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 .
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) 1-9,12,13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Flinn et al (20240046311).
As per claim 1, Flinn et al (20240046311) teaches a method for retrieving document data, which is performed by a computing device including at least one processor, the method comprising (as, the referenced information can come from documents – para 0275):
determining a first embedding vector by inputting retrieval word data into a first network model (as, from the mapping originally presented in the office action dated march 27, 2024, for claims 8, 9, reproduced here: -- (as using embedded vectors, para 0199, with embedded pointers – para 0273, 0274); determining a second embedding vector corresponding to the first embedding vector among a plurality of embedding vectors stored in a storage unit; and providing document data mapped to the second embedding vector (as calculating relationship scores – para 0354-0356; by developing a matrix of relationships – para 0353 – operating on topic relationships; then see para 0085, 0308 showing support vector machine models calculating such scoring);
wherein the document data has a high relevancy with the retrieval word data (as, scoring the similarity/relevance between the collection of system objects – 0275; and the content object to the topic objects – para 0276, 0277, and as an example, labeling the relationship as strong, based on a high total score – para 0355, 0356 and para 0361).
As per claim 2, Flinn et al (20240046311) teaches the method of claim 1, wherein the retrieval word data includes at least one of query type natural language sentence data, keyword data, subject word data, researcher name data, or title data (as, the meta information can include title, subtitle, abstract, etc. – para 0276).
As per claim 3, Flinn et al (20240046311) teaches the method of claim 1, wherein the document data includes at least one of thesis data related to the retrieval word data, keyword data related to the retrieval word data, or subject word data related to the retrieval word data (as the document data also includes thesis data relationship with keyword data – para 0284).
As per claim 4, Flinn et al (20240046311) teaches the method of claim 1, wherein the plurality of embedding vectors includes embedding vectors related to a plurality of items, respectively output by inputting each of the plurality of items into the first network model (as using neural network models – end of para 0383, to measure a ‘closest’ relationship between the content and topic – para 0383).
As per claim 5, Flinn et al (20240046311) teaches the method of claim 4, wherein the plurality of items includes at least one of a specific category among a plurality of categories included in the thesis data, the subject word related to the thesis data, or the keyword allocated to the thesis data (as developing object relationships with the thesis – para 0284, as well as subject/keyword – para 0277).
As per claim 6, Flinn et al (20240046311) teaches the method of claim 5, wherein the subject word is generated by a second network model performing subject word classification learned by using a learning data set in which the subject word is labeled to learning thesis data (as, a second network using a resolution function, to fine-tune the weighted relationships between objects and usage data, and incorporation the second network with the resolution function, with the first network – para 0227).
As per claim 7, Flinn et al (20240046311) teaches the method of claim 5, wherein an embedding vector related to the keyword is generated based on a common appearing matrix related to a keyword which appears in the learning thesis data at a predetermined number of times or more (as, a matrix relationship – para 0296 -- is used to measure sequential object relationship – para 0295, used to measure a degree of an object that supports a thesis – last half of para 0284);
and is acquired by using a third network model in which a loss value is set so that a similarity to an embedding vector of the learning thesis data related to the keyword increases on a space (as, calculating a degree of separation matrix – para 0296, which represents a distance relationship between object sequences – para 0295, which, towards the loss-aspect of the claim scope, is used to measure a thesis-object that disconfirms a relationships – para 0284) .
As per claim 8, Flinn et al (20240046311) teaches the method of claim 1, wherein the determining of the second embedding vector corresponding to the first embedding vector among the plurality of embedding vectors stored in the storage unit includes (as using embedded vectors, para 0199, with embedded pointers – para 0273, 0274)
generating a plurality of relation scores generated based on a similarity between each of the plurality of embedding vectors and the first embedding vector, and determining, as the second embedding vector, an embedding vector having a largest value among the plurality of relation scores (as calculating relationship scores – para 0354-0356; by developing a matrix of relationships – para 0353 – operating on topic relationships; then see para 0085, 0308 showing support vector machine models calculating such scoring).
As per claim 9, Flinn et al (20240046311) teaches the method of claim 1, wherein the determining of the second embedding vector corresponding to the first embedding vector among the plurality of embedding vectors stored in the storage unit includes:
generating a similarity value between each of the plurality of embedding vectors and the first embedding vector, and determining the second embedding vector based on the similarity value (as, mapped above with respect to vector representation and embedded pointer in the vectoring system, see also para 0366 showing the calculation of similarities between objects).
Claims 12,13 are device/non-transitory computer readable medium claims, performing steps that are commonly found in claims 1-9 above and as such, claims 12,13 are similar in scope and content to claims 12,13 above; therefore, claims 12,13 are rejected under similar rationale as presented against claims 1-9 above.
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) 10,11 are rejected under 35 U.S.C. 103 as being unpatentable over Flinn et al (20240046311) in view of Elisco (20220300711).
As per claims 10, 11, Flinn et al (20240046311) teaches the limitations of the claims (1,9) from which claims 10,11 depend; furthermore, Flinn et al (20240046311) teaches distance/similarity measures, but does not specify the distance measurement types; Elisco (20220300711) teaches vector similarity calculations using a cosine similarity – para 0037, along with a normalized vector as well – equation at the end of para 0035 and para 0036, and hence, by mathematical definition, Elisco (20220300711) is teaching the claimed equations because the cosine/dotproduct of between two vectors, normalized, meets the claim scope of claim 11 (with claim 10 scope covered as well, by the cosine calculation disclosed in Elisco (20220300711)). Therefore, it would have been obvious to one of ordinary skill in the art of vector calculations to further define the similarity calculation of Flinn et al (20240046311) with a normalized cosine distance calculation, as disclosed by Elisco (20220300711), because it would advantageously show a stronger similarity relationship between two vectors when using the cosine distance calculation (Elisco (20220300711), para 0037, last half, the description pertaining to cosine similarity calculations).
Response to Arguments
Applicant's arguments filed 01/14/2026 have been fully considered but they are not persuasive. As per applicants arguments against independent claim 1, examiner notes that although the para’s were not listed toward the vector aspect in claim 1, claims 8,9 contain more narrow claim features dependent upon claim 1, and that these paragraphs are further explanation of the vector relationships found in claim 1; namely – Flinn et al teaches the use of embedding vectors – para 0188, with embedded pointers -- para 0273, 0274; and calculating relationship cores – para 0354-0356, and using support vector machine models calculating each scoring., and para 0366 showing the calculation of similarities between objects. Further, to the amended claim language, Flinn et al teaches the comparison between objects (ie, content objects of the source with respect to topic objects stored; the comparison operates on the vector level – as shown in the paragraphs above). As to applicants arguments against the newly amended claim language toward high relevancy, examiner notes the new citations to Flinn et al showing a high correlation score/category in the comparisons performed in Flinn et al.
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.
Furthermore, note the following references toward applicants claim scope regarding word/topic recognition in documents to identify thesis components/subcomponents:
Flinn et al (20070203872) teaches tracking of the abstract, as an object of the metadata, from a thesis document (para 0287).
The following references were found, toward applicant claim scope using embedded vectors for word matching, topic related, in documents:
Zheng et al (20200167391) teaches the use of RNN/DNN, para 0020, with topic sensitive attention models (para 0021), and using word scores to select salient sentences in the document, relating to particular topics (para 0038-0040).
Janakiraman (20210136096) teaches natural language processing using neural networks to operate on dense vector representations on word embedding in documents (para 0023), with semantic considerations of the detected words (para 0018).
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 05/05/2026
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