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 Office Action is in response to correspondence filed 12 December 2025 in reference to application 18/527,910. Claims 1-7, 9-16, 18-22 are pending and have been examined.
Response to Amendment
The amendment filed 12 December 2025 has been accepted and considered in this office action. Claims 1-4, 6, 7, 9-16, 18-20 have been amended, 8 and 17 cancelled, and 21 and 22 added new.
Response to Arguments
Applicant’s arguments, see Remarks pages 11-18, filed 12 December 2025, with respect to rejections made under 35 USC 101 and 112 have been fully considered and are persuasive. The 35 USC 101 and 112 rejections of the claims has been withdrawn.
Applicant’s arguments with respect to claim(s) 1-7, 9-16, 18-22 and prior art have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 2 11, 12, 21, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Su et al. (US PAP 2016/0188619) in view of Zheng et al. (US PAP 2023/0229706).
Claim 1, Su teaches A computer-implemented method (abstract) comprising:
receiving, by one or more processors (0100, CPU, etc.) and via a graphical user interface, a word prefix from a computing device of a user (0046, receiving incomplete word prefix entered into GUI, 0050, retrieving a list of candidate query terms);
providing, by the one or more processors and to a personalized re-ranking module, a plurality of keywords to receive a plurality of predictions on the plurality of keywords (0050, retrieving a list of candidate query terms),wherein
(i) the plurality of keywords comprises one or more suggestion keywords that are associated with the word prefix, (0050, the candidate words that contain the prefix)
(ii) the plurality of predictions on the plurality of keywords is generated by the personalized re-ranking module based on a plurality of keyword feature vectors and one or more personalized feature vectors (0052, 0082, predicting a degree of similarity between candidate keywords and historical keywords; 0081, based on feature vectors);
(iii) the plurality of keyword feature vectors corresponds to the plurality of keywords (0081 candidate keyword vectors),
(iv) the one or more personalized feature vectors correspond to search session data that is associated with the user (0081, feature vectors of keywords previously searched),
(vi) a plurality of rankings is assigned to the plurality of keywords based on the plurality of predictions (0053, candidate query terms are ranked based on similarity); and
providing, by the one or more processors and via the graphical user interface, the, one or more suggestion keywords for the word prefix, responsive to the word prefix received from the computing device, based on the plurality of rankings (0053, providing candidate query term based on ranking to the user as a suggestion.).
Su does not specifically teach that the personalized re-ranking module is a a personalized re-ranking machine learning model; and
(v) the personalized re-ranking machine learning model is stored in association with one or more inferred relevant keywords.
In the same field of suggesting keywords, Zheng teaches that the personalized re-ranking module is a personalized re-ranking machine learning model (0068, 0048-49, machine learning re-ranking, trained using process of figure 4, which includes learning user historical queries); and
(v) the personalized re-ranking machine learning model is stored in association with one or more inferred relevant keywords (0068, 0048-49, machine learning re-ranking, trained using process of figure 4, which includes learning user historical queries which would include keywords).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use machine learning to re-rank as taught by Zheng in the system of Su in order to more accurately provide suggestions.
Consider claim 2, Zheng the computer-implemented method of claim 1 further comprising generating the plurality of predictions by using the personalized re-ranking machine learning model c (068, 0048-49, machine learning re-ranking of suggestions, trained using process of figure 4, which includes learning user historical queries).
Consider claim 11, Su teaches The computer-implemented method of claim 1, wherein the plurality of keywords are associated with a respective plurality of initial rankings, and assigning the plurality of rankings further comprises re-ranking the plurality of keywords by modifying the plurality of initial rankings based on the plurality of predictions (0056, initial ranking may be provided by candidate query term retrieval, and may be updated using re-ranking).
Claim 12, Su teaches A system (abstract) comprising:
one or more processors (0100, CPU, memory); and
one or more non-transitory computer readable media (0100, CPU, memory) storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving via a graphical user interface, a word prefix from a computing device of a user (0046, receiving incomplete word prefix entered into GUI, 0050, retrieving a list of candidate query terms);
providing, to a personalized re-ranking module, a plurality of keywords to receive a plurality of predictions on the plurality of keywords (0050, retrieving a list of candidate query terms),wherein
(i) the plurality of keywords comprises one or more suggestion keywords that are associated with the word prefix, (0050, the candidate words that contain the prefix)
(ii) the plurality of predictions on the plurality of keywords is generated by the personalized re-ranking module based on a plurality of keyword feature vectors and one or more personalized feature vectors (0052, 0082, predicting a degree of similarity between candidate keywords and historical keywords; 0081, based on feature vectors);
(iii) the plurality of keyword feature vectors corresponds to the plurality of keywords (0081 candidate keyword vectors),
(iv) the one or more personalized feature vectors correspond to search session data that is associated with the user (0081, feature vectors of keywords previously searched),
(vi) a plurality of rankings is assigned to the plurality of keywords based on the plurality of predictions (0053, candidate query terms are ranked based on similarity); and
providing, by the one or more processors and via the graphical user interface, the, one or more suggestion keywords for the word prefix, responsive to the word prefix received from the computing device, based on the plurality of rankings (0053, providing candidate query term based on ranking to the user as a suggestion.).
Su does not specifically teach that the personalized re-ranking module is a a personalized re-ranking machine learning model; and
(v) the personalized re-ranking machine learning model is stored in association with one or more inferred relevant keywords.
In the same field of suggesting keywords, Zheng teaches that the personalized re-ranking module is a personalized re-ranking machine learning model (0068, 0048-49, machine learning re-ranking, trained using process of figure 4, which includes learning user historical queries); and
(v) the personalized re-ranking machine learning model is stored in association with one or more inferred relevant keywords (0068, 0048-49, machine learning re-ranking, trained using process of figure 4, which includes learning user historical queries which would include keywords).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use machine learning to re-rank as taught by Zheng in the system of Su in order to more accurately provide suggestions.
Consider claim 21, Su teaches The computer-implemented method of claim 1 further comprising generating the one or more personalized feature vectors based on the search session data (0081, feature vectors of previous searches by user), wherein:
(i) the one or more personalized feature vectors comprise one or more embeddings of one or more features that are associated with the user (0081, feature vectors, i.e. embeddings of of previous searches by user), and
(ii) the one or more features comprise search session data, one or more word prefix embeddings, an age of the user, or a gender category corresponding to the user (0081, feature vectors of previous searches by user).
Claim 22 contains similar limitations as claim 21 and therefore is rejected for the same reasons.
Claim(s) 3-6 and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Su and Zheng as applied to claims 1 above, and further in view of Duan et al. (US PAP 2023/0359441).
Consider claim 3, Su and Zheng teach the computer-implemented method of claim 1 but do not specifically teach wherein the plurality of predictions comprise a respective plurality of probabilities of the user selecting the plurality of keywords.
In the same field of guess ahead completion, Duan teaches wherein the plurality of predictions comprise a respective plurality of probabilities of the user selecting the plurality of keywords (0046-49 0105, using a transformer model in order to predict most likely complete string given a prefix and input context, specifically 0047, output probabilities. ).
Therefore it would have been obvious to one of ordinary skill in the art at the time of effective filing in to use a transformer model and output probabilities as taught by Duan in the system of Su and Zheng in order to more accurately predict the completed strings (Duan 0003-06).
Consider claim 4, Su and Zheng teach the computer-implemented method of claim 1 but do not specifically teach further comprising generating the plurality of predictions based on a plurality of position embeddings associated with the list of suggestion keywords.
In the same field of guess ahead word completion, Duan teaches further comprising generating the plurality of predictions based on a plurality of position embeddings associated with the list of suggestion keywords. (0046-49 0105, using a transformer model in order to predict most likely complete string given a prefix and input context, using positional embeddings at 0049).
Therefore it would have been obvious to one of ordinary skill in the art at the time of effective filing in to use a transformer model as taught by Duan in the system of Su and Zheng in order to more accurately predict the completed strings (Duan 0003-06).
Consider claim 5, Su and and Zheng teach the computer-implemented method of claim 1 but do not specifically teach generating training data based on the search session data; and
training the personalized re-ranking machine learning model based on the training datal.
In the same field of guess ahead word completion, Duan teaches teach generating training data based on the search session data (0062-65 creating training data); and
training the personalized re-ranking machine learning model based on the training data (0066, training using the training data).
Therefore it would have been obvious to one of ordinary skill in the art at the time of effective filing in to use a transformer model that is trained on training data as taught by Duan in the system of Su and Zheng in order to more accurately predict the completed strings (Duan 0003-06).
Consider claim 6, Duan further suggests the computer-implemented method of claim 5, wherein generating the training data further comprises labeling one or more word prefix-suggestion pairs based on (i) occurrence of a click or a selection of one or more training typeahead suggestion keywords coinciding with one or more training word prefixes associated with the word prefix-suggestion pairs, or (ii) one or more inferred relevant keywords (0062-66, prefixes along with compete snippet which it is similar to or inferred.).
Claim 13 contains similar limitations as claim 4 and therefore is rejected for the same reasons.
Claim 14 contains similar limitations as claim 5 and therefore is rejected for the same reasons.
Claim 15 contains similar limitations as claim 6 and therefore is rejected for the same reasons.
Claim(s) 9, 10, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Su and Zheng as applied to claims 1 and 12 above, and further in view of Coulombe et al. (US PAP 2021/0158144).
Consider claim 9, Su and Zheng teach The computer-implemented method of claim 1, but does not specifically teach identifying the one or more equivalent keywords based on an equivalent keywords dictionary data object.
In the same field of natural language input, Coulombe teaches identifying the one or more equivalent keywords based on an equivalent keywords dictionary data object (0018, keyword matching to identify keywords that match user input).
Therefore it would have been obvious to one of ordinary skill in the art at the time of effective filing to use keyword matching as taught by Coulombe in the system of Su and Zheng in order to accurately process user input (Coulombe 0005).
Consider claim 10, Coulombe teaches The computer-implemented method of claim 9 further comprising generating the equivalent keywords dictionary data object by: generating a plurality of intersection over union measurements associated with a plurality of search results based on a comparison between the plurality of search results associated with a plurality of search queries (0018, intersection over union metrics for each node);
determining a subset of the plurality of search queries are equivalent one or more equivalent search queries based on the plurality of intersection over union measurements and expert label data (0018, subset of nodes that have score that meets threshold, 0027, based on knowledge graphs, which encodes previous knowledge, i.e expert label data); and
determining the one or more equivalent keywords based on the one or more equivalent search queries (0018, determining matching keywords based on metrics).
Claim 18 contains similar limitations as claim 9 and therefore is rejected for the same reasons.
Claim 19 contains similar limitations as claim 10 and therefore is rejected for the same reasons.
Allowable Subject Matter
Claims 7 and 16 would be allowable if rewritten to include all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter:
Consider claim 7, Duan further suggests The computer-implemented method of claim 6 further comprising extracting the one or more inferred relevant keywords from the search session data (0063, training data may be extracted from previously stored repositories.). However the prior art of record does not teach or fairly suggest the limitations of “wherein extracting the one or more inferred relevant keywords from the search session data further comprises determining the one or more inferred relevant keywords by one or more of (i) matching one or more search word prefixes with one or more beginning leading characters of one or more terms searched during a same search session associated with the search session data, (ii) matching the one or more search word prefixes with one or more leading characters of one or more middle words of the one or more terms searched, or (iii) matching the one or more search word prefixes with one or more beginning leading characters of one or more equivalent keywords with respect to the one or more terms searched” when combined with each and every other limitation of the claim. Rather the prior art does not provide details as to how training examples are inferred. Therefore claim 7 contains allowable subject matter.
Claim 16 contains similar limitations as claim 7 and therefore contains allowable subject matter as well.
Claim 20 is allowed. The following is an examiner’s statement of reasons for allowance:
Claim 20 contains similar subject matter as claim 7 and its independent claim 1 and therefore contains allowable subject matter as well.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 DOUGLAS C GODBOLD whose telephone number is (571)270-1451. The examiner can normally be reached 6:30am-5pm Monday-Thursday.
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DOUGLAS GODBOLD
Examiner
Art Unit 2655
/DOUGLAS GODBOLD/Primary Examiner, Art Unit 2655