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 .
Response to Amendment
2. Amendment filed 05/20/2026 has been considered by Examiner. Claims 1, 6, 7 and 12 have been amended. Claims 13 and 14 have been cancelled. Claims 1-4, 6-10 and 12 are pending, and likewise Claims 1-4, 6-10 and 12 have been examined.
Response to Arguments
Applicant’s amendments and arguments filed 05/20/2026, with respect to claim(s) 1-4, 6-10 and 12 have been fully considered. Applicant amended claims 1, 6, 7 and 12.
Claim objections for claim 13 have been withdrawn in view of the amended claims filed on 05/20/2026.
35 U.S.C. 112 rejections of claims 1-4, 6 have been withdrawn in view of the amended claims filed on 05/20/2026.
Applicant’s arguments in pages 1-2, filed 05/20/2026, with respect to 35 U.S.C 101 rejections of claims 1-4, 6-10 and 12 have been fully considered but they are not persuasive. Applicant argued that , “ the claimed invention provides for a technical improvement that is directed toward a document data processing system and a document data processing method, as described at paragraphs [0022]-[0068], in which different distributed representations of words are obtained even for the same word, depending on the text included. Accordingly, a block in a group of documents that is highly relevant to the information required by a user can be found with high precision”. Applicant further argued that “claim 1 recites a technical improvement to solve a specific problem, as described in the specification, encountered in the field of document structural analysis and provides the claimed invention as a solution to the identified document structural analysis problem. Applicant's disclosure clearly describes how this invention offers an improvement over existing document structural analysis methods by providing a particular document structural analysis technique that solves the problem of inefficient document location”. Examiner respectfully disagrees. Throughout the claims, no unique or specialized technique or invention has been provided, which shows the technological improvement. Different steps has been described to calculate cosine similarity of plurality of blocks. It is not clear how calculating the cosine similarity of plurality of blocks solves the problem of inefficient document location as described by the applicant. The claim recites the additional limitation of a “non-transitory computer-readable storage medium”, “language model” for performing the method, which are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The claim as drafted, is not patent eligible. According to MPEP § 2106.05(a), first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"). Therefore, 35 U.S.C 101 rejections of claims 1-4, 6-10 and 12 have been maintained. Please see the rejections below.
Applicant’s arguments filed 05/20/2026, with respect to claim(s) 1-4, 6-10 and 12, under 35 U.S.C. 103 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-4, 6-10 and 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Independent claim 1 recites “A non-transitory computer- readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for document search, the operations comprising: reading out a plurality of subject documents ;“ dividing each of the plurality of subject documents into a plurality of blocks ;” “acquiring [[a]] distributed representations of words in each of the plurality of blocks, using a language model that is learned such that the same word has different distributed representations depending on a context of each of the plurality of blocks with a self- attention function ;” “storing the distributed representation on a subject-document-by-subject-document basis, and on a block-by-block basis ;” “reading out query text ;” “acquiring representations of words included in the query text using a language model ; “storing the distributed representations of words included in the query text;” “words among the plurality of blocks that match a word included in the query text”; “calculating for each matching word that appears in both a block of the plurality of blocks and in the query text, a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the calculating a score of each of the plurality of blocks based on the cosine similarity.
A person can starts reading from a plurality of subject documents, dividing a plurality of subject documents into certain blocks or portions, acquiring/preparing distributed representation of a word based on subject and from the fact that same word can have different meaning based on context, by using language model and attention function, where language model can be some verified source of knowledge such as books or publications or a subject matter expert, self-attention function can be a mathematical formula used to compute weighted relationships between elements in a sequence to determine their context-dependent importance and store them per subject or per paragraph in writing, can read query text, extract words from query text, prepare distributed representation of the words in the query text, can calculate similarities, can search for a word that matches a word in the query text, and calculate cosine similarity to find the most relevant text. The above steps, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “non-transitory computer- readable storage medium”, “processors”, in the preamble, 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 claim recites the additional limitation of “language model”, “server” for performing the method, which is recited at a high level of generality and is 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 element does 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. Claim 1 is therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more than the abstract idea.
The Independent claim 7 recites “A document data processing method comprising the steps of: reading out a plurality of subject documents”; “dividing each of the plurality of subject documents into a plurality of blocks”; “acquiring [[a]] distributed representations of words in each of the plurality of blocks, using a language model that is learned such that the same word has different distributed representations depending on a context of each of the plurality of blocks with a self-attention function”; “storing the distributed representations on a subject-document-by-subject-document basis, and on a block-by-block basis”; “reading out query text”; “representations of words included in the query text using the language model”; storing the distributed representations of the words included in the query text; searching for words among the plurality of blocks that match a word included in the query text; calculating, for each matching word that appears in both a block of the plurality of blocks and in the query text, a cosine similarity between a distributed representation of the word in the block and a distributed representation of the word in the query text”; “and calculating a score of each of the plurality of blocks based on the cosine similarity”.[[;]]
A person can starts reading from a plurality of subject documents, dividing a plurality of subject documents into certain blocks or portions, acquiring/preparing distributed representation of a word based on subject and from the fact that same word can have different meaning based on context, by using language model and attention function, where language model can be some verified source of knowledge such as books or publications or a subject matter expert, self-attention function can be a mathematical formula used to compute weighted relationships between elements in a sequence to determine their context-dependent importance and store them per subject or per paragraph in writing, can read query text, extract words from query text, prepare distributed representation of the words in the query text, can calculate similarities, can search for a word that matches a word in the query text, and calculate cosine similarity to find the most relevant text. The above steps, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind. Thus, the claim recites a mental process.
The claim recites the additional limitation of “language model”, for performing the method, which is recited at a high level of generality and is 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 element does 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. Claim 7 is therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more than the abstract idea.
The dependent claims 2 and 8 recite “wherein each of the plurality of blocks comprises one or a plurality of paragraphs of the subject document”. Determining plurality of blocks are plurality of paragraphs of subject document is evaluation or judgment step that could be performed in the human mind or with the aid of pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 2 and 8 do not recite any additional limitations. The claims as drafted, are not patent eligible.
The dependent claims 3 and 9 recite “wherein each of the plurality of blocks comprises one or a plurality of sentences”. Determining plurality of blocks are composed of plurality of sentences is evaluation or judgment step that could be performed in the human mind or with the aid of pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 3 and 9 do not recite any additional limitations. The claims as drafted, are not patent eligible.
The dependent claims 4 and 10 recite “wherein the cosine similarity calculation is performed with respect to a predetermined part of speech only”. Calculating cosine similarity with respect to a predetermined speech can be done by assigning certain numbers to each word/speech and calculating, which are evaluation and mathematical steps that could be performed in the human mind or with the aid of pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 4 and 10 do not recite any additional limitations. The claims as drafted, are not patent eligible.
The dependent claims 6 and 12 recite “wherein, the score of each of the plurality of blocks is calculated by summing the cosine similarities for each of the plurality of blocks
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 1-3, 6-9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Le et al. (Distributed Representations of Sentences and Documents, arXiv:1405.4053v2[cs.CL] 22 May 2014 ), hereinafter referenced as Le, in view of Young et al. (Recent Trends in Deep Learning Based
Natural Language Processing, arXiv:1708.027098v8[cs.CL] 25 Nov 2018), hereinafter referenced as Young, further in view of Fukuda et al. ( US 10592542 B2), hereinafter referenced as Fukuda.
Regarding Claim 1, Le teaches a non-transitory computer- readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for document search, the operations comprising:
reading out a plurality of subject documents ( Le: Section 3.2, 5th paragraph, experimental protocols, training documents are read);
dividing each of the plurality of subject documents into a plurality of blocks( Le: Section 1, 3rd paragraph, section 3.2, 5th paragraph, documents are divided into paragraph vectors ( plurality of blocks));
acquiring [[a]] distributed representations of words in each of the plurality of blocks, [using a language model that is learned such that the same word has different distributed representations depending on a context of each of the plurality of blocks with a self- attention function] ( Le: Section 2.2, 2nd and 8th paragraph, Fig. 2, distributed representation of word in paragraph vector ( paragraph ID). Words are mapped to q dimensions, which means different distributed representations),
storing the distributed representation on a subject-document-by-subject-document basis, and on a block-by-block basis ( Le: Section 2.2, 2nd, 4th and 5th paragraph, Fig. 2, In the Distributed Memory Model of Paragraph Vectors (PV-DM), paragraph token acts as a memory that remembers the topic of the paragraph (subject-document-by-subject-document basis, and on a block-by-block basis));
reading out query text ( Le: Section 3, 3rd paragraph, a query is given );
searching for words among the plurality of blocks that match a word included in the query text ( Le: Section 3.3, 2nd, 3rd paragraph, search engine can search queries from dataset of paragraphs to match the query);
Le while teaching the non-transitory computer- readable storage medium of claim 1, fails to explicitly teach the claimed, [acquiring [[a]] distributed representations of words in each of the plurality of blocks,] using a language model that is learned such that the same word has different distributed representations depending on a context of each of the plurality of blocks with a self- attention function; acquiring representations of words included in the query text using a language model; storing the distributed representations of the words included in the query text ; , for each matching word that appears in both a block of the plurality of blocks and in the query text, a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the calculating a score of each of the plurality of blocks based on the cosine similarity.
However, Young does teach the claimed, [acquiring [[a]] distributed representations of words in each of the plurality of blocks,] using a language model that is learned such that the same word has different distributed representations depending on a context of each of the plurality of blocks with a self- attention function ( Young: Section II. A, 4th, 5th paragraph, language model which learned distributed representation of words. Section II.D, 2nd, 3rd paragraph, a word can have completely different senses or meanings in the contexts. For example, let’s consider these two sentences - 1) “The bank will not be accepting cash on Saturdays” 2) “The river overflowed the bank”. The word senses of “bank” are different in these two sentences depending on its context. Language model ( ELMo) produces word embeddings for each context where the word is used, thus allowing different representations for varying senses of the same word. Specifically, for N different sentences where a word w is present, ELMo generates N different representations. Section IV. D, 3rd, 9th paragraph, the attention mechanism can be used in NLP tasks such as language modeling, which can be broadly seen as mapping a query and a set of key-value pairs to an output, where all the mentioned components are vectors. The output is a combination of the values whose weights are determined by the compatibility between the query and the corresponding keys. This output amounts to the “context” of the input used in decoding the output. Section VIII. F, 1st paragraph, self-attention based model);
acquiring representations of words included in the query text using a language model ( Young: Section II. A, 4th, 5th paragraph, language model which learned distributed representation of words. Section II.D, 3rd paragraph, Language model ( ELMo) produces word embeddings for each context where the word is used, thus allowing different representations for varying senses of the same word. Specifically, for N different sentences where a word w is present, ELMo generates N different representations).
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 Young’s teaching of recent trends in deep learning based natural language processing, into the methods of distributed representations of sentences and documents, taught by Le, because, by summarizing, comparing and contrasting various models using deep learning, a better understanding of the past, present and future of deep learning in natural language processing can be achieved.( Young, [abstract ).
Le in view of Young while teaching the non-transitory computer- readable storage medium of claim 1, fails to explicitly teach the claimed, storing the distributed representations of the words included in the query text ; , for each matching word that appears in both a block of the plurality of blocks and in the query text, a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the calculating a score of each of the plurality of blocks based on the cosine similarity.
However, Fukuda does teach the claimed, storing the distributed representations of the words included in the query text (Fukuda: Column 10, lines 3-7, Fig. 2, data processing system 200 is generally configured with flash memory to provide non-volatile memory for storing operating system files and/or user generated data);
calculating, for each matching word that appears in both a block of the plurality of blocks and in the query text, a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the (Fukuda: Column 5, lines 49-63, column 11, lines 46-67, column 12, lines 1-17, Fig. 6A, a context vector of a keyword from the NL query can be compared with one or more context vectors of the same or similar keyword from a result. Through this comparison, the embodiment produces a similarity value for the result, which can be cosine similarity);
and calculating a score of each of the plurality of blocks based on the cosine similarity. ( Fukuda: Column 12, lines 18-33, Fig. 6D, Context vectors of the same or similar keywords are compared for similarities, and a document similarity score-Document X score-is computed. Column 5, lines 61-63, cosine similarity method can be used to determine similarity).
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 Fukuda’s teaching of document ranking by contextual vectors from natural language query, into the methods of distributed representations of sentences and documents, taught by Le in view of Young, because, this would provide an efficient way to determine whether a document in a result set is contextually relevant to the NL query. ( Fukuda, Column 4, lines 23-51).
Regarding Claim 7, Le teaches a document data processing method comprising the steps of:
reading out a plurality of subject documents ( Le: Section 3.2, 5th paragraph, experimental protocols, training documents are read);
dividing each of the plurality of subject documents into a plurality of blocks( Le: Section 1, 3rd paragraph, section 3.2, 5th paragraph, documents are divided into paragraph vectors ( plurality of blocks));
acquiring [[a]] distributed representations of words in each of the plurality of blocks [using a language model that is learned such that the same word has different distributed representations depending on a context of each of the plurality of blocks with a self-attention function] ( Le: Section 2.2, 2nd and 8th paragraph, Fig. 2, distributed representation of word in paragraph vector ( paragraph ID). Words are mapped to q dimensions, which means different distributed representations);
storing the distributed representation on a subject-document-by-subject-document basis, and on a block-by-block basis ( Le: Section 2.2, 2nd, 4th and 5th paragraph, Fig. 2, In the Distributed Memory Model of Paragraph Vectors (PV-DM), paragraph token acts as a memory that remembers the topic of the paragraph (subject-document-by-subject-document basis, and on a block-by-block basis));
reading out query text ( Le: Section 3, 3rd paragraph, a query is given );
searching for words among the plurality of blocks that match a word included in the query text ( Le: Section 3.3, 2nd, 3rd paragraph, search engine can search queries from dataset of paragraphs to match the query);
Le while teaching the document data processing method of claim 7, fails to explicitly teach the claimed, [acquiring [[a]] distributed representations of words in each of the plurality of blocks] using a language model that is learned such that the same word has different distributed representations depending on a context of each of the plurality of blocks with a self-attention function; representations of words included in the query text using the language model; storing the distributed representations of the words included in the query text; calculating, for each matching word that appears in both a block of the plurality of blocks and in the query text, a cosine similarity between a distributed representation of the word in the block and a distributed representation of the word in the query text; and calculating a score of each of the plurality of blocks based on the cosine similarity.
However, Young does teach the claimed, [acquiring [[a]] distributed representations of words in each of the plurality of blocks] using a language model that is learned such that the same word has different distributed representations depending on a context of each of the plurality of blocks with a self-attention function ( Young: Section II. A, 4th, 5th paragraph, language model which learned distributed representation of words. Section II.D, 2nd, 3rd paragraph, a word can have completely different senses or meanings in the contexts. For example, let’s consider these two sentences - 1) “The bank will not be accepting cash on Saturdays” 2) “The river overflowed the bank”. The word senses of “bank” are different in these two sentences depending on its context. Language model ( ELMo) produces word embeddings for each context where the word is used, thus allowing different representations for varying senses of the same word. Specifically, for N different sentences where a word w is present, ELMo generates N different representations. Section IV. D, 3rd, 9th paragraph, the attention mechanism can be used in NLP tasks such as language modeling, which can be broadly seen as mapping a query and a set of key-value pairs to an output, where all the mentioned components are vectors. The output is a combination of the values whose weights are determined by the compatibility between the query and the corresponding keys. This output amounts to the “context” of the input used in decoding the output. Section VIII. F, 1st paragraph, self-attention based model);
representations of words included in the query text using the language model ( Young: Section II. A, 4th, 5th paragraph, language model which learned distributed representation of words. Section II.D, 3rd paragraph, Language model ( ELMo) produces word embeddings for each context where the word is used, thus allowing different representations for varying senses of the same word. Specifically, for N different sentences where a word w is present, ELMo generates N different representations) ;
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 Young’s teaching of recent trends in deep learning based natural language processing, into the methods of distributed representations of sentences and documents, taught by Le, because, by summarizing, comparing and contrasting various models using deep learning, a better understanding of the past, present and future of deep learning in natural language processing can be achieved.( Young, [abstract ).
Le in view of Young while teaching the document data processing method of claim 7, fails to explicitly teach the claimed, storing the distributed representations of the words included in the query text; calculating, for each matching word that appears in both a block of the plurality of blocks and in the query text, a cosine similarity between a distributed representation of the word in the block and a distributed representation of the word in the query text; and calculating a score of each of the plurality of blocks based on the cosine similarity.
However, Fukuda does teach the claimed, storing the distributed representations of the words included in the query text (Fukuda: Column 10, lines 3-7, Fig. 2, data processing system 200 is generally configured with flash memory to provide non-volatile memory for storing operating system files and/or user generated data);
calculating, for each matching word that appears in both a block of the plurality of blocks and in the query text, a cosine similarity between a distributed representation of the word in the block and a distributed representation of the word in the query text (Fukuda: Column 5, lines 49-63, column 11, lines 46-67, column 12, lines 1-17, Fig. 6A, a context vector of a keyword from the NL query can be compared with one or more context vectors of the same or similar keyword from a result. Through this comparison, the embodiment produces a similarity value for the result, which can be cosine similarity);
and calculating a score of each of the plurality of blocks based on the cosine similarity ( Fukuda: Column 12, lines 18-33, Fig. 6D, Context vectors of the same or similar keywords are compared for similarities, and a document similarity score-Document X score-is computed. Column 5, lines 61-63, cosine similarity method can be used to determine similarity).
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 Fukuda’s teaching of document ranking by contextual vectors from natural language query, into the methods of distributed representations of sentences and documents, taught by Le in view of Young, because, this would provide an efficient way to determine whether a document in a result set is contextually relevant to the NL query. ( Fukuda, Column 4, lines 23-51).
Regarding Claim 2, Le in view of Young , further in view of Fukuda, teach the non-transitory computer- readable storage medium according to claim 1 . Le further teaches, wherein each of the plurality of blocks comprises one or a plurality of paragraphs of the subject document ( Le: Section 1, lines 21-32, paragraph vectors can comprise of variable-length pieces of texts, phrases, sentences ).
Claim 8 is method claim performing the steps in non-transitory computer- readable storage medium 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.
Regarding Claim 3, Le in view of Young , further in view of Fukuda, teach non-transitory computer- readable storage medium according to claim 1. Le further teaches, wherein each of the plurality of blocks comprises one or a plurality of sentences ( Le: Section 1, lines 21-32, paragraph vectors can comprise of variable-length pieces of texts, phrases, sentences ).
Claim 9 is method claim performing the steps in non-transitory computer- readable storage medium 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.
Regarding Claim 6, Le in view of Young , further in view of Fukuda, teach the non-transitory computer- readable storage medium according to claim 1. Fukuda further teaches, wherein, the score of each of the plurality of blocks is calculated by summing the cosine similarities for each of the plurality of blocks ( Fukuda: Column 13, lines 32-37, Fig. 9, sum all max_similarity values for all keywords to generate similarity score of the document. Column 5, lines 61-63, cosine similarity method can be used to determine similarity).
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 Fukuda’s teaching of document ranking by contextual vectors from natural language query, into the methods of distributed representations of sentences and documents, taught by Le in view of Young, because, this would provide an efficient way to determine whether a document in a result set is contextually relevant to the NL query. ( Fukuda, Column 4, lines 23-51).
Claim 12 is method claim performing the steps in non-transitory computer- readable storage medium 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.
Claims 4, 10 are rejected under 35 U.S.C. 103 as being unpatentable over Le et al. (Distributed Representations of Sentences and Documents, arXiv:1405.4053v2[cs.CL] 22 May 2014 ), hereinafter referenced as Le, in view of Young et al. (Recent Trends in Deep Learning Based
Natural Language Processing, arXiv:1708.027098v8[cs.CL] 25 Nov 2018), hereinafter referenced as Young, further in view of Fukuda et al. ( US 10592542 B2), hereinafter referenced as Fukuda, further in view of Nakajima et al. ( JP 2019082931 A), hereinafter referenced as Nakajima.
Regarding Claim 4, Le in view of Young , further in view of Fukuda, teach the non-transitory computer- readable storage medium according to claim 1. Le in view of Young , further in view of Fukuda, fail to explicitly teach, wherein the cosine similarity calculation is performed with respect to a predetermined part of speech only .
However, Nakajima does teach the claimed, wherein the cosine similarity calculation is performed with respect to a predetermined part of speech only ( Nakajima: Page 6, 4th, 5th paragraph, The inter-speech similarity specifying unit multiplies each of the inter-word similarity of each pair of words specified by the inter-word similarity specifying unit by the weighting factor which was determined according to the classification of the noun phrase and the verb phrase, or may be determined according to the part of speech of the corresponding word. By calculating an average value, inter statement similarity is calculated).
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 Nakajima’s teaching of similarity calculation method, into the systems and techniques, taught by Le in view of Young , further in view of Fukuda, because, this would improve the search result by efficiently calculating similarity between multiple query sentences. ( Nakajima, page 4, 4th paragraph ).
Claim 10 is method claim performing the steps in non-transitory computer- readable storage medium 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.
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 extension fee 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 date of this final action.
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/NADIRA SULTANA/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
08/02/2026
Prosecution Insights