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 .
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gilliam (US 20200258158) in view of Bhuyan (US 11736580)
In claims 1, 9, and 15 Gilliam discloses
At least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code that when executed causes the processor to (paragraph 33)
Receive, via a user device, user input requesting access to a gaming profile associated with an entity (paragraph 53 discloses entering login information and creation of a new users account as well as connection to a user’s personal banking account. It is noted that with respect to the term “gaming”, the BRI of “game” in this context is effectively just a user being provided with goals. Read in light of the instant application, no particulars of a traditional video game (such as an avatar, or series of rules governing the game, or a virtual world) have been claimed or described in the specification, instead the specification appears to simply describe the “game” as users saving money towards their financial goals. Paragraphs 105-107 of the instant application “when the user accesses the gaming functionality […] the user may receive rewards for accomplishing various goals related to financial wellness”, paragraphs 140-142 of the instant application describes “at any point during the gaming process, a user may decide to change their financial goals, and do so accordingly. In these instances the system would just create new games or gaming actions for the user to complete to achieve their desired goals”.)
Receive entity data from the user’s gaming profile (paragraph 53, the entity would be the bank, with the personal banking account being the entity data)
Collect entity data from a plurality of user profiles associated with an entity (paragraph 52 “the system uses artificial intelligence and machine learning to track the savings and spending habits of participants 24”)
Extract resource data from the entity data for the user and the plurality of users (paragraph 52, the savings and spending habits are the resource data)
Deploy the trained model, predict, using the trained model based on a machine learning dataset and the extracted resource data, a first objectively likely to be preferred by the user (paragraph 52 “the system uses AI and machine learning to determine which of those means of encouragement are the most effective for a particular participant 24, allowing the system to target specific types of encouragement to specific participants 24 based on the methods that work best for that participant 24.”)
Based on the predicted first objective likely to be preferred by the user, determine one or more gaming actions capable of being performed via the user device and associated with the gaming profile that would further the first objectively likely to be preferred by the user (paragraph 52 “offering rewards or incentives to the participants for making certain progress in their savings journey”. The “certain progress” would be the gaming action)
Assign a remuneration amount to each gaming action of the one or more gaming actions, monitor the gaming profile of the user for completion of the one or more gaming actions, and allocate to the gaming profile, remuneration in the amount assigned to the one or more gaming actions completed by the user (paragraph 52 “offering rewards or incentives to the participants for making certain progress in their savings journey”.)
Gilliam fails to disclose iteratively train a computer implemented machine learning model wherein the model is trained by: inserting training data into an iterative training and testing loop to predict a target variable, repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined acceptable level, however Bhuyan discloses iteratively train a computer implemented machine learning model wherein the model is trained by: inserting training data into an iterative training and testing loop to predict a target variable, repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined acceptable level (column 6 lines 54-67, column 7 lines 1-33). As Gilliam generally discluses use of machine learning models, It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Gilliam with Bhuyan to use a specific known machine learning model to allow for the invention of Gilliam to be able to more accurately predict the objectives to be preferred by the user.
In claims 2, 3, 10, 16 and 17, Gilliam discloses the machine learning dataset is generated based on data collected from a plurality of sources (paragraph 52 “the system uses artificial intelligence and machine learning to track the savings and spending habits of participants 24”, participants would be plurality of sources) the plurality of sources comprises an entity data source, wherein the entity data source comprises historical data associated with the user’s prior interactions with the gaming system (paragraph 52 discloses “savings and spending habits” which would be prior interactions with the bank entity)
In claims 4, 5, 11, 12, 15, 18, and 19 Gilliam discloses in response to extracting the resource data, predict, based on the machine learning dataset and the extracted resource data, a second objective likely to be preferred by the entity, wherein the second objective likely to be preferred by the entity, determine one or more gaming actions capable of being performed via the user device and associated with the gaming profile that would further the second objective likely to be preferred by the entity (paragraph 49 discloses that participants may be provided with a loan, which would be provided once a savings goal is reached as per paragraph 52, which would be “preferred by the entity” as it would be a bank product)
In claims 6 and 13, Gilliam discloses the first objective is at least one of saving, purchasing, retiring, increasing credit score, and funding for an emergency (It is noted by examiner that as this is a Markush group, only one of these limitations needs to be taught. paragraph 52)
In claims 7 and 14, Gilliam discloses the second objective is at least one of increasing user savings, increasing user engagement, promoting cross selling with partner entities, promoting fiscal learning, borrowing facility initiation, and increasing user retention (It is noted by examiner that as this is a Markush group, only one of these limitations needs to be taught. paragraphs 49 and 52)
In claim 8, Gilliam discloses predict a first objective likely to be preferred by the suer, the processor is configured to prioritize a first objective that furthers one or more objectives of the entity (paragraphs 49 and 52 disclose that the objectives provide progress in the savings journey, which may then complete with the user procuring a loan)
In claim 20, Gilliam in view of Bhuyan discloses the claimed invention except display via the user device, the first objective likely to be preferred by the entity, the second objective likely to be preferred by the user, and receive via the user device, a user selection of at least one of the first and the second objective, however Official notice is taken that display of pertinent data to the user as well as providing the user with a choice between two options was notoriously well known in the art. It would have been obvious before the effective filing date of the invention to one of ordinary skill in the art to combine Gilliam in view of Bhuyan with this well known technique in order to allow for the user to be able to understand the objectives that need to be completed in exchange for remuneration, as well as to allow for the users to have some amount of control over the objectives required of them.
Response to Arguments
Applicant’s amendments overcome the 101 rejection.
Applicant’s amended claim language overcomes the previous art rejection, however a new rejection is made in view of Bhuyan as set forth above.
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 THOMAS HAYNES HENRY whose telephone number is (571)270-3905. The examiner can normally be reached M-F 10-6.
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/THOMAS H HENRY/Primary Examiner, Art Unit 3715
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