DETAILED ACTION
Claims 1 – 12, which are currently pending, are fully considered below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on February 3, 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 .
35 USC § 101
Note: The instant claims recite significantly more than an abstract idea. Instant paragraph [0016] discloses that the information processing system calculates each vector of a high-dimensional vector using significantly more than a mental process (for example, word2vec has 200 dimensions and BERT has 768 dimensions) using a Gini coefficient that indicates the density of the vector.
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 1 – 12 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pranitha Bhimanagouda Patil (U.S. Patent Publication 20240194342).
With respect to claims 1, 11, and 12, Patil teaches:
data acquisition means for acquiring vector data of a plurality of dimensions (see paragraph [0086], and Fig. 3A, where clustering models 304 are generated in a vector space having dimensions that correspond to features 305);
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clustering means for grouping, based on relative distances between pieces of the vector data acquired by the data acquisition means, the pieces of the data in which the relative distances therebetween are close to each other, and performing hierarchical clustering for repeating the grouping (see paragraph [0104], where clustering model 304 uses distance based clustering and where the clustering model may be a hierarchical based clustering model as in Fig. 6D); and
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density calculation means for calculating density of each of clusters of the pieces of the vector data, wherein the clustering means divides or integrates the clusters based on the density of each of the clusters calculated by the density calculation means when the clustering means performs the hierarchical clustering (see paragraph [0104], for density based clustering, and hierarchy based clustering, also see Fig. 6A, 6B, 6C, and 6D for density based clustering and other clustering).
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With respect to claim 2, Patil teaches:
the clustering means extracts at least two vectors by comparing densities of vector components of the vector data of a plurality of dimensions with each other, extracts from each of the extracted vectors dense parts in which vector values are dense, extracts from each of the extracted vectors a region of interest onto which clustering is concentrated based on the extracted dense parts, and performs the hierarchical clustering on the extracted region of interest and a peripheral region surrounding the region of interest (see paragraph [0104], for density and hierarchical clustering, where the vector data components (or features), are clustered by density).
With respect to claim 3, Patil teaches:
when the clustering means determines that the density of each of the clusters calculated by the density calculation means is equal to or greater than a threshold, the clustering means divides the cluster, and when the clustering means determines that the density of each of the clusters is not equal to or greater than a threshold, the clustering means determines whether or not the current number of the clusters is equal to or greater than a target value, and then when the clustering means determines that the current number of the clusters is equal to or greater than the target value, the clustering means stops the hierarchical clustering (see paragraph [0104], where data sets are divided into clusters, see paragraph [0101], for clustering using a threshold).
With respect to claim 4, Patil teaches:
when the clustering means determines that the density of each of the clusters has reached a predetermined value during the hierarchical clustering, the clustering means temporarily stops the hierarchical clustering and outputs the current number of the clusters and vectors included in each of the clusters (see paragraph [0104], for clustering termination logic).
With respect to claim 5, Patil teaches:
the density is a Gini coefficient (see paragraph [0104] for the Gini coefficient).
With respect to claim 6, Patil teaches:
the data acquisition means, clustering means, and the density calculation means are a processor (see paragraph [0005], where processors and memory are used to implement clustering methods).
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With respect to claim 7, Patil teaches:
the data acquisition means, clustering means, and the density calculation means are a processor (see paragraph [0005], where processors and memory are used to implement clustering methods).
With respect to claim 8, Patil teaches:
the data acquisition means, clustering means, and the density calculation means are a processor (see paragraph [0005], where processors and memory are used to implement clustering methods).
With respect to claim 9, Patil teaches:
the data acquisition means, clustering means, and the density calculation means are a processor (see paragraph [0005], where processors and memory are used to implement clustering methods).
With respect to claim 10, Patil teaches:
the data acquisition means, clustering means, and the density calculation means are a processor (see paragraph [0005], where processors and memory are used to implement clustering methods).
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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/ALEXANDRIA Y BROMELL/Primary Examiner, Art Unit 2167 January 7, 2026