DETAILED ACTION
Notice of 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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
The Independent claims 1, 7 and 13 recite “obtaining content pieces included in the domain-specific store pertaining to a particular domain”; “for each content piece, identifying domain-specific terms in a respective content piece”; “obtaining domain-adapted embedding vectors corresponding to the domain- specific terms as identified in the content piece”, “and generating a generic embedding vector corresponding to the respective content piece using a large language model”, “and combining the generic embedding vector with the domain-adapted embedding vectors to provide a combined domain-adapted embedding vector for the respective content piece”; “and storing the content pieces indexed with combined domain-adapted embedding vectors into the domain-specific vector search database to be used for executing semantic searching”. The limitations above as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process, as this could be performed in the human mind or with the aid of pen and paper.
The limitation of "obtaining ... ",” identifying…“, ” generating…”, “combining…”, “storing…” as drafted covers mental activities. More specifically, a human can receive a content piece which can be an input phrase or sentence, can identify the specific domain or area for those phrase or sentence, can generate an embedded vector which can be a number or format or certain way to represent the phrase in certain domain, can generate a generic embedding vector corresponding to the input phrase or sentence by using certain knowledge repository, which can be a human or book and by previously identified similar vectors and combine the generic and previously obtained embedding vector to create a domain specific vector for the input and store it to be used in semantic search. The above steps, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind. There is, nothing in the claim element precludes the step from practically being performed in the human mind. Additionally, the mere nominal recitation of a generic computer appliance does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
The claims recites the additional limitation of “large language model”, for performing the method, which is recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Throughout the specification LLM or large language model is recited as generic component and not sufficient to amount to significantly more than the judicial exception. Claim 7 recites additional limitation of “processor” and “ non transitory computer readable storage medium”, claim 13 recites additional limitation of “ computing device” and “computer readable storage device” for performing the method. All those are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. This is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. There is no indication
that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claims 1, 7 and 13 are therefore not drawn to eligible subject matter as this is directed to an abstract idea without
significantly more than the abstract idea.
Claims 2, 8 and 14 recite “wherein obtaining the domain-adapted embedding vector comprises obtaining the domain-adapted embedding vector from a domain-specific dictionary”, obtaining the vector from a domain specific library or dictionary, is an observation, evaluation, could be performed in the human mind or with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 2, 8, 14 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claims 3, 9, 15 recite “wherein the domain-specific dictionary is built from a log of executed queries gathered from a user application, wherein the executed queries are gathered based on evaluation of metrics defining a criterion for a type of actions included in the respective queries”, to determine that the domain specific dictionary is built from the log of executed queries, is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 3, 9, 15 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claims 4, 10, 16 recite “receiving a request for a semantic query at the domain-specific store, the request including query text”; “obtaining an embedding vector for the query text for use in executing a semantic searching”; “and providing the embedding vector for the query text for searching the domain-specific vector search database to identify one or more content pieces of the content pieces stored with domain- adapted embedding vectors that match to the domain-adapted embedding vector for the query text ”, to find a similar embedding vector from the domain specific dictionary or library, corresponding to the input query text, could be performed in the human mind or with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 4, 10, 16 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claim 5, 11, 17 recite “wherein obtaining the embedding vector for the query text comprises: computing the embedding vector as a domain-adapted embedding vector based on combining a generic embedding vector generated for the query text and one or more domain- adapted embedding vector for one or more domain-specific terms found within the query text”, determining the embedding vector for the query text can be done by combining the generic embedding vector for the query text and some other domain specific vector within the query text and could be performed with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 5, 11, 17 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claim 6, 12, 18 recite “executing a search at the domain-specific vector search database is based on a similarity calculation to compute similarities between the domain-adapted embedding vector and each of the embedding vectors in the domain-specific vector search database to determine the match”, to determine that the search is based on a similarity calculation between the vectors is an observation, evaluation, could be performed in the human mind or with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 6, 12, 18 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Double Patenting
The non-statutory 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 non-statutory 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).
The filing of a terminal disclaimer by itself is not a complete reply to a non-statutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based e-Terminal Disclaimer may be filled out completely online using web-screens. An e-Terminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about e-Terminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 2, 7, 8, 13, 14 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 21, 30 and 37 of co pending U.S. Application No. 18,673,224, in view of Osuala (as rejected in the prior art rejections below). The claims 21, 30 and 37 of the co pending U.S. Application No. 18,673,224 and claims 1, 2, 7, 8, 13, 14 of the instant application are the same except for the limitation of “ storing the content pieces indexed with combined domain-adapted embedding vectors into the domain-specific vector search database to be used for executing semantic searching”. Osuala does disclose the mentioned limitation in column 12, lines 2-4, column 14, lines 54-57, where the representative vectors may be generated such that the first entries may each indicate an index of one of the textual elements according to the list of indexed textual elements of the word embedding model 312. In Fig. 2, the step 222 of the text searching process 200, the first word embedding is stored in a storage. This storage may occur in a storage of the computer 102 or in the server 112 or in another computer or another server to be used for semantic searching. It would have been obvious to one of ordinary skilled in the art to have modified the co pending application by incorporating the system and method of semantic searching using concatenated word and context, taught by Osuala to have an improved searching which helps the search engine to recognize semantically similar words and concepts. (see Osuala, column 2, lines 12-33). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 2, 7, 8, 13, 14 of the instant application similar in scope and content of the claims 21, 30 and 37 of the co-pending application no 18,673,224 from the same Applicant.
Application No: 18/807,518
Application No: 18/673,224
1.A computer-implemented method for building a domain-specific vector search database for a domain-specific store, the domain-specific vector search database comprising embedding vectors corresponding to domain-specific terms, the method comprising:
obtaining content pieces included in the domain-specific store pertaining to a particular domain;
for each content piece, identifying domain-specific terms in a respective content piece;
obtaining domain-adapted embedding vectors corresponding to the domain- specific terms as identified in the content piece,
and generating a generic embedding vector corresponding to the respective content piece using a large language model,
and combining the generic embedding vector with the domain-adapted embedding vectors to provide a combined domain-adapted embedding vector for the respective content piece;
and storing the content pieces indexed with combined domain-adapted embedding vectors into the domain-specific vector search database to be used for executing semantic searching.
21. A computer implemented method, the method comprising:
receiving an input phrase for generating a domain-adapted embedding vector for the phrase;
scanning the input phrase to identify one or more domain-specific terms from the input phrase that are included in a domain-specific dictionary;
obtaining, from the domain-specific dictionary, one or more domain-adapted embedding vectors for each domain-specific term of the one or more domain-specific terms from the domain- specific dictionary;
generating a generic embedding vector for the input phrase using a large language model,
wherein the generic embedding vector is configured for storage in a domain-specific dictionary and subsequent use in semantic query processing;
and combining the generic embedding vector with the one or more domain-adapted embedding vectors to provide the domain-adapted embedding vector for the input phrase.
2.The method of claim 1, wherein obtaining the domain-adapted embedding vector comprises obtaining the domain-adapted embedding vector from a domain-specific dictionary.
21. obtaining, from the domain-specific dictionary, one or more domain-adapted embedding vectors for each domain-specific term of the one or more domain-specific terms from the domain- specific dictionary;
3.The method of claim 2, wherein the domain-specific dictionary is built from a log of executed queries gathered from a user application, wherein the executed queries are gathered based on evaluation of metrics defining a criterion for a type of actions included in the respective queries.
4.The method of claim 1, the method comprising: receiving a request for a semantic query at the domain-specific store, the request including query text;
obtaining an embedding vector for the query text for use in executing a semantic searching;
and providing the embedding vector for the query text for searching the domain-specific vector search database to identify one or more content pieces of the content pieces stored with domain- adapted embedding vectors that match to the domain-adapted embedding vector for the query text.
5.The method of claim 4, wherein obtaining the embedding vector for the query text comprises: computing the embedding vector as a domain-adapted embedding vector based on combining a generic embedding vector generated for the query text and one or more domain- adapted embedding vector for one or more domain-specific terms found within the query text.
6. The method of claim 5, the method comprising executing a search at the domain-specific vector search database is based on a similarity calculation to compute similarities between the domain-adapted embedding vector and each of the embedding vectors in the domain-specific vector search database to determine the match.
7. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
obtaining content pieces included in the domain-specific store pertaining to a particular domain;
for each content piece, identifying domain-specific terms in a respective content piece;
obtaining domain-adapted embedding vectors corresponding to the domain- specific terms as identified in the content piece,
and generating a generic embedding vector corresponding to the respective content piece using a large language model,
and combining the generic embedding vector with the domain-adapted embedding vectors to provide a combined domain-adapted embedding vector for the respective content piece;
and storing the content pieces indexed with combined domain-adapted embedding vectors into the domain-specific vector search database to be used for executing semantic searching.
30. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
receiving an input phrase for generating a domain-adapted embedding vector for the phrase;
scanning the input phrase to identify one or more domain-specific terms from the input phrase that are included in a domain-specific dictionary;
obtaining, from the domain-specific dictionary, one or more domain-adapted embedding vectors for each domain-specific term of the one or more domain-specific terms from the domain- specific dictionary;
generating a generic embedding vector for the input phrase using a large language model,
and combining the generic embedding vector with the one or more domain-adapted embedding vectors to provide the domain-adapted embedding vector for the input phrase.
8. The non-transitory computer-readable storage medium of claim 7, wherein obtaining the domain-adapted embedding vector comprises obtaining the domain-adapted embedding vector from a domain-specific dictionary.
30. obtaining, from the domain-specific dictionary, one or more domain-adapted embedding vectors for each domain-specific term of the one or more domain-specific terms from the domain- specific dictionary;
9.The non-transitory computer-readable storage medium of claim 8, wherein the domain-specific dictionary is built from a log of executed queries gathered from a user application, wherein the executed queries are gathered based on evaluation of metrics defining a criterion for a type of actions included in the respective queries.
10. The non-transitory computer-readable storage medium of claim 9, the method comprising: receiving a request for a semantic query at the domain-specific store, the request including query text;
obtaining an embedding vector for the query text for use in executing a semantic searching;
and providing the embedding vector for the query text for searching the domain-specific vector search database to identify one or more content pieces of the content pieces stored with domain- adapted embedding vectors that match to the domain-adapted embedding vector for the query text.
11. The non-transitory computer-readable storage medium of claim 10, wherein obtaining the embedding vector for the query text comprises: computing the embedding vector as a domain-adapted embedding vector based on combining a generic embedding vector generated for the query text and one or more domain- adapted embedding vector for one or more domain-specific terms found within the query text.
12. The non-transitory computer-readable storage medium of claim 7, the method comprising executing a search at the domain-specific vector search database is based on a similarity calculation to compute similarities between the domain-adapted embedding vector and each of the embedding vectors in the domain-specific vector search database to determine the match.
13. A system, comprising: a computing device; and a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations, the operations comprising:
obtaining content pieces included in the domain-specific store pertaining to a particular domain;
for each content piece, identifying domain-specific terms in a respective content piece;
obtaining domain-adapted embedding vectors corresponding to the domain- specific terms as identified in the content piece,
and generating a generic embedding vector corresponding to the respective content piece using a large language model,
and combining the generic embedding vector with the domain-adapted embedding vectors to provide a combined domain-adapted embedding vector for the respective content piece;
and storing the content pieces indexed with combined domain-adapted embedding vectors into the domain-specific vector search database to be used for executing semantic searching.
37. A system, comprising: a computing device; and a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations, the operations comprising:
receiving an input phrase for generating a domain-adapted embedding vector for the phrase;
scanning the input phrase to identify one or more domain-specific terms from the input phrase that are included in a domain-specific dictionary;
obtaining, from the domain-specific dictionary, one or more domain-adapted embedding vectors for each domain-specific term of the one or more domain-specific terms from the domain- specific dictionary;
generating a generic embedding vector for the input phrase using a large language model,
and combining the generic embedding vector with the one or more domain-adapted embedding vectors to provide the domain-adapted embedding vector for the input phrase.
14. The system of claim 13, wherein obtaining the domain-adapted embedding vector comprises obtaining the domain-adapted embedding vector from a domain-specific dictionary.
37. obtaining, from the domain-specific dictionary, one or more domain-adapted embedding vectors for each domain-specific term of the one or more domain-specific terms from the domain- specific dictionary;
15. The system of claim 14, wherein the domain-specific dictionary is built from a log of executed queries gathered from a user application, wherein the executed queries are gathered based on evaluation of metrics defining a criterion for a type of actions included in the respective queries.
16. The system of claim 13, the method comprising: receiving a request for a semantic query at the domain-specific store, the request including query text;
obtaining an embedding vector for the query text for use in executing a semantic searching;
and providing the embedding vector for the query text for searching the domain-specific vector search database to identify one or more content pieces of the content pieces stored with domain- adapted embedding vectors that match to the domain-adapted embedding vector for the query text.
17. The system of claim 16, wherein obtaining the embedding vector for the query text comprises: computing the embedding vector as a domain-adapted embedding vector based on combining a generic embedding vector generated for the query text and one or more domain- adapted embedding vector for one or more domain-specific terms found within the query text.
18. The system of claim 13, the method comprising executing a search at the domain-specific vector search database is based on a similarity calculation to compute similarities between the domain-adapted embedding vector and each of the embedding vectors in the domain-specific vector search database to determine the match.
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 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 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.
Claims 1, 2, 4-8, 10-14 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Osuala et al. ( US 11983208 B2), hereinafter referenced as Osuala, in view of Ling et al. ( US 20210232768 A1), hereinafter referenced as Ling, further in view of Batina et al. (US 20240289361 A1), hereinafter referenced as Batina.
Regarding Claim 1, Osuala teaches a computer-implemented method for building a domain-specific vector search database for a domain-specific store, the domain-specific vector search database comprising embedding vectors corresponding to domain-specific terms, the method comprising:
obtaining content pieces included in the domain-specific store pertaining to a particular domain ( Osuala: Column 3, lines 32-44, column 5, lines 4-20, 43-47, Figs. 1, 2, 3 illustrate a computing device 102, text searching program 110a, 110b which may interact with database 114. In the step 202 of the text searching process 200, a text for a search query is received, which is a medical journal article 302 ( domain specific, pertaining to medical domain). Column 6, lines 33-38, The processor 104 may access a technical or specialized dictionary which may include the technical
vocabulary of a certain field of research, for example general medicine or oncology or gynecology) ;
obtaining domain-adapted embedding vectors corresponding to the domain- specific terms as identified in the content piece ( Osuala: Column 8, lines 63-67, column 9, lines 1- 15, Figs 2, 3, In the step 218 of the text searching process 200, the sequence 310 is input into a word embedding model 312 to obtain embedding vector. Column 13, lines 39-44, column 14, lines 16-17, Fig.4 illustrates a deep neural network 404 implemented as part of the word embedding model 312. The deep neural network 404 may be a transformer neural network. A query word embedding 412b is generated as output of the deep neural network 404 ),
and generating a generic embedding vector corresponding to the respective content piece [using a large language model] ( Osuala: Column 13, lines 39-42, column 14, lines 22-24, Fig. 4, generic or additional query embedding vector is generated from the multiple words included in the query text by transformer neural network 404),
and combining the generic embedding vector with the domain-adapted embedding vectors to provide a combined domain-adapted embedding vector for the respective content piece ( Osuala: Column 14, lines 24-33, Fig. 4, the final output query embedding may be formed from combining the query word embedding 412b and the additional query word embedding 412a);
and storing the content pieces indexed with combined domain-adapted embedding vectors into the domain-specific vector search database to be used for executing semantic searching ( Osuala: Column 12, lines 2-4, the representative vectors may be generated such that the first entries may each indicate an index of one of the textual elements according to the list of indexed textual elements of the word embedding model 312. Column 14, lines 54-57, Figs. 1,2, In the step 222 of the text searching process 200, the first word embedding is stored in a storage. This storage may occur in a storage of the computer 102 or in the server 112 or in another computer or another server);
Osuala while teaching the method of claim 1, fails to explicitly teach the claimed, for each content piece, identifying domain-specific terms in a respective content piece; [and generating a generic embedding vector corresponding to the respective content piece] using a large language model.
However, Ling does teach the claimed, for each content piece, identifying domain-specific terms in a respective content piece ( Ling: Para.[0072], Fig. 2, the phrase "prostate cancer" is mapped in TRIE dictionary 220, which is built from domain specific vocabulary database 210 and the dictionary 220 is queried when the input sentence with input phrase is received);
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 Ling’s teaching of a machine learning model with evolving domain-specific lexicon features for natural language processing, into the system and method of semantic searching using concatenated word and context, taught by Osuala, because, specific domain knowledge would improve the search engine to accurately index annotations such that user can easily find relevant articles to enhance their knowledge .(Ling, Para.[0002], [0061]).
Osuala in view of Ling, while teaching the method of claim 1, fails to explicitly teach the claimed, [and generating a generic embedding vector corresponding to the respective content piece] using a large language model.
However, Batina does teach the claimed, and generating a generic embedding vector corresponding to the respective content piece using a large language model ( Batina: Para.[0064], Fig. 6, generating embedding vector 60 by using large language model, which is a learned numerical representation of a token that captures some semantic meaning of the text segment represented by the token 56).
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 Batina’s teaching of search engine technology and, more particularly, to search techniques that leverage use of large language models (LLMs ), into the system and method, taught by Osuala in view of Ling, because, the use of LLM in processing user inputs and performing vector search by generating data that complements/ enhances user input would improve the search technology (Batina, Para.[0033],[0034]).
Claim 7 is non-transitory computer-readable storage medium claim coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to ( Osuala: Column 22, lines 28-49, column 23, lines 29-53, Fig. 7, processor 706, one or more computer-readable RAMs 708 and one or more computer-readable ROMs 710 on one or more buses 712, and one or more operating systems 714 and one or more computer-readable tangible storage devices 716, that can retain and store instructions for use by a processor), performing the steps in method claim 1 above and as such, claim 7 is similar in scope and content to claim 1 and therefore, claim 7 is rejected under similar rationale as presented against claim 1 above.
Claim 13 is system claim comprising: a computing device; and a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to ( Osuala: Column 22, lines 28-49, column 23, lines 29-53, Fig. 7, computer 102, server 112, processor 706, one or more computer-readable RAMs 708 and one or more computer-readable ROMs 710 on one or more buses 712, and one or more operating systems 714 and one or more computer-readable tangible storage devices 716, that can retain and store instructions for use by an instruction execution device), performing the steps in method claim 1 above and as such, claim 13 is similar in scope and content to claim 1 and therefore, claim 13 is rejected under similar rationale as presented against claim 1 above.
Regarding Claim 2, Osuala in view of Ling, further in view of Batina teach the method of claim 1. Osuala further teaches, wherein obtaining the domain-adapted embedding vector comprises obtaining the domain-adapted embedding vector from a domain-specific dictionary ( Osuala: Column 11, lines 30-33, 55-65, Fig. 3, The discrete representation of the words of the search text may be in the form of respective numbers for the words of the text, may be in the form of a representative vector. The representative vectors may be obtained by using a dictionary of the word embedding model 312).
Claim 8 is non-transitory computer-readable storage medium claim performing the steps in method claim 2 above and as such, claim 8 is similar in scope and content to claim 2 and therefore, claim 8 is rejected under similar rationale as presented against claim 2 above.
Claim 14 is system claim performing the steps in method claim 2 above and as such, claim 14 is similar in scope and content to claim 2 and therefore, claim 14 is rejected under similar rationale as presented against claim 2 above.
Regarding Claim 4, Osuala in view of Ling, further in view of Batina teach the method of claim 1. Osuala further teaches, the method comprising: receiving a request for a semantic query at the domain-specific store, the request including query text ( Osuala: Column 5, lines 16-20, column 8, lines 3-12, Fig. 2, at step 202 of the text searching process 200, a text for search query is received. At step 206, words are selected surrounding the query. The context, and the order of the words in the context, with respect to each other and with respect to the query word or words may provide information regarding the semantic meaning of the query);
obtaining an embedding vector for the query text for use in executing a semantic searching ( Osuala: Column 8, lines 63-67, column 9, lines 1- 15, Figs 2, 3, In the step 218 of the text searching process 200, the sequence 310 is input into a word embedding model 312 to obtain embedding vector. Column 13, lines 39-44, column 14, lines 16-17, Fig.4 illustrates a deep neural network 404 implemented as part of the word embedding model 312. The deep neural network 404 may be a transformer neural network. A query word embedding 412b is generated as output of the deep neural network 404 );
and providing the embedding vector for the query text for searching the domain-specific vector search database to identify one or more content pieces of the content pieces stored with domain- adapted embedding vectors that match to the domain-adapted embedding vector for the query text (Osuala: Column 18, lines 46-58, Fig. 2, in the step 224 of the text searching process 200, the first word embedding from step 222 ( storage database) is compared with the individual other word embeddings. Column 20, lines 30-50, Fig. 2, in the step 226, the similarities of the first word embedding as compared to the other word embeddings are ranked. in the step 228, a candidate match of the other texts is presented that is most similar to the first word embedding).
Claim 10 is non-transitory computer-readable storage medium claim performing the steps in method claim 4 above and as such, claim 10 is similar in scope and content to claim 4 and therefore, claim 10 is rejected under similar rationale as presented against claim 4 above.
Claim 16 is system claim performing the steps in method claim 4 above and as such, claim 16 is similar in scope and content to claim 4 and therefore, claim 16 is rejected under similar rationale as presented against claim 4 above.
Regarding Claim 5, Osuala in view of Ling, further in view of Batina teach the method of claim 4. Osuala further teaches, wherein obtaining the embedding vector for the query text comprises: computing the embedding vector as a domain-adapted embedding vector based on combining a generic embedding vector generated for the query text and one or more domain- adapted embedding vector for one or more domain-specific terms found within the query text (Osuala: Column 14, lines 24-33, Fig. 4, the final output query embedding may be formed from combining the query word embedding 412b and the additional query word embedding 412a ( generic))
Claim 11 is non-transitory computer-readable storage medium claim performing the steps in method claim 5 above and as such, claim 11 is similar in scope and content to claim 5 and therefore, claim 11 is rejected under similar rationale as presented against claim 5 above.
Claim 17 is system claim performing the steps in method claim 5 above and as such, claim 17 is similar in scope and content to claim 5 and therefore, claim 17 is rejected under similar rationale as presented against claim 5 above.
Regarding Claim 6, Osuala in view of Ling, further in view of Batina teach the method of claim 5. Osuala further teaches, the method comprising executing a search at the domain-specific vector search database is based on a similarity calculation to compute similarities between the domain-adapted embedding vector and each of the embedding vectors in the domain-specific vector search database to determine the match (Osuala: Column 18, lines 46-67, column 19, lines 1-18, Fig. 2, in the step 224 of the text searching process 200, the first word embedding from step 222 ( storage database) is compared with the individual other word embeddings. The performing of the comparison may include calculating a measure for assessing a similarity ( such as cosine distance) between the query word embedding or vector of the selected word or the query word and the word embedding or vector generated from the text portions of the other texts. The measures for the various other texts may then be compared, to determine which of the other texts is most similar to the search text).
Claim 12 is non-transitory computer-readable storage medium claim performing the steps in method claim 6 above and as such, claim 12 is similar in scope and content to claim 6 and therefore, claim 12 is rejected under similar rationale as presented against claim 6 above.
Claim 18 is system claim performing the steps in method claim 6 above and as such, claim 18 is similar in scope and content to claim 6 and therefore, claim 18 is rejected under similar rationale as presented against claim 6 above.
Claims 3, 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Osuala et al. ( US 11983208 B2), hereinafter referenced as Osuala, in view of Ling et al. ( US 20210232768 A1), hereinafter referenced as Ling, further in view of Batina et al. (US 20240289361 A1), hereinafter referenced as Batina, further in view of Huang et al. (US 20050065773 A1), hereinafter referenced as Huang.
Regarding Claim 3, Osuala in view of Ling, further in view of Batina teach the method of claim 2. Osuala in view of Ling, further in view of Batina fail to explicitly teach the claimed, wherein the domain-specific dictionary is built from a log of executed queries gathered from a user application, wherein the executed queries are gathered based on evaluation of metrics defining a criterion for a type of actions included in the respective queries.
However, Huang does teach the claimed, wherein the domain-specific dictionary is built from a log of executed queries gathered from a user application ( Huang: Para.[0036], Figs. 5, 6, the system database 402 is queried by new query terms, the system accesses the log database 502 (step 600) and places the new terms in a file in the dictionary builder 506 (Step 602). The text analyzer 504 analyzes the query terms in the file (step 604) and engages dictionary builder 506 to associate the query terms in the document with one or more semantic nodes of the semantic taxonomy 400 in the query semantic dictionary 508 (step 606). Para.[0042],[0046], Fig. 9, The dictionary builder 506 can include a sub-module 900 that identifies domain specific terms in a given query, using domain specific glossary 902 relating to the semantic taxonomy ),
wherein the executed queries are gathered based on evaluation of metrics defining a criterion for a type of actions included in the respective queries ( Huang: Para.[0038],[0039], Fig. 8, the search application 804 places the query term 802 into the log files 810. The text analyzer 812 scans through the log files to the find query keywords processed by the search application 804 and calculates how many times a query has been submitted and records that into the log database, along with the query. The query terms are arranged in the query semantic dictionary in order of "most often queried terms" ( evaluation of metrices)).
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 Huang’s teaching of a method of search content enhancement, into the system and method, taught by Osuala in view of Ling, further in view of Batina, because, by enhancing the search content, effectiveness of searches in obtaining desired search results can be improved. (Huang, Para.[0011]).
Claim 9 is non-transitory computer-readable storage medium claim performing the steps in method claim 3 above and as such, claim 9 is similar in scope and content to claim 3 and therefore, claim 9 is rejected under similar rationale as presented against claim 3 above.
Claim 15 is system claim performing the steps in method claim 3 above and as such, claim 15 is similar in scope and content to claim 3 and therefore, claim 15 is rejected under similar rationale as presented against claim 3 above.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant's disclosure.
Ramkumar et al. (US 20220358150 A1) teaches systems and methods which may use natural language processing (NLP) and machine-learning techniques to detect an impact that an event will have on a domain-specific topic. For example, the system may use multi-stage cleaning using a rules-based and an artificial intelligence (AI)-based filter to filter large quantities of event items that may not be relevant to a domain of interest. The AI-based filter may be trained using labeled event items that were previously known to be impactful. The system may cluster the cleaned event items to group similar event items and eliminate redundancy. The system may then predict and quantify the impact that events described by clustered event items will have on the domain-specific topic. Such prediction may be based on a classifier trained using various model features that correlate with impactful events, including prior similar events.
Bakis et al. (US 20180357216 A1) teaches system and method which performs automated domain concept discovery and clustering using word embeddings by receiving a set of documents for natural language processing for a domain, representing a plurality of entries in the set of documents as continuous vectors in a high dimensional continuous space, applying a clustering algorithm based on a mutual information optimization criterion to form a set of clusters, associating each entry of the plurality of entries with each cluster in the set of clusters through formalizing an evidence based model of each cluster given each entry, calculating a mutual information metric between each entry and each cluster using the evidence based model, and identifying a nominal center of each cluster by maximizing the mutual information.
Gong et al. (US 20220138424 A1) teaches a domain-specific phrase mining method, apparatus and electronic device. A specific implementation includes: performing word vector conversion on a domain-specific phrase in a target text to obtain a first word vector, and performing word vector conversion on an unknown phrase in the target text to obtain a second word vector, where the domain-specific phrase is a phrase in a domain to which the target text belongs; obtaining a word vector space formed by the first and second word vectors, and identifying a preset quantity of target word vectors around the second word vector in the word vector space; determining, based on similarity values indicative of similarity between the preset quantity of target word vectors and the second word vector, whether the unknown phrase is a phrase in the domain to which the target text belong..
Gui et al. (CN 107291914 A ) teaches a method for generating search engine query expansion terms, wherein the method comprises the following steps: obtaining network session log, extracting the search word of all network user to obtain query terms training database, using the query term training database word vector model for training, receiving query text and inputting it to trained word vector model, to obtain an expanded word. The invention realizes the query words according to semantic expansion accuracy so as to improve search engine query results and the recall rate, which effectively solves the problem of search query results and result.
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/NADIRA SULTANA/Examiner, Art Unit 2653
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