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 Status
Claims 1- 20 are pending. Claims 1-8 are rejected. Claims 9-20 are allowed.
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).
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Claims 1-8 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8 of U.S. Patent No. 12,259,896 application 18/592,512 in view of Choudhary.
19/065,567
1. A method, comprising:
18/592,512
1. A method, comprising:
19/065,567
generating, by an embedding model, a user query embedding for a user query
received from a user query answer service;
18/592,512
generating, by an embedding model, a new user query embedding for a new user query received from a user
19/065,567
obtaining, from a search engine index, an indexed user query matching the user
query, a vector index corresponding to the indexed user query, and a relevancy
score corresponding to the indexed user query;
18/592,512
obtaining, by a search engine from a search engine an indexed user query matching the new user query index
19/065,567
selecting, from a vector store, a vector structure corresponding to the vector index;
18/592,512
a first vector index corresponding to the indexed user query, and a relevancy score corresponding to the indexed user query
selecting, from a plurality of vector structures in a vector store, a vector structure corresponding to the first vector index
19/065,567
obtaining, from the vector structure, a result embedding matching the user query
embedding;
18/592,512
obtaining, from the vector structure, a result embedding matching the new user query embedding
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transmitting, by the user query answer service, the result embedding to an answer
generation model; and
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transmitting, by a user query answer service to an answer generation model, the result embedding and
19/065,567
receiving, by the user query answer service, an answer to the user query from the
answer generation model.
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receiving, by the user query answer service, an answer to the new user query from the answer generation model
19/065,567
2. The method of claim 1, further comprising:
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2. The method of claim 1, further comprising:
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generating a result similarity score for the result embedding;
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generating a result similarity score for the result embedding;
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determining an index similarity score for the vector index based on the result
similarity score; and
18/592,512
determining an index similarity score for the first vector index based on the result similarity score
19/065,567
determining a composite score corresponding to the vector index based on the
relevancy score of the indexed user query and the index similarity score of the
vector index.
18/592,512
determining a composite score corresponding to the first vector index based on the
relevancy score of the indexed user query and the index similarity score of the
first vector index.
19/065,567
3. The method of claim 2, further comprising:
generating, by the answer generation model, the answer to the user query, based on
the result embedding corresponding to the vector index, and responsive to the
composite score being higher than a composite score threshold.
18/592,512
3. The method of claim 2, further comprising:
generating, by the answer generation model, the answer to the new user query, based on
the result embedding corresponding to the first vector index, and responsive to the
composite score being higher than a composite score threshold.
19/065,567
4. The method of claim 1, further comprising:
generating a result similarity score for the result embedding;
determining an index similarity score for the vector index based on the result
similarity score;
determining a composite score for the vector index based on the relevancy score of
the indexed user query and the index similarity score of the vector index; and
obtaining an alternative result embedding responsive to the composite score being
lower than a composite store threshold,
wherein the answer generation model generates the answer to the user query based
on the alternative result embedding.
18/592,512
4. The method of claim 1, further comprising:
generating a result similarity score for the result embedding;
determining an index similarity score for the first vector index based on the result
similarity score;
determining a composite score for the first vector index based on the relevancy score of
the indexed user query and the index similarity score of the first vector index; and
obtaining an alternative result embedding responsive to the composite score being
lower than a composite store threshold,
wherein the answer generation model generates the answer to the new user query based at least one alternative result embedding.
19/065,567
5. The method of claim 1, further comprising:
generating a result similarity score for the result embedding;
determining an index similarity score for the vector index based on the result
similarity score;
determining a composite score for the vector index based on the relevancy score of
the indexed user query and the index similarity score of the vector index; and
initiating an iteration of feedback re-indexing for the search engine index responsive
to the composite score being lower than a composite score threshold.
18/592,512
5. The method of claim 1, further comprising:
generating a result similarity score for the result embedding;
determining an index similarity score for the first vector index based on the result
similarity score;
determining a composite score for the first vector index based on the relevancy score of
the indexed user query and the index similarity score of the first vector index; and
initiating an iteration of feedback re-indexing for the search engine index responsive
to the composite score being lower than a composite score threshold.
19/065,567
6. The method of claim 5, wherein the feedback re-indexing comprises:
obtaining a selected response corresponding to the user query;
obtaining a second vector index corresponding to the selected response;
updating a labeled dataset with the user query and the second vector index; and
generating an updated search engine index based at least on the labeled dataset.
18/592,512
6. The method of claim 5, wherein the feedback re-indexing comprises:
obtaining a selected response corresponding to the new user query;
obtaining a second vector index corresponding to the selected response;
updating a labeled dataset with the new user query and the second vector index; and
generating an updated search engine index based at least on the labeled dataset.
19/065,567
7. The method of claim 1, further comprising:
populating the vector store during a population phase, wherein populating
comprises:
ingesting content corresponding to a content domain store from a data
repository, and
generating, by the embedding model, an embedding corresponding to an
utterance of the content, and transmitting the embedding to the vector store.
18/592,512
7. The method of claim 1, further comprising:
populating the vector store during a population phase, wherein populating
comprises:
ingesting content corresponding to a content domain store from a data
repository, and
generating, by the embedding model, an embedding corresponding to an
utterance of the content, and transmitting the embedding to the vector store.
19/065,567
8. The method of claim 7, further comprising:
creating a new vector structure comprising:
the embedding corresponding to the utterance, and
a new vector index corresponding to the new vector structure, wherein the new
vector index identifies the content domain store; and
committing the new vector structure to the vector store.
18/592,512
8. The method of claim 7, further comprising:
creating a new vector structure comprising:
the embedding corresponding to the utterance, and
a new vector index corresponding to the new vector structure, wherein the new
vector index identifies the content domain store; and
committing the new vector structure to the vector store.
Continuing Data
This application is a CON of 18/592,512 filed 2/29/2024 now PAT 12,259,896
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Regarding claim 1, the prior art does not teach, disclose or fairly suggest at least the following element of claim 1: “obtaining, from a search engine index, an indexed user query matching the user query, a vector index corresponding to the indexed user query, and a relevancy score corresponding to the indexed user query.”
For the sake of completeness, consider Chowdhary’s disclosure in US PAT. No. 12,259,896,
“obtaining, by a search engine from a search engine index an indexed user query matching the new user query, a first vector index corresponding to the indexed user query and a relevancy score corresponding to the indexed user query.”
However, Chowdhary’s disclosure in US PAT. No. 12,259,896 is not prior art because the present application 19/065,567 is a continuation of US PAT. No. 12,259,896.
Regarding independent claim 9, the prior art does not teach, disclose or fairly suggest at least the following element of claim 9, “selecting, for a response embedding of the set of responses embeddings, a vector structure from a vector store having an embedding matching the response embedding, to obtain a set of corresponding vector structures.”
For the sake of completeness, consider Chowdhary’s disclosure in US PAT. No. 12,259,896,
“select a vector structure corresponding to the first vector index from a plurality of vector structures in the vector store.”
However, Chowdhary’s disclosure in US PAT. No. 12,259,896 is not prior art because the present application 19/065,567 is a continuation of US PAT. No. 12,259,896.
Regarding independent claim 12, the prior art does not teach, disclose or fairly suggest at least the following element of claim 12, “the search engine is configured to cause the at least one computer processor to obtain an indexed user query matching the user query from a search
engine index, a vector index corresponding to the indexed user query, and a relevancy score corresponding to the indexed user query.”
For the sake of completeness, consider Chowdhary’s disclosure in US PAT. No. 12,259,896,
“obtaining, by a search engine from a search engine index an indexed user query matching the new user query, a first vector index corresponding to the indexed user query and a relevancy score corresponding to the indexed user query.”
However, Chowdhary’s disclosure in US PAT. No. 12,259,896 is not prior art because the present application 19/065,567 is a continuation of US PAT. No. 12,259,896.
Sanz (US 12,008,026) claim 8 includes “wherein identifying of the one or more candidate content embedding vectors that match the query embedding vector includes determining by the one or more computing systems and for each of the one or more candidate content embedding vectors, that the content embedding vector has a degree of matching to the query embedding vector that exceeds a defined threshold.”
Amity (US 2005/0138007) abstract discloses A search system includes a search engine to search through an index of documents and an index enhancer to enhance the index with at least some user queries. The index may include a listing of terms found in documents to be indexed and at least in user queries used to find said documents and a listing at least of how frequently such terms occurred in the documents and user queries.
Kharbanda (US 2024/0403362) abstract discloses Furthermore, the system can determine one or more video results based on the video embeddings and the audio data. Subsequently, the system can transmit, to the user device, the one or more video results.
Schillace (US 12,405,934) abstract discloses he content data associated with the content item may be provided to one or more semantic embedding models that generate semantic embeddings. From one or more of the semantic embedding models, one or semantic embeddings may be received. The one or more semantic embeddings may then be inserted into the embedding object memory. The semantic embeddings may be associated with respective indications corresponding to a reference to source data associated with the semantic embeddings. Further, the insertion may trigger a spatial storage operation to store a vector representation of the one or more semantic embeddings. A plurality of collections of stored embeddings may be received from the embedding object memory, based on a provided input, to determine an action.
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/ETIENNE P LEROUX/Primary Examiner of Art Unit 2161