DETAILED ACTION
This action is in response to the application filed 07/10/2026. Claims 1-21 are pending and have been examined.
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a system and is thus an apparatus, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
Generate… candidate solutions to the user query; (This limitation is a mental process as it encompasses a human mentally creating solutions and is thus an evaluation.)
evaluate candidate solutions using the fitness function; (This limitation is a mental process as it encompasses a human mentally evaluating solutions using a function and is thus an evaluation.)
use a genetic algorithm to generate, based at least in part on the evaluations of candidate solutions, a population of candidate solutions (This limitation is a mental process as it encompasses a human mentally creating a group of solutions using an algorithm and is thus an evaluation.)
evaluate the population of candidate solutions using the fitness function; (This limitation is a mental process as it encompasses a human mentally evaluating the group of solutions using the function and is thus an evaluation.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
A computer system associated with a user, the computer system comprising: a network interface; at least one processing device operable to: (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
receive a user query via the network interface, from a user device (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
receive fitness function; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
using a large language model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
based at least in part on the evaluation of the population of candidate solutions, transmit, via the network interface, at least a portion of the population of candidate solutions to the user device for reproduction via a user device display and/or a speaker. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A computer system associated with a user, the computer system comprising: a network interface; at least one processing device operable to: uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
receive a user query via the network interface, from a user device is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
receive fitness function; is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
using a large language model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
based at least in part on the evaluation of the population of candidate solutions, transmit, via the network interface, at least a portion of the population of candidate solutions to the user device for reproduction via a user device display and/or a speaker is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites
based at least in part on the evaluation of the population of candidate solutions, using the fitness function, that indicates that the population of candidate solutions does not contain at least one suitable solution, generate another population of candidate solutions. (This limitation is a mental process as it encompasses a human mentally creating another group of solutions and is thus an evaluation.)
Therefore, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional elements of
wherein the system is operable to (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the system is operable to uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 2 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites
optimize one or more large language model hyperparameters, comprising learning rate, number of layers, number of neurons, dropout rate, and/or batch size (This limitation is a mental process as it encompasses a human mentally optimizing hyperparameters and is thus an evaluation.)
Therefore, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 further recites additional elements of
wherein the system is operable to (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 3 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the system is operable to uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites
optimize one or more large language model hyperparameters using at least one genetic algorithm (This limitation is a mental process as it encompasses a human mentally optimizing hyperparameters and is thus an evaluation.)
Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 further recites additional elements of
wherein the system is operable to (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 4 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the system is operable to uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites the same abstract ideas as claim 1. Therefore, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 further recites additional elements of
wherein the user query and the fitness function are receivable via respective webpage fields respectively configured to receive the user query and the fitness function at the user device. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the user query and the fitness function are receivable via respective webpage fields respectively configured to receive the user query and the fitness function at the user device.is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 5 is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites
modify the user query in generating the population of candidate solutions. (This limitation is a mental process as it encompasses a human mentally modifying a query and is thus an evaluation.)
Therefore, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 further recites additional elements of
wherein the system is operable to (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 6 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the system is operable to uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 6 is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites the same abstract ideas as claim 1. Therefore, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 further recites additional elements of
wherein the large language model comprises an input layer, an output layer, one or more hidden layers, and a max pooling layer. (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 7 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the large language model comprises an input layer, an output layer, one or more hidden layers, and a max pooling layer uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 7 is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 1:
Claim 8 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites
Generating… candidate solutions to the user query; (This limitation is a mental process as it encompasses a human mentally creating solutions and is thus an evaluation.)
evaluating candidate solutions using the fitness function; (This limitation is a mental process as it encompasses a human mentally evaluating solutions using a function and is thus an evaluation.)
using a genetic algorithm to generate, based at least in part on the evaluations of candidate solutions, a population of candidate solutions (This limitation is a mental process as it encompasses a human mentally creating a group of solutions using an algorithm and is thus an evaluation.)
evaluating the population of candidate solutions using the fitness function; (This limitation is a mental process as it encompasses a human mentally evaluating the group of solutions using the function and is thus an evaluation.)
Therefore, claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 further recites additional elements of
receiving a user query via the network interface, from a user device (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
receiving fitness function; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
using a large language model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
based at least in part on the evaluation of the population of candidate solutions, transmitting, via the network interface, at least a portion of the population of candidate solutions to the user device for reproduction via a user device display and/or a speaker. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 8 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 8 do not provide significantly more than the abstract idea itself, taken alone and in combination because
receiving a user query via the network interface, from a user device is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
receiving fitness function; is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
using a large language model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
based at least in part on the evaluation of the population of candidate solutions, transmitting, via the network interface, at least a portion of the population of candidate solutions to the user device for reproduction via a user device display and/or a speaker is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 8 is subject-matter ineligible.
Regarding claim 9, claim 9 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 10, claim 10 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 11, claim 11 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 12, claim 12 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 13, claim 13 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding claim 14, claim 14 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis.
Regarding Claim 15:
Subject Matter Eligibility Analysis Step 1:
Claim 15 recites computer-readable, non-transitory medium and is thus an article of manufacture, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 15 recites
Generate… candidate solutions to the user query; (This limitation is a mental process as it encompasses a human mentally creating solutions and is thus an evaluation.)
evaluate candidate solutions using the fitness function; (This limitation is a mental process as it encompasses a human mentally evaluating solutions using a function and is thus an evaluation.)
use a genetic algorithm to generate, based at least in part on the evaluations of candidate solutions, a population of candidate solutions (This limitation is a mental process as it encompasses a human mentally creating a group of solutions using an algorithm and is thus an evaluation.)
evaluate the population of candidate solutions using the fitness function; (This limitation is a mental process as it encompasses a human mentally evaluating the group of solutions using the function and is thus an evaluation.)
Therefore, claim 15 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 15 further recites additional elements of
A computer system associated with a user, the computer system comprising: a network interface; at least one processing device operable to: (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
receive a user query via the network interface, from a user device (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
receive fitness function; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
using a large language model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
based at least in part on the evaluation of the population of candidate solutions, transmit, via the network interface, at least a portion of the population of candidate solutions to the user device for reproduction via a user device display and/or a speaker. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 15 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 15 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A computer system associated with a user, the computer system comprising: a network interface; at least one processing device operable to: uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
receive a user query via the network interface, from a user device is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
receive fitness function; is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
using a large language model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
based at least in part on the evaluation of the population of candidate solutions, transmit, via the network interface, at least a portion of the population of candidate solutions to the user device for reproduction via a user device display and/or a speaker is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 15 is subject-matter ineligible.
Regarding claim 16, claim 16 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 17, claim 17 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 18, claim 18 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 19, claim 19 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 20, claim 20 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding claim 21, claim 21 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis.
Claim Rejections - 35 USC § 103
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 6, 8-11, 13, 15-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (“EvoPrompting: Language Models for Code-Level Neural Architecture Search”) (hereafter referred to as Chen) in view of Liang et al. (US 2024/0354580 A1) (hereafter referred to as Liang).
Regarding claim 1, Chen teaches
generate, using a large language model, candidate solutions to the user query (Chen, page 2, Figure 2 caption, “First, our code-pretrained LM uses the seeds as in-context prompt examples to generate candidate architectures.”);
evaluate candidate solutions using the fitness function (Chen, page 2, Figure 2 caption, “Next, the most fit member of the population are selected as in-context examples for the next meta-learning loop and all evaluated individuals are used as training data for prompt-tuning the LM.”);
use a genetic algorithm to generate, based at least in part on the evaluations of candidate solutions, a population of candidate solutions (Chen, page 4, 1st column, 3.2 LMs for evolutionary crossover and mutation, “The goal of our algorithm is to generate a set C consisting of k neural network architectures that maximize the reward EVALT(c, D) for arbitrary pairs of (D, T):
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Since this optimization problem is generally intractable, we turn to a black-box evolutionary approach for iteratively generating, scoring, and selecting the best neural network architectures.” Examiner notes that the genetic algorithm is the evolutionary algorithm.);
evaluate the population of candidate solutions using the fitness function (Chen, page 5, 2nd column, Filtering and scoring child samples, “To score and filter child samples c generated by πθ, we use the evaluation function of EVALT(c, D), which trains the model encoded by c on the dataset D and returns the lowest validation error encountered during training.” Examiner notes that the EVAL function is the fitness function);
Chen does not teach, but Liang does teach
A computer system associated with a user, the computer system comprising: a network interface; at least one processing device operable to (Liang, page 18, paragraph 0163, “The computer instructions may be stored in a computer-readable storage medium or may be transmitted from a computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website, computer, server, or data center in a wired (for example, a coaxial optical cable, an optical fiber, or a digital subscriber line) or wireless (for example, infrared, radio, or microwave) manner. The computer-readable storage medium may be any usable medium accessible by a server or a terminal, or a data storage device, such as a server or a data center, integrating one or more usable media. The usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, or a magnetic tape), an optical medium (for example, a DIGITAL VERSATILE DISC (DVD)), a semiconductor medium (for example, a solid-state drive).”):
receive a user query via the network interface, from a user device; receive fitness function (Liang, page 10, paragraph 0047, “In embodiments of this disclosure, a user logs in by using a terminal device of the user, and connects the terminal device to the search apparatus. For example, the user submits an optimization request to the search apparatus by using the terminal device. The optimization request includes a model file and an optimization requirement of a to-be-optimized model.” Examiner notes that the user query is the optimization request. Examiner further notes that the fitness function is the optimization requirement.);
and based at least in part on the evaluation of the population of candidate solutions, transmit, via the network interface, at least a portion of the population of candidate solutions to the user device for reproduction via a user device display and/or a speaker (Liang, page 11-12, paragraph 0064-0066, “[0064] In this embodiment, after receiving the model file and the optimization requirement, the search apparatus obtains the search space, and performs neural architecture search processing in the search space based on the model file. To obtain the neural network architecture that meets the optimization requirement. The neural architecture search herein may also be referred to as neural network architecture search. [0065] Step 403: Return the neural network architecture. [0066]In this embodiment, after obtaining the neural network architecture through search, the search apparatus sends the neural network architecture obtained through the search to the user, where there may be one or more neural network architectures returned to the user. For example, a plurality of neural network architectures may be returned to the user, or the architecture of one of the neural network architectures may be returned to the user. After obtaining the neural network architecture returned by the search apparatus, the user may perform full training or model fine-tuning based on data of a local service, in other words, obtain a neural network model suitable for the local service” and Liang, page 3, Figure 3,
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Examiner notes that the evaluation is the search and the candidate solutions are the plurality of neural networks returned to a user. Examiner further notes that, as depicted in figure 3, the user receives the architecture on a computer which includes a display.).
Chen and Liang are analogous to the claimed invention because they both create solutions to a query via generative models. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to have implemented the generative model of Chen on the computer system of Liang. Thus, this would be applying a known technique (creating solutions to queries) to a known device (a computer system) ready for improvement to yield predictable results (generating solutions to queries) (MPEP 2143 I. (C) Use of known technique to improve similar devices (methods, or products) in the same way). It also would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Chen to transmit information to and from users. Doing so is advantageous because “In this method, a user does not need to input a training data set, and optimization of an original user input model is completed based on a model file provided by the user and an optimization requirement of the user, to improve convenience and security of model architecture search. In addition, the method farther obtains, based on the model input by the user, a neural network architecture that better matches a user service” (Liang, page 8, paragraph 0008).
Regarding claim 2, Chen in view of Liang teaches the computer system as defined in claim 1. Chen in view of Liang further teaches
wherein the system is operable to, based at least in part on the evaluation of the population of candidate solutions, using the fitness function, that indicates that the population of candidate solutions does not contain at least one suitable solution, generate another population of candidate solutions (Chen, page 2, Figure 2 caption, “An overview of EvoPrompting. After initializing the search with a handful of manually designed program seeds, the meta-learning loop begins. First, our code-pretrained LM uses the seeds as in-context prompt examples to generate candidate architectures. Those candidate architectures are then trained on the task training data and evaluated on the task validation set. Next the most fit members of the population are selected as in-context examples for the next meta-learning loop and all evaluated individuals are used as training data for prompt-tuning the LM. From there, the meta-learning loop begins again.” Examiner notes that the loop provides multiple iterations of generating candidate solutions.).
Regarding claim 3, Chen in view of Liang teaches the computer system as defined in claim 1. Chen in view of Liang further teaches
wherein the system is operable to optimize one or more large language model hyperparameters, comprising learning rate, number of layers, number of neurons, dropout rate, and/or batch size (Chen, page 6, 2nd column, EvoPrompting finds smaller and more accurate models, “EvoPrompting possesses the Pareto frontier closest to the origin, indicating that it finds more optimal models in terms of accuracy and size” where “We also have an evaluation function EVALT(c, D): V* x D [Wingdings font/0xE0]
R
that trains the model architecture given by code c and D and outputs some real-valued fitness s
∈
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, which can be a function of model accuracy and other model characteristics.” Examiner notes that by training the models on the LM to find the optimal models, the LM hyperparameters such as size, or number of layers, is optimized.).
Regarding claim 4, Chen in view of Liang teaches the computer system as defined in claim 1. Chen in view of Liang further teaches
wherein the system is operable to optimize one or more large language model hyperparameters using at least one genetic algorithm (Chen, page 6, 2nd column, EvoPrompting finds smaller and more accurate models, “EvoPrompting possesses the Pareto frontier closest to the origin, indicating that it finds more optimal models in terms of accuracy and size” where “We also have an evaluation function EVALT(c, D): V* x D [Wingdings font/0xE0]
R
that trains the model architecture given by code c and D and outputs some real-valued fitness s
∈
R
, which can be a function of model accuracy and other model characteristics.” Examiner notes that by training the models on the LM to find the optimal models, the LM hyperparameters such as size, or number of layers, is optimized. Examiner further notes that EvoPrompting is the genetic algorithm.).
Regarding claim 6, Chen in view of Liang teaches the computer system as defined in claim 1. Chen in view of Liang further teaches
wherein the system is operable to modify the user query in generating the population of candidate solutions (Chen, page 2, Figure 2 (see below)
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Examiner notes that the prompt-tuning is modifying the user query.).
Regarding claim 8, Chen teaches
generating, using a large language model, candidate solutions to the user query (Chen, page 2, Figure 2 caption, “First, our code-pretrained LM uses the seeds as in-context prompt examples to generate candidate architectures.”);
evaluating candidate solutions using the fitness function (Chen, page 2, Figure 2 caption, “Next, the most fit member of the population are selected as in-context examples for the next meta-learning loop and all evaluated individuals are used as training data for prompt-tuning the LM.”);
using a genetic algorithm to generate, based at least in part on the evaluations of candidate solutions, a population of candidate solutions (Chen, page 4, 1st column, 3.2 LMs for evolutionary crossover and mutation, “The goal of our algorithm is to generate a set C consisting of k neural network architectures that maximize the reward EVALT(c, D) for arbitrary pairs of (D, T):
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Since this optimization problem is generally intractable, we turn to a black-box evolutionary approach for iteratively generating, scoring, and selecting the best neural network architectures.” Examiner notes that the genetic algorithm is the evolutionary algorithm.);
evaluating the population of candidate solutions using the fitness function (Chen, page 5, 2nd column, Filtering and scoring child samples, “To score and filter child samples c generated by πθ, we use the evaluation function of EVALT(c, D), which trains the model encoded by c on the dataset D and returns the lowest validation error encountered during training.” Examiner notes that the EVAL function is the fitness function);
Chen does not teach, but Liang does teach
A computer-implemented method, the method comprising: receiving a user query via the network interface, from a user device; receive fitness function (Liang, page 10, paragraph 0047, “In embodiments of this disclosure, a user logs in by using a terminal device of the user, and connects the terminal device to the search apparatus. For example, the user submits an optimization request to the search apparatus by using the terminal device. The optimization request includes a model file and an optimization requirement of a to-be-optimized model.” Examiner notes that the user query is the optimization request. Examiner further notes that the fitness function is the optimization requirement.);
and based at least in part on the evaluation of the population of candidate solutions, transmitting, via the network interface, at least a portion of the population of candidate solutions to the user device for reproduction via a user device display and/or a speaker (Liang, page 11-12, paragraph 0064-0066, “[0064] In this embodiment, after receiving the model file and the optimization requirement, the search apparatus obtains the search space, and performs neural architecture search processing in the search space based on the model file. To obtain the neural network architecture that meets the optimization requirement. The neural architecture search herein may also be referred to as neural network architecture search. [0065] Step 403: Return the neural network architecture. [0066]In this embodiment, after obtaining the neural network architecture through search, the search apparatus sends the neural network architecture obtained through the search to the user, where there may be one or more neural network architectures returned to the user. For example, a plurality of neural network architectures may be returned to the user, or the architecture of one of the neural network architectures may be returned to the user. After obtaining the neural network architecture returned by the search apparatus, the user may perform full training or model fine-tuning based on data of a local service, in other words, obtain a neural network model suitable for the local service” and Liang, page 3, Figure 3,
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Examiner notes that the evaluation is the search and the candidate solutions are the plurality of neural networks returned to a user. Examiner further notes that, as depicted in figure 3, the user receives the architecture on a computer which includes a display.).
Chen and Liang are analogous to the claimed invention because they both create solutions to a query via generative models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Chen to transmit information to and from users. Doing so is advantageous because “In this method, a user does not need to input a training data set, and optimization of an original user input model is completed based on a model file provided by the user and an optimization requirement of the user, to improve convenience and security of model architecture search. In addition, the method farther obtains, based on the model input by the user, a neural network architecture that better matches a user service” (Liang, page 8, paragraph 0008).
Regarding claim 9, claim 9 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 10, claim 10 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 11, claim 11 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 13, claim 13 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding claim 15, Chen teaches
generate, using a large language model, candidate solutions to the user query (Chen, page 2, Figure 2 caption, “First, our code-pretrained LM uses the seeds as in-context prompt examples to generate candidate architectures.”);
evaluate candidate solutions using the fitness function (Chen, page 2, Figure 2 caption, “Next, the most fit member of the population are selected as in-context examples for the next meta-learning loop and all evaluated individuals are used as training data for prompt-tuning the LM.”);
use a genetic algorithm to generate, based at least in part on the evaluations of candidate solutions, a population of candidate solutions (Chen, page 4, 1st column, 3.2 LMs for evolutionary crossover and mutation, “The goal of our algorithm is to generate a set C consisting of k neural network architectures that maximize the reward EVALT(c, D) for arbitrary pairs of (D, T):
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Since this optimization problem is generally intractable, we turn to a black-box evolutionary approach for iteratively generating, scoring, and selecting the best neural network architectures.” Examiner notes that the genetic algorithm is the evolutionary algorithm.);
evaluate the population of candidate solutions using the fitness function (Chen, page 5, 2nd column, Filtering and scoring child samples, “To score and filter child samples c generated by πθ, we use the evaluation function of EVALT(c, D), which trains the model encoded by c on the dataset D and returns the lowest validation error encountered during training.” Examiner notes that the EVAL function is the fitness function);
Chen does not teach, but Liang does teach
A computer -readable, non-transitory medium that stores program instructions that when executed by a computer system, cause the computer system to perform operations comprising (Liang, page 18, paragraph 0163, “The computer instructions may be stored in a computer-readable storage medium or may be transmitted from a computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website, computer, server, or data center in a wired (for example, a coaxial optical cable, an optical fiber, or a digital subscriber line) or wireless (for example, infrared, radio, or microwave) manner. The computer-readable storage medium may be any usable medium accessible by a server or a terminal, or a data storage device, such as a server or a data center, integrating one or more usable media. The usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, or a magnetic tape), an optical medium (for example, a DIGITAL VERSATILE DISC (DVD)), a semiconductor medium (for example, a solid-state drive).”):
receive a user query via the network interface, from a user device; receive fitness function (Liang, page 10, paragraph 0047, “In embodiments of this disclosure, a user logs in by using a terminal device of the user, and connects the terminal device to the search apparatus. For example, the user submits an optimization request to the search apparatus by using the terminal device. The optimization request includes a model file and an optimization requirement of a to-be-optimized model.” Examiner notes that the user query is the optimization request. Examiner further notes that the fitness function is the optimization requirement.);
and based at least in part on the evaluation of the population of candidate solutions, transmit, via the network interface, at least a portion of the population of candidate solutions to the user device for reproduction via a user device display and/or a speaker (Liang, page 11-12, paragraph 0064-0066, “[0064] In this embodiment, after receiving the model file and the optimization requirement, the search apparatus obtains the search space, and performs neural architecture search processing in the search space based on the model file. To obtain the neural network architecture that meets the optimization requirement. The neural architecture search herein may also be referred to as neural network architecture search. [0065] Step 403: Return the neural network architecture. [0066]In this embodiment, after obtaining the neural network architecture through search, the search apparatus sends the neural network architecture obtained through the search to the user, where there may be one or more neural network architectures returned to the user. For example, a plurality of neural network architectures may be returned to the user, or the architecture of one of the neural network architectures may be returned to the user. After obtaining the neural network architecture returned by the search apparatus, the user may perform full training or model fine-tuning based on data of a local service, in other words, obtain a neural network model suitable for the local service” and Liang, page 3, Figure 3,
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Examiner notes that the evaluation is the search and the candidate solutions are the plurality of neural networks returned to a user. Examiner further notes that, as depicted in figure 3, the user receives the architecture on a computer which includes a display.).
Chen and Liang are analogous to the claimed invention because they both create solutions to a query via generative models. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to have implemented the generative model of Chen on the computer system of Liang. Thus, this would be applying a known technique (creating solutions to queries) to a known device (a computer system) ready for improvement to yield predictable results (generating solutions to queries) (MPEP 2143 I. (C) Use of known technique to improve similar devices (methods, or products) in the same way). It also would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Chen to transmit information to and from users. Doing so is advantageous because “In this method, a user does not need to input a training data set, and optimization of an original user input model is completed based on a model file provided by the user and an optimization requirement of the user, to improve convenience and security of model architecture search. In addition, the method farther obtains, based on the model input by the user, a neural network architecture that better matches a user service” (Liang, page 8, paragraph 0008).
Regarding claim 16, claim 16 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 17, claim 17 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 18, claim 18 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 20, claim 20 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Claim(s) 5, 12, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Liang in further view of Nordfors (US 2024/0403697 A1) (hereafter referred to as Nordfors).
Regarding claim 5, Chen in view of Liang teach the computer system as defined in claim 1. Chen in view of Liang further teaches
wherein the user query and the fitness function are receivable via respective webpage … respectively configured to receive the user query and the fitness function at the user device (Liang, page 10, paragraph 0047, “In embodiments of this disclosure, a user logs in by using a terminal device of the user, and connects the terminal device to the search apparatus. For example, the user submits an optimization request to the search apparatus by using the terminal device. The optimization request includes a model file and an optimization requirement of a to-be-optimized model” where “the computer instructions may be transmitted from a website, computer, server, or data center to another website, computer, server or data center” Examiner notes that the user query is the optimization request. Examiner further notes that the fitness function is the optimization requirement.)
Chen in view of Liang doesn’t explicitly disclose respective webpage fields. Nordfors, however discloses
wherein the user query and the fitness function are receivable via respective webpage fields respectively configured to receive the user query and the fitness function at the user device (Nordfors, page 12, paragraph 0059-0060, “FIG. 4A depicts an example screen 400 for input of data within a prompt entry field of an ML model interface window of the parallel interaction user interface. As illustrated, the sample screen 400 includes a prompt entry field 303, a save ML model prompt button 404, a plurality of save prompt fields 406, and a plurality of user entries 408. [0060] In some examples, the prompt entry field 303 is a placeholder for the content of the entry field 415, as described below. The entry field 415 comprises an input text field, in which text is inputted and thereafter inserted into prompt entry field 303” where “the client device 210 may include a web browser and be configured to connect to the computing device 110 via one or more networks, such as the internet” (Nordfors, page 9, paragraph 0034). Examiner notes that the entry field is configured to receive input from a user.)
Chen, Liang, and Nordfors are analogous to the claimed invention because they both use user input for machine learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Chen and Liang to use fields for user input. Doing so is advantageous because “Generally, the purpose of the prompt may include an objective, a goal, and a function that is associated with the prompt. By way of example, a respective user entry 408 for the "purpose of the prompt" save prompt field 406 includes "Language check all responses." Generally, the known requirements may include requirements that are needed for processing the prompt, such as particular characters and/or particular identifiers and/or particular data formatting.” (Nordfors, page 12, paragraph 0062).
Regarding claim 12, claim 12 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 19, claim 19 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Claim(s) 7, 14, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Liang in further view of Shao et al. (“Transformer-Based Neural Network for Answer Selection in Question Answering”) (hereafter referred to as Shao).
Regarding claim 7, Chen in view of Liang teach the computer system as defined in claim 1. Chen in view of Liang further teaches
wherein the large language model comprises an input layer, an output layer (Chen, page 2, Figure 2
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Examiner notes that the input layer is the initialization of seed code samples and the output is the select in-context and prompt-tuning examples.)
Chen in view of Liang does not explicitly disclose hidden layers, and a max pooling layer. Shao, however, discloses
wherein the large language model comprises an input layer, an output layer, one or more hidden layers, and a max pooling layer (Shao, page 4, 1st column, 2nd paragraph, “we design a Transformer-based feature extractor followed by three aggregated strategies in the relevance matching layer, leading to three corresponding answer selection models, i.e., Transformer-based model with weighted mean pooling, max pooling…and attentive pooling” and Shao, page 4, Figure 3, See below
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Examiner notes that the pooling layer is the max pooling layer.)
Chen, Liang, and Shao are analogous to the claimed invention because they use language models that use user prompts or queries to get solutions. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Chen and Liang to use hidden layers and a max pooling layer. Doing so is advantageous because “that our proposed Transformer-based answer selection models can produce a better performance compared with several competitive baselines” (Shao, page 1, abstract).
Regarding claim 14, claim 14 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis.
Regarding claim 21, claim 21 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lanzi et al. (“ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game Design”) also discloses using genetic algorithms in LLMs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAITLYN R LAU whose telephone number is (571)272-1429. The examiner can normally be reached Monday - Thursday: 8:00 am - 6:00 pm EST.
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/K.R.L./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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