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 .
Examiner’s Note
Examiner has cited particular columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to
fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 102
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) 1-3, 5, 8-9, 14, 16-17 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Qu et al. (US 2022/0366893).
As to claim 1, Qu teaches a method, comprising: receiving, input text data 112a including a named entity 602; receiving, by the processing system, a plurality of few-shot examples (predefined intent labels Fig.2) in a support set, the plurality of few-shot examples corresponding to a plurality of predetermined intent labels; performing, by the processing system, annotation of the input text data using a few-shot learning algorithm using the plurality of few-shot examples 604-606; generating, by the processing system, labeled data including the annotated input text data 604-606; identifying, by the processing system, a label noise/error on the labeled 608-610 data using a confident learning based label noise detection algorithm 510; and generating cleaned labeled data 612 excluding one or more noisy labels from the labeled data (Pars.19, 24-29, 48-53; Figs.1-6)
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As to claim 2, Qu teach teaches wherein the generating the labeled data further comprises annotating each sentence included in the input text data to recognize the named entity and mark the named entity with a corresponding label (Fig.1).
As to claim 3, according to Qu performing the annotation using the few-shot learning algorithm further comprises performing the annotation using prompt-free few-shot learning algorithm which is using no manually drafted prompt (Figs.2, 5).
As to claim 5, Qu teaches where a first machine learning model that implements the few-shot learning algorithm using a train dataset, wherein the train dataset is greater than the plurality of few-shot examples; and fine-tuning the first machine learning model using the plurality of few-shot examples (Figs.2, 4).
As to claim 8, Qu teaches training, by the processing system, a second machine learning model 432 that implements the confident learning based label noise detection algorithm; and predicting and identifying, by the processing system, a label noise on the labeled data with the trained second machine learning model (Fig.5, 510-512).
Regarding claims 9, 14, 16, 17 and 19-20, the corresponding instructions and device comprising the steps similar to the claims addressed above are analogous, therefore rejected as being anticipated by Qu for the foregoing reasons.
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) 4, 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Qu et al. (US 2022/0366893) as applied above, and further in view of Zhang et al. (US 2025/0103813).
As to claims 4, 15 and, Qu doesn’t explicitly teach the entity recognition process comprising marking the named entity with a corresponding label and position information of the named entity in each sentence. However, Zhang teaches a method and system comprising name entity recognition models for identifying a named entity within an input sentence and annotating the named entity with labels wherein the annotated text are marked with the corresponding label and position information in the input sentence (Fig.3, 314-316; Fig.5).
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The combination of the analogous teachings would have been obvious to one of ordinary skill in the art before the time of applicant’s invention for the purpose of efficiently processing the identified entities.
Allowable Subject Matter
Claims 6-7 and 10-13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claims 6-7 and 10-13 are allowable because Qu doesn’t teach the system further comprising training a first machine learning model that implements the prompt-free few-shot learning algorithm by: tokenizing each sentence included in the input text data into one or more tokens; generating a template for each of the one or more tokens; sampling a first pair including a positive template and the template and a second pair including a negative template and the template from a templates pool; sampling a third pair including a positive sentence template and the template and a fourth pair including a negative sentence template and the templates for each token.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Song et al. (US 2022/0138572).
Tensmeyer et al. (US 2023/00334244)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL DEMELASH ABEBE whose telephone number is (571)272-7615. The examiner can normally be reached monday-friday 7-4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at 571-272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL ABEBE/Primary Examiner, Art Unit 2657
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