DETAILED ACTION
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 .
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/06/2023 and 08/09/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Maitra et al. (Maitra), US Patent Application Publication No. US 2018/0341871 A1.
As to independent claim 1, Maitra discloses a method for universal self-adaptive prompting, comprising:
receiving, by one or more processors, a query describing a machine learning task (paragraph [0003]: one or more processors may receive a question associated with a restricted domain);
generating, by the one or more processors, a plurality of candidate responses to the query using a machine learning model (paragraph [0003]: the one or more processors may identify candidate answers to the questions and may process the candidate answers and the classification type for the question using a machine learning model));
categorizing, by the one or more processors, the machine learning task into one of a plurality of task types (paragraph [0003]: the one or more processors may process the question with a machine learning model to determine a classification type for the question; paragraphs [0032],[0034]: the question answering platform may process the processed question, with a machine learning model, to classify the question as a factoid question type or a descriptive question type);
selecting, by the one or more processors, one or more candidate responses of the plurality of candidate responses to be pseudo-demonstrations based on the task type for the machine learning task (paragraphs [0054]-[0055], [0094]: the question answering platform may select the answer from the scored and ranked candidate answers based on the classification type of the question (e.g., a factoid question type, a descriptive question type, or a list question type);
prepending, by the one or more processors, the pseudo-demonstrations to the query (Figure 1J and paragraphs [0040],[0055], [0089]: the answer “Satisfy the customer, welcome changing requirements, etc.” to the question “Can you list the principles of Agile Testing?”); and
generating, by the one or more processors, a response to the query using the machine learning model based on the query prepended with the pseudo-demonstrations (Figure 1J and paragraph [0055]: the user device may display the information indicating the answer to the user of the user device via a user interface).
As to dependent claim 2, Maitra discloses wherein the plurality of task types comprises classification, short form generation, and long form generation (paragraph [0023]).
As to dependent claim 3, Maitra discloses wherein selecting the one or more candidate responses is based on an entropy metric for classification task types, a consistency metric for short form generation task types, and an overlap metric for long form generation task types (paragraphs [0054]-[0056]).
As to dependent claim 4, Maitra discloses wherein the query received comprises an unlabeled dataset (paragraph [0020]).
As to dependent claim 5, Maitra discloses wherein categorizing the machine learning task is based on an amount of possible responses and an amount of correct responses (paragraph [0054]).
As to dependent claim 6, Maitra discloses wherein the response to the query is generated based on a maximum likelihood estimated output (paragraph [0056]).
As to dependent claim 7, Maitra discloses wherein generating the response to the query is repeated a plurality of times using the machine learning model based on the query prepended with the pseudo-demonstrations (paragraph [0027]).
As to dependent claim 8, Maitra discloses further comprises generating a final response to the query based on a majority voting output (paragraph [0054]).
As to dependent claim 9, Maitra discloses wherein the machine learning model is a large language model (paragraph [0033]).
Claims 10-17 are system claims that contain similar limitations of claims 1-8, respectively. Therefore claims 10-17 are rejected under the same rationale.
Claims 18-20 are medium claims that contain similar limitations of claims 1-3, respectively. Therefore, claims 18-20 are rejected under the same rationale.
Conclusion
Any inquiry concerning this communication should be directed to CHAU T NGUYEN at telephone number (571)272-4092. The examiner can normally be reached on M-F from 8am to 5pm (PT).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula, can be reached at telephone number 5712724128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
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