DETAILED ACTION
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 Statutory Analysis
The claims do not recite any of the judicial exceptions enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance. Further, the claims do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claims do not recite a mathematical relationship, formula, or calculation. Thus, the claims are eligible because they do not recite a judicial exception.
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.
Claims 1-3, 6-12 and 15-18 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Chabanne et al. (U.S. Patent Application Publication No. US 2020/0356840 A1) (hereafter referred to as “Chabanne”).
With regard to claim 1, Chabanne describes creating a vector database based on vector data derived from a first set of layers up to an intermediate layer of a neural network, wherein the vector data corresponds to a plurality of objects and a plurality of attributes processed by the neural network based on a first set of training data used to train the neural network for a first classification objective (refer for example to paragraphs [0040] through [0043]); retraining each of a second set of layers in the neural network excluding the first set of layers and the intermediate layer based on a second classification objective and a second set of training data (refer for example to paragraph [0075] and to paragraphs [0080] through [0083]); processing the vector database by the second set of layers retrained based on the second classification objective (refer for example to paragraphs [0080] through [0083]); and generating at least one of an object classification or an attribute classification based on the processing (refer to paragraphs [0087] and [0088]).
As to claim 2, Chabanne describes wherein the neural network comprises the first set of layers, the intermediate layer, and the second set of layers, and wherein the first set of layers precedes the intermediate layer, and the second set of layers succeeds the intermediate layer (refer to paragraphs [0049] through [0056]).
In regard to claim 3, Chabanne describes wherein the vector data comprises: a plurality of object vectors (refer for example to paragraphs [0005] and [0046]); a plurality of attribute vectors (refer for example to paragraph [0047]); and metadata associated with at least one of the plurality of objects and at least one of the plurality of attributes (refer for example to paragraph [0024] and [0094]).
In regard to claim 6, Chabanne describes wherein retraining the second set of layers comprises receiving, by the neural network, the second classification objective and the second set of training data as an input from a user (refer for example to paragraphs [0044] and [0045]); analyzing, by the neural network, each of the second set of training data based on the second classification objective (refer for example to paragraphs [0080] through [0083]); and generating, by the neural network, at least one of an object classification and an attribute classification for each of the second set of training data based on the second classification objective to retrain the neural network (refer for example to paragraphs [0087] and [0088]).
With regard to claim 7, Chabanne describes separating the second set of layers from the neural network post retraining of the neural network, wherein the vector database is processed by the second set of layers after separating from the neural network (refer for example to paragraphs [0040] through [0043]).
As to claim 8, Chabanne describes wherein generating the at least one of the object classification or the attribute classification comprises retaining data privacy regulatory compliance requirements based on the processing of the vector database (refer for example to paragraph [0043] and [0044]).
In regard to claim 9, Chabanne describes receiving a second classification objective as a user input from a user (refer for example to paragraphs [0044] and [0045]); identifying a second set of retrained layers from a plurality of second sets of retrained layers based on the second classification objective, wherein each of the plurality of second sets of retrained layers is mapped to a corresponding second classification objective (refer for example to paragraph [0075] and to paragraphs [0080] through [0083]); processing a vector database by the second set of retrained layers, wherein the vector database is created based on vector data derived from a first set of layers up to an intermediate layer of a neural network, and wherein the vector data corresponds to a plurality of objects and a plurality of attributes processed by the neural network based on a first set of training data used to train the neural network for a first classification objective (refer for example to paragraphs [0080] through [0083]); and generating at least one of an object classification or an attribute classification based on the processing (refer for example to paragraphs [0087] and [0088]).
With regard to claim 10, Chabanne describes a processor and a memory coupled to the processor, wherein the memory stores processor executable instructions, which, on execution, causes the processor (refer for example to paragraph [0041]); to create a vector database based on vector data derived from a first set of layers up to an intermediate layer of a neural network, wherein the vector data corresponds to a plurality of objects and a plurality of attributes processed by the neural network based on a first set of training data used to train the neural network for a first classification objective (refer for example to paragraphs [0040] through [0043]); retrain each of a second set of layers in the neural network excluding the first set of layers and the intermediate layer based on a second classification objective and a second set of training data (refer for example to paragraph [0075] and to paragraphs [0080] through [0083]); process the vector database by the second set of layers retrained based on the second classification objective (refer for example to paragraphs [0080] through [0083]); and generate at least one of an object classification or an attribute classification based on the processing (refer for example to paragraphs [0087] and [0088]).
As to claim 11, Chabanne describes wherein the neural network comprises the first set of layers, the intermediate layer, and the second set of layers, and wherein the first set of layers precedes the intermediate layer, and the second set of layers succeeds the intermediate layer (refer to paragraphs [0049] through [0056]).
In regard to claim 12, Chabanne describes wherein the vector data comprises: a plurality of object vectors (refer for example to paragraphs [0005] and [0046]); a plurality of attribute vectors (refer for example to paragraph [0047]); and metadata associated with at least one of the plurality of objects and at least one of the plurality of attributes (refer for example to paragraph [0024] and [0094]).
In regard to claim 15, Chabanne describes wherein, to retrain the second set of layers, the processor-executable instructions, on execution, further cause the processor (refer for example to paragraph [0041] to receive, by the neural network, the second classification objective and the second set of training data as an input from a user (refer for example to paragraphs [0044] and [0045]); analyze, by the neural network, each of the second set of training data based on the second classification objective (refer for example to paragraphs [0080] through [0083]); and generate, by the neural network, at least one of an object classification and an attribute classification for each of the second set of training data based on the second classification objective to retrain the neural network (refer for example to paragraphs [0087] and [0088]).
With regard to claim 16, Chabanne describes the processor-executable instructions, on execution, further cause the processor to separate the second set of layers from the neural network post retraining of the neural network, wherein the vector database is processed by the second set of layers after separating from the neural network (refer for example to paragraphs [0040] through [0043]).
As to claim 17, Chabanne describes wherein, to generate the at least one of the object classification or the attribute classification, the processor-executable instructions, on execution, further cause the processor to retain data privacy regulatory compliance requirements based on the processing of the vector database (refer for example to paragraph [0043] and [0044]).
In regard to claim 18, Chabanne describes a processor and a memory coupled to the processor, wherein the memory stores processor executable instructions, which, on execution, causes the processor (refer for example to paragraph [0041]) to receive a second classification objective as a user input from a user (refer for example to paragraphs [0044] and [0045]); identify a second set of retrained layers from a plurality of second sets of retrained layers based on the second classification objective, wherein each of the plurality of second sets of retrained layers is mapped to a corresponding second classification objective (refer for example to paragraph [0075] and to paragraphs [0080] through [0083]); process a vector database by the second set of retrained layers, wherein the vector database is created based on vector data derived from a first set of layers up to an intermediate layer of a neural network, and wherein the vector data corresponds to a plurality of objects and a plurality of attributes processed by the neural network based on a first set of training data used to train the neural network for a first classification objective (refer for example to paragraphs [0080] through [0083]); and generate at least one of an object classification or an attribute classification based on the processing (refer for example to paragraphs [0087] and [0088]).
Allowable Subject Matter
Claims 4-5 and 13-14 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.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sanjeevan, Beilis, Ji, Thakker, Tars, Skans, Xu, Lewis, Kadejshvili and Yang all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of an application may be obtained from the Patent Center information webpage on the USPTO website. For more information about the Patent Center, see https://www.uspto.gov/patents/apply/patent-center. Should you have questions about access to the Patent Center, contact the Patent Electronic Business Center (EBC) at 571-272-4100 or via email at: ebc@uspto.gov .
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
/JOSE L COUSO/Primary Examiner, Art Unit 2667
June 18, 2026
Prosecution Insights