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) 1-4, 7-8, 11-13, 16-18 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Li et al (“UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance” Oct 2022)
1. A large model optimization training method, comprising:
taking, as candidate queries, queries collected from a predetermined data source and capable of serving as input to a large model in response to determining that an optimization triggering condition is met (see section 2.1 on training dataset consists of text prompt (i.e. candidate queries));
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screening out target queries from the candidate queries, the target queries being queries which cannot be correctly processed by the large model (See section 2.1 e.g. noised sample (i.e. sample that cannot be correctly processed)); and
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constructing respectively corresponding training samples according to the target queries, the training samples being used for carrying out optimization training on the large model (See section 2.1 on denoising (i.e. constructing denoised samples)).
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2. The method according to claim 1, wherein screening out the target queries from the candidate queries comprises: performing the following processing on each candidate query: taking the candidate query as the input to the large model to obtain a reply corresponding to the candidate query and generated by the large model, and taking the candidate query as the screened-out target query in response to determining that the reply is an error reply which is not matched with the candidate query (See section 2.1 on error loss).
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3. The method according to claim 2, wherein determining that the reply is an error reply which is not matched with the candidate query comprises: inputting the candidate query and the reply into an evaluation model obtained by pre-training to obtain an evaluation result indicating whether the reply is matched with the candidate query and output by the evaluation model (See section 2.2. on image text matching).
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4. The method according to claim 1, wherein constructing respectively corresponding training samples according to the target queries comprises: for each target query, performing the following processing: determining a problem type corresponding to the target query, determining a sample type corresponding to the problem type, and constructing the training sample of the sample type according to the target query (See section 1 on problem type and sample type such as styles, features, descriptions).
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7. The method according to claim 1, further comprising: before screening out the target queries from the candidate queries, determining the query types of the candidate queries; and after screening out the target queries from the candidate queries, counting numbers of the target queries belonging to different query types, and determining processing capacities of the large model for different query types according to the counting result (See section 2.1 on signal to noise ratio. Examiner Note: signal is the original data/query types while the noise is the different data/query type; the ratio implied the numbers).
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8. The method according to claim 1, further comprising: performing optimization training on the large model by using the training samples (See section 2.2).
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Claims 11-13, 16-18 are drawn to claims above and are rejected for the same reason. See e.g. section 4 on running experiments with computing system which inherently has processor, memory and non-transitory computer readable storage medium.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bai et al (US 20230334834 A1) disclose model training based on synthetic data. See abstract.
Croitoru et al (“Diffusion Models in Vision: A Survey” March 2023) disclose a survey of diffusion models including supervised fine tuning, desire for data quality, coverage and diversity. See abstract and pg. 10860-10861.
Lian et al (“LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models” Oct 2023) disclose in-context learning and desire for curating a diverse and high quality dataset, fine tuning diffusion model. See section 1
Zhang et al (“Text-to-image Diffusion Models in Generative AI: A Survey” 2015) disclose a survey of Text-to-image Diffusion Models in Generative AI. See abstract.
Chin et al (“Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts” sept 2023) disclose tool that automatically finds problematic prompts for diffusion models. See abstract.
Wen et al (“Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery” Dec 18, 2023) disclose prompt tuning and discovery. See abstract.
Allowable Subject Matter
Claims 5-6, 9-10, 14-15, 19-20 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:
Claim 5 recites ”wherein the problem types comprise a data coverage problem, a model capability problem and a data quality problem”. While Lian et al (“LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models” Oct 2023) disclose in-context learning and desire for curating a diverse and high quality dataset; Croitoru et al (“Diffusion Models in Vision: A Survey” March 2023) disclose desire for data quality, coverage and diversity; none of the prior arts alone or in combination render obvious “wherein the problem types comprise a data coverage problem, a model capability problem and a data quality problem”.
Claim 9 recites “The method according to claim 8, wherein the training samples comprise a first type of training samples and a second type of training samples; wherein performing optimization training on the large model by using the training samples comprises: pre-training the large model by using the first type of training samples to obtain a first model, performing supervised fine-tuning training on the first model by using the second type of training samples to obtain a second model, and performing actual inference application by the second model; or pre-training the large model by using the first type of training samples, performing supervised fine-tuning training on the large model by using the second type of training samples, and in response to the fact that the pre-training is not completed after the supervised fine-tuning training is completed to obtain a third model, performing actual inference application by the third model, and in response to completion of the pre-training, obtaining a first model, performing supervised fine-tuning training on the first model by using the second type of training samples to obtain a second model, and replacing the third model by the second model for actual inference application.” While the concept of pre-trained model is well known in the art, the specific of training a pre-trained model using the training samples as generated in claim 1 is not anticipated or render obvious from the prior arts.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUT WONG whose telephone number is (571)270-1123. The examiner can normally be reached M-F 10am-6pm EST.
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/LUT WONG/ Primary Examiner, Art Unit 2127
