DETAILED ACTION
Claims 1 – 20, which are currently pending, are fully considered below.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 20, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
35 USC § 101
Claims 1 – 20 have been evaluated under 35 USC 101. Examiner determines that the limitation of independent claims 1 and 14 of “modifying at least one relationship between an entity in the at least one entity group and the at least one data item to produce an anonymized dataset such that entity is indistinguishable from at least one other entries in the anonymized dataset” recites significantly more than an abstract idea.
Priority
This application is a CON of 18/345,657 06/30/2023 PAT 12164673; 18/345,657 is a CON of 17/016,788 09/10/2020 PAT 11727147.
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)(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.
Claims 1 – 3, 5 – 6, 8 – 10, 14, 16 – 17, and 19 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Vincent Huang et al. (U.S. Patent Publication 20130198188).
With respect to claims 1 and 14, Huang teaches:
a plurality of entities and at least one data item, wherein the dataset is indicative of relationships between the plurality of entities and the at least one data item (see paragraph [0009], where there are entities which are partitioned into groups based on at least a common relationship with one data item (attribute));
grouping, by the computing system, the plurality of entities into at least one entity group (see paragraph [0009], where there are entities which are partitioned into groups based on at least one data item (attribute));
determining, by the computing system, that a majority of the plurality of entities in the at least one entity group are associated with the at least one data item (see paragraph [0009], where there are entities which are partitioned into groups based on at least one data item (attribute), where the majority of entities may be grouped according to similarities to a reference entity’s attributes); and
in response to determining, by the computing system, that the majority of the plurality of entities in the at least one entity group are associated with the at least one data item, modifying at least one relationship between an entity in the at least one entity group and the at least one data item to produce an anonymized dataset such that entity is indistinguishable from at least one other entities in the anonymized dataset (see paragraphs [0032] and [0033], where anonymized datasets are produced, where the dataset is indistinguishable).
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With respect to claims 2, Huang teaches:
after said modifying, distributing, by the computing system, the anonymized to an external computing system (see paragraph [0041] and Fig. 1, where the dataset may be distributed over an external system over a network).
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With respect to claims 3, Huang teaches:
wherein the dataset comprises at least one of federated learning training data, personally identifiable information, bipartite graph data, or parameters of a machine-learned model (see paragraph [0032], where user data includes personally identifyiable information).
With respect to claims 5, and 16 Huang teaches:
determining, by the computing system, that a majority of the plurality of entities in the at least one entity group are not associated with the at least one data item (see paragraphs [0094] and [0095], where entities or users may be removed whose preferences or data items are least like the others in the group); and
removing, by the computing system, at least one relationship between the entities in the at least one entity group and the at least one data item (see paragraphs [0094] and [0095], where entities or users may be removed whose preferences or data items are least like the others in the group).
With respect to claims 6 and 17, Huang teaches:
wherein determining that the majority of the plurality of entities in the at least one entity group are not associated with the at least one data item comprises: determining that a majority of the entities in the at least one entity group do not have a relationship with the at least one data item (see paragraphs [0094] and [0095], where entities or users may be removed whose preferences or data items are least like the others in the group).
With respect to claims 8, Huang teaches:
wherein grouping the plurality of entities comprises: mapping the plurality of entities and the at least one data item to a plurality of points in a dimensional space (see Fig. 8, where entities are mapped in dimensional space); establishing one or more centers in the dimensional space; and
distributing the plurality of entities among the one or more centers based at least in part on a plurality of distances between the plurality of points and the one or more centers (see Fig. 8, where entities are distributed among centers or clustered based on a plurality of distances between points).
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With respect to claims 9, Huang teaches:
wherein distributing the plurality of entities among the one or more centers comprises: selecting the one or more centers from the plurality of points; and assigning each point of the plurality of points to a center of the one or more centers such that a total distance of the points from their assigned centers is minimized (see paragraph [0065], Fig. 5, and Fig. 8, where the distance is calculated from the center to the entity to reduce sparseness).
With respect to claims 10, Huang teaches:
wherein at least k points are assigned to each center of the one or more centers, wherein k is a number of entities from which data for each entity of the plurality of entities is indistinguishable (see paragraphs [0032] and [0033], where k anonymized data points and datasets are produced, where the dataset is indistinguishable).
With respect to claim 19, Huang teaches:
mapping the plurality of entities and the at least one data item to a plurality of points in a dimensional space (see Fig. 8, where entities are mapped in dimensional space);
establishing one or more centers in the dimensional space (see paragraph [0065], Fig. 5, and Fig. 8, where the distance is calculated from the center to the entity to reduce sparseness); and
distributing the plurality of entities among the one or more centers based at least in part on a plurality of distances between the plurality of points and the one or more centers (see Fig. 8, where entities are distributed among centers or clustered based on a plurality of distances between points).
Allowable Subject Matter
Claims 4, 7, 11 – 13, 15, 18, and 20 are objected to as being dependent upon a rejected base claim, but may be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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, Robert Beausoliel can be reached at 571-272-3645. 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 March 6, 2026
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