DETAILED ACTION
Claims 1-33 are presented for examination.
Notice of Pre-AIA or AIA Status
2. 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 § 102
3. 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.
4. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
5. Claims 1-2, 4-5, 8, 12-13, 15-16, 19, 23-24, 26-27 and 30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Green (US 2018/0096071).
In claim 1, Green teaches
A method for providing relevant search results for search queries, the method comprising, by a client computing device:
receiving a query, wherein the query is associated with a user account, and the user account is associated with a user account vector (FIG. 8 [0090] at step 810, where the social-networking system 160 may receive, from a client system of a user of a communications network, a query inputted by the user, wherein the query comprises one or more n-grams. At step 830, the social-networking system 160 may generating a reconstructed embedding of a search context for a current search session of the first user based on a reconstructed embedding of the query and a reconstructed embedding of the user, wherein the query inputted by the user is inputted during the current search session);
generating a query vector based at least in part on the query ([0090] a reconstructed embedding of the query);
generating an output vector based at least in part on the query vector and the user account vector ([0090] generating a reconstructed embedding of a search context for a current search session of the first user based on a reconstructed embedding of the query and a reconstructed embedding of the user);
obtaining, based at least in part on the query, a plurality of digital asset vectors, wherein each digital asset vector of the plurality of digital asset vectors corresponds to a respective digital asset ([0090] At step 820, the social-networking system 160 may identify one or more objects matching the query. At step 840, the social-networking system 160 may calculate, for each identified object, a context-score based on a similarity metric of the reconstructed embedding of the search context and the object embedding associated with the identified object);
comparing the output vector to the plurality of digital asset vectors to generate respective similarity scores for the plurality of digital asset vectors ([0090] At step 840, the social-networking system 160 may calculate, for each identified object, a context-score based on a similarity metric of the reconstructed embedding of the search context and the object embedding associated with the identified object);
filtering the plurality of digital asset vectors in accordance with the similarity scores to establish a filtered plurality of digital asset vectors ([0090] At step 850, the social-networking system 160 may generate one or more search results based on the calculated context-scores, wherein each search result corresponds to one of the identified objects); and
displaying, in accordance with the filtered plurality of digital asset vectors, respective affordances for the respective digital assets that correspond to the filtered plurality of digital asset vectors ([0090] At step 860, the social-networking system 160 may send, to the client system in response to the query, a search-results interface for display, wherein the search-results interface comprises one or more of the search results, and wherein each search result comprises a reference to its corresponding identified object).
In claim 2, Green teaches
The method of claim 1, wherein the query comprises text content, image content, audio content, video content, or some combination thereof ([0039] the social-networking system 160 may receive, from a client system 130 of a user of a communications network, a query inputted by the user, wherein the query may comprise one or more n-grams. As used herein, n-grams may be words or groups of words, any part of speech…).
In claim 4, Green teaches
The method of claim 1, wherein the user account vector is generated based at least in part on:
a first set digital asset vectors that correspond to digital assets marked as favorites in association with the user account;
a second set of digital asset vectors that correspond to digital assets that are frequently accessed in association with the user account; and
a third set of query history vectors that correspond to queries provided in association with the user account within a threshold period of time ([0068] generating a reconstructed embedding of the querying user may comprise pooling one or more object embeddings associated with one or more objects interacted with by the querying user within a particular timeframe, respectively. As an example and not by way of limitation, a reconstructed embedding of a querying user may be generated based on objects which the querying user has interacted with within the past 30 days. In particular embodiments, generating a reconstructed embedding of the querying user may comprise pooling the one or more object embeddings, wherein the one or more object embeddings are associated with a particular number of the one or more objects interacted most recently with by the querying user, respectively. As an example and not by way of limitation, a reconstructed embedding of a querying user may be generated based on the most recent 128 objects which the querying user has interacted with).
In claim 5, Green teaches
The method of claim 1, further comprising, prior to generating the output vector based at least in part on the query vector and the user account vector: concatenating the query vector to the user account vector, or vice-versa ([0071] for a search context c of for a search session of querying user pq who inputted query q, the reconstructed embedding of the querying user's search context may be the sum pooling π (c)= π (pq) + π (q). Referencing FIG. 5, query 510 may be the query q=“French restaurants San Francisco duck confit” and the querying user may be Steven. If the reconstructed embedding of the querying user is an average pooling of objects Steven has interacted with e1 through e4
In claim 8, Green teaches
The method of claim 1, wherein filtering the plurality of digital asset vectors in accordance with the similarity scores to establish the filtered plurality of digital asset vectors comprises: excluding, from the filtered plurality of digital asset vectors, digital asset vectors having respective similarity scores that do not satisfy a threshold similarity score ([0064] the relevance-score of an identified object may be used to determine whether a corresponding search result is generated. As an example and not by way of limitation, search results corresponding to identified objects above a threshold relevance-score may be generated).
Claim Rejections - 35 USC § 103
6. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
7. 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 of this title, 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.
8. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
9. Claims 3, 6-7, 9, 14, 17-18, 20, 25, 28-29 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Green (US 2018/0096071), in view of Penta et al. (US 2025/0238470) hereinafter Penta.
In claim 3, per rejection in claim 1
Green does not appear to explicitly disclose however, Penta discloses “The method of claim 1, wherein the query vector is generated based at least in part on the query using a transformer-based large language model (LLM) ([0020] the personalized retrieval-augmented generation system determines a data context specific to the entity by comparing the query embedding with a plurality of vectorized segments of content items associated with the entity stored in a database. Subsequently, the personalized retrieval-augmented generation system can generate a personalized response by providing the data context and the query to a large language model)”.
Hence, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Green and Penta, the suggestion/motivation for doing so would have been to provide an accurate and flexible retrieval-augmented generation systems (Abstract).
In claim 6, per rejection in claim 1
Green does not appear to explicitly disclose however, Penta discloses “The method of claim 1, wherein: the output vector is generated based at least in part on the query vector and the user account vector using a transformer-based large language model (LLM), and the transformer-based LLM implements a set of fully connected layers and a set of input normalization layers ([0048] the personalized retrieval-augmented generation system 106 can compare a query embedding (from the embedding model 204) with content embeddings (e.g., vectorized segments of content items), user account embeddings, relationship embeddings, etc. stored in the vector database 206. Based on the comparison, the personalized retrieval-augmented generation system 106 can determine which data contexts 208 associated with the entity should be used in the large language model 210)”.
Hence, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Green and Penta, the suggestion/motivation for doing so would have been to provide an accurate and flexible retrieval-augmented generation systems (Abstract).
In claim 7, per rejection in claim 1
Green does not appear to explicitly disclose however, Penta discloses “The method of claim 1, wherein a given digital asset vector of the plurality of digital asset vectors is generated by: obtaining, from a transformer-based LLM, a first digital asset vector based at least in part on metadata associated with the corresponding respective digital asset; obtaining, from a machine learning model, a second digital asset vector based at least in part on data content of the corresponding respective digital asset; and generating the digital asset vector based at least in part on combining the first and second digital asset vectors ([0076] the personalized retrieval-augmented generation system 106 can input a single query into two different embedding models to generate two different query embeddings. The personalized retrieval-augmented generation system 106 can compare the two different query embeddings with content item embeddings in two different databases. Subsequently, the personalized retrieval-augmented generation system 106 can generate one or more data contexts from the content items in the two different databases and provide those data contexts to a large language model to generate a personalized response for the entity. In some cases, the personalized retrieval-augmented generation system 106 can utilize multiple embedding models, databases, and/or large language models to generate a personalized response for the entity)”.
Hence, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Green and Penta, the suggestion/motivation for doing so would have been to provide an accurate and flexible retrieval-augmented generation systems (Abstract).
In claim 9, per rejection in claim 1
Green does not appear to explicitly disclose however, Penta discloses “The method of claim 1, further comprising: determining that the query includes at least one question; providing, to at least one machine learning model, (1) the query, and (2) the respective digital assets that correspond to the filtered plurality of digital asset vectors, to cause the at least one machine learning model to generate a descriptive answer to the at least one question; and displaying an affordance for the descriptive answer ([0050] the personalized retrieval-augmented generation system 106 can generate a personalized response that considers and utilizes content items specific to the entity. In one or more implementations, the personalized response 212 can take on various formats. For example, the personalized response 212 can be abstractive question answering, where the personalized retrieval-augmented generation system 106 provides a natural language response to a query in the form of a question. In some cases, the personalized response 212 can be semantic by using the intent and contextual meaning of the query 202)”.
Hence, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Green and Penta, the suggestion/motivation for doing so would have been to provide an accurate and flexible retrieval-augmented generation systems (Abstract).
Allowable Subject Matter
10. Claims 10, 21 and 32 are objected to as being dependent upon a rejected baseclaim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
None of the prior arts of record teaches "wherein obtaining, based at least in part on the query, the plurality of digital asset vectors comprises: generating at least one retrieval tree based at least in part on at least one keyword associated with the query; comparing the at least one retrieval tree against at least one keyword index to identify a first plurality of digital asset vectors using keyword-based searching; generating at least one embedding based at least in part on the query; comparing the at least one embedding against at least one vector index to identify a second plurality of digital asset vectors using semantic-based searching; and generating the plurality of digital asset vectors based at least in part on the first and second plurality of digital asset vectors" as recited in claims 10, 21 and 32.
Their dependent claims 11, 22 and 33 are also objected based on the same rationale as applied to their parent claim.
Claims 12-20 are essentially same as claims 1-9 except that they recite claimed invention as a non-transitory computer readable storage medium and are rejected for the same reasons as applied hereinabove.
Claims 23-31 are essentially same as claims 1-9 except that they recite claimed invention as a device and are rejected for the same reasons as applied hereinabove.
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed on 892 form.
Examiner’s Note: Examiner has cited particular figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUAWEN A PENG whose telephone number is (571)270-5215. The examiner can normally be reached Mon thru Fri 9 am to 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUAWEN A PENG/Primary Examiner, Art Unit 2169