rejection, the Applicants only requests “withdrawal of all rejections”. The Examiner disagrees and maintains that the claims stand rejection under 35 U.S.C. 101.
Applicant’s arguments with respect to the 103 rejection have been considered but are moot based on new grounds of rejection.
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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