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 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.
(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 1-20 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by LaBute et al. (US 11,442,976).
Regarding Claims 12 and 1, LaBute discloses a computer system comprising one or more processors, the system comprising:
a client comprising a user interface operable to receive a diversity query from a user (Col. 3, line 45-55, LaBute); and
a server comprising a vector database that stores a plurality of vectors that are indexed into a plurality of clusters (Col. 4, lines 51-62, LaBute);
wherein the system is configured to:
form a query vector vc based on the diversity query (Fig. 8, Col. 4, lines 10-13, “Resource identification application 208 comprises an application for resource identification, for example, an application for determining a first clustering and a second clustering, for associating a received identifier query with a cluster of a first clustering and a cluster of a second clustering,” wherein since the identifier is being associated with a cluster of the first clustering and a cluster of the second clustering, then the identifier corresponds to the diversity query as claimed; Col. 8, lines 17-37, LaBute);
find a vector vr with an approximate maximum distance from vc by selecting a furthest cluster having a centroid furthest from vc, wherein vr is a furthest vector from vc in the furthest cluster (Fig. 8, Col. 5, lines 10-25, “grouping process analyzes the distance between vector 342 and the predetermined clusters (e.g., wherein distance comprises distance between vector 342 and the cluster centroid, average distance between vector 342 and the cluster's constituent vectors, the maximum distance between vector 342 and the cluster vectors, distance from vector 342 to the cluster edge, distance from vector 342 to the cluster farthest vector, etc.;” wherein calculating the maximum distance corresponds to selecting a furthest cluster as claimed; Col. 8, lines 17-37, LaBute);
place vc and vr into a subset P and add at least one diverse vector into P by performing one or more repetitions comprising (Fig. 8, Col. 8, lines 17-37, LaBute):
determining another furthest cluster having another centroid furthest from all vectors in P (Fig. 8, Col. 8, lines 17-37, LaBute);
selecting another vector from the other furthest cluster that is furthest from all the vectors in P (Fig. 8, Col. 8, lines 17-37, LaBute); and
insert the other vector into P (Fig. 8, Col. 8, lines 17-37, LaBute); and
use the subset P to provide a response to the diversity query (Col. 7, lines 3-16, LaBute).
Regarding Claim 5, LaBute discloses a method of claim 1, further comprising:
receiving input data that is a subject of the diversity query (Col. 7, lines 3-16, LaBute), and
transforming the input data to the query vector via an embedding model, wherein the plurality of vectors in the vector database are obtained by transforming corresponding data objects into the plurality of vectors using the embedding model, the data objects belonging to a same embedding model domain as the input data (Col. 7, lines 3-16, LaBute).
Regarding Claims 13 and 2, LaBute discloses a system of claim 12, wherein determining the other furthest cluster comprises determining that a minimum distance of the other centroid from all of the vectors in P is a maximum (Col. 8, lines 17-37, LaBute).
Regarding Claims 14 and 3, LaBute discloses a system of claim 12, wherein determining the other vector comprises determining that a minimum distance of the other vector from all of the vectors in P is a maximum (Col. 8, lines 17-37, LaBute).
Regarding Claims 17 and 6, LaBute discloses a system of claim 16, wherein the type of the input data comprises at least one of text, imagery, video, and audio (Col. 5, lines 55-64, LaBute).
Regarding Claims 18 and 7, LeBute discloses a system of claim 16, further comprising, based on the subset P after the one or more repetitions, returning a subset of the data objects corresponding to the vectors in P (Col. 8, lines 17-37, LaBute).
Regarding Claim 8, LaBute discloses a method of claim 1, further comprising, before receiving the query, precomputing data structures that index the plurality of vectors into the plurality of clusters (Col. 4, lines 51-62, LaBute).
Regarding Claim 9, LaBute discloses a method of claim 8, wherein the precomputing of the data structures comprises using one of a K-means clustering or a density based clustering (Col. 5, lines 1-9, LaBute).
Regarding Claims 20 and 10, LaBute discloses a system of claim 12, wherein finding the vector vr and performing the one or more repetitions involve determining Euclidean distance or cosine distance between two vectors and between a selected vector and a selected centroid (Col. 7, lines 3-16, LaBute).
Regarding Claim 11, LaBute discloses a method of claim 1, wherein the diversity query is submitted via a user interface of a computer, and wherein the subset P is used to return the response to the user via the user interface (Fig. 2, 202, LaBute).
Regarding Claims 15 and 4, LaBute discloses a system of claim 12, wherein the query defines a number of results N, and wherein the one or more repetitions complete when a size of P = N+1.
Regarding Claim 16, LaBute discloses a system of claim 12, further: wherein the client receives input data that is a subject of the diversity query, wherein the system is operable to transform the input data to the query vector via an embedding model, and wherein the plurality of vectors in the vector database are obtained by transforming corresponding data objects into the plurality of vectors using the embedding model, the data objects belonging to a same embedding model domain as the input data (Col. 7, lines 3-16, LaBute).
Regarding Claim 19, LaBute discloses a system of claim 12, further comprising, before receiving the query, precomputing data structures that index the plurality of vectors into the plurality of cluster (Col. 4, lines 51-62, LaBute), wherein the precomputing of the data structures comprises using one of a K-means clustering or a density based clustering (Col. 5, lines 1-9, LaBute).
Response to Arguments
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “a diversity query is a search request that seeks diverse, or dissimilar, data”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant argues that the applied art fails to disclose; “a diversity query”
The Examiner respectfully disagrees. The applied art does disclose: form a query vector vc based on the diversity query (Fig. 8, Col. 4, lines 10-13, “Resource identification application 208 comprises an application for resource identification, for example, an application for determining a first clustering and a second clustering, for associating a received identifier query with a cluster of a first clustering and a cluster of a second clustering,” wherein since the identifier is being associated with a cluster of the first clustering and a cluster of the second clustering, then the identifier corresponds to the diversity query as claimed; Col. 8, lines 17-37, LaBute).
Applicant argues that the applied art fails to disclose; “Moreover, Applicant's independent claims 1 and 12 recite responding to the diversity query by selecting a furthest cluster from the query vector, selecting a furthest vector within the furthest cluster, adding that furthest vector into a response subset, and then finding one or more additional diverse vectors from clusters determined to be furthest from all the other vector results.”
The Examiner respectfully disagrees. The applied art does disclose: find a vector vr with an approximate maximum distance from vc by selecting a furthest cluster having a centroid furthest from vc, wherein vr is a furthest vector from vc in the furthest cluster (Fig. 8, Col. 5, lines 10-25, “grouping process analyzes the distance between vector 342 and the predetermined clusters (e.g., wherein distance comprises distance between vector 342 and the cluster centroid, average distance between vector 342 and the cluster's constituent vectors, the maximum distance between vector 342 and the cluster vectors, distance from vector 342 to the cluster edge, distance from vector 342 to the cluster farthest vector, etc.;” wherein calculating the maximum distance corresponds to selecting a furthest cluster as claimed; Col. 8, lines 17-37, LaBute); place vc and vr into a subset P and add at least one diverse vector into P by performing one or more repetitions comprising (Fig. 8, Col. 8, lines 17-37, LaBute): determining another furthest cluster having another centroid furthest from all vectors in P (Fig. 8, Col. 8, lines 17-37, LaBute); selecting another vector from the other furthest cluster that is furthest from all the vectors in P (Fig. 8, Col. 8, lines 17-37, LaBute); and insert the other vector into P (Fig. 8, Col. 8, lines 17-37, LaBute); and use the subset P to provide a response to the diversity query (Col. 7, lines 3-16, LaBute).
Conclusion
THIS ACTION IS MADE FINAL. 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 GIOVANNA B COLAN whose telephone number is (571)272-2752. The examiner can normally be reached Mon - Fri 8:30-5:00.
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/GIOVANNA B COLAN/Primary Examiner, Art Unit 2165 February 8, 2026