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 .
Information Disclosure Statement
The information disclosure statement filed 02/06/2023 fails to comply with 37 CFR 1.98(a)(1), which requires the following: (1) a list of all patents, publications, applications, or other information submitted for consideration by the Office; (2) U.S. patents and U.S. patent application publications listed in a section separately from citations of other documents; (3) the application number of the application in which the information disclosure statement is being submitted on each page of the list; (4) a column that provides a blank space next to each document to be considered, for the examiner’s initials; and (5) a heading that clearly indicates that the list is an information disclosure statement. The information disclosure statement has been placed in the application file, the references have been considered; what is missing, is, conference source, page numbers, etc. Examiner managed to decipher, and track down, the references; however, in some instances, similarly titled references were found, which were NOT the document listed. As mentioned above, what is needed, is the conference name/date/ and when applicable, page numbers; so as to distinguish the listed references from similar references. Correction is required.
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) 1-8, 10-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gupta et al (“Document Informed Neural…Distributional Prior”, The Thirty-Third AAAI Conferences on Artificial Intelligence, 2019, pp 6505 - 6512).
As per claim 1, Gupta et al teaches a computer-implemented method for a topic modeling with a continuous learning, the method comprising:
extracting a current topic representation which represents a topic distribution over vocabulary within a current document (as “H” being the number of topics – pp 6506, second column, under “DocNADE”); adjusting a size of the vocabulary of the current topic representation based on words used in a topic pool by masking at least one word of the vocabulary (as, tracking the vocabulary size K – pp 6506, second column, “DocNADE”; and, calculating probability tree for words, in the hidden topic layers “h” – pp6508, first column, “learning”, with assignments/masking of 0 and 1 trees – pp 6508, “Algorithm 1 shows….left subtree or 1 otherwise” – effectively, any potential word that cannot be traced to the root will be given a value of “zero”, thereby being masked since this removes/reduces the number of computations based on a word that cannot be evaluated) ), wherein the topic pool includes past topic representations accumulated by each of past documents (as, topic distribution is from pre-training on text/documents – pp6507, “DocNADEe and iDocNADEe with embedding priors” – “via its pre-train embedding…topic distributions…for short texts”;
regularizing the current topic representation by controlling a degree of topic imitation with past topic representations, based on comparison of the current topic representation and each of the past topic representations; and accumulating the regularized current topic representation into the topic pool (as, pre-trained embedding matrix E, containing the prior knowledge – then computing the hidden layers H – [[6507, “DocNADEe iDocNADEe with embedding priors”, reflecting back on hidden units (topics)).
As per claim 2, Gupta et al teaches the method of claim 1, further comprising: Extracting the current topic representation based on a hidden vector and at least one parameter; wherein the hidden vector is configured to encode a topic proportion within the current document to represent a conditional probability of a word included in the current document based on a proceeding word of the word (as, forward/backward hidden layers hi – page 6507, first column, under “DocNADEe and IDOcNADEe with embedding priors” ); and sharing the at least one parameter is shared in calculating the hidden vector for another word included in the current document (as l is the mixture coefficient – page 6507, 1st column, last 6 lines).
As per claim 3, Gupta et al teaches the method of claim 1, wherein: adjusting the size of the vocabulary includes masking at least one word of the vocabulary of the current topic representation; and the at least one masked word is not found in the topic pool (as, calculating probability tree for words, in the hidden topic layers “h” – pp6508, first column, “learning”, with assignments/masking of 0 and 1 trees – pp 6508, “Algorithm 1 shows….left subtree or 1 otherwise”).
As per claim 4, Gupta et al teaches the method of claim 1, wherein: regularizing the current topic representation includes calculating a loss function which is related to probabilities of words in the adjusted size of vocabulary; and the loss function is defined in terms of the current topic representation and at least one parameter (as, computing the negative log-likelihood for every word – pp 6510, 1st column, first paragraph, which refers to figure 2a, 2b on pp 6509 – examiner notes, applicants spec, refers to the claimed ‘loss function’ as a negative log-likelihood – pp4, first col., step 8, in applicants pgpub – 2023/0289533).
As per claim 5, Gupta et al teaches the method of claim 4, wherein regularizing the current topic representation includes adapting the current topic representation and the at least one parameter which minimize a value of the loss function (as, minimizing the negative log-likelihood function over the document, with the hidden vector topic parameter – pp, 6508, second page, “The DocNADE variants…negative log-likelihood…gradient descent”).
As per claim 6, Gupta et al teaches the method of claim 5, further comprising using the adapted parameter for extracting a future topic representation of a future document (as, using the “DocNADEe/iDocNADEe with embedding priors” – pp 6507, 1st column, using previously trained distribution on previous documents, for topic/word distributions of the current document text – examiner notes, that in this instance, the “current” document is a future document, compared to the trained embeddings, which are based on a “past” text).
As per claims 7,8, Gupta et al teaches a perplexity parameter across document data sets – pp 6509, first column, “Generalization (Perplexity, PPL), and second column, “Quantitative” – computed over a number of documents, as well as a log-likelihood function.
Claims 10-14 are method claims whose steps are covered by the claim scope of claims 1-8 above and as such, claims 10-14 are similar in scope and content to claims 1-8 above; therefore, claims 10-14 are rejected under similar rationale as presented against claim 1-8 above. Further, to additional claim elements in claims 10-14, Gupta et al teaches semantic analysis as well (pp 6507, 1st column, “DocNADEe and iDocNADEe with embedding priors”, “we introduce additional semantic information….”); two different word embeddings – pp 6507, 1st column, “embedding pairs”.
Response to Arguments
Applicant's arguments filed 09/02/25 have been fully considered but they are not persuasive. As per applicants arguments on pp-8 of the response, examiner notes that the binary tree calculations of Gupta, gives a weight of “0” to the word that cannot be traced to a root, and hence “masked”, which reduces the number of calculations and hence, more efficient. See mapping/explanation 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 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. Please see related art listed on the PTO-892 form.
The following references were found, pertinent to applicants claims/spec, qualifying as prior art:
Gupta et al, “Document Informed Neural Autoregressive Topic Models”, Section 2, DocNADE; Section 3.2 [Wingdings font/0xE0] perplexity, quantitative. Section 3.3, Topic Coherence; Section 3.4, Document Categorization.
Chen et al, “Topic Modeling…Lifelong Learning and Big Data”, Section 3, Overall Algorithm, including Lifelong Learning re: documents/topics.
Andrassy et al (20200151207) –abstract.
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 12/11/2025
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