DETAILED ACTION
The instant application is a continuation of Application No. 18/384,010. Claims 1-20 are currently pending.
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 § 102
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.
Claim(s) 1, 2, 4, 14, 15, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gregovic (US 2021/0192496)
Regarding claim 1,
A system comprising:
one or more processors; and
a non-transitory computer-readable memory coupled to the one or more processors, the non-transitory computer-readable memory storing instructions thereon that, when executed by the one or more processors, cause the one or more processors to:
obtain data from a set of data sources, the data being associated with a set of electronic accounts linked to a digital wallet of a user (see paragraph 0019);
generate, using at least one machine learning (ML) model based on the data, a recommendation for the user related to the set of electronic accounts (see paragraphs 0019-0021); and
display the recommendation in a digital wallet application that (i) implements the digital wallet and (ii) is integrated with a decentralized web technology (see paragraph 0024);
regarding claim 2, the system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
train the at least one ML model with a set of training data from the set of data sources to generate a set of training recommendations as outputs, wherein each training recommendation in the set of training recommendations is related to an electronic account of the set of electronic accounts (see paragraphs 0018 and 0019);
regarding claim 4, the system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
receive user input corresponding to at least one electronic account;
analyze, by the at least one ML model, the user input and a data set from one or more electronic accounts that are different from the at least one electronic account;
generate, by the at least one ML model based on the user input, a subsequent recommendation indicating a predicted impact the user input has on the at least one electronic account of the one or more electronic accounts that are different from the at least one electronic account; and
cause a user interface to display the subsequent recommendation for analysis by the user (see paragraph 0051);
regarding claim 14,
a method comprising:
obtaining, by one or more processors, data from a set of data sources, the data being associated with a set of electronic accounts linked to a digital wallet of a user;
generating, by the one or more processors using at least one machine learning (ML) model based on the data, a recommendation for the user related to the set of electronic accounts; and
displaying, by the one or more processors, the recommendation in a digital wallet application that (i) implements the digital wallet and (ii) is integrated with a decentralized web technology (see paragraph 0024);
regarding claim 15, the method of claim 14, further comprising:
training, by the one or more processors, the at least one ML model with a set of training data from the set of data sources to generate a set of training recommendations as outputs, wherein each training recommendation in the set of training recommendations is related to an electronic account of the set of electronic accounts (see paragraphs 0018 and 0019);
regarding claim 20,
a non-transitory computer-readable storage medium having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:
obtain data from a set of data sources, the data being associated with a set of electronic accounts linked to a digital wallet of a user;
generate, using at least one machine learning (ML) model based on the data, a recommendation for the user related to the set of electronic accounts; and
display the recommendation in a digital wallet application that (i) implements the digital wallet and (ii) is integrated with a decentralized web technology.
Allowable Subject Matter
Claims 3, 5-13, and 16-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the prior art of record fails to teach or fairly suggest either alone or in combination thereof:
regarding claim 3, the system of claim 1, wherein a first data source of the set of data sources is a campaign corresponding to a first electronic account of the set of electronic accounts, and wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
verify that the first data source satisfies a security threshold;
responsive to determining that the first data source fails to satisfy the security threshold, block data transmission from the first data source; responsive to determining that the first data source satisfies the security threshold, obtain a first data set from the first data source; and
apply the at least one ML model to user post language and security compliance data from the first data set to generate the recommendation for the user;
regarding claim 5, the system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
aggregate security data from one or more data sources that are external to the digital wallet application, the aggregated security data being associated with one or more electronic accounts of the set of electronic accounts;
update a set of security data that is stored as part of the digital wallet application based on the aggregated security data;
generate, by the at least one ML model, (i) an interference prediction based on the data from the set of data sources and the updated set of security data and (ii) a security recommendation for the user based on the interference prediction, wherein the interference prediction indicates at least one of the one or more electronic accounts represented in the aggregated security data; and
cause a user interface to display the interference prediction and the security recommendation for analysis by the user;
regarding claim 7, the system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
aggregate security data from one or more data sources that are external to the digital wallet application, the aggregated security data being associated with one or more electronic accounts of the set of electronic accounts;
update a set of security data that is stored as part of the digital wallet application based on the aggregated security data;
generate, by the at least one ML model, a security recommendation for the user based on the updated set of security data, wherein the security recommendation indicates at least one of the one or more electronic accounts represented in the aggregated security data;
cause a user interface to display the security recommendation for analysis by the user;
receive, from the user, an input related to the security recommendation; and
modify a security operation of the at least one of the one or more electronic accounts represented in the aggregated security data based on the user input;
regarding claim 8, the system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
construct, by the at least one ML model based on historical user data stored as part of the digital wallet application, an action pattern of the user;
generate, by the at least one ML model based on the action pattern, a set of predicted action patterns of the user;
organize the set of predicted action patterns into a ranked list based on one or more criteria; and
modify subsequent outputs of the digital wallet application in accordance with the ranked list.
Regarding claim 9, the system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
construct, by the at least one ML model based on historical user data stored as part of the digital wallet application, an action pattern of the user; generate, by the at least one ML model based on the action pattern, (i) a short-term recommendation for the user, (ii) a medium-term recommendation for the user, and (iii) a long- term recommendation for the user, wherein each recommendation is associated with one or more of the set of electronic accounts; and
modify subsequent outputs of the digital wallet application in accordance with the short-term recommendation, the medium-term recommendation, and the long-term recommendation.
Regarding claim 10, the system of claim 1, wherein the digital wallet is hosted on a distributed ledger platform with access to a distributed ledger and each transaction input by the user is recorded on the distributed ledger, and wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
receive a transaction listing from the user corresponding to one or more of the set of electronic accounts, the transaction listing including (i) user data corresponding to the user and (ii) an updated state of an asset related to the one or more of the set of electronic accounts;
generate a transaction including a description of the transaction listing; and
record the transaction in the distributed ledger;
regarding claim 12, the system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
aggregate historical user data for one or more users accessing a digital wallet platform that hosts the digital wallet application;
construct, by the at least one ML model based on historical user data stored as part of the digital wallet application and the aggregated historical user data, a group action pattern of the user and other users indicated in the aggregated historical user data that are substantially similar to the user based on one or more similarity metrics;
generate, by the at least one ML model based the group action pattern, the recommendation for the user; and
modify subsequent outputs of the digital wallet application in accordance with the recommendation;
regarding claim 13, the system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
generate, by the at least one ML model based on (i) a set of virtual assets of the user and (ii) a set of real assets of the user, an allocation recommendation;
receive a user input from the user corresponding to the allocation recommendation;
adjust an allocation of the set of virtual assets of the user or the set of real assets of the user based on the user input; and
update the at least one ML model in accordance with the user input;
regarding claim 16, the method of claim 14, wherein a first data source of the set of data sources is a campaign corresponding to a first electronic account of the set of electronic accounts, and the method further comprises:
verifying, by the one or more processors, that the first data source satisfies a security threshold;
responsive to determining that the first data source fails to satisfy the security threshold, blocking, by the one or more processors, data transmission from the first data source;
responsive to determining that the first data source satisfies the security threshold, obtaining, by the one or more processors, a first data set from the first data source; and
applying, by the one or more processors, the at least one ML model to user post language and security compliance data from the first data set to generate the recommendation
for the user;
regarding claim 17, the method of claim 14, further comprising:
aggregating, by the one or more processors, security data from one or more data sources that are external to the digital wallet application, the aggregated security data being associated with one or more electronic accounts of the set of electronic accounts;
updating, by the one or more processors, a set of security data that is stored as part of the digital wallet application based on the aggregated security data;
generating, by the at least one ML model, a security recommendation for the user based on the updated set of security data, wherein the security recommendation indicates at least one of the one or more electronic accounts represented in the aggregated security data;
causing, by the one or more processors, a user interface to display the security recommendation for analysis by the user;
receiving, from the user, an input related to the security recommendation; and
modifying, by the one or more processors, a security operation of the at least one of the one or more electronic accounts represented in the aggregated security data based on the user input;
regarding claim 18, the method of claim 14, further comprising:
constructing, by the at least one ML model based on historical user data stored as part of the digital wallet application, an action pattern of the user;
generating, by the at least one ML model based on the action pattern, a set of predicted action patterns of the user;
organizing, by the one or more processors, the set of predicted action patterns into a ranked list based on one or more criteria; and
modifying, by the one or more processors, subsequent outputs of the digital wallet application in accordance with the ranked list;
regarding claim 19, the method of claim 14, wherein the digital wallet is hosted on a distributed ledger platform with access to a distributed ledger and each transaction input by the user is recorded on the distributed ledger, and the method further comprises:
receiving, by the one or more processors, a transaction listing from the user corresponding to one or more of the set of electronic accounts, the transaction listing including (i) user data corresponding to the user and (ii) an updated state of an asset related to the one or more of the set of electronic accounts;
generating, by the one or more processors, a transaction including a description of the transaction listing; and
recording, by the one or more processors, the transaction in the distributed ledger.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMARA ALZAIDA FRANKLIN whose telephone number is (571)272-2389. The examiner can normally be reached Monday-Friday, 8:00am-4:30pm
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March 05, 2026
/JAMARA A FRANKLIN/ Primary Examiner, Art Unit 2876