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 § 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) 1,4,7 are rejected under 35 U.S.C. 103 as being unpatentable over Panuganty et al (20210248136) in view of Tran (20230252224).
As per claim 1, Panuganty et al (20210248136) teaches a method of generating, by a recommendation apparatus, content to a user and recommending an asset to be displayed in the content (as displaying content – figures 3,4,6; by use of the personalized analytics system – figure 5b, 5c), the method comprising:
receiving a sentence for generating the content from a user; transforming the sentence into a story type text through a language model (as, taking in a Natural Language query – para 0105; eventually generating a storyline – para 0121 – output of story narrator);
transforming the story type text into a storyline including 1) a background, 2) a main character, and 3) a main element through the language model (as, the storyline/story narrator utilizes background information and displays it – para 0147, generates a main character/object – para 0215 – product/event/concept; main summary – para 0242 – extracting summary points from a chart);
transforming the storyline into sentence data for generating the content or recommending the asset through the language model; and generating the content or recommending the asset based on the sentence data (wherein the output of the story narrator used is a descriptive output, as well as using charts,graphs, etc. – para 0121);
wherein the sentence data includes for each individual source word in the sentence, an English word corresponding to the source word, parts of speech, and importance value (as, using part of speech as attributes – para 0474); assigned by the language model, wherein the sentence data is provided as a machine readable structured data set that is linked or associated with metadata of an asset so as to enable automated retrieval of the asset based on the linguistic attributes (as, using the metadata to find/prioritize what is needed – para 0117; including search for assets, such as image/sound/text/documents, etc – para 0102; using a sentence structure – para 0518; wherein the matching is toward the attributes stored for the storyline database – see para 0540, and the matching is toward the query and the stored attributes of the item of interest – see para 0302 – “matching the keywords to content included in the metadata, and generating tag information; examiner further notes, to the “linked or associated with metadata”, after matching the keywords to content, the current “keywords” “attributes” to the stored asset, can be updated via longer term data curation – see para 0117 – with a scoring based on higher priority – para 0117; the curation engine uses natural language processing models as well as semantic models, to perform the matching, and updating the models themselves – para 0219).
Panuganty et al (20210248136) does not explicitly teach that the keywords/attributes/ metatags are part of a JSON format; Tran (20230252224) teaches generative models producing descriptions based on certain keywords (abstract), and further details of content generations – para 0006 – 0010), wherein the formatting of the information is in JSON format – para 0281. Therefore, it would have been obvious to one of ordinary skill in the art of content generation to further define the information file/storage, as taught by Panuganty et al (20210248136) with specifying the data to be in JavaScriptObjectNotation format, as taught by Tran (20230252224) , because then the data can be easily downloaded/accessed/manipulated by many, as JSON is a well known used format that provides a consistent schema (Tran (20230252224), para 0281).
As per claim 4, the combination of Panuganty et al (20210248136) in view of Tran (20230252224) teaches the method of claim 1, wherein the story type text is transformed through the language model based on a command for writing a synopsis (see Panuganty et al (20210248136), generating a summary based upon the narrated analytics results – para 0396, see, ‘represents a summary’).
Claim 7 is an apparatus claim whose steps are performed by the various features in the method claims 1,4 above; as such, claim 7 is similar in scope and content to claims 1,4 above; therefore, claim 7 is rejected under similar rationale as presented against claims 1,4 above.
Response to Arguments
Applicant's arguments filed 4/10/2026 have been fully considered but they are not persuasive. On pp 5-6 of the response, applicants argue that the Panuganty reference does not teach the arrangement of the word/metatags/sentence data, in a JSON format; these arguments are toward the amended claim language, which now is rejected under Panuganty et al (20210248136) in view of Tran (20230252224). On pp 7-8 of the response, applicants argue that Panuganty et al (20210248136) does not teach the arrangements of the JSON dataset is used to update asset metadata and to search metadata – examiner argues, that with the JSON limitation, and these accompanying features, are now taught by the combination of Panuganty et al (20210248136) in view of Tran (20230252224) (see Tran (20230252224) above; furthermore, the JSON dataset disclosed in Tran (20230252224) Is used for crawling and indexing of future text – see para 0281).
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.
Prior art toward JSON files in organizing/searching data:
Agarwal et al (10,277,743) teaching:
In another example, contact flows may be developed using a contact flow development interface that allows contact flow developers to construct a contact flow documents using a data interchange document format, such as, but not limited to, JSON (JavaScript Object Notation) or XML (eXtensible Markup Language). (54) In one example, the input data may be provided to the natural language service 510 to analyze the input data for an intent identifier and the natural language service 510 may return metadata to the contact flow execution module 516 and the metadata may be matched to an action defined in contact flow component. For example, the natural language service 510 may be configured to analyze audio or textual data using a natural language technique that may use any of: machine translation, coreference resolution, discourse analysis, morphological segmentation, named entity recognition, natural language generation and understanding, part-of-speech tagging, parsing, relationship extraction, sentiment analysis, speech recognition, speech segmentation, topic segmentation, etc.
Strassner (20160188609) teaching JSON encoding (para 0037) when processing POS/metadata and comparing the context tokens to potential matches (para 0114).
Amer et al (20190304157) teaches tokenization of an input sentence structure based on linguistics – para 0062, reflecting back on attributes of the storyline – para 0038.
Lewis (20240046074) teaches the use of tokenized descriptors, on a language level, to represent the translation – see para 0035.
Kapoor et al (20180089156) teaches query understanding, and generating reports/slides based on the input (Fig. 1)
Panuganty (20200401593) teaches taking a user query, generating a storyline, and displaying – see Fig. 1-3, para 0045-0053).
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
06/22/2026
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