Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Examiner recommend language in the title, to more accurately encapsulate the claim/invention scope towards the detection of sentence delimited sequence of words, for the eventual document classification.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4,6,7,9-11,13,15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Elchik (20170213469).
As per claim 1, Elchik (20170213469) teaches a method for performing subject word classification of document data, which is performed by a computing device including at least one processor (as classification – para 0062, document classification/categorization; operating on digital content – para 0030, defining digital content to include documents), the method comprising: acquiring a plurality of sentence data by using document data; and determining a class of the document data by inputting each of the plurality of sentence data into at least one network model (as extracting sentences, and using a content engine to determine the topic/keywords, and the like – para 0007) by:
Outputting a plurality of embedding vectors using a first network model by receiving each of the plurality of sentence data (as, processing for sentences, as noted above in claims 1-4, and using neural networks and vectors – para 0070; along with marking and operating on the portion that is identified as “key” – para 0062 – and the resulting output is used/integrated into a query/answer format – see para 0063) ), and
mapping the plurality of embedding vectors onto a vector space using a second network model, and determining the class by analyzing a similarity between plurality of embedding vectors mapped on to the vector space ((as, determining the class/type of information/document – beginning of para 0070, and using neural networks/vectors to analyze the relationships between the topic and subjects found in the text – last half of para 0070); Elchik (20170213469) further teaches the odds/probability that there is a NamedEntityRecognition given a vector x:
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(before para 0046 in Elchik (20170213469)). Furthermore, Elchik (20170213469) teaches the use of similarity metrics when comparing the vector representations of the extracted topics – para 0072.
As per claim 2, Elchik (20170213469) teaches the method of claim 1, wherein the acquiring of the plurality of sentence data by using document data includes determining a sentence delimiter in the document data, and acquiring the plurality of sentence data based on the sentence delimiter (as determining NER – named entity recognition, wherein the system determines the sentences by an end-of-sentence – para 0039 – end of sentence punctuation).
As per claim 3, Elchik (20170213469) teaches the method of claim 2, wherein the determining of the sentence delimiter in the document data includes determining the sentence delimiter based on a normal expression equation (as determining the delimiter by a period/question mark/exclamation point – para 0039) and a pretrained sentence distinguishing model for a plurality of text data included in the document data (as, also identifying by, a pre-set list of keyword/series of keywords – para 0062, last half of paragraph).
As per claim 4, Elchik (20170213469) teaches the method of claim 3, wherein the pretrained sentence distinguishing model receives a sentence included in the document data and outputs a segment result for the input sentence (as, marking and operating on the portion that is identified as “key” – para 0062 – and the resulting output is used/integrated into a query/answer format – see para 0063).
As per claim 6, Elchik (20170213469) teaches the method of claim 5, wherein the second network model includes an encoder encoding sequences of the plurality of embedding vectors, and outputting a first vector expression for each of the sequences or a second vector expression for all sequences, and a neural network classifier determining the class by receiving the first vector expression or the second vector expression (as using deep neural networks and support vector machines – para 0070, for the process of identifying sequences – see beginning of para 0070, extracted keywords mapped to topics such as sports/politics; and also for the process of analyzing a whole sentence determined by delimiters – para 0039).
As per claim 7, Elchik (20170213469) teaches the method of claim 6, wherein the class is related to the subject word of the document data (as, the derived topics can be related to sports/politics – beginning of para 0070 – examiner notes that, that particular example, the subject word could be a subset of sports or politics).
Claims 9-11, 13 are device claims that perform the method steps of claims 1-4, 6, 7 above and as such, claims 9-11, 13 are similar in scope and content to claims 1-4,6,7 above; therefore, claims 9-11, 13 are rejected under similar rationale as presented against claims 1-4,6,7 above. Furthermore, Elchik (20170213469) teaches processor and computer readable memory – para 0034).
Claim 15 is a non-transitory computer readable medium device that performs steps commonly found in claims 1-4,6,7,9-11,13 above and as such, claim 15 is similar in scope and content to claims 1-4,6,7,9-11,13; therefore, claim 15 is rejected under similar rationale as presented against claims 1-4,6,7,9-11,13 above. Furthermore, Elchik (20170213469) teaches such non-transitory storage media – para 0030.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 8,14 are rejected under 35 U.S.C. 103 as being unpatentable over Elchik (20170213469) in view of Flinn et al (20240046311).
As per claims 8,14, Elchik (20170213469) teaches the limitations independent claims 1,9, as mapped above; however, Elchik (20170213469) does not explicitly teach detailing, as part of a topic list, the outline of a technical paper that would include the abstract of a thesis document; however, Flinn et al (20240046311) teaches as, part of the metadata mapped to topic information, includes an object that details the abstract of the documents (para 0276). Therefore, it would have been obvious to one of ordinary skill in the art of document analysis/tagging to further detail as taught by Flinn et al (20240046311) because it would advantageously allow for the rating of the relationship between the abstract, and either confirmation or disconfirming the thesis (Flinn et al (20240046311), para 0284).
Response to Arguments
Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection relies upon a newly cited section of the prior art reference used, Elchik (20170213469). Applicants arguments are toward the newly presented claim limitations, namely, the introduction of a similarity measure of the vectors representing topics. Examiner notes the further recitations to Elchik (20170213469) to teach probability scores toward the matching of vectors, as well as a similarity calculation toward the representative vectors of topics that are searched/measured. Lastly, examiner notes, Zadeh et al (20140201126) teaches similarity calculations in determining topics in a document – para 0826-0828, para 0845.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See references listed on the PTO-892 form.
Furthermore, the following references were found, toward sentence delimiters and topic finding in documents:
Michels (20210374677) teaches sentence delimiting in documents, and deriving topics from these sentences (para 0074-0078) using neural networks.
Hilleli et al (20210099317) teaches tokenization of document phrases (para 0062-0063), delimiting the sentences -- para 0072, and using HMM/NN to calculate probability sequences (para 0073-0074)
Zadeh et al (20140201126) teaches similarity calculations in determining topics in a document – para 0826-0828, para 0845.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 05/04/2026
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