Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Response to Arguments
Applicant’s arguments with respect to the pending claims have been considered but are moot because of the new ground of rejection below.
Claim 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) 1-6, 9-15, 17- 19, 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Froy (US 2023/0169823 A1) in view of Ruppert (US 2009/0124392 A1).
1. Froy discloses a method comprising:
aggregating, by an electronic processor, gaming data generated by casino devices communicatively coupled to a casino network (i.e. system continuously feed the model with player data in association with a gameplay session), [0016], [0057], [0120];
accessing, by the electronic processor, a machine learning model trained, via exploratory data analysis of the aggregated gaming data, to map input features of the aggregated gaming data to model parameters used to predict a target output value, [0016], [0057], [0064], [0091]-[0092];
predicting, by the electronic processor using the machine learning model to analyze at least some portion of the aggregated gaming data associated with a specific user account logged onto one of the casino devices, a user-specific output value that identifies a player behavior [0053], [0163]; and
automatically modifying, based on the identified player behavior, a configuration associated with the one of the casino devices to optimize, for the specific user account, an operation associated with the one of the casino devices, [0112]-[0113].
Froy does not expressly disclose wherein the configuration comprises at least one of a volatility, a payout structure, a bonus frequency, a game theme, a denomination range, a game grouping, or a promotion-offering configuration of the one of the casino devices.
Ruppert teaches a download and configuration management system that automatically modifies configurations of casino gaming machines, including paytables (payout structure), game themes, denominations, and related device-level parameters, by downloading and applying new configurations from a central system to the EGMs [0131], [0285], (Fig. 18). Ruppert further teaches managing and grouping games/configurations across the casino floor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Froy’s machine-learning-driven automatic modification of game parameters with Ruppert’s download-and-configuration management engine so that the predicted player behavior triggers a device-level reconfiguration of one or more of volatility, payout structure, bonus frequency, game theme, denomination range, game grouping, or promotion-offering configuration. One of ordinary skill would have been motivated to combine the references because both are directed to adaptive casino gaming systems. Froy supplies the predictive intelligence (ML model of player behavior) and Ruppert supplies the proven mechanism for safely and automatically pushing the resulting configuration changes to the actual casino devices. The combination yields the predictable result of more precise, player-specific optimization of the EGM’s operating parameters while retaining centralized control and regulatory compliance.
2. Froy and Ruppert discloses the method of claim 1, wherein the predicting the user-specific output value associated with the player behavior comprises analyzing the at least some portion of the aggregated gaming data for recency, frequency, and monetary engagement metrics associated with the specific user account, and wherein the automatically modifying the configuration associated with the one of the casino devices is based on the analysis of the at least some portion of the aggregated gaming data for the recency, frequency, and monetary engagement metrics, Froy [0065]-[0068], [0112].
3. Froy and Ruppert discloses the method of claim 1, further comprising identifying, based on analysis of the at least some portion of the aggregated gaming data, a suboptimal game configuration at the one of the casino devices, and wherein the automatically modifying the configuration includes optimizing the suboptimal game configuration for the specific user account, Froy [0112]-[0119].
4. Froy and Ruppert discloses the method of claim 3, wherein detecting the suboptimal game configuration comprises identifying an underperforming game available at the one of the casino devices, and wherein the modifying the configuration comprises automatically adjusting one or more of a volatility, a payout structure, a bonus frequency, a game theme, or a denomination range of the underperforming game, Froy [0112]-[0119].
5. Froy and Ruppert discloses the method of claim 1, wherein the modifying the configuration comprises automatically grouping, by the electronic processor, one or more games provided by the one of the casino devices based on at least one of a game theme, a game volatility, or an in-game feature, Froy [0112]-[0119].
6. Froy and Ruppert discloses the method of claim 1, further comprising determining, by the electronic processor using the predicted user-specific output value, a risk level associated with the player behavior, wherein the automatically modifying the configuration comprises modifying the configuration to a specific level based on the determined risk level, Froy [0061], [0221].
9. Froy and Ruppert discloses the method of claim 1, wherein the automatically modifying the configuration causes optimization of the operation to perform at least one of fine-tuning a loyalty program, generating a promotion, modifying a game mechanic, targeting a specific player segment, optimizing marketing spending, or selecting an intervention strategy associated with a responsible gaming restriction, Froy [0112]-[0119].
10. Froy and Ruppert discloses the method of claim 1, wherein the predicting the user-specific output value associated with the player behavior comprises analyzing real-time gaming data associated with a current gaming session associated with the specific user account, and wherein the automatically modifying the configuration associated with the one of the casino devices comprises configuring the one of the casino devices to dynamically offer a promotion specifically based, at least in part, on the real-time gaming data, Froy [0213].
11-15, 17, 18. Froy and Ruppert discloses a gaming system comprising: a network communication device configured to communicate with a casino network; and one or more processors configured to execute instructions, wherein execution of the instructions cause the gaming system to perform operations to: aggregate gaming data generated by casino devices communicatively coupled to the casino network; access a machine learning model trained, via exploratory data analysis of at least a portion of the aggregated gaming data, to map input features of the at least a portion of the aggregated gaming data to model parameters used to predict a target output value; predict, using the machine learning model to analyze at least some portion of the aggregated gaming data associated with a specific user account logged onto one of the casino devices, a user-specific output value that identifies a player behavior; and automatically modify, based on the identified player behavior, a configuration associated with the one of the casino devices to optimize, for the specific user account an operation associated with the one of the casino devices wherein the configuration comprises at least one of a volatility, a payout structure, a bonus frequency, a game theme, a denomination range, a game grouping, or a promotion-offering configuration of the one of the casino devices as similarly discussed above.
19. Froy and Ruppert discloses one or more non-transitory, machine-readable mediums having instructions stored thereon, which when executed by one or more electronic processors of a gaming system cause the gaming system to perform operations comprising: aggregating gaming data generated by casino devices communicatively coupled to a casino network; accessing a machine learning model trained, via exploratory data analysis of the aggregated gaming data, to map input features of the aggregated gaming data to model parameters used to predict a target output value; predicting, using the machine learning model to analyze at least some portion of the aggregated gaming data associated with a specific user account logged onto one of the casino devices, a user-specific output value that identifies a player behavior; and automatically modifying, based on the identified player behavior, a configuration associated with the one of the casino devices to optimize, for the specific user account, an operation associated with the one of the casino devices wherein the configuration comprises at least one of a volatility, a payout structure, a bonus frequency, a game theme, a denomination range, a game grouping, or a promotion-offering configuration of the one of the casino devices as similarly discussed above.
21. Froy and Ruppert discloses the method of claim 1, wherein the aggregating gaming data further comprises aggregating gaming data from a plurality of different casino accounts associated with respective casino networks of a plurality of different casino operators, and wherein the machine learning model is trained using the aggregated gaming data from the plurality of different casino accounts, Froy [0064], [0121].
22. Froy and Ruppert discloses the method of claim 1, wherein the modified configuration is generated by the electronic processor based on performance metrics for a plurality of candidate game configurations, the performance metrics comprising at least one of engagement, revenue, bet size, or time on device, Ruppert [0131], [0285], (Fig. 18), Froy [0064], [0121]-[0122].
Claim(s) 20 is rejected under 35 U.S.C. 103 as being unpatentable over Froy (US 2023/0169823 A1) and Ruppert (US 2009/0124392 A1) as applied above and further in view of Chen (US 11,704,577 B1).
20. Froy and Ruppert discloses the method of claim 1, but does not expressly disclose wherein the machine learning model is trained by a data platform system associated with a casino account of the casino network, wherein the accessing the machine learning model comprises accessing the machine learning model at a portable prediction server associated with at least one of the casino devices, the portable prediction server having received a copy of the machine learning model from the data platform system via a telecommunications network and via an integration framework communicatively coupled to the casino network, and wherein the integration framework comprises the portable prediction server and a model messenger.
Chen teaches a high-performance machine learning inference framework for edge devices in which machine learning models are trained or prepared in a central/provider network (cloud) environment and then deployed to heterogeneous edge devices so that the models become “extremely portable” and can perform inference locally on (or associated with) those edge devices. Chen further teaches a server module that allows users to deploy and manage the ML models running on the connected edge devices, including the distribution of model copies over a network so that prediction/inference occurs at the edge rather than solely in the central system (col. 2, lines 10-55; col. 6, lines 7-56). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Froy’s multi-server model training and export architecture with the portable/edge inference framework of Chen so that the trained machine learning model is accessed at a portable prediction server associated with at least one of the casino devices, the portable prediction server having received a copy of the model from the data platform system via a telecommunications network and an integration framework that comprises the portable prediction server and a model-messenger component. One of ordinary skill would have been motivated to make this combination because Froy already teaches centralized training with export of the model to a casino-side server for real-time use. Chen provides a proven framework for making ML models portable and deployable to edge devices for local inference. Implementing Froy’s model deployment through Chen’s edge-inference framework would yield the predictable benefits of lower latency, improved reliability of real-time predictions at or near the casino devices, reduced dependence on continuous central-server connectivity, and better handling of sensitive player data. The specific nomenclature “portable prediction server” and “model messenger” constitutes an obvious descriptive labeling of the edge inference server and the communication/data-pipeline component that feeds prepared data to the local model and returns prediction results, both of which are conventional elements of distributed machine-learning systems taught by the combination of references.
Claim(s) 23 is rejected under 35 U.S.C. 103 as being unpatentable over Froy (US 2023/0169823 A1) and Ruppert (US 2009/0124392 A1) as applied above and further in view of Kipnis (US 2022/0370913 A1).
23. Froy and Ruppert discloses the method of claim 1, but does not expressly disclose identifying, by the electronic processor using natural language processing to analyze player reviews and comments, negative sentiment associated with the configuration associated with the one of the casino devices, and wherein the automatically modifying the configuration is based, at least in part, on the identified negative sentiment. Kipnis teaches the use of natural language processing (NLP) on text associated with game events and player experience, including performing sentiment analysis on such text to determine characteristics of the player’s experience (Abstract), [0003]-[0006], [0052]-[0059].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Froy’s system by incorporating natural language processing to analyze player reviews and comments for negative sentiment associated with a configuration, and to base the automatic modification of the configuration at least in part on that identified negative sentiment, as taught by Kipnis. One of ordinary skill would have been motivated to make this combination because Froy already relies on player-reaction and feedback signals to drive configuration changes. Applying a known NLP sentiment-analysis technique would provide an additional, low-cost, and more precise signal of negative reaction to a particular configuration.
Filing of New or Amended Claims
The examiner has the initial burden of presenting evidence or reasoning to explain why persons skilled in the art would not recognize in the original disclosure a description of the invention defined by the claims. See Wertheim, 541 F.2d at 263, 191 USPQ at 97 (“[T]he PTO has the initial burden of presenting evidence or reasons why persons skilled in the art would not recognize in the disclosure a description of the invention defined by the claims.”). However, when filing an amendment an applicant should show support in the original disclosure for new or amended claims. See MPEP § 714.02 and § 2163.06 (“Applicant should specifically point out the support for any amendments made to the disclosure.”). Please see MPEP 2163 (II) 3. (b)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SENG H LIM whose telephone number is (571)270-3301. The examiner can normally be reached Monday-Friday (9-5).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Seng H Lim/Primary Examiner, Art Unit 3715
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