DETAILED ACTION
Claims 2 – 21, which are currently pending, are fully considered below.
Claim 1 is canceled.
Claims 2 – 21 are new.
No claims are amended.
Priority
This application is a CON of 18/890,448 09/19/2024 PAT 12353441.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 15, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 2 – 21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wyatt Wade Berlinic et al. (U.S. Patent Publication 20260037547).
With respect to claims 2, 20, and 21, Berlinic teaches:
receiving, through an interactive user interface, a request for information associated with an entity (see paragraph [0024], where an organization may want to request entity information);
retrieving, from at least one data structure and based on the request, a plurality of records that are associated with the entity (see paragraph [0018], where records associated with an entity are received);
generating a plurality of similarity metrics that indicate respective levels of similarity between pairs of records from the plurality of records (see paragraphs [0020] [0021], and [0049], where similarity metrics are used to determine similarity between records, also see Fig. 7);
clustering the plurality of records based on the plurality of similarity metrics to identify a plurality of clusters of records (see paragraphs [0018] and [0020], where groups of similar records are clustered);
identifying the information associated with the entity based on the plurality of clusters of records (see paragraph [0025], where entities are identified which are linked to records in entity resolution); and
outputting, through the interactive user interface, a response to the request, wherein the response is indicative of the information associated with the entity (see paragraph [0025], where the entity information request is output and stored).
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With respect to claim 3, Berlinic teaches:
wherein the request for the information is a verification request to verify the information, wherein identifying the information includes verifying the information, and wherein the response indicates that the information is verified (see paragraph [0018], where an entity is matched with their records (verified)).
With respect to claim 4, Berlinic teaches:
initiating processing of a transaction being indicative of the information associated with the entity, wherein the entity is a person, and wherein the information includes an identity of the person (see paragraph [0018], where the entity is a person).
With respect to claim 5, Berlinic teaches:
wherein the interactive user interface includes a chat interface (see paragraph [0022], for user interface).
With respect to claim 6, Berlinic teaches:
generating an entity matrix; and partitioning the entity matrix into groups of records, wherein clustering the plurality of records is based on the plurality of similarity metrics and the groups of records (see paragraph [0021], for similarities).
With respect to claim 7, Berlinic teaches:
wherein the plurality of records include a first record and a second record, and wherein the plurality of similarity metrics include at least one similarity metric indicating a level of similarity between the first record and the second record (see paragraphs [0020] [0021], and [0049], where similarity metrics are used to determine similarity between records, also see Fig. 7).
With respect to claim 8, Berlinic teaches:
wherein the plurality of records include a first record and a second record, wherein the first record includes first data categorized into a first field and second data categorized into a second field, wherein the second record includes third data categorized into the first field and fourth data categorized into the second field, and wherein the plurality of similarity metrics include at least one similarity metric that is based on a first comparison between the first data and the third data and a second comparison between the second data and the fourth data (see paragraphs [0020] [0021], and [0049], where similarity metrics are used to determine similarity between records, also see Fig. 7).
With respect to claim 9, Berlinic teaches:
standardizing a field to have a consistent format between a first record and a second record, wherein the plurality of records include the first record and the second record (see paragraph [0044], for a standardized format).
With respect to claim 10, Berlinic teaches:
generating an entity graph based on the plurality of clusters of records, wherein identifying the information associated with the entity based on the plurality of clusters of records includes identifying the information based on the entity graph (see paragraph [0031], for an entity graph).
With respect to claim 11, Berlinic teaches:
wherein receiving the request includes receiving the request over a network from a user device that includes the through the interactive user interface, wherein outputting the response includes sending the response over the network to the user device for output through the interactive user interface (see paragraph [0023], for networks and interfaces).
With respect to claim 12, Berlinic teaches:
generating the plurality of similarity metrics includes processing at least a subset of the plurality of records using a trained machine learning model that generates at least a subset of the plurality of similarity metrics (see paragraph [0021], for similarities).
With respect to claim 13, Berlinic teaches:
receiving, through the interactive user interface, feedback associated with the response; and updating the trained machine learning model based on the feedback (see paragraph [0024], for updates).
With respect to claim 14, Berlinic teaches:
clustering the plurality of records includes processing the plurality of records and the plurality of similarity metrics using a trained machine learning model that identifies the plurality of clusters of records (see paragraphs [0018] and [0020], where groups of similar records are clustered).
With respect to claim 15, Berlinic teaches:
receiving, through the interactive user interface, feedback associated with the response; and updating the trained machine learning model based on the feedback (see paragraph [0024], for updates).
With respect to claim 16, Berlinic teaches:
receiving, through the interactive user interface, feedback associated with the response; and updating the response based on the feedback (see paragraph [0024], for updates).
With respect to claim 17, Berlinic teaches:
filtering out a subset of the plurality of records based on a corresponding subset of the plurality of similarity metrics falling below a similarity threshold (see paragraph [0049], for threshold).
With respect to claim 18, Berlinic teaches:
wherein generating the plurality of similarity metrics includes generating the plurality of similarity metrics using a fuzzy matching algorithm (see paragraph [0021], for similarity metrics).
With respect to claim 19, Berlinic teaches:
filtering out a subset of the plurality of records based on clustering of the subset of the plurality of records among the plurality of clusters of records (see paragraph [0021], for filtering).
Conclusion/Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDRIA Y BROMELL whose telephone number is (571)270-3034. The examiner can normally be reached M-F 8-4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached at 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDRIA Y BROMELL/Primary Examiner, Art Unit 2156 June 13, 2026
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