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. Specification The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The portion of the title “Model Optimization” does not describe the type of invention. It is noted the rest of the current title is merely different embodiments. 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. (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. Claim(s) 1 - 4, 6-7 , 1 0 - 20 is/are rejected under 35 U.S.C. 102 (a)(1), (a)(2) as being anticipated by Lin et al., (US Publication No. 2023/0082597), hereinafter “ Lin ” . Regarding claims 1, 14, 20 Lin discloses encapsulating a model operator in a project model to obtain a super-model corresponding to the project model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] , the model operator at least including: a network layer in the project model, the super-model being a model with a dynamically variable space structure [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; determining a configuration search space corresponding to the project model according to the model operator and a control parameter [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; training the super-model based on the configuration search space and the project model and obtaining a convergence super-model corresponding to the project model in response to that a training end condition is reached [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; and searching the convergence super-model for an adjusted model corresponding to the project model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] . Regarding claims 2, 15, Lin further discloses wherein the project model includes the plurality of model operators, and encapsulating the model operator comprises: classifying the plurality of model operators of the project model into at least one operator set according to a connection relationship between the plurality of model operators in the project model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; allocating corresponding encapsulation variables for each operator set [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; encapsulating the model operators contained in each operator set using the encapsulation variable corresponding to each operator set, and obtaining a plurality of encapsulation operators corresponding to the plurality of model operators upon completion of encapsulation, space structures of the encapsulation operators being dynamically variable [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; and connecting the plurality of encapsulation operators according to the connection relationship between the plurality of model operators to obtain the super-model corresponding to the project model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] . Regarding claim 3 , Lin further discloses wherein classifying the plurality of model operators comprises: determining an output operator corresponding to each model operator according to the connection relationship between the plurality of model operators in the project model, input data of the output operators being output data of the model operators [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; and performing, according to the output operators, set classification on the plurality of model operators of the project model to obtain at least one operator set, the model operators in the same operator set having the same output operators [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] . Regarding claim 4 , Lin further discloses performing the encapsulation of the model operators contained in each operator set by fusing the encapsulation variable corresponding to each operator set and an output channel number of the model operators in each operator set [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] . Regarding claim 6 , Lin further discloses wherein the control parameter includes a plurality of sub-model configuration parameters, and determining the configuration search space comprises: adjusting a space structure parameter of each model operator using the plurality of sub-model configuration parameters to obtain a plurality of updated structure parameters corresponding to each model operator [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; determining a vector constituted by the plurality of updated structure parameters corresponding to each model operator as a search vector of each model operator [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; and determining a search space constituted by the plurality of search vectors corresponding to the plurality of model operators as the configuration search space corresponding to the project model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] . Regarding claim 7 , Lin further discloses wherein training the super-model comprises: determining a copy of the project model as a teacher model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; performing the following processing by iterating i, 1≤i≤N, N being a total number of iterations: sampling the configuration search space for an i th time to obtain i th model configuration information [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; creating a sub-model corresponding to the i th model configuration information from the super-model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; training the sub-model based on the teacher model to obtain a convergence sub-model corresponding to the i th model configuration information [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; and determining, in response to that i is iterated to N, a set of the convergence sub-models corresponding to the N pieces of model configuration information as the convergence super-model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] . Regarding claims 10 , 1 6 , Lin further discloses screening sub-models in the convergence super-model having a same prediction accuracy as a prediction accuracy of the project model to obtain an initial compressed model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; and searching sub-models in the initial compressed model having a prediction speed greater than a prediction speed of the project model for the adjusted model corresponding to the project model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] . Regarding claims 11 , 1 7 , Lin further discloses screening sub-models in the convergence super-model having the same prediction speed as the prediction speed of the project model to obtain an initial adjusted model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] ; and searching sub-models in the initial adjusted model having a prediction accuracy greater than a prediction accuracy of the project model for the adjusted model corresponding to the project model [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] . Regarding claims 1 2, 1 8 , Lin further discloses performing, in response to an image reconstruction request transmitted by a terminal device for a to-be-reconstructed image, super-resolution reconstruction on the to-be-reconstructed image using the adjusted model to obtain a super-resolution image of the to-be-reconstructed image, and returning the super-resolution image to the terminal device [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] . Regarding claims 13 , 1 9 , Lin further discloses performing, in response to an image classification request transmitted by a terminal device for a to-be-classified image, image classification on the to-be-classified image using the adjusted model to obtain classification information of the to-be-classified image, and returning the classification information to the terminal device [ Lin, figures 6-8, 12-13, paragraphs 115-116, 119-123, 134, 208 ] . Allowable Subject Matter Claims 5, 8-9 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. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT WILLIAM J GOODCHILD whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1589 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 8am-4:30pm . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /William J. Goodchild/ Primary Examiner, Art Unit 2433