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 .
This is Non-Final Office Action in response to application filed on August 7, 2025. Claims 1-20 are presented for examination.
Information Disclosure Statement
The references listed in the IDS filed on June 23, 2026 has been considered and entered into record. A copy of the signed or initialed IDS is hereby attached.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 11-14 and 20 of U.S. Patent No. US 12/393,596B2. Although the claims at issue are not identical, they are not patentably distinct from each other because they are directed toward the same subject matter.
All limitations and elements in claim 1 of the instant application are found in claim 1 of Cooley except “generating a training set comprising a plurality of training samples, the plurality of training samples comprising a plurality of search phrases, each training sample corresponding to a search query of the item query engine” have been omitted. But the ‘019 teaches at paragraph [0033] “generates an embedding for a query to retrieve…a list of items returned by a query engine in response to the search phrase”. It is similar to “generating a training set comprising a plurality of training samples” in ‘596. Although the claims at issue are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the similar limitations as showed in the Claims Comparison Table below as the claims of the cited patent teach every claims of the instant application, as such, anticipate the claims of the instant application.
Claim 1 of the above patent recites every element of claim 1 of the instant application.
Claim 10 of the above patent recites every element of claim 16 of the instant application.
Claim 19 of the above patent recites every element of claim 20 of the instant application.
Claims Comparison Table:
Instant application #19/294,019
Patent # 12/393,596
Claim 1. A computer-implemented method, comprising:
at a computer system that comprises memory and one or more processors:
retrieving a set of historical query records, each historical query record including (i) a search phrase entered by a user and (ii) a list of items returned by a query engine in response to the search phrase;
determining, for each distinct search phrase, a search-frequency value representing a number of occurrences of the search phrase among the historical query records;
stratifying the historical query records into a plurality of bins, each bin corresponding to a different range of search-frequency values;
sampling, from each bin, at least one historical query record to produce an evaluation set that collectively contains historical query records from the plurality of bins;
applying a machine learning model to generate, for each historical query record in the representative evaluation set, a quality-metric value that characterizes a relevance of the list of returned items to the corresponding search phrase; and storing the quality-metric values in association with the evaluation set for training of the query engine.
16. A non-transitory computer-readable medium configured to store code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to: retrieve a set of historical query records, each historical query record including (i) a search phrase entered by a user and (ii) a list of items returned by a query engine in response to the search phrase; determine, for each distinct search phrase, a search-frequency value representing a number of occurrences of the search phrase among the historical query records; stratify the historical query records into a plurality of bins, each bin corresponding to a different range of search-frequency values; sample, from each bin, at least one historical query record to produce an evaluation set that collectively contains historical query records from the plurality of bins; apply a machine learning model to generate, for each historical query record in the representative evaluation set, a quality-metric value that characterizes a relevance of the list of returned items to the corresponding search phrase; and store the quality-metric values in association with the evaluation set for training of the query engine.
20. A system comprising: one or more processors; and memory configured to store code comprising instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: retrieve a set of historical query records, each historical query record including (i) a search phrase entered by a user and (ii) a list of items returned by a query engine in response to the search phrase; determine, for each distinct search phrase, a search-frequency value representing a number of occurrences of the search phrase among the historical query records; stratify the historical query records into a plurality of bins, each bin corresponding to a different range of search-frequency values; sample, from each bin, at least one historical query record to produce an evaluation set that collectively contains historical query records from the plurality of bins; apply a machine learning model to generate, for each historical query record in the representative evaluation set, a quality-metric value that characterizes a relevance of the list of returned items to the corresponding search phrase; and store the quality-metric values in association with the evaluation set for training of the query engine.
Claim 1. A method, comprising:
at a computer system comprising at least one processor and memory:
receiving a plurality of search queries from a plurality of users directed at an item query engine of an online system, each search query including a search phrase used by a user to conduct the search query;
generating a training set comprising a plurality of training samples, the plurality of training samples comprising a plurality of search phrases, each training sample corresponding to a search query of the item query engine and comprising a search phrase and a list of items returned by the item query engine;
generating a representative set of training samples, wherein generating the representative set of training samples comprises: determining search frequencies of the search phrases used in the training samples,
stratifying the set of training samples into a plurality of bins according to the search frequencies of the search phrases, wherein each bin of the plurality of bins includes a subset of training samples, wherein each bin defines a range of numbers of times a search phrase is used by the plurality of users, and sampling the training samples from the plurality of bins to collect the representative set of training samples; and
training the item query engine using the representative set of training samples.
2. The method of claim 1, wherein training the item query engine using the representative set of training samples comprises: initiating a plurality of parameters of the item query engine and a loss function; applying the item query engine to the training samples to generate predictions of labels of the training samples; comparing the predictions of labels of the training samples to the training labels of the training samples based on the loss function; and backpropagating the loss function to adjust the plurality of parameters of the item query engine.
10. A non-transitory computer-readable medium configured to store code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to: receive a plurality of search queries from a plurality of users directed at an item query engine of an online system, each search query including a search phrase used by a user to conduct the search query; generate a training set comprising a plurality of training samples, the plurality of training samples comprising a plurality of search phrases, each training sample corresponding to a search query of the item query engine and comprising a search phrase and a list of items returned by the item query engine; generate a representative set of training samples, wherein generating the representative set of training samples comprises: determining search frequencies of the search phrases used in the training samples, stratifying the set of training samples into a plurality of bins according to the search frequencies of the search phrases, wherein each bin of the plurality of bins includes a subset of training samples, wherein each bin defines a range of numbers of times a search phrase is used by the plurality of users, and sampling the training samples from the plurality of bins to collect the representative set of training samples; and training the item query engine using the representative set of training samples.
19. A system comprising: one or more processors; and memory configured to store code comprising instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: receive a plurality of search queries from a plurality of users directed at an item query engine of an online system, each search query including a search phrase used by a user to conduct the search query; generate a training set comprising a plurality of training samples, the plurality of training samples comprising a plurality of search phrases, each training sample corresponding to a search query of the item query engine and comprising a search phrase and a list of items returned by the item query engine; generate a representative set of training samples, wherein generating the representative set of training samples comprises: determining search frequencies of the search phrases used in the training samples, stratifying the set of training samples into a plurality of bins according to the search frequencies of the search phrases, wherein each bin of the plurality of bins includes a subset of training samples, wherein each bin defines a range of numbers of times a search phrase is used by the plurality of users, and sampling the training samples from the plurality of bins to collect the representative set of training samples; and training the item query engine using the representative set of training samples.
Allowable Subject Matter
Claims 1-20 are allowed over the art of record.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding independent claim 1, similar claim 10 and claim 19, the closest art, Yang et al. (US 20200250197 A1)Yang discloses a method, comprising: at a computer system comprising at least one processor and memory (Fig.1 of Yang): receiving a plurality of search queries from a plurality of users directed at an item query engine of an online system (step 410 of Fig.4, Yang, i.e., retrieving plurality of historical queries), each search query including search phrases (step 410 of Fig.4; ¶[0013], Yang), wherein each historical query record of the set of historical query records is associated with a search phrase (¶[0013]-[0014], Yang, i.e., search phrase) )and a list of items returned by the item query engine (step 410 of Fig.4; ¶[0013]-[0014], [0019] and [0027], Yang, i.e., search phrase and list of retrieved records); generating the representative set of training samples (¶[0032]-[0033], Yang) comprises: determining search frequencies for the plurality of search phrases included in the set of the historical query records (¶[0029]-[0030], Yang, i.e., determining relevancy of search records for search phrases in the historical query); sampling the historical query records from the plurality of bins to collect a representative set of historical query records (¶[0032] and [0034]-[0035], Yang); and training the item query engine using representative set of historical query records for rating (¶[0032] and [0034]-[0035], Yang). Ryger et al. (US 20150310115 A1) discloses method and system for retrieving and presenting electronic documents responsive to user queries including stratifying the text fields into levels (¶[0154] and [0156], Ryger). However, the prior art fails to disclose or suggest “generating a training set comprising a plurality of training samples, the plurality of training samples comprising a plurality of search phrases, each training sample corresponding to a search query of the item query engine and comprising a search phrase and a list of items returned by the item query engine; generating a representative set of training samples, wherein generating the representative set of training samples comprises: determining search frequencies of the search phrases used in the training samples, stratifying the set of training samples into a plurality of bins according to the search frequencies of the search phrases, wherein each bin of the plurality of bins includes a subset of training samples, wherein each bin defines a range of numbers of times a search phrase is used by the plurality of users”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Giallorenzi et al. (US 7602834 B1) disclose system and method for determining a fine frequency offset of a received signal.
Lu et al. (US 20040095998 A1) disclose method and apparatus for motion estimation with all binary representation.
Goldberg et al. (US 20170193579 A1) disclose system and method to calculate session-based price demand on e-commerce site.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HANH B THAI whose telephone number is (571)272-4029. The examiner can normally be reached Mon-Friday 7-4:30.
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, Tony Mahmoudi can be reached at 571-272-4078. 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. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HANH B THAI/Primary Examiner, Art Unit 2163
June 26, 2026
Prosecution Insights