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 .
Response to Amendment
This Office action is in response to Applicant's amendment filed on 12/31/2025.
Claim 1-19 and 21 are pending. Claim 1, 7-8 and 14-15 are amended. Claim 20 is cancelled. Claim 21 is new. Claim 1-19 and 21 are rejected.
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.
Claim 1-4, 8-11 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Masse, Christopher et al (PGPUB Document No. 20250272581), hereafter referred as to “Masse”, in view of Hudson, Micheal et al (PGPUB Document No. 20250390786), hereafter, referred to as “Hudson”, in further view of Farid, Maor et al (PGPUB Document No. 20230290451), hereafter, referred to as “Farid”.
Regarding claim 1(Currently Amended), Masse teaches A system comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, configure the system to perform operations comprising (Masse, para 0022 discloses a computing system comprising of a processor, memory for storing executable instructions “system that comprises a processor and a memory storing instructions which, when executed by the processor, cause the processor to perform operations”): receive, via a query interface, a query related to a medical topic(Masse, element 310 of Fig. 3 and para 0061 disclose a query is being received by virtual assistant interface “The UI may prompt the entity to enter a question that the virtual assistant application 110 is configured to answer, and responsive to user input”; where prior art Farid discussed later teaches vector database for medical records and querying the database);
transmit the query to one or more vector DBs(Masse, para 0061 further teaches transmitting the query “the entity device 130 may provide (e.g., send or transmit, such as via the one or more networks 160”; para 0080 further teaches sending the query to a vector database ”the machine learning model 404 may be configured to identify one or more document vectors 412 associated with one or more documents 418 that are relevant to the question indicated within the user input 402. To do so, the first machine learning model 404 may be configured to query a document vector database (such as one of the databases 132) to identify the document vector 412 that may be most similar to the input vector 414”);
retrieve result sets from the one or more vector DBs, each result set associated with a respective vector DB of the one or more vector DBs(Masse, para 0081 discloses retrieving vector related result set from vector database “the machine learning model 404 may return document vectors 412 that satisfy a predetermined threshold (such as a maximum number of document vectors 412 that satisfy the threshold, all of the document vectors 412 that satisfy the threshold)”), each result set comprising result records, each result record being associated with content and a respective content vector(Masse, para 0074 discloses providing response or result set in response to user query “the virtual assistant may be configured to answer the input question 310 by retrieving one or more corresponding responses from a database and output at least one of the one or more responses.”);
But Masse does not explicitly teach But Masse does not explicitly teach determine a plurality of vectorization algorithms used by the one or more vector DBs; select a vectorization algorithm of the plurality of vectorization algorithms based on a frequency of use: normalize, based on the vectorization algorithm, one or more of the result records in the result sets to update a normalized result set; generate, based on the normalized result set, a unified result set; store the unified result set; and return the unified result set to a medical decision system that provides medical recommendations to a system user.
However, in the same field of endeavor of content vectorization Hudson teaches determine a plurality of vectorization algorithms used by the one or more vector DBs; select a vectorization algorithm of the plurality of vectorization algorithms based on a frequency of use(Hudson, para 0062 discloses selection vectorization algorithm based on the previous usage “During subsequent operation, intent model 335 may convert each input into a vector embedding, according to the same embedding algorithm, determine the nearest cluster to that vector embedding according to any suitable similarity metric”; para 0062 further discloses plurality of available vectorization algorithms to choose from “embedding algorithms include, without limitation, Word2Vec, Global Vectors for Word Representation (GloVe), Term Frequency and Inverse Document Frequency (TF-IDF), BERT, Doc2Vec, Skip-Thought vector embedding, the Probabilistic Latent Semantic Indexing (PLSI) model, Latent Dirichlet Allocation (LDA)”):
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of vectorization of contents of Hudson into the feature of querying vector database of Masse to produce an expected result of improving the retrieval process. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the system by intelligently selecting the relevant learning model (Hudson, para 0104).
But Masse and Hudson don’t explicitly teach normalize, based on the vectorization algorithm, one or more of the result records in the result sets to update a normalized result set; generate, based on the normalized result set, a unified result set; store the unified result set; and return the unified result set to a medical decision system that provides medical recommendations to a system user.
However, in the same field of endeavor of session management Farid teaches normalize, based on the vectorization algorithm, one or more of the result records in the result sets to update a normalized result set; generate, based on the normalized result set, a unified result set (Farid, para 0110 discloses normalizing/unifying vectors for medical data “The medical data obtained by obtaining module 231 can be used by unified representation module 232 to generate unified representation vectors of the medical data. Unified representation vectors can be generated by the feature extraction module 233 including a plurality of AI models configured for processing the medical data into feature vectors”); store the unified result set(Farid, para 0110 further discloses storing of unified vector data “AI fusion module 234 configured to fuse the feature vectors into a unified representation vector. The generated unified representation vector can be stored in storage 220”); and return the unified result set to a medical decision system that provides medical recommendations to a system user(Farid, Fig. 6 & para 0144 discloses providing back the unified vectorized medical data to the user for professional review “retrieving the stored medical data from local clinical data storage 310 (block 650). The medical data can be provided back to the user workstation 240 and can be displayed on display 243 to the professional review (block 660)”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of unifying vectorized medical data and storing it for retrieval of Farid into the feature of querying vector database of Masse and Hudson to produce an expected result of unifying different type of vector representation of patient data into a common format. The modification would be obvious because one of ordinary skill in the art would be motivated to retrieve associated similar medical data from local clinical data storage for providing it to professional review(Farid, para 0144-0145).
Regarding claim 2 (Original), Masse, Hudson and Farid teach all the limitations of claim 1 and Farid further teaches wherein normalizing the one or more of the result records in the result sets further comprises generating, using the vectorization algorithm, a new content vector for the content associated with each of the one or more of the result records(Farid, para 0110 further discloses that extracted medical data vectors are getting unified by applying AI(algorithm) “Unified representation vectors can be generated by the feature extraction module 233 including a plurality of AI models configured for processing the medical data into feature vectors, and by AI fusion module 234 configured to fuse the feature vectors into a unified representation vector”).
Regarding claim 3(Original), Masse, Hudson and Farid teach all the limitations of claim 2 and Farid further teaches wherein generating the unified result set based on the normalized result set further comprises(Farid, para 0110 further discloses that extracted medical data vectors are getting unified by applying AI(algorithm) “Unified representation vectors can be generated by the feature extraction module 233 including a plurality of AI models configured for processing the medical data into feature vectors, and by AI fusion module 234 configured to fuse the feature vectors into a unified representation vector”):
Masse teaches ranking the one or more result records in the normalized result set based on relevance to the query to generate a ranked result set; and generating the unified result set based on the ranked result set and one or more parameters(Masse, in para 0022 discloses scoring/ranking the response similarity of vectors (which can similarly be applied to unified/normalized vectors) “providing, based on the comparison of the first similarity score and the second similarity score, the input vector and at least one of (i) the document vector, (ii) the response, or (iii) a combination thereof to a large language model (LLM) to generate response content; and outputting the response content as an answer to the question” ).
Using the broadest reasonable interpretation consistent with the specification (paragraph 0016) as it would be interpreted by one of ordinary skill in the art, examiner is interpreting the limitation “parameters” to mean at least any factor that impacts the relevance similarity determination.
Regarding claim 4(Original), Masse, Hudson and Farid teach all the limitations of claim 3 and Farid further teaches wherein the one or more parameters further comprise a maximum result set size and a minimum set relevance score(Masse, in para 0081 discloses similarity score is considering a parameter such as maximum or minimum relevance “……the machine learning model 404 may return document vectors 412 that satisfy a predetermined threshold (such as a maximum number of document vectors 412 that satisfy the threshold, all of the document vectors 412 that satisfy the threshold”; where para 0092 further discloses threshold can be minimum or maximum “thresholds for making one or more determinations…….comparison that is described as “greater than” may be implemented with a “less than or equal to” condition” ).
Regarding claim 8(Currently Amended), Masse teaches receiving via a query interface, a query related to a medical topic (Masse, element 310 of Fig. 3 and para 0061 disclose a query is being received by virtual assistant interface “The UI may prompt the entity to enter a question that the virtual assistant application 110 is configured to answer, and responsive to user input”; where prior art Farid discussed later teaches vector database for medical records and querying the database);
transmitting the query to one or more vector DBs(Masse, para 0061 further teaches transmitting the query “the entity device 130 may provide (e.g., send or transmit, such as via the one or more networks 160”; para 0080 further teaches sending the query to a vector database ”the machine learning model 404 may be configured to identify one or more document vectors 412 associated with one or more documents 418 that are relevant to the question indicated within the user input 402. To do so, the first machine learning model 404 may be configured to query a document vector database (such as one of the databases 132) to identify the document vector 412 that may be most similar to the input vector 414”);
retrieving result sets from the one or more vector DBs, each result set associated with a respective vector DB of the one or more vector DBs (Masse, para 0081 discloses retrieving vector related result set from vector database “the machine learning model 404 may return document vectors 412 that satisfy a predetermined threshold (such as a maximum number of document vectors 412 that satisfy the threshold, all of the document vectors 412 that satisfy the threshold)”), each result set comprising result records, each result record being associated with content and a respective content vector(Masse, para 0074 discloses providing response or result set in response to user query “the virtual assistant may be configured to answer the input question 310 by retrieving one or more corresponding responses from a database and output at least one of the one or more responses.”);
But Masse does not explicitly teach determining a plurality of vectorization algorithms used by the one or more vector DBs; selecting a vectorization algorithm of the plurality of vectorization algorithms based on a frequency of use; normalizing, based on the vectorization algorithm, one or more of the result records in the result sets to update a normalized result set; generating, based on the normalized result set, a unified result set; storing the unified result set; and returning the unified result set to a medical decision system that provides medical recommendations to a system user.
However, in the same field of endeavor of content vectorization Hudson teaches determining a plurality of vectorization algorithms used by the one or more vector DBs; selecting a vectorization algorithm of the plurality of vectorization algorithms based on a frequency of use(Hudson, para 0062 discloses selection vectorization algorithm based on the previous usage “During subsequent operation, intent model 335 may convert each input into a vector embedding, according to the same embedding algorithm, determine the nearest cluster to that vector embedding according to any suitable similarity metric”; para 0062 further discloses plurality of available vectorization algorithms to choose from “embedding algorithms include, without limitation, Word2Vec, Global Vectors for Word Representation (GloVe), Term Frequency and Inverse Document Frequency (TF-IDF), BERT, Doc2Vec, Skip-Thought vector embedding, the Probabilistic Latent Semantic Indexing (PLSI) model, Latent Dirichlet Allocation (LDA)”):
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of vectorization of contents of Hudson into the feature of querying vector database of Masse to produce an expected result of improving the retrieval process. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the system by intelligently selecting the relevant learning model (Hudson, para 0104).
But Masse and Hudson don’t explicitly teach normalizing, based on the vectorization algorithm, one or more of the result records in the result sets to update a normalized result set; generating, based on the normalized result set, a unified result set.
However, in the same field of endeavor of session management Farid teaches normalizing, based on the vectorization algorithm, one or more of the result records in the result sets to update a normalized result set; generating, based on the normalized result set, a unified result set (Farid, para 0110 discloses normalizing/unifying vectors for medical data “The medical data obtained by obtaining module 231 can be used by unified representation module 232 to generate unified representation vectors of the medical data. Unified representation vectors can be generated by the feature extraction module 233 including a plurality of AI models configured for processing the medical data into feature vectors”); storing the unified result set (Farid, para 0110 further discloses storing of unified vector data “AI fusion module 234 configured to fuse the feature vectors into a unified representation vector. The generated unified representation vector can be stored in storage 220”); and returning the unified result set to a medical decision system that provides medical recommendations to a system user(Farid, Fig. 6 & para 0144 discloses providing back the unified vectorized medical data to the user for professional review “retrieving the stored medical data from local clinical data storage 310 (block 650). The medical data can be provided back to the user workstation 240 and can be displayed on display 243 to the professional review (block 660)”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of unifying vectorized medical data and storing it for retrieval of Farid into the feature of querying vector database of Masse and Hudson to produce an expected result of unifying different type of vector representation of patient data into a common format. The modification would be obvious because one of ordinary skill in the art would be motivated to retrieve associated similar medical data from local clinical data storage for providing it to professional review(Farid, para 0144-0145).
Regarding claim 9(Original), Masse, Hudson and Farid teach all the limitations of claim 8 and Farid further teaches wherein normalizing the one or more of the result records in the result sets further comprises generating, using the vectorization algorithm, a new content vector for the content associated with each of the one or more of the result records (Farid, para 0110 further discloses that extracted medical data vectors are getting unified by applying AI(algorithm) “Unified representation vectors can be generated by the feature extraction module 233 including a plurality of AI models configured for processing the medical data into feature vectors, and by AI fusion module 234 configured to fuse the feature vectors into a unified representation vector”).
Regarding claim 10(Original), Masse, Hudson and Farid teach all the limitations of claim 9 and Farid further teaches wherein generating the unified result set based on the normalized result set further comprises(Farid, para 0110 further discloses that extracted medical data vectors are getting unified by applying AI(algorithm) “Unified representation vectors can be generated by the feature extraction module 233 including a plurality of AI models configured for processing the medical data into feature vectors, and by AI fusion module 234 configured to fuse the feature vectors into a unified representation vector”):
Masse teaches ranking the one or more result records in the normalized result set based on relevance to the query to generate a ranked result set; and generating the unified result set based on the ranked result set and one or more parameters (Masse, in para 0022 discloses scoring/ranking the response similarity of vectors (which can similarly be applied to unified/normalized vectors) “providing, based on the comparison of the first similarity score and the second similarity score, the input vector and at least one of (i) the document vector, (ii) the response, or (iii) a combination thereof to a large language model (LLM) to generate response content; and outputting the response content as an answer to the question” ).
Using the broadest reasonable interpretation consistent with the specification (paragraph 0016) as it would be interpreted by one of ordinary skill in the art, examiner is interpreting the limitation “parameters” to mean at least any factor that impacts the relevance similarity determination.
Regarding claim 11(Original), Masse, Hudson and Farid teach all the limitations of claim 8 and Farid further teaches wherein the one or more parameters further comprise a maximum result set size and a minimum set relevance score(Masse, in para 0081 discloses similarity score is considering a parameter such as maximum or minimum relevance “……the machine learning model 404 may return document vectors 412 that satisfy a predetermined threshold (such as a maximum number of document vectors 412 that satisfy the threshold, all of the document vectors 412 that satisfy the threshold”; where para 0092 further discloses threshold can be minimum or maximum “thresholds for making one or more determinations…….comparison that is described as “greater than” may be implemented with a “less than or equal to” condition” ).
Regarding claim 15 (Currently Amended), Masse teaches A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to (Masse, para 0022 discloses a computing system comprising of a processor, memory for storing executable instructions “system that comprises a processor and a memory storing instructions which, when executed by the processor, cause the processor to perform operations”): receive, via a query interface, a query related to a medical topic (Masse, element 310 of Fig. 3 and para 0061 disclose a query is being received by virtual assistant interface “The UI may prompt the entity to enter a question that the virtual assistant application 110 is configured to answer, and responsive to user input”; where prior art Farid discussed later teaches vector database for medical records and querying the database);
transmit the query to one or more vector DBs(Masse, para 0061 further teaches transmitting the query “the entity device 130 may provide (e.g., send or transmit, such as via the one or more networks 160”; para 0080 further teaches sending the query to a vector database ”the machine learning model 404 may be configured to identify one or more document vectors 412 associated with one or more documents 418 that are relevant to the question indicated within the user input 402. To do so, the first machine learning model 404 may be configured to query a document vector database (such as one of the databases 132) to identify the document vector 412 that may be most similar to the input vector 414”);
retrieve result sets from the one or more vector DBs, each result set associated with a respective vector DB of the one or more vector DBs(Masse, para 0081 discloses retrieving vector related result set from vector database “the machine learning model 404 may return document vectors 412 that satisfy a predetermined threshold (such as a maximum number of document vectors 412 that satisfy the threshold, all of the document vectors 412 that satisfy the threshold)”), each result set comprising result records, each result record being associated with content and a respective content vector (Masse, para 0074 discloses providing response or result set in response to user query “the virtual assistant may be configured to answer the input question 310 by retrieving one or more corresponding responses from a database and output at least one of the one or more responses.”);
But Masse does not explicitly teach determine a plurality of vectorization algorithms used by the one or more vector DBs; select a vectorization algorithm of the plurality of vectorization algorithms based on a frequency of use; normalize, based on the vectorization algorithm, one or more of the result records in the result sets to update a normalized result set; generate, based on the normalized result set, a unified result set; store the unified result set; and return the unified result set to a medical decision system that provides medical recommendations to a system user.
However, in the same field of endeavor of content vectorization Hudson teaches determine a plurality of vectorization algorithms used by the one or more vector DBs; select a vectorization algorithm of the plurality of vectorization algorithms based on a frequency of use(Hudson, para 0062 discloses selection vectorization algorithm based on the previous usage “During subsequent operation, intent model 335 may convert each input into a vector embedding, according to the same embedding algorithm, determine the nearest cluster to that vector embedding according to any suitable similarity metric”; para 0062 further discloses plurality of available vectorization algorithms to choose from “embedding algorithms include, without limitation, Word2Vec, Global Vectors for Word Representation (GloVe), Term Frequency and Inverse Document Frequency (TF-IDF), BERT, Doc2Vec, Skip-Thought vector embedding, the Probabilistic Latent Semantic Indexing (PLSI) model, Latent Dirichlet Allocation (LDA)”):
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of vectorization of contents of Hudson into the feature of querying vector database of Masse to produce an expected result of improving the retrieval process. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the system by intelligently selecting the relevant learning model (Hudson, para 0104).
But Masse and Hudson don’t explicitly teach normalize, based on the vectorization algorithm, one or more of the result records in the result sets to update a normalized result set; generate, based on the normalized result set, a unified result set.
However, in the same field of endeavor of session management Farid teaches normalize, based on the vectorization algorithm, one or more of the result records in the result sets to update a normalized result set; generate, based on the normalized result set, a unified result set (Farid, para 0110 discloses normalizing/unifying vectors for medical data “The medical data obtained by obtaining module 231 can be used by unified representation module 232 to generate unified representation vectors of the medical data. Unified representation vectors can be generated by the feature extraction module 233 including a plurality of AI models configured for processing the medical data into feature vectors”); store the unified result set(Farid, para 0110 further discloses storing of unified vector data “AI fusion module 234 configured to fuse the feature vectors into a unified representation vector. The generated unified representation vector can be stored in storage 220”); and return the unified result set to a medical decision system that provides medical recommendations to a system user (Farid, Fig. 6 & para 0144 discloses providing back the unified vectorized medical data to the user for professional review “retrieving the stored medical data from local clinical data storage 310 (block 650). The medical data can be provided back to the user workstation 240 and can be displayed on display 243 to the professional review (block 660)”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of unifying vectorized medical data and storing it for retrieval of Farid into the feature of querying vector database of Masse and Hudson to produce an expected result of unifying different type of vector representation of patient data into a common format. The modification would be obvious because one of ordinary skill in the art would be motivated to retrieve associated similar medical data from local clinical data storage for providing it to professional review(Farid, para 0144-0145).
Regarding claim 16(Original), Masse, Hudson and Farid teach all the limitations of claim 15 and Farid further teaches wherein normalizing the one or more of the result records in the result sets further comprises generating, using the vectorization algorithm, a new content vector for the content associated with each of the one or more of the result records (Farid, para 0110 further discloses that extracted medical data vectors are getting unified by applying AI(algorithm) “Unified representation vectors can be generated by the feature extraction module 233 including a plurality of AI models configured for processing the medical data into feature vectors, and by AI fusion module 234 configured to fuse the feature vectors into a unified representation vector”).
Regarding claim 17(Original), Masse, Hudson and Farid teach all the limitations of claim 16 and Farid further teaches wherein generating the unified result set based on the normalized result set further comprises(Farid, para 0110 further discloses that extracted medical data vectors are getting unified by applying AI(algorithm) “Unified representation vectors can be generated by the feature extraction module 233 including a plurality of AI models configured for processing the medical data into feature vectors, and by AI fusion module 234 configured to fuse the feature vectors into a unified representation vector”):
Masse teaches ranking the one or more result records in the normalized result set based on relevance to the query to generate a ranked result set; and generating the unified result set based on the ranked result set and one or more parameters (Masse, in para 0022 discloses scoring/ranking the response similarity of vectors (which can similarly be applied to unified/normalized vectors) “providing, based on the comparison of the first similarity score and the second similarity score, the input vector and at least one of (i) the document vector, (ii) the response, or (iii) a combination thereof to a large language model (LLM) to generate response content; and outputting the response content as an answer to the question” ).
Using the broadest reasonable interpretation consistent with the specification (paragraph 0016) as it would be interpreted by one of ordinary skill in the art, examiner is interpreting the limitation “parameters” to mean at least any factor that impacts the relevance similarity determination.
Regarding claim 18(Original), Masse, Hudson and Farid teach all the limitations of claim 17 and Farid further teaches wherein the one or more parameters further comprise a maximum result set size and a minimum set relevance score (Masse, in para 0081 discloses similarity score is considering a parameter such as maximum or minimum relevance “……the machine learning model 404 may return document vectors 412 that satisfy a predetermined threshold (such as a maximum number of document vectors 412 that satisfy the threshold, all of the document vectors 412 that satisfy the threshold”; where para 0092 further discloses threshold can be minimum or maximum “thresholds for making one or more determinations…….comparison that is described as “greater than” may be implemented with a “less than or equal to” condition” ).
Claim 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Masse, Christopher et al (PGPUB Document No. 20250272581), hereafter referred as to “Masse”, in view of Hudson, Micheal et al (PGPUB Document No. 20250390786), hereafter, referred to as “Hudson”, in view of Farid, Maor et al (PGPUB Document No. 20230290451), hereafter, referred to as “Farid”, in further view of Bonnell, Phillip (US Patent No. 10860396), hereafter, referred to as “Bonnell”.
Regarding claim 5(Original), Masse, Hudson and Farid teach all the limitations of claim 1 but don’t explicitly teach wherein the operations further comprise: upon detecting that the vectorization algorithm matches a local vectorization algorithm of a vector DB of the one or more vector DBs, add result records of a result set retrieved from the vector DB to the normalized result set.
However, in the same field of endeavor of feature embedding Bonnell teaches wherein the operations further comprise: upon detecting that the vectorization algorithm matches a local vectorization algorithm of a vector DB of the one or more vector DBs, add result records of a result set retrieved from the vector DB to the normalized result set(Bonnell, col 17:38~54 discloses matching local (message) vector to vector DBs (group vector) and if found similar then add vector “the message vector may be compared with one or more group vectors to determine a similarity score that represents how close the message vector matches each group vector………. if a message vector may be determined to be similar to a group vector, the message vector may be added (component-wise) to that group vector” ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of added similar vector in a same database/group of Bonnell into the feature of querying vector database of Masse, Hudson and Farid to produce an expected result of having similar vector type together. The modification would be obvious because one of ordinary skill in the art would be motivated to improve by treating the newly added vectors as a known class rather than having to classify new vectors using different classifier/algorithm(Bonnell, col 21: 1~5).
Regarding claim 12(Original), Masse, Hudson and Farid teach all the limitations of claim 8 but don’t explicitly teach further comprising: upon detecting that the vectorization algorithm matches a local vectorization algorithm of a vector DB of the one or more vector DBs, adding result records of a result set retrieved from the vector DB to the normalized result set.
However, in the same field of endeavor of feature embedding Bonnell teaches further comprising: upon detecting that the vectorization algorithm matches a local vectorization algorithm of a vector DB of the one or more vector DBs, adding result records of a result set retrieved from the vector DB to the normalized result set (Bonnell, col 17:38~54 discloses matching local (message) vector to vector DBs (group vector) and if found similar then add vector “the message vector may be compared with one or more group vectors to determine a similarity score that represents how close the message vector matches each group vector………. if a message vector may be determined to be similar to a group vector, the message vector may be added (component-wise) to that group vector” ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of added similar vector in a same database/group of Bonnell into the feature of querying vector database of Masse, Hudson and Farid to produce an expected result of having similar vector type together. The modification would be obvious because one of ordinary skill in the art would be motivated to improve by treating the newly added vectors as known class rather than having to classify new vectors using different classifier/algorithm(Bonnell, col 21: 1~5).
Regarding claim 19(Original), Masse, Hudson and Farid teach all the limitations of claim 15 but don’t explicitly teach wherein the instructions further cause the computer to: upon detecting that the vectorization algorithm matches a local vectorization algorithm of a vector DB of the one or more vector DBs, add result records of a result set retrieved from the vector DB to the normalized result set.
However, in the same field of endeavor of feature embedding Bonnell teaches wherein the instructions further cause the computer to: upon detecting that the vectorization algorithm matches a local vectorization algorithm of a vector DB of the one or more vector DBs, add result records of a result set retrieved from the vector DB to the normalized result set(Bonnell, col 17:38~54 discloses matching local (message) vector to vector DBs (group vector) and if found similar then add vector “the message vector may be compared with one or more group vectors to determine a similarity score that represents how close the message vector matches each group vector………. if a message vector may be determined to be similar to a group vector, the message vector may be added (component-wise) to that group vector” ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of added similar vector in a same database/group of Bonnell into the feature of querying vector database of Masse, Hudson and Farid to produce an expected result of having similar vector type together. The modification would be obvious because one of ordinary skill in the art would be motivated to improve by treating the newly added vectors as a known class rather than having to classify new vectors using different classifier/algorithm (Bonnell, col 21: 1~5).
Claim 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Masse, Christopher et al (PGPUB Document No. 20250272581), hereafter referred as to “Masse”, in view of Hudson, Micheal et al (PGPUB Document No. 20250390786), hereafter, referred to as “Hudson”, in view of Farid, Maor et al (PGPUB Document No. 20230290451), hereafter, referred to as “Farid”, in further view of Xu, Wenhua et al(PGPUB Document No. 20200327107), hereafter, referred to as “Xu”.
Regarding claim 6 (Original), Masse, Hudson and Farid teach all the limitations of claim 1 but don’t explicitly teach wherein the operations further comprise: storing, using a cache, a result record associated with content and a new content vector generated using the vectorization algorithm, the cache including a cache index associated with the result record, the cache index associated with the result record corresponding to an original content vector generated based on the content and on a local vectorization algorithm for a vector DB of the one or more vector DBs; upon retrieving an additional result record from the vector DB: determining that an additional content vector associated with the additional result record corresponds to the cache index; retrieving, from the cache, the new content vector associated with the result record and the cache index; and adding the result record and the new content vector to the normalized result set.
However, in the same field of endeavor Xu teaches wherein the operations further comprise: storing, using a cache, a result record associated with content and a new content vector generated using the vectorization algorithm, the cache including a cache index associated with the result record, the cache index associated with the result record corresponding to an original content vector generated based on the content and on a local vectorization algorithm for a vector DB of the one or more vector DBs(Xi, para 0012 discloses storing data (which can be for vector data as well) in a cache index “that the index persistence apparatus writes the index record and the operation for the index record in the cache apparatus to the database includes………. the index persistence apparatus, a database operation statement based on the index record and the operation for the index record”; where prior art Farid in para 0110 discloses vectorization of records ), upon retrieving an additional result record from the vector DB: determining that an additional content vector associated with the additional result record corresponds to the cache index; retrieving, from the cache, the new content vector associated with the result record and the cache index; and adding the result record and the new content vector to the normalized result set(Xi, fig. 1 and para 0038 disclose using cache index adding data to database which can similarly be added to normalized vector data taught by Farid in para 0110 “The data processing apparatus may be configured to: handle access of service software 16, define metadata; generate an index; cache an index in the cache apparatus; and access the database to implement operation such as query, addition, modification, and deletion on data stored in the database. The cache apparatus may be configured to cache an index sent by the data processing apparatus”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of storing records in a cache index of Xu into the feature of querying vector database of Masse, Hudson and Farid to produce an expected result of performing data operations using cache index. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the adding, deleting and querying data operations using corresponding cache index entries(Xi, 0014).
Regarding claim 13(Original), Masse, Hudson and Farid teach all the limitations of claim 8 but don’t explicitly teach further comprising: storing, using a cache, a result record associated with content and a new content vector generated using the vectorization algorithm, the cache including a cache index associated with the result record, the cache index corresponding to an original content vector generated based on the content and on a local vectorization algorithm for a vector DB of the one or more vector DBs; and upon retrieving an additional result record from the vector DB: determining that an additional content vector associated with the additional result record corresponds to the cache index; retrieving, from the cache, the new content vector associated with the result record and the cache index; and adding the result record and the new content vector to the normalized result set.
However in the same field of endeavor Xu teaches further comprising: storing, using a cache, a result record associated with content and a new content vector generated using the vectorization algorithm, the cache including a cache index associated with the result record, the cache index corresponding to an original content vector generated based on the content and on a local vectorization algorithm for a vector DB of the one or more vector DBs (Xi, para 0012 discloses storing data (which can be for vector data as well) in a cache index “that the index persistence apparatus writes the index record and the operation for the index record in the cache apparatus to the database includes………. the index persistence apparatus, a database operation statement based on the index record and the operation for the index record”; where prior art Farid in para 0110 discloses vectorization of records); and upon retrieving an additional result record from the vector DB: determining that an additional content vector associated with the additional result record corresponds to the cache index; retrieving, from the cache, the new content vector associated with the result record and the cache index; and adding the result record and the new content vector to the normalized result set (Xi, fig. 1 and para 0038 disclose using cache index adding data to database which can similarly be added to normalized vector data taught by Farid in para 0110 “The data processing apparatus may be configured to: handle access of service software 16, define metadata; generate an index; cache an index in the cache apparatus; and access the database to implement operation such as query, addition, modification, and deletion on data stored in the database. The cache apparatus may be configured to cache an index sent by the data processing apparatus”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of storing records in a cache index of Xu into the feature of querying vector database of Masse, Hudson and Farid to produce an expected result of performing data operations using cache index. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the adding, deleting and querying data operations using corresponding cache index entries(Xi, 0014).
Claim 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Masse, Christopher et al (PGPUB Document No. 20250272581), hereafter referred as to “Masse”, in view of Hudson, Micheal et al (PGPUB Document No. 20250390786), hereafter, referred to as “Hudson”, in view of Farid, Maor et al (PGPUB Document No. 20230290451), hereafter, referred to as “Farid”, in view of Xu, Wenhua et al(PGPUB Document No. 20200327107), hereafter, referred to as “Xu”, in further view of Peterke, Detmar (PGPUB Document No. 20180357252), hereafter, referred to as “Peterke”.
Regarding claim 7 (Currently Amended), Masse, Hudson, Farid and Xi teach all the limitations of claim 6 but don’t explicitly teach wherein cache entries in the cache are associated with timestamps; and wherein the operations further comprise deleting the cache entries based on a recency threshold and the timestamps.
However, in the same field of endeavor of caching records Peterke teaches wherein cache entries in the cache are associated with timestamps; and wherein the operations further comprise deleting the cache entries based on a recency threshold and the timestamps(Peterke. Para 0027 discloses cache entries are having timestamps and cleaning the cache based on timestamp threshold “the client application can scan the file cache at startup and identify and delete any files in the cache that are older than some timestamp or threshold age, such as, for example, one week, two weeks, or more”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of cleaning up the contents in cache based on their timestamp/age in the cache of Peterke into the feature of querying vector database of Masse, Hudson, Farid and Xu to produce an expected result of making available space in the cache. The modification would be obvious because one of ordinary skill in the art would be motivated to have space in faster memory such as cache by cleaning up unnecessary content in the cache (Peterke. Para 0027).
Regarding claim 14(Currently Amended), Masse, Hudson, Farid and Xi teach all the limitations of claim 13 but don’t explicitly teach wherein cache entries in the cache are associated with timestamps, and wherein the method further comprises deleting the cache entries based on a recency threshold and the timestamps.
However, in the same field of endeavor of caching records Peterke teaches wherein cache entries in the cache are associated with timestamps, and wherein the method further comprises deleting the cache entries based on a recency threshold and the timestamps(Peterke. Para 0027 discloses cache entries are having timestamps and cleaning the cache based on timestamp threshold “the client application can scan the file cache at startup and identify and delete any files in the cache that are older than some timestamp or threshold age, such as, for example, one week, two weeks, or more”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of cleaning up the contents in cache based on their timestamp/age in the cache of Peterke into the feature of querying vector database of Masse, Hudson, Farid and Xu to produce an expected result of making available space in the cache. The modification would be obvious because one of ordinary skill in the art would be motivated to have space in faster memory such as cache by cleaning up unnecessary content in the cache (Peterke. Para 0027).
Claim 20, cancelled.
Claim 21 rejected under 35 U.S.C. 103 as being unpatentable over Masse, Christopher et al (PGPUB Document No. 20250272581), hereafter referred as to “Masse”, in view of Hudson, Micheal et al (PGPUB Document No. 20250390786), hereafter, referred to as “Hudson”, in view of Farid, Maor et al (PGPUB Document No. 20230290451), hereafter, referred to as “Farid”, in further view of Pilitsis, Julie et al (PGPUB Document No. 20240282459), hereafter, referred to as “Pilitsis”.
Regarding claim 21 (New), Masse, Hudson and Farid teach all the limitations of claim 1 and Hudson further teaches wherein selecting the vectorization algorithm of the plurality of vectorization algorithms based on the frequency of use comprises(Hudson, para 0062 discloses selection vectorization algorithm based on the previous usage “During subsequent operation, intent model 335 may convert each input into a vector embedding, according to the same embedding algorithm, determine the nearest cluster to that vector embedding according to any suitable similarity metric”; para 0062 further discloses plurality of available vectorization algorithms to choose from “embedding algorithms include, without limitation, Word2Vec, Global Vectors for Word Representation (GloVe), Term Frequency and Inverse Document Frequency (TF-IDF), BERT, Doc2Vec, Skip-Thought vector embedding, the Probabilistic Latent Semantic Indexing (PLSI) model, Latent Dirichlet Allocation (LDA)”):
But Masse, Hudson and Farid don’t explicitly teach determine which vectorization algorithm of the plurality of vectorization algorithms is used most frequently by the one or more vector DBs; and select the vectorization algorithm used most frequently.
However, in the same field of endeavor of algorithm Pilitsis teaches determine which vectorization algorithm of the plurality of vectorization algorithms is used most frequently by the one or more vector DBs; and select the vectorization algorithm used most frequently (Pilitsis, para 0024 discloses selection of most frequently used algorithm “The K-means algorithm was used to discover patient subgroups from a mere data-driven perspective. The K-means algorithm is one of the simplest and most frequently used clustering algorithms. The K-Means clustering uses a simple iterative technique to group points in a dataset into clusters that contain similar characteristics. Initially, a specific number of clusters (K) are decided” ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of selecting most frequently used algorithm of Pilitsis into the feature of querying vector database of Masse, Hudson and Farid to produce an expected result of having similar vector type together clustering. The modification would be obvious because one of ordinary skill in the art would be motivated to implement an algorithm which is simplest to use(Pilitsis, para 0024).
Response to Arguments
I. 35 U.S.C §103
Applicant’s arguments filed on 12/31/2025 have been fully considered but are
moot because the independent claim 1, 8 and 15 have been amended with newly added
features which applicant’s arguments are directed towards. Since claims have been amended with new features, a new ground of rejection is presented.
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.
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/ABDULLAH A DAUD/Examiner, Art Unit 2164
/AMY NG/Supervisory Patent Examiner, Art Unit 2164
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