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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claimed invention is directed to one or more abstract ideas without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below.
Step 1:
The claimed method (claims 1-8), system (claims 9-14), and computer readable storage media (claims 15-20) are directed to one of the eligible categories of subject matter and therefore satisfies step 1.
Step 2A, Prong One:
Independent claims recite the following limitations that can be practically performed in the mind and/or with a pen and a piece of paper.
Claims 1 and 15:
identifying a search query performed; generating a query vector for the search query by aggregating item listing vectors of search results from the search query;
determining similarities between the query vector and the item listing vectors; and
generating an intent specificity of the search query based on aggregating the similarities between the query vector and the item listing vectors.
Claim 9:
identifying a first set of queries having determined intent specificities; using the intent specificities for each of the first set of queries, to generate intent specificity scores for additional search queries;
Step 2A, Prong Two:
The additional elements are:
Claims 1 and 15:
performed using a search engine.
Claim 9:
providing a second query to the trained intent specificity machine learning model; and
providing, using the trained intent specificity machine learning model, an intent specificity score for the second query.
These additional elements are using generic computer functions as a tool to perform.
Step 2B:
For Step 2B, the additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the identified judicial exception. MPEP 2106.07(a)(III)(B) identifies the list of cases in MPEP 2106. 05(d)(II) as available bases. Taking these aforementioned additional elements as an ordered combination, these additional elements add nothing that is not already present when the elements are considered separately.
As per dependent claims:
Step 2A, Prong One:
Claim 2, wherein the search results have user interaction histories.
Claim 3, wherein aggregating the item listing vectors to generate the query vector includes determining means for the item listing vectors.
Claims 7 and 16. The computer-implemented method of claim 1, further comprising: identifying a second search query; generating at least one query vector for the second search query by aggregating item listing vectors of search results from the second search query; determining similarities between the at least one query vector for the second search query and the item listing vectors of the search results from the second search query; and generating a second intent specificity of the second search query based on aggregating the similarities between the at least one query vector for the second search query and the item listing vectors of the search results from the second search query.
Claim 10, generating item listing vectors for each set of search results corresponding to each of the first set of queries;
generating a query vector for at least one of the first set of queries using the item listing vectors of the search results for the at least one of the first set of queries; and
determining a mean of cosine similarity between the query vector and the item listing vectors of the search results for the at least one of the first set of queries to determine the intent specificity for the at least one of the first set of queries.
Claim 11, wherein the first set of queries each have prior user interaction histories with search results for each of the first set of queries that is above a threshold.
Claim 13, identifying a second set of queries having determined intent specificities, the first set of queries having a higher number of user interactions with search results compared to the second set of queries, the second set of queries having prior user interaction histories above the threshold.
Claim 14, determining that the intent specificity score for the second query is below an intent specificity score threshold; based on determining that the intent specificity score for the second query is below the intent specificity score threshold, determining that the second query is a query-independent factor; based on determining that the second query is the query-independent factor, ranking search results for the second query; and providing the search results for the second query based on the ranking.
The aforementioned limitations that can be practically performed in the mind and/or with a pen and a piece of paper.
As per dependent claims:
Step 2A, Prong Two:
Claim 2, by a user device that provided the search query to the search engine.
Claim 4, wherein the similarities between the query vector and the item listing vectors are determined by using a Euclidean distance.
Claim 5,, wherein the similarities between the query vector and the item listing vectors are determined using cosine similarities between the query vector and the item listing vectors.
Claim 6, wherein aggregating the similarities between the query vector and the item listing vectors includes determining a mean of the cosine similarities.
Claim 8 and 17, training an intent specificity machine learning model using the intent specificity and the second intent specificity for generating intent specificity scores for additional search queries; providing a third query to the trained intent specificity machine learning model; and providing, using the trained intent specificity machine learning model, an intent specificity score for the third query.
Claim 12, wherein the second query provided to the trained intent specificity machine learning model has prior user interaction histories with search results for the second query that is below a threshold.
Claim 13, training the intent specificity machine learning model using the intent specificities for each of the second set of queries; and providing the intent specificity score for the second query based on training the intent specificity machine learning model using the intent specificities for each of the second set of queries.
The additional elements of dependent claims are directed to generic computer functions.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Duzhik (US Pub. 2019/0391982) in view of Joshi (US Pub. 7,840,538).
Regarding claim 1 (15), Duzhik discloses a computer-implemented method comprising:
identifying a search query performed using a search engine (¶ [0005], the search engine will then present a ranked list of Internet resources that are potentially relevant to the search query);
generating a query vector for the search query by aggregating item listing vectors of search results from the search query (¶ [0109], The training set generation routine includes an aggregator, a query vector generator 440, and a similarity parameter generator);
determining similarities between the query vector and the item listing vectors (¶ [0017], for each possible pair of queries within the plurality of search queries, based on the respective query vectors of each query of the pair of queries); and
the search query based on aggregating the similarities between the query vector and the item listing vectors (¶ [0017])
Duzhik does not explicitly disclose generating an intent specificity; however, Joshi discloses generating an intent specificity (col. 9, lines 26-33, constructs a query intent vector based on the aggregated frequency vector).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Joshie into Duzhik to select highest possibility based on the query intent (¶ [0006], past users’ interactions).
Regarding claim 2, Duzhik in view of Joshi discloses the computer-implemented method of claim 1, wherein the search results have user interaction histories by a user device that provided the search query to the search engine (¶ [0006]).
Regarding claim 3, Duzhik in view of Joshi discloses the computer-implemented method of claim 1, wherein aggregating the item listing vectors to generate the query vector includes determining means for the item listing vectors (¶ [0049]).
Regarding claim 4, Duzhik in view of Joshi discloses the computer-implemented method of claim 1, wherein the similarities between the query vector and the item listing vectors are determined by using a Euclidean distance (col. 6, lines 60-67; Euclidean-it’s also well known).
Regarding claim 5, Duzhik in view of Joshi discloses the computer-implemented method of claim 1, wherein the similarities between the query vector and the item listing vectors are determined using cosine similarities between the query vector and the item listing vectors.
Regarding claim 6, Duzhik in view of Joshi discloses the computer-implemented method of claim 5, wherein aggregating the similarities between the query vector and the item listing vectors includes determining a mean of the cosine similarities (¶ [0019]; cosine similarity).
Regarding claim 7 (16) , Duzhik in view of Joshi discloses the computer-implemented method of claim 1, further comprising: identifying a second search query performed using the search engine;
generating at least one query vector for the second search query by aggregating item listing vectors of search results from the second search query (¶ [0008]);
determining similarities between the at least one query vector for the second search query and the item listing vectors of the search results from the second search query (¶ [0008]); and
generating a second intent specificity of the second search query based on aggregating the similarities between the at least one query vector for the second search query and the item listing vectors of the search results from the second search query (¶ [0008]).
Regarding claim 8 (17), Duzhik in view of Joshi discloses the computer-implemented method of claim 1, further comprising: training an intent specificity machine learning model using the intent specificity and the second intent specificity for generating intent specificity scores for additional search queries (col. 9, lines 8-15); providing a third query to the trained intent specificity machine learning model (col. 9, lines 8-15); and providing, using the trained intent specificity machine learning model, an intent specificity score for the third query (col. 9, lines 8-15).
Regarding claim 9. A computer system comprising:
a processor; and
a computer storage medium storing computer-useable instructions that, when used by the processor, causes the computer system to perform operations comprising:
identifying a first set of queries having determined [intent specificities] (¶ [0005], the search engine will then present a ranked list of Internet resources that are potentially relevant to the search query);
Joshie discloses the followings that Dushik does not explicitly teach, training an intent specificity (col. 9, lines 26-33) machine learning model, using the intent specificities for each of the first set of queries, to generate intent specificity scores for additional search queries; providing a second query to the trained intent specificity (col. 9, lines 26-33) machine learning model; and
providing, using the trained intent specificity machine learning model, an intent specificity score for the second query (col. 9, lines 26-33, constructs a query intent vector based on the aggregated frequency vector).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Joshie into Duzhik to select highest possibility based on the query intent (¶ [0006], past users’ interactions).
Regarding claim 10, Duzhik in view of Joshi discloses the computer system of claim 9, wherein the intent specificities of the first set of queries are determined by:
generating item listing vectors for each set of search results corresponding to each of the first set of queries (¶¶ [0016]-[0017]);
generating a query vector for at least one of the first set of queries using the item listing vectors of the search results for the at least one of the first set of queries (¶¶ [0016]-[0017]); and
determining a mean of cosine similarity between the query vector and the item listing vectors of the search results for the at least one of the first set of queries to determine the intent specificity for the at least one of the first set of queries (¶¶ [0016]-[0017]; Joshi, col. 9).
Regarding claim 11, Duzhik in view of Joshi discloses the computer system of claim 9, wherein the first set of queries each have prior user interaction histories with search results for each of the first set of queries that is above a threshold (¶ [0122], a predetermined threshold).
Regarding claim 12, Duzhik in view of Joshi discloses the computer system of claim 11, wherein the second query provided to the trained intent specificity machine learning model has prior user interaction histories with search results for the second query that is below a threshold (¶ [0122]; Joshi, col. 9).
Regarding claim 13, Duzhik in view of Joshi discloses the computer system of claim 12, further comprising: identifying a second set of queries having determined intent specificities, the first set of queries having a higher number of user interactions with search results compared to the second set of queries, the second set of queries having prior user interaction histories above the threshold (¶ [0122]);
training the intent specificity machine learning model using the intent specificities for each of the second set of queries (¶ [0131]); and
providing the intent specificity score for the second query based on training the intent specificity machine learning model using the intent specificities for each of the second set of queries (¶ [0131], Joshi, col. 9).
Regarding claim 14, Duzhik in view of Joshi discloses the computer system of claim 9, further comprising: determining that the intent specificity score for the second query is below an intent specificity score threshold (Joshi, col. 9, lines 26-37);
based on determining that the intent specificity score for the second query is below the intent specificity score threshold, determining that the second query is a query-independent factor (Joshi, col. 9, lines 26-37);
based on determining that the second query is the query-independent factor, ranking search results for the second query (col. 9, lines 26-37); and providing the search results for the second query based on the ranking (col. 9, lines 26-37).
Regarding claim 18, Duzhik in view of Joshi discloses the one or more non-transitory computer storage media of claim 17, wherein the search result embeddings include item listing embeddings (¶ [0006]).
Regarding claim 19, Duzhik in view of Joshi discloses the one or more non-transitory computer storage media of claim 17, further comprising: determining that the intent specificity score for the third query is above an intent specificity score threshold (Joshie, col. 9); and based on determining that the intent specificity score for the third query is above the intent specificity score threshold, providing an indication that retrieval for search results using the third query is precise instead of an indication for recall (Joshie, col. 9; most to least relevant).
Regarding claim 20, Duzhik in view of Joshi discloses the one or more non-transitory computer storage media of claim 17, further comprising: determining that the intent specificity score for the third query is above an intent specificity score threshold (col. 4); based on determining that the intent specificity score for the third query is above the intent specificity score threshold, determining that the third query is a query-dependent factor (col. 4); based on determining that the third query is the query-dependent factor, ranking search results for the third query; and providing the search results for the third query based on the ranking (col. 4).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUANKHANH D PHAN whose telephone number is (571)270-3047. The examiner can normally be reached on Mon-Fri, 10:00am-18:00pm.
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/TUANKHANH D PHAN/ Examiner, Art Unit 2154