CTNF 19/042,654 CTNF 81883 DETAILED ACTION 07-03-aia AIA 15-10-aia 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-06 AIA 15-10-15 2. 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. 3. Claims 1-20, filed on 1/31/2025, are pending in this office action. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 4. 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 – 07-12-aia AIA (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. 07-15-03-aia AIA 5. Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Aher et al. (US Publication 2022/0398279 A1) As per claim 1 , Aher teaches A system (see Abstract) comprising a processor and a non-transitory computer-readable medium storing computing instructions that, when executed on the processor, cause the processor to perform operations comprising: (Figure 8 reference 811, control circuitry, reference 814, storage) generating a first ambiguity score for a first query; (paragraphs 0043, 0044, a query is received and confidence score and values computed utilized in disambiguating the received query) propagating the first ambiguity score for the first query to generate a second ambiguity score for a second query; (paragraph 0044, 0045, 0050, the query and scores are utilized to generate a disambiguating query with an associated score) training a machine-learning classifier at least based on the first query and the second query; (paragraph 0038, 0044, machine learning models are employed to classify objects, paragraph 0083, 0084, classification of objects based on confidence levels) and generating, using the machine-learning classifier, a third ambiguity score for a third query. (paragraph 0049, 0088, 0090, aggregated scores are generated for objects to be associated with queries) As per claim 2, Aher teaches generating the first ambiguity score for the first query further comprises: determining a specificity count of unique product types purchased based on the first query; determining a co-purchase probability for the first query; and generating the first ambiguity score for the first query based on the specificity count for the first query and the co-purchase probability for the first query. (paragraph 0043, objects that are scored includes products such as shirts, cars, including make and models, paragraph 0048, 0050, object attributes of media assets) As per claim 3 , Aher teaches propagating the first ambiguity score for the first query to generate the second ambiguity score for the second query further comprises: determining that the second query is equivalent to the first query; and setting the second ambiguity score for the second query to be equivalent to the first ambiguity score for the first query. (paragraph 0045, minimize number of disambiguating queries) As per claim 4 , Aher teaches propagating the first ambiguity score for the first query to generate the second ambiguity score for the second query further comprises: determining that the second query is a subquery of the first query; and setting the second ambiguity score for the second query to represent a lower ambiguity than the first ambiguity score for the first query. (paragraph 0042, 0043, sub-elements of objects) As per claim 5 , Aher teaches propagating the first ambiguity score for the first query to generate the second ambiguity score for the second query further comprises: determining that the second query is a sibling of the first query; and setting the second ambiguity score for the second query and the first ambiguity score for the first query to be equivalent to a minimum ambiguity of queries that are siblings to the first query and the second query. (paragraph 0034, 0046, 0047, disambiguating query is confirmation reply) As per claim 6 , Aher teaches determining that the second query is the sibling of the first query further comprises: excluding attributes of product type, or product type descriptor in determining that the second query is the sibling of the first query. (paragraph 0048, 0082, 0083, dynamic attributes for classification) As per claim 7 , Aher teaches propagating the first ambiguity score for the first query to generate the second ambiguity score for the second query further comprises: applying token-level comparison attributes across identical attributes of the first query and the second query. (paragraph 0077, 0089, compare results) As per claim 8 , Aher teaches performing a score-back propagation on the first ambiguity score for the first query to generate the second ambiguity score for the second query, wherein the score-back propagation corresponds to a ratio between a current query and a sub-query of the current query. (paragraph 0040, 0084, percentage for confidence) As per claim 9 , Aher teaches determining whether the third ambiguity score for the third query meets a predetermined threshold. (paragraph 0049, 0087, success rate) As per claim 10 , Aher teaches when the third ambiguity score for the third query meets the predetermined threshold, displaying one or more items in one or more sections of a graphical user interface in response to a search using the third query. (paragraph 0040, 0084, percentage for confidence, paragraph 0050, 0076, display snapshot) As per claim 11 , Aher teaches A computer-implemented method comprising: (see Abstract) generating a first ambiguity score for a first query; (paragraphs 0043, 0044, a query is received and confidence score and values computed utilized in disambiguating the received query) propagating the first ambiguity score for the first query to generate a second ambiguity score for a second query; (paragraph 0044, 0045, 0050, the query and scores are utilized to generate a disambiguating query with an associated score) training a machine-learning classifier at least based on the first query and the second query; (paragraph 0038, 0044, machine learning models are employed to classify objects, paragraph 0083, 0084, classification of objects based on confidence levels) and generating, using the machine-learning classifier, a third ambiguity score for a third query. (paragraph 0049, 0088, 0090, aggregated scores are generated for objects to be associated with queries) As per claim 12 , Aher teaches generating the first ambiguity score for the first query further comprises: determining a specificity count of unique product types purchased based on the first query; determining a co-purchase probability for the first query; and generating the first ambiguity score for the first query based on the specificity count for the first query and the co-purchase probability for the first query. (paragraph 0043, objects that are scored includes products such as shirts, cars, including make and models, paragraph 0048, 0050, object attributes of media assets) As per claim 13 , Aher teaches propagating the first ambiguity score for the first query to generate the second ambiguity score for the second query further comprises: determining that the second query is equivalent to the first query; and setting the second ambiguity score for the second query to be equivalent to the first ambiguity score for the first query. (paragraph 0045, minimize number of disambiguating queries) As per claim 14 , Aher teaches propagating the first ambiguity score for the first query to generate the second ambiguity score for the second query further comprises: determining that the second query is a subquery of the first query; and setting the second ambiguity score for the second query to represent a lower ambiguity than the first ambiguity score for the first query. (paragraph 0042, 0043, sub-elements of objects) As per claim 15 , Aher teaches propagating the first ambiguity score for the first query to generate the second ambiguity score for the second query further comprises: determining that the second query is a sibling of the first query; and setting the second ambiguity score for the second query and the first ambiguity score for the first query to be equivalent to a minimum ambiguity of queries that are siblings to the first query and the second query. (paragraph 0034, 0046, 0047, disambiguating query is confirmation reply) As per claim 16 , Aher teaches determining that the second query is the sibling of the first query further comprises: excluding attributes of product type, or product type descriptor in determining that the second query is the sibling of the first query. (paragraph 0048, 0082, 0083, dynamic attributes for classification) As per claim 17 , Aher teaches propagating the first ambiguity score for the first query to generate the second ambiguity score for the second query further comprises: applying token-level comparison attributes across identical attributes of the first query and the second query. (paragraph 0077, 0089, compare results) As per claim 18 , Aher teaches performing a score-back propagation on the first ambiguity score for the first query to generate the second ambiguity score for the second query, wherein the score-back propagation corresponds to a ratio between a current query and a sub-query of the current query. (paragraph 0040, 0084, percentage for confidence) As per claim 19 , Aher teaches A non-transitory computer-readable medium storing computing instructions that, when executed on a processor, cause the processor to perform operations comprising: (see Abstract) generating a first ambiguity score for a first query; (paragraphs 0043, 0044, a query is received and confidence score and values computed utilized in disambiguating the received query) propagating the first ambiguity score for the first query to generate a second ambiguity score for a second query; (paragraph 0044, 0045, 0050, the query and scores are utilized to generate a disambiguating query with an associated score) training a machine-learning classifier at least based on the first query and the second query; (paragraph 0038, 0044, machine learning models are employed to classify objects, paragraph 0083, 0084, classification of objects based on confidence levels) and generating, using the machine-learning classifier, a third ambiguity score for a third query. (paragraph 0049, 0088, 0090, aggregated scores are generated for objects to be associated with queries) As per claim 20 , Aher teaches generating the first ambiguity score for the first query further comprises: determining a specificity count of unique product types purchased based on the first query; determining a co-purchase probability for the first query; and generating the first ambiguity score for the first query based on the specificity count for the first query and the co-purchase probability for the first query. (paragraph 0043, objects that are scored includes products such as shirts, cars, including make and models, paragraph 0048, 0050, object attributes of media assets) Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zilka (US Publication 2023/0350978 A1) Nallapati (US Publication 2023/0325384 A1) Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANGELINO N GORTAYO whose telephone number is (571)272-7204. The examiner can normally be reached Monday-Friday 7:00am - 3:30pm. 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. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at 571-272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DANGELINO N GORTAYO/Primary Examiner, Art Unit 2168 Application/Control Number: 19/042,654 Page 2 Art Unit: 2168 Application/Control Number: 19/042,654 Page 3 Art Unit: 2168 Application/Control Number: 19/042,654 Page 4 Art Unit: 2168 Application/Control Number: 19/042,654 Page 5 Art Unit: 2168 Application/Control Number: 19/042,654 Page 6 Art Unit: 2168 Application/Control Number: 19/042,654 Page 7 Art Unit: 2168 Application/Control Number: 19/042,654 Page 8 Art Unit: 2168 Application/Control Number: 19/042,654 Page 9 Art Unit: 2168 Application/Control Number: 19/042,654 Page 10 Art Unit: 2168
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