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
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Harrington et al. (Publication No. 2026/0101019 filed January 31, 2025, priority to provisional application 63/704857 filed October 8, 2024, hereinafter Harrington); Webster et al. (Publication No. 2026/0030283 filed April 3, 2025, priority to provisional application No. 63/378,559 filed October 6, 2022, Webster); and Edward S. Epstein, “A Scoring System for Probability of Ranked Categories,” Journal of Applied Meterology (1962-1982), Volume 8, pages 985-987.
Regarding Claims 1 and 11, teaches Harrington teaches ingesting an input comprising data in any of a plurality of formats, wherein the input is tokenized into a model-readable format based on its data type (Abstract - a request to connect an ingestion server to a conference to which the client device is connected. The request comprises a token for authentication of the ingestion server and an identifier of the ingestion server. [0019] The request includes an identifier of the ingestion server, and a token for authenticating the ingestion server to have access permissions to the conference based on the access permissions of the client device. The conferencing server establishes a communication connection with the ingestion server, using the token to determine the access permissions of the ingestion server to the conference. [0052] The user interface 212 includes one or more input interfaces and/or output interfaces. [0086] data types); processing the tokenized input through classification ([0088] topic classifications).
Harrington does not expressly teach hierarchical classification.
Webster teaches tokenized input through hierarchical classification model (Abstract - generates variation hierarchical classifications by varying the initial hierarchical classifications assigned, selects at least one hierarchical classification from the initial hierarchical classifications and variation hierarchical classifications, and produces a token stream of tokens.), wherein the hierarchical classification model first a super-category for the input and subsequently refines the classification by predicting a corresponding sub-category (0159] SVO entries are indexed (the SVO Token Category Index) according to a classification structure based on the WordNet lexicon data file (i.e., “lexfile”) category); and a super-category and sub-category for the input data are output as a result of the hierarchical classification model (Abstract - generates variation hierarchical classifications by varying the initial hierarchical classifications assigned, selects at least one hierarchical classification from the initial hierarchical classifications and variation hierarchical classifications, and produces a token stream of tokens.).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the method of Harrington with the method of Webster because Harrington’s method enables a request comprising a token for authentication of an ingestion server an identifier of the ingestion server but does not include a hierarchical classification. Webster teaches a system that allows for characterization of natural language document and of search queries to locate those documents including hierarchical classifications that may represent natural language documents. Incorporating the method of Webster into the method of Harrington would improve Harrington’s authentication of ingested identifiers to consist of a structured hierarchy of classified identifiers that could be classified based on a selection request.
Webster does not expressly teach predictions of categories.
Epstein teaches applying smoothing techniques during training and inference to mitigate overconfidence in predictions (page 1, 1. Introduction, two forecasts including the last category. This conclusion is based on the notion that categories 3 and 4 are closer to one another than categories 1 and 4), wherein label smoothing is applied during training to adjust target probabilities away from extreme values, and normalization is applied during inference to generate calibrated probability distributions for predicted categories (page 986, Figure 1, Bounds on the expected kernel utility of a prediction, probabilities as well as the forecasters, page 987, categories of probability and distribution of probabilities); providing an indication of a predicted classification (page 986, Figure 1, Bounds on the expected kernel utility of a prediction, probabilities as well as the forecasters, page 987, categories of probability and distribution of probabilities).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of Webster’s method with the method of Epstein because Webster’s method includes hierarchical structured categories but does not include predictions of the categories. Epstein teaches probability forecasts of ranked categories including proper scoring for ranked categories. Incorporating the method of Epstein into the method of Webster would improve Webster’s categories to consist of a ranking and scoring of categories, wherein a resulting score includes a value of the categories prediction.
Regarding Claims 2 and 12, Epstein generating super-category and sub-category predictions utilizes selective activation of sub-layers within the hierarchical classification model, wherein only sub-model layers corresponding to an identified super-category are activated to process inputs further into sub-categories, thereby reducing computational costs (page 985, costs of degress, C is the cost of complete).
Regarding Claims 3 and 13, Harrington teaches wherein synthetic data is generate using Large Language Models ([0073] LLMs) to supplement a training dataset ([0073] a large language model (LLM)].
Regarding Claims 4 and 14, Harrington teaches the hierarchical classification model is trained using an automated data labeling pipeline, wherein the pipeline utilizes Large Language ([0073] LLMs) to supplement a training dataset ([0073] a large language model (LLM)].
Regarding Claims 5 and 15, Webster teaches logits associated with each hierarchical layer are analyzed and a category with a highest probability for the super-category is selected, followed by a selection of a sub-category based on hierarchical predictions corresponding to the identified super-category (Abstract - generates variation hierarchical classifications by varying the initial hierarchical classifications assigned, selects at least one hierarchical classification from the initial hierarchical classifications and variation hierarchical classifications, and produces a token stream of tokens.).
Regarding Claims 6 and 16, Webster teaches the hierarchical category classification includes providing detailed outputs that specify a hierarchical path traversed during classification, comprising the identified super-category and sub-category (Abstract - generates variation hierarchical classifications by varying the initial hierarchical classifications assigned, selects at least one hierarchical classification from the initial hierarchical classifications and variation hierarchical classifications, and produces a token stream of tokens.).
Regarding Claims 7 and 17, Harrington teaches one or more modifications to one or more machine learning models associated with the hierarchical classification model to reduce latency ([0073] LLMs) to supplement a training dataset ([0073] a large language model (LLM)].
Regarding Claims 8 and 18, Harrington teaches the one or more modifications include any of removing, from the one or more machine learning models, non-English words, removing stop words, and performing lemmatization ([0073] LLMs) to supplement a training dataset ([0073] a large language model (LLM)].
Regarding Claims 9 and 19, Harrington teaches the one or more modifications include enforcing a file size maximum, wherein the file size maximum is determined based on one or more estimated load time vs image size trend graphs ([0073] LLMs) to supplement a training dataset ([0073] a large language model (LLM)].
Regarding Claims 10 and 20, Harrington teaches wherein the hierarchical classification model performs dimensional reduction during preprocessing using tokenization techniques, including Bert-tiny tokenization, to create compact yet meaningful representations of the input data formats prior to classification ([0073] LLMs) to supplement a training dataset ([0073] a large language model (LLM)].
Conclusion
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/CHERYL LEWIS/Primary Examiner, Art Unit 2166 May 30, 2026
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