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 .
DETAILED ACTION
This Office Action corresponds to application 19/348,133 which was filed on 10/2/2025 and is a CON of 17/934,688 filed 9/23/2022. Claims 1-20 are currently pending.
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 and 9 of U.S. Patent No. 12,468,765. Although the claims at issue are not identical, they are not patentably distinct from each other because all the limitations of the instant application are taught by the parent patent 12,468,765.
Application 19/348,133
Patent 12,468,765
1, 11, 17. A computer-implemented method comprising:
receiving, by one or more processors, a set of one or more session-agnostic autocomplete scores corresponding to one or more of a set of candidate search results for a search query prefix, wherein the set of one or more session-agnostic autocomplete scores comprises:
(i) a first subset of session-agnostic autocomplete scores respectively corresponding to a first subset of the set of candidate search results located within an individual curated search corpus,
and (ii) a second subset of session-agnostic autocomplete scores for a second subset of the set of candidate search results located within a general search result corpus;
receiving, by the one or more processors, a set of one or more session-aware autocomplete scores corresponding to one or more of the set of candidate search results;
generating, by the one or more processors and based on the set of one or more session-agnostic autocomplete scores and the set of one or more session-aware autocomplete scores, a set of hybrid autocomplete scores respectively corresponding to the set of candidate search results, wherein (i) a hybrid autocomplete score, for a candidate search result of the set of candidate search results, of the set of hybrid autocomplete scores comprises a weighted combination of (a) a session-agnostic autocomplete score of the set of one or more session-agnostic autocomplete scores and (b) a session-aware autocomplete score of the set of one or more session-agnostic autocomplete scores; and
providing, by the one or more processors, an autocomplete prediction based on the set of hybrid autocomplete scores.
A computer-implemented method comprising:
receiving, by one or more processors, a search query prefix;
generating, by the one or more processors, a set of session-agnostic autocomplete scores for a set of candidate search results by:
generating a first subset of the set of session-agnostic autocomplete scores for a first subset of the set of candidate search results within an individual curated search corpus based at least in part on an edit distance measure between the search query prefix and each of the first subset of the set of candidate search results, and
generating a second subset of the set of session-agnostic autocomplete scores for a second subset of the set of candidate search results within a general search result corpus based at least in part on a trie-based frequency score that is generated based at least in part on one or more qualifying trie leaf node subsets of a general search result corpus trie data object for the general search result corpus and a general corpus historical frequency score for each of the second subset of the set of candidate search results;
generating, by the one or more processors, a set of session-aware autocomplete scores for the set of candidate search results;
generating (i) a weighted set of session-agnostic autocomplete scores by applying a first weight measure to the set of session-agnostic autocomplete scores and (ii) a weighted set of session-aware autocomplete scores by applying a second weight measure to the set of session-aware autocomplete scores, wherein the first weight measure exceeds the second weight measure; and
wherein (i) a weighted summation, of the set of weighted summations, for the candidate search result comprises a weighted combination of at least one weighted session-agnostic autocomplete score of the weighted set of session-agnostic autocomplete scores and at least one weighted session-aware autocomplete score of the weighted set of session-aware autocomplete scores, and (ii) a weight of the weighted combination is based at least in part on a presence of the candidate search result within the individual curated search corpus or the general search result corpus;
generating, by the one or more processors, a set of hybrid autocomplete scores for the set of candidate search results based at least in part on the set of weighted summations between the first subset of the set of session-agnostic autocomplete scores, the second subset of the set of session-agnostic autocomplete scores, and the set of session-aware autocomplete scores, wherein the set of weighted summations is based at least in part on one or more locations of a candidate search result of the set of candidate search results, and the one or more locations comprise the individual curated search corpus or the general search result corpus;
generating, by the one or more processors, a ranked autocomplete prediction based at least in part on the set of hybrid autocomplete scores; and
initiating, using the one or more processors, the performance of one or more prediction-based actions based at least in part on the ranked autocomplete prediction.
2, 12. The computer-implemented method of claim 1, wherein the set of one or more session-aware autocomplete scores is based on a determination that the search query prefix is associated with a series of search queries over a period of time.
9. The computer-implemented method of claim 1, wherein the set of session-aware autocomplete scores for the set of candidate search results is based at least in part on a determination that the search query prefix is associated with a series of search queries over a period of time.
3, 13. The computer-implemented method of claim 1, wherein each of the set of candidate search results is associated with at least one session-agnostic autocomplete score, at least one session-aware autocomplete score, or at least one session-agnostic autocomplete score and at least one session-aware autocomplete score.
1 …
generating, by the one or more processors, a set of hybrid autocomplete scores for the set of candidate search results based at least in part on the set of weighted summations between the first subset of the set of session-agnostic autocomplete scores, the second subset of the set of session-agnostic autocomplete scores, and the set of session-aware autocomplete scores, wherein the set of weighted summations is based at least in part on one or more locations of a candidate search result of the set of candidate search results, and the one or more locations comprise the individual curated search corpus or the general search result corpus;
generating, by the one or more processors, a ranked autocomplete prediction based at least in part on the set of hybrid autocomplete scores; and
initiating, using the one or more processors, the performance of one or more prediction-based actions based at least in part on the ranked autocomplete prediction.
4, 14. The computer-implemented method of claim 1, wherein the hybrid autocomplete score comprises a weighted combination of (a) a weighted session-agnostic autocomplete score and (b) a weighted session-aware autocomplete score.
1…
wherein (i) a weighted summation, of the set of weighted summations, for the candidate search result comprises a weighted combination of at least one weighted session-agnostic autocomplete score of the weighted set of session-agnostic autocomplete scores and at least one weighted session-aware autocomplete score of the weighted set of session-aware autocomplete scores, and (ii) a weight of the weighted combination is based at least in part on a presence of the candidate search result within the individual curated search corpus or the general search result corpus;
generating, by the one or more processors, a set of hybrid autocomplete scores for the set of candidate search results based at least in part on the set of weighted summations between the first subset of the set of session-agnostic autocomplete scores, the second subset of the set of session-agnostic autocomplete scores, and the set of session-aware autocomplete scores, wherein the set of weighted summations is based at least in part on one or more locations of a candidate search result of the set of candidate search results, and the one or more locations comprise the individual curated search corpus or the general search result corpus;
…
5, 15. The computer-implemented method of claim 4, wherein (i) the weighted session-agnostic autocomplete score is generated by applying a first weight measure to the session-agnostic autocomplete score, (ii) the weighted session-aware autocomplete score is generated by applying a second weight measure to the session-aware autocomplete score, and (iii) the first weight measure is different than the second weight measure.
1 …
generating (i) a weighted set of session-agnostic autocomplete scores by applying a first weight measure to the set of session-agnostic autocomplete scores and (ii) a weighted set of session-aware autocomplete scores by applying a second weight measure to the set of session-aware autocomplete scores, wherein the first weight measure exceeds the second weight measure; and
…
6, 16. The computer-implemented method of claim 5, wherein the first weight measure exceeds the second weight measure.
1 …
generating (i) a weighted set of session-agnostic autocomplete scores by applying a first weight measure to the set of session-agnostic autocomplete scores and (ii) a weighted set of session-aware autocomplete scores by applying a second weight measure to the set of session-aware autocomplete scores, wherein the first weight measure exceeds the second weight measure; and
…
7, 18. The computer-implemented method of claim 1, wherein a weight of the weighted combination is based on a presence of the candidate search result within the individual curated search corpus or the general search result corpus.
1…
wherein (i) a weighted summation, of the set of weighted summations, for the candidate search result comprises a weighted combination of at least one weighted session-agnostic autocomplete score of the weighted set of session-agnostic autocomplete scores and at least one weighted session-aware autocomplete score of the weighted set of session-aware autocomplete scores, and (ii) a weight of the weighted combination is based at least in part on a presence of the candidate search result within the individual curated search corpus or the general search result corpus;
…
8, 19. The computer-implemented method of claim 7, further comprising: determining the presence of the candidate search result within the individual curated search corpus and the general search result corpus; and selecting the session-agnostic autocomplete score from the first subset of session-agnostic autocomplete scores respectively corresponding to the first subset of the set of candidate search results located within the individual curated search corpus.
1 …
generating (i) a weighted set of session-agnostic autocomplete scores by applying a first weight measure to the set of session-agnostic autocomplete scores and (ii) a weighted set of session-aware autocomplete scores by applying a second weight measure to the set of session-aware autocomplete scores, wherein the first weight measure exceeds the second weight measure; and
wherein (i) a weighted summation, of the set of weighted summations, for the candidate search result comprises a weighted combination of at least one weighted session-agnostic autocomplete score of the weighted set of session-agnostic autocomplete scores and at least one weighted session-aware autocomplete score of the weighted set of session-aware autocomplete scores, and (ii) a weight of the weighted combination is based at least in part on a presence of the candidate search result within the individual curated search corpus or the general search result corpus;
generating, by the one or more processors, a set of hybrid autocomplete scores for the set of candidate search results based at least in part on the set of weighted summations between the first subset of the set of session-agnostic autocomplete scores, the second subset of the set of session-agnostic autocomplete scores, and the set of session-aware autocomplete scores, wherein the set of weighted summations is based at least in part on one or more locations of a candidate search result of the set of candidate search results, and the one or more locations comprise the individual curated search corpus or the general search result corpus;
…
9, 20. The computer-implemented method of claim 7, wherein the weight corresponds to the session-agnostic autocomplete score, and the computer-implemented method further comprises: determining the presence of the candidate search result within the individual curated search corpus; and increasing the weight of the session-agnostic autocomplete score.
1 …
generating (i) a weighted set of session-agnostic autocomplete scores by applying a first weight measure to the set of session-agnostic autocomplete scores and (ii) a weighted set of session-aware autocomplete scores by applying a second weight measure to the set of session-aware autocomplete scores, wherein the first weight measure exceeds the second weight measure; and
wherein (i) a weighted summation, of the set of weighted summations, for the candidate search result comprises a weighted combination of at least one weighted session-agnostic autocomplete score of the weighted set of session-agnostic autocomplete scores and at least one weighted session-aware autocomplete score of the weighted set of session-aware autocomplete scores, and (ii) a weight of the weighted combination is based at least in part on a presence of the candidate search result within the individual curated search corpus or the general search result corpus;
generating, by the one or more processors, a set of hybrid autocomplete scores for the set of candidate search results based at least in part on the set of weighted summations between the first subset of the set of session-agnostic autocomplete scores, the second subset of the set of session-agnostic autocomplete scores, and the set of session-aware autocomplete scores, wherein the set of weighted summations is based at least in part on one or more locations of a candidate search result of the set of candidate search results, and the one or more locations comprise the individual curated search corpus or the general search result corpus;
…
10. The computer-implemented method of claim 1, wherein the search query prefix is received via user input to a search query box and the autocomplete prediction comprises a complete search query for the search query prefix.
1 …
receiving, by one or more processors, a search query prefix;
generating, by the one or more processors, a ranked autocomplete prediction based at least in part on the set of hybrid autocomplete scores; and
initiating, using the one or more processors, the performance of one or more prediction-based actions based at least in part on the ranked autocomplete prediction.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US2016/0078101), hereinafter Somaiya, in view of Su et al. (US2016/0188619), hereinafter Su.
Regarding Claim 1:
Somaiya teaches:
A computer-implemented method comprising: receiving, by one or more processors, a set of one or more session-agnostic autocomplete scores corresponding to one or more of a set of candidate search results for a search query prefix (Somaiya, figure 2, [0019, 0030-0032, 0041], note using a global scoring function for a session-agnostic autocomplete scores),
wherein the set of one or more session-agnostic autocomplete scores comprises: (i) a first subset of session-agnostic autocomplete scores respectively corresponding to a first subset of the set of candidate search results located within an individual curated search corpus (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077], note using an expansion result module to generate search query suggestions using the query portion received from the user and the user’s search histories; note a selection module selects query suggestions from the expansion results model using a scoring function, which means autocomplete scores for the query suggestions from the expansion result module have been generated and received. Since the expansion results module uses the users own query history across multiple sessions it is interpreted as an individual curated search corpus that is session agnostic), and
(ii) a second subset of session-agnostic autocomplete scores for a second subset of the set of candidate search results located within a general search result corpus (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077], note using a native result module to generate search query suggestions using the query portion received from the user and the query histories of multiple users; note a selection module selects query suggestions from the native results model using a scoring function, which means autocomplete scores for the query suggestions from the native result module have been generated and received. Since the native results module uses the query histories of multiple users across multiple sessions it is interpreted as a general search corpus that is session agnostic);
generating, by the one or more processors and based on the set of one or more session-agnostic autocomplete scores and the set of one or more session-aware autocomplete scores, a set of hybrid autocomplete scores respectively corresponding to the set of candidate search results, wherein (i) a hybrid autocomplete score, for a candidate search result of the set of candidate search results, of the set of hybrid autocomplete scores comprises a weighted combination of (a) a session-agnostic autocomplete score of the set of one or more session-agnostic autocomplete scores and (b) a session-aware autocomplete score of the set of one or more session-agnostic autocomplete scores (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077], note generating a combined weighted autocomplete score for the autocomplete search results which include the session-agnostic autocomplete scores; note generating a set of hybrid scores to merge the subsets; note weighting the results different based on where the candidate results are from, for example generating an ordered list by placing the second subset in an ordered list above the first subset (e.g., weighting the first subset lower than the second subset) or by ordering the first and second subsets based on a scores, e.g., the scoring functions for NQ for the native results and EQ for the expansion results means the results are weighted different based on whether they are from the individual curated corpus or the general corpus); and
providing, by the one or more processors, an autocomplete prediction based on the set of hybrid autocomplete scores (Somaiya, figures 2-3, [0019, 0030, 0043, 0065-0066, 0120], note providing a combined autocomplete score for ranking/predicting the autocomplete search results).
While Somaiya teaches incorporating multiple sources for ranking and presenting autocomplete results, Somaiya doesn’t specifically teach receiving, by the one or more processors, a set of one or more session-aware autocomplete scores corresponding to one or more of the set of candidate search results. However, Su is in the same field of endeavor, information retrieval, and Su teaches:
receiving, by the one or more processors, a set of one or more session-aware autocomplete scores corresponding to one or more of the set of candidate search results (Su, abstract, figure 2, [0011-0014, 0057], note receiving session-aware autocomplete scores);
generating, by the one or more processors and based on the set of one or more session-agnostic autocomplete scores and the set of one or more session-aware autocomplete scores, a set of hybrid autocomplete scores respectively corresponding to the set of candidate search results, wherein (i) a hybrid autocomplete score, for a candidate search result of the set of candidate search results, of the set of hybrid autocomplete scores comprises a weighted combination of (a) a session-agnostic autocomplete score of the set of one or more session-agnostic autocomplete scores and (b) a session-aware autocomplete score of the set of one or more session-agnostic autocomplete scores (Su, figure 2, [0011-0014, 0050-0053], note candidate query terms with corresponding scores may be received session-aware autocomplete scores and session-agnostic autocomplete scores such as ones received using data from other users; note generating scores for similarity between session-aware and session-agnostic candidate terms, e.g., hybrid autocomplete scores; note generating the scores may comprise a weighted sum function. When combined with the previously cited references this would be include the session-agnostic scores as taught by Somaiya).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Su because all references are directed towards information retrieval and because Su would expand upon the teachings of the previously cited references in query processing which would improve autocomplete performance by utilizing query context (Su, [0009]).
Regarding Claim 2:
Somaiya as modified shows the method as disclosed above;
Somaiya as modified further teaches:
wherein the set of one or more session-aware autocomplete scores is based on a determination that the search query prefix is associated with a series of search queries over a period of time (Su, abstract, figure 2, [0011-0014, 0057], note receiving session-aware autocomplete scores for queries within a first session, e.g., period of time);
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Su because all references are directed towards information retrieval and because Su would expand upon the teachings of the previously cited references in query processing which would improve autocomplete performance by utilizing query context (Su, [0009]).
Regarding Claim 3:
Somaiya as modified shows the method as disclosed above;
Somaiya as modified further teaches:
wherein each of the set of candidate search results is associated with at least one session-agnostic autocomplete score, at least one session-aware autocomplete score, or at least one session-agnostic autocomplete score and at least one session-aware autocomplete score (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077], note candidate results are associated with session-agnostic scores) (Su, figure 2, [0011-0014, 0050-0053], note candidate results are associated with session-aware and session-agnostic scores. When combined with the previously cited references this would be include the session-agnostic scores as taught by Somaiya).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Su because all references are directed towards information retrieval and because Su would expand upon the teachings of the previously cited references in query processing which would improve autocomplete performance by utilizing query context (Su, [0009]).
Regarding Claim 4:
Somaiya as modified shows the method as disclosed above;
Somaiya as modified further teaches:
wherein the hybrid autocomplete score comprises a weighted combination of (a) a weighted session-agnostic autocomplete score and (b) a weighted session-aware autocomplete score (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077], note generating a combined weighted autocomplete score for the autocomplete search results which include the session-agnostic autocomplete scores; note generating a set of hybrid scores to merge the subsets; note weighting the results different based on where the candidate results are from, for example generating an ordered list by placing the second subset in an ordered list above the first subset (e.g., weighting the first subset lower than the second subset) or by ordering the first and second subsets based on a scores, e.g., the scoring functions for NQ for the native results and EQ for the expansion results means the results are weighted different based on whether they are from the individual curated corpus or the general corpus) (Su, figure 2, [0011-0014, 0050-0053], note candidate query terms with corresponding scores may be received session-aware autocomplete scores and session-agnostic autocomplete scores such as ones received using data from other users; note generating scores for similarity between session-aware and session-agnostic candidate terms, e.g., hybrid autocomplete scores; note generating the scores may comprise a weighted sum function. When combined with the previously cited references this would be include the session-agnostic scores as taught by Somaiya).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Su because all references are directed towards information retrieval and because Su would expand upon the teachings of the previously cited references in query processing which would improve autocomplete performance by utilizing query context (Su, [0009]).
Regarding Claim 5:
Somaiya as modified shows the method as disclosed above;
Somaiya as modified further teaches:
wherein (i) the weighted session-agnostic autocomplete score is generated by applying a first weight measure to the session-agnostic autocomplete score, (ii) the weighted session-aware autocomplete score is generated by applying a second weight measure to the session-aware autocomplete score, and (iii) the first weight measure is different than the second weight measure (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077, 0106, 0132], note generating a combined weighted autocomplete score for the autocomplete search results which include the session-agnostic autocomplete scores; note generating a set of hybrid scores to merge the subsets; note weighting the results different based on where the candidate results are from, for example generating an ordered list by placing the second subset in an ordered list above the first subset (e.g., weighting the first subset lower than the second subset) or by ordering the first and second subsets based on a scores, e.g., the scoring functions for NQ for the native results and EQ for the expansion results means the results are weighted different based on whether they are from the individual curated corpus or the general corpus; note the use of weights) (Su, figure 2, [0011-0014, 0050-0053, 0062, 0077, 0085, 0094], note candidate query terms with corresponding scores may be received session-aware autocomplete scores and session-agnostic autocomplete scores such as ones received using data from other users; note generating scores for similarity between session-aware and session-agnostic candidate terms, e.g., hybrid autocomplete scores; note generating the scores may comprise a weighted sum function; note the weights applied may be different. When combined with the previously cited references this would be include the session-agnostic scores as taught by Somaiya).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Su because all references are directed towards information retrieval and because Su would expand upon the teachings of the previously cited references in query processing which would improve autocomplete performance by utilizing query context (Su, [0009]).
Regarding Claim 6:
Somaiya as modified shows the method as disclosed above;
Somaiya as modified further teaches:
wherein the first weight measure exceeds the second weight measure (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077, 0106, 0132], note generating a combined weighted autocomplete score for the autocomplete search results which include the session-agnostic autocomplete scores; note generating a set of hybrid scores to merge the subsets; note weighting the results different based on where the candidate results are from, for example generating an ordered list by placing the second subset in an ordered list above the first subset (e.g., weighting the first subset lower than the second subset) or by ordering the first and second subsets based on a scores, e.g., the scoring functions for NQ for the native results and EQ for the expansion results means the results are weighted different based on whether they are from the individual curated corpus or the general corpus; note the use of weights) (Su, figure 2, [0011-0014, 0050-0053, 0062, 0077, 0085, 0094], note candidate query terms with corresponding scores may be received session-aware autocomplete scores and session-agnostic autocomplete scores such as ones received using data from other users; note generating scores for similarity between session-aware and session-agnostic candidate terms, e.g., hybrid autocomplete scores; note generating the scores may comprise a weighted sum function; note the weights applied may be different, e.g., a first measure may be more than a second. When combined with the previously cited references this would be include the session-agnostic scores as taught by Somaiya).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Su because all references are directed towards information retrieval and because Su would expand upon the teachings of the previously cited references in query processing which would improve autocomplete performance by utilizing query context (Su, [0009]).
Regarding Claim 7:
Somaiya as modified shows the method as disclosed above;
Somaiya as modified further teaches:
wherein a weight of the weighted combination is based on a presence of the candidate search result within the individual curated search corpus or the general search result corpus (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077, 0106, 0132], note generating a combined weighted autocomplete score for the autocomplete search results which include the session-agnostic autocomplete scores; note generating a set of hybrid scores to merge the subsets; note weighting the results different based on where the candidate results are from, for example generating an ordered list by placing the second subset in an ordered list above the first subset (e.g., weighting the first subset lower than the second subset) or by ordering the first and second subsets based on a scores, e.g., the scoring functions for NQ for the native results and EQ for the expansion results means the results are weighted different based on whether they are from the individual curated corpus or the general corpus; note the use of weights) (Su, figure 2, [0011-0014, 0050-0053, 0062, 0077, 0085, 0094], note candidate query terms with corresponding scores may be received session-aware autocomplete scores and session-agnostic autocomplete scores such as ones received using data from other users; note generating scores for similarity between session-aware and session-agnostic candidate terms, e.g., hybrid autocomplete scores; note generating the scores may comprise a weighted sum function; note the weights applied may be different, e.g., a first measure may be more than a second. When combined with the previously cited references this would be include the session-agnostic scores as taught by Somaiya).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Su because all references are directed towards information retrieval and because Su would expand upon the teachings of the previously cited references in query processing which would improve autocomplete performance by utilizing query context (Su, [0009]).
Regarding Claim 8:
Somaiya as modified shows the method as disclosed above;
Somaiya as modified further teaches:
determining the presence of the candidate search result within the individual curated search corpus and the general search result corpus (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077], note selecting a first subset and a second subset, one subset being suggestions generated by the native model, e.g., general search corpus, and one subset being suggestions from the expansion result module which, e.g., the individual curated search corpus); and
selecting the session-agnostic autocomplete score from the first subset of session-agnostic autocomplete scores respectively corresponding to the first subset of the set of candidate search results located within the individual curated search corpus (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077], note selecting a first subset and a second subset, one subset being suggestions generated by the native model, e.g., general search corpus, and one subset being suggestions from the expansion result module which, e.g., the individual curated search corpus. note this limitation is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
Regarding Claim 9:
Somaiya as modified shows the method as disclosed above;
Somaiya as modified further teaches:
wherein the weight corresponds to the session-agnostic autocomplete score, and the computer-implemented method further comprises: determining the presence of the candidate search result within the individual curated search corpus (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077], note selecting a first subset and a second subset, one subset being suggestions generated by the native model, e.g., general search corpus, and one subset being suggestions from the expansion result module which, e.g., the individual curated search corpus); and
increasing the weight of the session-agnostic autocomplete score (Somaiya, [0036-0042, 0050, 0064-0066, 0074-0077, 0106, 0132], note generating a combined weighted autocomplete score for the autocomplete search results which include the session-agnostic autocomplete scores; note generating a set of hybrid scores to merge the subsets; note weighting the results different based on where the candidate results are from, for example generating an ordered list by placing the second subset in an ordered list above the first subset (e.g., weighting the first subset lower than the second subset) or by ordering the first and second subsets based on a scores, e.g., the scoring functions for NQ for the native results and EQ for the expansion results means the results are weighted different based on whether they are from the individual curated corpus or the general corpus; note the use of weights) (Su, figure 2, [0011-0014, 0050-0053, 0062, 0077, 0085, 0094], note candidate query terms with corresponding scores may be received session-aware autocomplete scores and session-agnostic autocomplete scores such as ones received using data from other users; note generating scores for similarity between session-aware and session-agnostic candidate terms, e.g., hybrid autocomplete scores; note generating the scores may comprise a weighted sum function; note the weights applied may be different, e.g., a first measure may be more than a second. When combined with the previously cited references this would be include the session-agnostic scores as taught by Somaiya).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Su because all references are directed towards information retrieval and because Su would expand upon the teachings of the previously cited references in query processing which would improve autocomplete performance by utilizing query context (Su, [0009]).
Regarding Claim 10:
Somaiya as modified shows the method as disclosed above;
Somaiya as modified further teaches:
wherein the search query prefix is received via user input to a search query box and the autocomplete prediction comprises a complete search query for the search query prefix (Somaiya, figures 2-3, [0018-0019, 0030, 0043, 0065-0066, 0120], note entering a query in a search box for autocomplete prediction) (Su, [0006-0008], note query input box).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Su because all references are directed towards information retrieval and because Su would expand upon the teachings of the previously cited references in query processing which would improve autocomplete performance by utilizing query context (Su, [0009]).
Claim 11 discloses substantially the same limitations as claim 1 respectively, except claim 11 is directed to an apparatus comprising processor and memory (Somaiya, figure 10, note processor and memory) while claim 1 is directed to a method. Therefore claim 11 is rejected under the same rationale set forth for claim 1.
Claim 12 discloses substantially the same limitations as claim 2 respectively, except claim 12 is directed to an apparatus comprising processor and memory (Somaiya, figure 10, note processor and memory) while claim 2 is directed to a method. Therefore claim 12 is rejected under the same rationale set forth for claim 2.
Claim 13 discloses substantially the same limitations as claim 3 respectively, except claim 13 is directed to an apparatus comprising processor and memory (Somaiya, figure 10, note processor and memory) while claim 3 is directed to a method. Therefore claim 13 is rejected under the same rationale set forth for claim 3.
Claim 14 discloses substantially the same limitations as claim 4 respectively, except claim 14 is directed to an apparatus comprising processor and memory (Somaiya, figure 10, note processor and memory) while claim 4 is directed to a method. Therefore claim 14 is rejected under the same rationale set forth for claim 4.
Claim 15 discloses substantially the same limitations as claim 5 respectively, except claim 15 is directed to an apparatus comprising processor and memory (Somaiya, figure 10, note processor and memory) while claim 5 is directed to a method. Therefore claim 15 is rejected under the same rationale set forth for claim 5.
Claim 16 discloses substantially the same limitations as claim 6 respectively, except claim 16 is directed to an apparatus comprising processor and memory (Somaiya, figure 10, note processor and memory) while claim 6 is directed to a method. Therefore claim 16 is rejected under the same rationale set forth for claim 6.
Claim 17 discloses substantially the same limitations as claim 1 respectively, except claim 17 is directed to a non-transitory computer-readable media comprising processor and memory (Somaiya, figure 10, note processor and memory) while claim 1 is directed to a method. Therefore claim 17 is rejected under the same rationale set forth for claim 1.
Claim 18 discloses substantially the same limitations as claim 7 respectively, except claim 18 is directed to a non-transitory computer-readable media comprising processor and memory (Somaiya, figure 10, note processor and memory) while claim 7 is directed to a method. Therefore claim 18 is rejected under the same rationale set forth for claim 7.
Claim 19 discloses substantially the same limitations as claim 8 respectively, except claim 19 is directed to a non-transitory computer-readable media comprising processor and memory (Somaiya, figure 10, note processor and memory) while claim 8 is directed to a method. Therefore claim 19 is rejected under the same rationale set forth for claim 8.
Claim 20 discloses substantially the same limitations as claim 9 respectively, except claim 20 is directed to a non-transitory computer-readable media comprising processor and memory (Somaiya, figure 10, note processor and memory) while claim 9 is directed to a method. Therefore claim 20 is rejected under the same rationale set forth for claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhu et al. (US2017/0097939) and Hsieh et al. (US2021/0174164) teach generating autocomplete scores.
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/JOHN J MORRIS/Examiner, Art Unit 2151 9/24/2026
/James Trujillo/Supervisory Patent Examiner, Art Unit 2151
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