Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims are directed to the abstract idea of mental processes and/ or certain methods of organizing human activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Step 1 of the 2019 Revised Patent Subject Matter
More specifically, regarding Step 1, of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a machine, process, and/or an article of manufacturer, which are statutory categories of invention.
Step 2a – Prong 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims are analyzed to determine whether it is directed to a judicial exception.
Independent claim:
[Claim 1] A method for generating a trained model for predicting an action to be selected by a user in a game that proceeds in accordance with actions selected by the user, while updating game states, the method comprising:
a step of determining weights for individual history-data element groups included in history data concerning the game, on the basis of user information associated with the individual history-data element groups;
a step of generating game state text and action text, which are text data expressed in a prescribed format, from data of game states and actions included in the history-data element groups included in the history data, and generating training data including pairs of game state text and action text corresponding to pairs of one game state and an action selected in the one game state; and
a step of generating a trained model on the basis of the generated training data,
wherein the step of generating training data includes generating a number of items of game state text as game state text corresponding to one game state, including items of game state text having different orders of a plurality of text elements included in the game state text, the number being based on the weight determined for the history-data element group including data of the one game state, and of generating training data including pairs of the individual generated items of game state text and action text corresponding to an action selected in the one game state.
[Claim 7] A system for generating a trained model for predicting an action to be selected by a user in a game that proceeds in accordance with actions selected by the user, while updating game states, the system:
determining weights for individual history-data element groups included in history data concerning the game, on the basis of user information associated with the individual history-data element groups;
generating game state text and action text, which are text data expressed in a prescribed format, from data of game states and actions included in the history-data element groups included in the history data, and
generating training data including pairs of game state text and action text corresponding to pairs of one game state and an action selected in the one game state; and
generating a trained model on the basis of the generated training data, wherein the generation of training data includes generating a number of items of game state text as game state text corresponding to one game state, including items of game state text having different orders of a plurality of text elements included in the game state text, the number being based on the weight determined for the history-data element group including data of the one game state, and of generating training data including pairs of the individual generated items of game state text and action text corresponding to an action selected in the one game state.
Claim 1 is exemplary to each of the independent claims, as claim 7 recite a system that store instructions to facilitate substantially the same functional steps as claim 1.
The underlined limitations in claim 1 recite an abstract idea included in the groupings of mental processes and/or method of organizing human activity, connected to technology only through application thereof using generic computing elements, specifically:
Mathematical concepts — Data augmentation via permutation, weighted sampling/oversampling, and model training are mathematical algorithms for manipulating data to improve predictions.
Mental processes — Evaluating historical gameplay, weighting by skill, and generating variations can be practically performed in the human mind or with pen/paper at a conceptual level.
Certain methods of organizing human activity — Predicting human behavior in games based on past actions resembles fundamental economic/practical activities like strategy optimization.
Recent Federal Circuit decisions, such as Recentive Analytics v. Fox Corp. (2025), confirm that claims involving training and using machine learning models— including data collection, iterative training, and generating predictions/optimizations—are abstract ideas when described functionally without a specific technological improvement. Similarly, general data augmentation techniques (e.g., permuting elements) and weighted sampling based on metadata (here, user rank) are conventional ML practices, not transformative.
Step 2a – Prong 2 of the 2019 Revised Patent Subject Matter Eligibility Guidance
The second prong of step 2a is the consideration if the claim limitations are directed to a practical application.
Limitations that are indicative of integration into a practical application:
-Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
-Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition - see Vanda Memo
-Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
-Effecting a transformation or reduction of a particular article to a different state or thing – see MPEP 2106.05(c)
-Applying or using 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 - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
-Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea- see MPEP 2106.05(f)
-Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
-Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h)
Claims 1-7 apply the abstract idea on generic computers ("a trained model," deep learning, pretrained natural language models) in the context of text-based game action prediction. Elements like permuting text orders for augmentation and weighting by user rank are improvements to the abstract idea itself (better training data for more accurate behavioral prediction), not to computer functionality or another technology.
USPTO guidance (2019 PEG, updated 2024 for AI) and examples emphasize that mere improvements in ML accuracy, data efficiency, or prediction quality—without a specific, non-conventional technical solution—do not integrate the idea into a practical application. For instance:
Example 39 (facial detection training with specific two-stage neural network) is eligible due to its particular training architecture.
In contrast, generic augmentation and weighting (common in imbalanced datasets or EDA techniques) lack such specificity.
The game context confines the idea to a field of use, which is insufficient (per Recentive and earlier cases like Electric Power Group).
Step 2b of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims as a whole are analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception.
The ordered combination adds nothing "significantly more" than the abstract idea. Components (text generation from states/actions, permutation augmentation, weighted replication, contrastive training with incorrect pairs, generic deep/sequential/pretrained models) are well-understood, routine, and conventional in ML and game AI as of the filing date.
No unconventional arrangement or novel hardware is claimed. As in Recentive, simply using conventional ML techniques (even with domain-specific data like ranked gameplay history) to achieve better predictions does not supply an inventive concept.
Consequently, consideration of each and every element of each and every claim, both individually and as an ordered combination, leads to the conclusion that the claims are not patent-eligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Kipnis (US 2023/0330526 A1) in view of Rico (US 2020/0289943 A1).
Claim 1. Kipnis discloses method for generating a trained model, while updating game states, the method comprising:
a step of determining weights for data element groups included in data concerning the game [0085];
a step of generating game state text and action text, which are text data expressed in a prescribed format, from data of game states and actions included in the data element groups included in the data, and generating training data including pairs of game state text and action text corresponding to pairs of one game state and an action selected in the one game state [0011], [0013], [0027], [0057]; and
a step of generating a trained model on the basis of the generated training data [0013], [0019], [0024],
wherein the step of generating training data includes generating a number of items of game state text as game state text corresponding to one game state, including items of game state text having different orders of a plurality of text elements included in the game state text, the number being based on the weight determined for the data element group including data of the one game state, and of generating training data including pairs of the individual generated items of game state text and action text corresponding to an action selected in the one game state [0011], [0013], [0027], [0057], [0085].
Kipnis does not expressly disclose training data used for predicting an action to be selected by a user in a game that proceeds in accordance with actions selected by the user, on the basis of user information associated with the individual history-data element groups. Rico disclose training data used for predicting an action to be selected by a user in a game that proceeds in accordance with actions selected by the user, on the basis of user information associated with the individual history-data element groups (i.e. to implement an AI bot agent configured to perform gameplay of the video game on behalf of the user), [0017]-[0019], [0051]-[0053]. It would have been obvious to a person of ordinary skilled in the art to modify Kipnis with Rico and would have been motivated to do so to expand the use case of the AI training in game prediction model for the user to be implemented as an AI bot agent.
Claim 2. Kipnis and Rico disclose the method according to Claim 1, wherein in the step of generating a trained model, a trained model is generated by training a deep learning model with the generated training data, the deep learning model being directed to learning sequential data, Kipnis [0019] or Rico [0010], [0049].
Claim 3. Kipnis and Rico disclose the method according to Claim 1, wherein in the step of determining weights, weights are determined so as to have magnitudes corresponding to the levels of user ranks included in the user information, Kipnis [0085].
Claim 4. Kipnis and Rico disclose the method according to Claim 1, wherein the step of generating a trained model includes generating a trained model by training a pretrained natural language model with the generated training data, the pretrained natural language model having learned in advance grammatical structures and text-to- text relationships concerning a natural language, Kipnis [0013].
Claim 5. Kipnis and Rico disclose the method according to Claim 1, wherein: the step of generating training data includes implicitly generating training data including first pairs and second pairs, the first pairs being pairs of game state text and action text corresponding to pairs of one game state and an action selected in the one game state, generated on the basis of data of game states and actions included in the history-data element groups included in the history data, and the second pairs being pairs of the one game state text and action text corresponding to actions that are randomly selected from actions selectable by a user and that are not included in the first pairs; and the step of generating a trained model includes generating a trained model by performing training with the first pairs as correct data and performing training with the second pairs as incorrect data for improving the training model, Kipnis [0013] or Rico [0038]-[0039], [0049].
Claim 6. Kipnis and Rico disclose a non-transitory computer readable medium storing a program that causes a computer to execute the steps of the method according to Claim 1 (Fig. 1).
Claim 7. Kipnis and Rico disclose a system for generating a trained model for predicting an action to be selected by a user in a game that proceeds in accordance with actions selected by the user, while updating game states, the system: determining weights for individual history-data element groups included in history data concerning the game, on the basis of user information associated with the individual history-data element groups; generating game state text and action text, which are text data expressed in a prescribed format, from data of game states and actions included in the history-data element groups included in the history data, and generating training data including pairs of game state text and action text corresponding to pairs of one game state and an action selected in the one game state; and generating a trained model on the basis of the generated training data, wherein the generation of training data includes generating a number of items of game state text as game state text corresponding to one game state, including items of game state text having different orders of a plurality of text elements included in the game state text, the number being based on the weight determined for the history-data element group including data of the one game state, and of generating training data including pairs of the individual generated items of game state text and action text corresponding to an action selected in the one game state as similarly discussed above (Fig. 1).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached USPTO form PTO-892.
Filing of New or Amended Claims
The examiner has the initial burden of presenting evidence or reasoning to explain why persons skilled in the art would not recognize in the original disclosure a description of the invention defined by the claims. See Wertheim, 541 F.2d at 263, 191 USPQ at 97 (“[T]he PTO has the initial burden of presenting evidence or reasons why persons skilled in the art would not recognize in the disclosure a description of the invention defined by the claims.”). However, when filing an amendment an applicant should show support in the original disclosure for new or amended claims. See MPEP § 714.02 and § 2163.06 (“Applicant should specifically point out the support for any amendments made to the disclosure.”). Please see MPEP 2163 (II) 3. (b)
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SENG H LIM whose telephone number is (571)270-3301. The examiner can normally be reached Monday-Friday (9-5).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David L. 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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/Seng H Lim/Primary Examiner, Art Unit 3715