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 .
Continued Examination Under 37 CFR 1. 114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1 .114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/26/2026 has been entered.
Response to Amendment
3. Amendment filed 01/26/2026 has been considered by Examiner. Claims 1-4, 6 and 7 have been amended. Claims 1-4, 6-10 and 12-14 are pending, and likewise Claims 1-4, 6-10 and 12-14 have been examined.
Response to Arguments
Applicant’s amendments and arguments filed 01/26/2026, with respect to claim(s) 1-4, 6-10 and 12 -14 have been fully considered. Applicant amended claims 1-4, 6, and 7.
Applicant’s arguments in page 1, filed 01/26/2026, with respect to 35 U.S.C 101 rejections of claims 1-4, 6-10 and 12-14 have been fully considered but they are not persuasive. Applicant argued that the claim 1 has been amended to be directed to a non-transitory computer-readable storage medium, and claims 1 and 7 have been amended to recite "acquiring a distributed representation of a word 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". Examiner respectfully disagrees. A human can obtain a distributed representation of the word, can learn/recognize that that same word can have different meaning based on the context and can have different distributed representations. The amended limitation is using language model and self-attention function to determine this, 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. 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. Thus, 35 U.S.C 101 rejections of claims 1-4, 6-10 and 12-14 have been maintained. Please see the rejections below.
Applicant’s arguments filed 01/26/2026, with respect to claim(s) 1-4, 6-10 and 12-14, 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 Objections
Claims 13 is objected to because of the following informalities:
Claim 13 recites in line 1, “The document data processing system according to claim 1”, whereas claim 1 is amended as “non-transitory computer- readable storage medium…”. Since claim 13 is dependent on claim 1, for examination purpose, examiner has interpreted line 1 of claim 13 as “The non-transitory computer- readable storage medium according to claim 1”.
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, 12-14 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 representation of a word 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 and transmit to the server ;” “extracting a word included in the query text and acquire a distributed representation of the word included in the query text using a language model ; “storing the distributed representation ;” “comparing the distributed representation of the word included in the query text and the distributed representation of the word included in each of the plurality of blocks and calculates cosine similarities of each of the plurality of blocks ;” “searching for a word that matches a word included in the query text from words included in the block; “calculating a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the matching word in the query text”, “calculating a score of each of the plurality of blocks based on the cosine similarity”; “andsynchronized with each other”. 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.
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 compare the words from query text with the previously acquired words, 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. The claim as drafted, is not patent eligible (see BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 119 USPQ2d 1236 (Fed. Cir. 2016 which notes server vs client structured to be well known).
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 representation of a word 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”; “reading out query text; extracting a word included in the query text and acquiring a distributed representation of the word included in the query text”; “comparing the distributed representation of the word included in the query text and the distributed representation of the word included in each of the plurality of blocks and calculating cosine similarities similarity of each of the plurality of blocks”; “calculating a score of each of the plurality of blocks based on the cosine similarities similarity”; “displaying the score in a score display unit ; displaying the plurality of blocks in a text display unit; and synchronizing the score display unit and the text display unit that are synchronized with each other, wherein, in the step of calculating cosine similarities similarity of each of the plurality of blocks, a word that matches a word included in the query text is searched for from words included in the block, and a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the matching word in the query text is calculated.” 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.
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 compare the words from query text with the previously acquired words, 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, in a case where there is more than one matching word in the query text and the block, the sum of cosine similarities of distributed representations of matching words is a score of the block”. Determining whether there is matching word in between the query text and the block and determining sum of the similarities of the matching word as a score of the block are evaluation or judgment 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 6 and 12 do not recite any additional limitations. The claims as drafted, are not patent eligible.
The dependent claims 13 and 14 recite “wherein the text display unit changes a display method in accordance with the score”. Changing the display based on the score is evaluation or judgment step that could be performed in the human mind or with the aid of pen and paper. Text display unit is an additional element, which is not sufficient to amount to significantly more than the judicial exception. The claims 13 and 14 as drafted, are not patent eligible.
Claim Rejections - 35 USC § 112
8. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-4, 6 are rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 1 recites in line 33, “ the score display unit” and “the text display unit” .There is insufficient antecedent basis for this limitation in the claim. For examination purposes the examiner has interpreted“ the score display unit” and “the text display unit” of line 33, to be “ a score display unit” and “a text display unit”.
Any claim not specifically treated is rejected by virtue of its dependency.
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 and 7-9 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 Domeniconi et al. ( US 20200394186 A1), hereinafter referenced as Domeniconi.
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 representation of a word 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));
Le while teaching the non-transitory computer- readable storage medium of claim 1, fails to explicitly teach the claimed, [acquiring a distributed representation of a word 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; reading out query text and transmit to the server; extracting a word included in the query text and acquire a distributed representation of the word included in the query text [using the language model]; storing the distributed representation ; comparing the distributed representation of the word included in the query text and the distributed representation of the word included in each of the plurality of blocks and calculates cosine similarities of each of the plurality of blocks; searching for a word that matches a word included in the query text from words included in the block; calculating a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the matching word in the query text, calculating a score of each of the plurality of blocks based on the cosine similarity; and displaying the score and the plurality of blocks, wherein the score display unit and the text display unit are synchronized with each other.
However, Young does teach the claimed, acquiring a distributed representation of a word 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);
[extracting a word included in the query text and] acquire a distributed representation of the word [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 non-transitory computer- readable storage medium of claim 1, fails to explicitly teach the claimed, reading out query text and transmit to the server; extracting a word included in the query text and acquire a distributed representation of the word included in the query text [using the language model]; storing the distributed representation ; comparing the distributed representation of the word included in the query text and the distributed representation of the word included in each of the plurality of blocks and calculates cosine similarities of each of the plurality of blocks; searching for a word that matches a word included in the query text from words included in the block; calculating a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the matching word in the query text, calculating a score of each of the plurality of blocks based on the cosine similarity; and displaying the score and the plurality of blocks, wherein the score display unit and the text display unit are synchronized with each other.
However, Domeniconi does teach the claimed, reading out query text and transmit to the server ( Domeniconi: Para.[0027], Fig. 1, a user provides a query 110, which can be individual words 111, lines 112, or paragraphs 113. Para.[0064], Fig. 4, server 412);
extracting a word included in the query text and acquire a distributed representation of the word included in the query text [using the language model] ( Domeniconi: Para.[0037],[0038],[0040], Fig. 3, query is parsed at step 314. At step 322 of the flow chart 320, the related distributed embedding representation, is computed for each word using the word embedding model) ;
storing the distributed representation ( Domeniconi: Para.[0068], Fig. 4,storage system 434);
comparing the distributed representation of the word included in the query text and the distributed representation of the word included in each of the plurality of blocks and calculates cosine similarities of each of the plurality of blocks ( Domeniconi: Para.[0040],[0041], Fig. 3, distributed representation of query and the distributed representation of log segments are compared and similarity metric such as cosine similarity is calculated);
searching for a word that matches a word included in the query text from words included in the block ( Domeniconi: Para.[0042], searching for word wq in similar pattern of words);
calculating a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the matching word in the query text ( Domeniconi: Para.[0040], calculating similarity metric such as cosine similarity between distributed representation of query and the distributed representation of log segments);
calculating a score of each of the plurality of blocks based on the cosine similarity ( Domeniconi: Para.[0040], [0041], Fig. 3, at step 324, a ranked list of log segments of N lines is retrieved that have higher similarity of aggregated word embeddings therein with respect to the query representation. Similarity score is calculated based on cosine similarity);
and displaying the score and the plurality of blocks, wherein the score display unit and the text display unit are synchronized with each other ( Domeniconi: Para.[0039], [0041], Fig. 3 illustrates calculated similarity score and the log segments are output at step 316, where 3 similarity scores are presented with 3 log segments);
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 Domeniconi’s teaching of NLP based context-aware data mining of a text document, into the methods of distributed representations of sentences and documents, taught by Le in view of Young, because, this would improve the ability of a user to find meaningful events in the log.( Domeniconi, Para.[0004]).
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 representation of a word 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),
Le while teaching the document data processing method of claim 7, fails to explicitly teach the claimed, [acquiring a distributed representation of a word 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; reading out query text ; extracting a word included in the query text and acquiring a distributed representation of the word included in the query text ; comparing the distributed representation of the word included in the query text and the distributed representation of the word included in each of the plurality of blocks and calculates cosine similarities of each of the plurality of blocks; calculating a score of each of the plurality of blocks based on the cosine similarities; displaying the score in a score display unit; displaying the plurality of blocks in a text display unit; and synchronizing the score display unit and the text display unit with each other, wherein, in the step of calculating cosine similarities of each of the plurality of blocks, a word that matches a word included in the query text is searched for from words included in the block, and a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the matching word in the query text is calculated.
However, Young does teach the claimed, acquiring a distributed representation of a word 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);
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, reading out query text and transmit to the server; extracting a word included in the query text and acquire a distributed representation of the word included in the query text [using the language model]; storing the distributed representation ; comparing the distributed representation of the word included in the query text and the distributed representation of the word included in each of the plurality of blocks and calculates cosine similarities of each of the plurality of blocks; searching for a word that matches a word included in the query text from words included in the block; calculating a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the matching word in the query text, calculating a score of each of the plurality of blocks based on the cosine similarity; and displaying the score and the plurality of blocks, wherein the score display unit and the text display unit are synchronized with each other.
However, Domeniconi does teach the claimed, reading out query text and transmit to the server ( Domeniconi: Para.[0027], Fig. 1, a user provides a query 110, which can be individual words 111, lines 112, or paragraphs 113. Para.[0064], Fig. 4, server 412);
extracting a word included in the query text and acquire a distributed representation of the word included in the query text ( Domeniconi: Para.[0037],[0038],[0040], Fig. 3, query is parsed at step 314. At step 322 of the flow chart 320, the related distributed embedding representation, is computed for each word using the word embedding model) ;
comparing the distributed representation of the word included in the query text and the distributed representation of the word included in each of the plurality of blocks and calculates cosine similarities of each of the plurality of blocks ( Domeniconi: Para.[0040],[0041], Fig. 3, distributed representation of query and the distributed representation of log segments are compared and similarity metric such as cosine similarity is calculated);
calculating a score of each of the plurality of blocks based on the cosine similarity ( Domeniconi: Para.[0040], [0041], Fig. 3, at step 324, a ranked list of log segments of N lines is retrieved that have higher similarity of aggregated word embeddings therein with respect to the query representation. Similarity score is calculated based on cosine similarity);
displaying the score in a score display unit ( Domeniconi: Para.[0039], [0041], Fig. 3 illustrates calculated similarity scores are output at step 316);
displaying the plurality of blocks in a text display unit ( Domeniconi: Para.[0039], [0041], Fig. 3 illustrates calculated similarity score and the log segments are output at step 316);
and synchronizing the score display unit and the text display unit with each other ( Domeniconi: Para.[0039], [0041], Fig. 3 illustrates calculated similarity score and the log segments are output at step 316, where 3 similarity scores are presented with 3 log segments);
wherein, in the step of calculating cosine similarities of each of the plurality of blocks, a word that matches a word included in the query text is searched for from words included in the block, and a cosine similarity between a distributed representation of the matching word in the block and a distributed representation of the matching word in the query text is calculated ( Domeniconi: Para.[0042], searching for word wq in similar pattern of words. Para.[0040], calculating similarity metric such as cosine similarity between distributed representation of query and the distributed representation of log segments).
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 Domeniconi’s teaching of NLP based context-aware data mining of a text document, into the methods of distributed representations of sentences and documents, taught by Le in view of Young, because, this would improve the ability of a user to find meaningful events in the log.( Domeniconi, Para.[0004]).
Regarding Claim 2, Le in view of Young , further in view of Domeniconi, 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 Domeniconi, 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.
Claims 4, 6, 10 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 Domeniconi et al. ( US 20200394186 A1), hereinafter referenced as Domeniconi, 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 Domeniconi, teach the non-transitory computer- readable storage medium according to claim 1. Le in view of Young , further in view of Domeniconi, 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 Domeniconi, 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.
Regarding Claim 6, Le in view of Young , further in view of Domeniconi, teach the non-transitory computer- readable storage medium according to claim 1. Le in view of Young , further in view of Domeniconi,, fail to explicitly teach, wherein, in a case where there is more than one matching word in the query text and the block, the sum of cosine similarities of distributed representations of matching words is a score of the block.
However, Nakajima does teach the claimed, wherein, in a case where there is more than one matching word in the query text and the block, the sum of cosine similarities of distributed representations of matching words is a score of the block ( Nakajima: Page 5, 7th paragraph, the inter-text similarity specifying unit of the search device 100 calculates the inter-text similarity based on the sum of the inter-word similarity of each pair of words).
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 Domeniconi, because, this would improve the search result by efficiently calculating similarity between multiple query sentences. ( Nakajima, page 4, 4th paragraph ).
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 13 and 14 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 Domeniconi et al. ( US 20200394186 A1), hereinafter referenced as Domeniconi, further in view of Sims et al. ( US 20200201895 A1), hereinafter referenced as Sims.
Regarding claim 13, Le in view of Young , further in view of Domeniconi, teach the non-transitory computer- readable storage medium according to claim 1. Le in view of Young , further in view of Domeniconi, fail to explicitly teach the claimed, wherein the text display unit changes a display method in accordance with the score.
However, Sims does teach the claimed, wherein the text display unit changes a display method in accordance with the score (Sims: Para.[0163], [0164], Based on a request from a user, the display of document summary can be changed, The original sentences corresponding to n records can be re-ordered to be displayed/presented in the order in which they appeared in the original document. Notably, reordering as to original document order may result in a sentence having a lower similarity score being returned/presented to a user before a sentence with a higher similarity score).
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 Sim’s teaching of identifying one or more documents of potential relevance to an input query, into the systems and techniques, taught by Le in view of Young , further in view of Domeniconi, because, this would improve the management of a large number of content in digital form and would facilitate effective and efficient searching of relevant content. (Sims, para.[0004] ).
Claim 14 is method claim performing the steps in the non-transitory computer- readable storage medium claim 13 above and as such, claim 14 is similar in scope and content to claim 13 and therefore, claim 14 is rejected under similar rationale as presented against claim 13 above.
Conclusion
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/NADIRA SULTANA/Examiner, Art Unit 2653