CTNF 18/187,938 CTNF 81576 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 07-07-aia AIA 07-07 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 – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1 – 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lester et al. (US PAP 2023/0325725) . As per claims 1, 8, and 15, Lester et al. teach a method, comprising: receiving a textual prompt word and textual context data (“obtaining input data (e.g., a set of text, audio data, visual data, and/or latent encoding data). A prompt can be obtained… the soft-prompt can modulate the frozen network's behavior in the same way as textual context preceding the input”; paragraphs 39, 137); generating an interim vector by encoding the textual prompt word and the textual context data using an encoder machine learning model (“To create a soft prompt for a given task, the system may first initialize the prompt as a fixed-length sequence of vectors (e.g., 20 tokens long)”; paragraphs 54, 137); generating an augmented prompt vector by processing the interim vector using a sequence generation machine learning model, the sequence generation machine learning model trained based on at least one sequence of vectors comprising a training prompt word, a training related word, and a plurality of intermediate vectors (“the systems and methods can attach these vectors to the beginning of each embedded input and feed the combined sequence into the model. Alternatively and/or additionally, the systems and methods can put the prompts at different parts of the input and analyze the effect of the different positions. The model's prediction can be compared to the target to calculate a loss, and the error can be back-propagated to calculate gradients, however the system may only apply these gradient updates to our new learnable vectors”; paragraphs 54, 137); and generating model output by processing the augmented prompt vector using a language machine learning model (“machine-learned model can be conditioned by the prompt to generate output data associated with the particular task…The prompts may have been trained with varying training datasets. Prompt ensembling can enable the weighting of a plurality of outputs to get a generalized output…The plurality of outputs can be descriptive of outputs associated with a plurality of different tasks.”; paragraphs 45 – 54). As per claims 2, 9, and 16, Lester et al. further disclose the encoder machine learning model was trained during training of a translation machine learning model, the translation machine learning model comprising the encoder machine learning model and a decoder machine learning model (“the machine-learned model(s) can process the speech data to generate a speech translation output… the system can learn to translate from German to English by manipulating the task prompt for an English to German task”; paragraphs 85, 176). As per claims 3, 10, Lester et al. further disclose the translation machine learning model is a multilingual translation model(“the machine-learned model(s) can process the speech data to generate a speech translation output… the system can learn to translate from German to English by manipulating the task prompt for an English to German task”; paragraphs 85, 176). As per claims 4, 11, and 17, Lester et al. further disclose generating the model output further comprises processing the textual context data using the language machine learning model (“machine-learned model can be conditioned by the prompt to generate output data associated with the particular task…The prompts may have been trained with varying training datasets. Prompt ensembling can enable the weighting of a plurality of outputs to get a generalized output…The plurality of outputs can be descriptive of outputs associated with a plurality of different tasks.”; paragraphs 45 – 54). As per claims 5, 12, and 18, Lester et al. further disclose generating the at least one sequence of vectors; and training the sequence generation machine learning model based at least in part on the at least one sequence of vectors (“the “tokens” of the soft prompt can be learnable vectors. The configuration can lead a soft prompt to be optimized end-to-end over a training dataset.”; paragraphs 53, 54). As per claims 6, 13, and 19 Lester et al. further disclose generating the at least one sequence of vectors comprises: mapping the training textual prompt word and the training related word in a vector space, and sampling one or more vectors from the vector space between the mapped training textual prompt word and the mapped training related word (paragraphs 45, 53, 54, 87). As per claims 7, 14, and 20, Lester et al. further disclose generating the training related word by processing the training textual prompt word and the training textual context data using an attention mechanism (paragraphs 45 – 54) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Park et al. teach text-based image generation using text prompt. Huang et al. teach Generating Query Results Based On Domain-specific Dynamic Word Embeddings . 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To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /LEONARD SAINT-CYR/ Primary Examiner, Art Unit 2658 Application/Control Number: 18/187,938 Page 2 Art Unit: 2658 Application/Control Number: 18/187,938 Page 3 Art Unit: 2658 Application/Control Number: 18/187,938 Page 4 Art Unit: 2658 Application/Control Number: 18/187,938 Page 5 Art Unit: 2658
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