DETAILED ACTION
This Action is a response to the filing received 20 June 2024. No claims appear to have been presented at that time. A first set of claims was filed on 3 September 2024. In a Preliminary Amendment filed 9 September 2024, claims 1-5, 7-19 and 24 were amended; claims 20-23 and 25 were canceled; and no claims were newly added. Claims 1-19 and 24 remain pending for examination.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 1-7, 11-18 and 24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
At Step 1, the claims are evaluated for whether they recite statutory categories of subject matter. Claims 1-11 recite processes; claims 12-19 recite machines; and claim 24 recites an article of manufacture. The analysis proceeds to Step 2.
At Step 2A, Prong 1, the claims are evaluated for whether they recite (set forth or describe) a judicial exception, such as an abstract idea. Claim 1 recites the following mental process step: determining a target interface configuration based on the test results corresponding to the plurality of model interface sets. This is an evaluation, judgment or opinion a human user can make based on the information obtained in the previous steps of the method. That is, based on obtaining test results obtained by testing model interface configurations sets, a human user would be able to review test results to determine a best model interface configuration. The analysis proceeds to Step 2A, Prong 2.
At Prong 2, the claims are evaluated for whether they recite additional elements that integrate the abstract idea into a practical application. Claim 1 recites the following additional elements: (1) obtaining a test data set search space of a model interface configuration comprising a candidate model interface and a hyperparameter value range; (2) obtaining a plurality of model interface configuration sets based on the search space, each set comprising a candidate model interface and a hyperparameter value; and (3) obtaining a test result for each model interface configuration set by using the test data to test a large model called based on the configuration set. Elements (1) and (2) represent necessary pre-solution data gathering. That is, this information is necessary in order to perform the tests to derive the test results. Element (3) also recites mere data gathering, wherein a system (the large model) is tested for a response, the response being to determine a system malfunction (model performance). Accordingly, the claim does not recite additional elements that integrate the abstract idea into a practical application. The analysis proceeds to Step 2B.
At Step 2B, the claims are evaluated for whether they recite additional elements that amount to significantly more than the judicial exception. In particular, these additional elements are evaluated for whether they recite other than what is well-understood, routine and/or conventional activity in the field. The above-cited additional elements are directed to receiving or transmitting data over a network / storing and retrieving information from memory (at least elements (1) and (2)) and performing repetitive calculations (element (3), wherein the repetitive calculations are executions of the large model to produce a result). Whether considered individually or in any combination, the additional elements do not amount to significantly more than the judicial exception. Accordingly, claim 1 is ineligible under 35 U.S.C. § 101.
Claims 12 and 24 are rejected for similar reasons as claim 1. They further recite the use of computer instructions stored on computer-readable media and executable by a processor to perform the method steps. These represent the use of a general-purpose computer and/or computing environment as a tool for performing the judicial exception, and do not integrate the judicial exception into a practical application or recite significantly more.
Claims 2 and 13 recite an additional mental process step (determining the target interface configuration based on the evaluation results) and additional pre-solution data gathering (obtaining a first evaluation identifier and corresponding rule, and obtaining an evaluation result by evaluating the item to be evaluated in the test result based on the rule).
Claims 3 and 14 recite an iterative version of claim 2, and the analysis therefore does not change for claim 3.
Claims 4 and 15 recite an additional mental process step, that being generating first prompt information based on the configuration sets, evaluation results, search space, and an obtained first output requirement. A human user would be able to draft a prompt including the preceding information. The claims also recite an additional data gathering step (obtaining a first output requirement for the configuration), and an insignificant application (inputting the prompt information into the large model for processing to obtain a result).
Claims 5 and 16 further recite that the evaluation result comprises a score (further describing the obtaining of the evaluation result as set forth in claim 3); obtaining a transformed evaluation score by performing a method for power transformation on the evaluation score (either an additional mental process step or, alternatively, performing repetitive calculations); and searching for a new configuration in the search space by using the large model based on the transformed evaluation scores (an insignificant application of inputting a prompt or other data into the large model to obtain an output result).
Claims 6 and 17 recite determining a goal of recommending the configuration that matches the item to be evaluated (data gathering) and determining the target configuration based on the goal and the evaluation results (an additional mental process step).
Claims 7 and 18 recite obtaining a second output requirement for the configuration (data gathering), generating second prompt information based on the search space and the second requirement (a mental process for the reasons given with respect to claim 4), and obtaining the configuration sets by inputting the second prompt into the large model for processing (an insignificant application).
Claim 11 recites obtaining second evaluation information to be registered that comprises an identifier and a rule (data gathering), storing the second evaluation information in a database if not previously in the database and updating the rule by using the second rule if the second identifier exists in the database (an insignificant application).
In view of the foregoing, claims 2-7, 11-18 and 24 are also ineligible under 35 U.S.C. § 101.
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 1-3, 6, 8, 11-14, 17, 19 and 24 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Ma et al., U.S. 2019/0258904 A1 (“Ma”).
Regarding claim 1, Ma teaches: A computer-implemented method for recommending a large model interface configuration (Ma, e.g., ¶¶6-8, “A best predictive model is determined based on the stored, computed assessment criterion value of each validated predictive type model … a method of providing training and selection of a predictive model is provided.” See also, e.g., ¶84, “tenth indicator further may include an indicator of values for one or more optimization parameters used to determine when training/validation of the predictive type model is complete … a maximum number of configuration evaluations … a maximum number of configurations to evaluate in a single iteration …”), the method comprising:
obtaining a search space of a model interface configuration and a test data set (Ma, e.g., ¶58, “a user may execute predictive model selection application 122, which causes presentation of a first user interface windows, which may include a plurality of menus and selectors … An indicator may indicate one or more user trainings from a user interface, one or more data entries into a data field of the user interface …” See also, e.g., ¶59, “a first indicator may be received that indicates input dataset …”), wherein the search space comprises at least one candidate model interface and a value range of a hyperparameter (Ma, e.g., ¶69, “a tenth indicator of a plurality of predictive type models may be received. A champion predictive model is determined by training and validating a model of each predictive type model … where a predictive type model may be listed zero or more times …” See also, e.g., ¶70, “an eleventh indicator may include specified values for one or more of the hyperparameters and/or specified values for an automatic tuning method (autotune option) associated with each of the plurality of predictive type models …” and ¶71, “decision tree predictive type model hyperparameters may include a maximum number of decision tree levels (depth), a maximum number of child nodes for each parent node …”); and
obtaining a plurality of model interface configuration sets based on the search space, wherein each model interface configuration set comprises a candidate model interface and a value of the hyperparameter (Ma, e.g., ¶119, “a predictive type model and its associated selections in operation 220 are selected. For example, on a first iteration of operation 226, a first predictive type model is selected … on a second iteration of operation 226, a second predictive type model is selected …” See also, e.g., ¶72, “One or more values of a maximum number of decision tree levels … number of bins … split criterion … may be specified as options … a tuneDecisionTree action selects different hyperparameter configurations to run a dtreeTrain option … and a dtreeScore action multiple times …”); and
obtaining a test result corresponding to each model interface configuration set, by using the test data set to test a large model called based on each model interface configuration set (Ma, e.g., ¶123, “trained predictive model is validated using each observation vector read from the selected validation sample … and a validation criterion value or values is computed …” See also, e.g., ¶125, “the parameters that describe each validated predictive model and the computed validation criterion value(s) may be stored … in association with the hyperparameters used to train the validated predictive model.” See also, e.g., ¶128, “selected best predictive model of the selected predictive type model is scored using each observation vector read from assessment dataset 1502 and the associated hyperparameters and other characteristics of the selected best predictive model to compute an assessment criterion value based on the assessment criterion selected in operation 222 …”); and
determining a target interface configuration based on the test results corresponding to the plurality of model interface configuration sets (Ma, e.g., ¶¶147-149, “a determination is made concerning whether there is another predictive type model … to process. When there is another predictive type model to process, processing continues in operation 226 to select and process the next predictive type model … When there is not another predictive type model to process, processing continues in an operation 248. In operation 248, a trained predictive model of the plurality of predictive type models having the best assessment criterion value is determined …”).
Claims 12 and 24 are rejected for the reasons given in the rejection of claim 1 above. Examiner notes that with respect to claim 12, Ma further teaches: An electronic device, comprising: at least one processor (Ma, e.g., FIG. 1, processor 110); and a memory communicatively coupled to the at least one processor and storing instructions executable by the at least one processor (Ma, e.g., FIG. 1, computer-readable medium 108; see also, e.g., ¶48, “predictive model selection application 122 is implemented in software (comprised of computer-readable and/or computer-executable instructions) stored in computer-readable medium 108 and accessible by processor 110 for execution of the instructions that embody the operations of predictive model selection application 122 …”); wherein the at least one processor is configured to: [[[perform the method of claim 1]]]; and with respect to claim 24, Ma further teaches: A non-transitory computer readable storage medium, storing computer instructions, wherein the computer instructions are caused to enable a computer to perform a method for recommending a large model interface configuration (Ma, e.g., ¶48, “predictive model selection application 122 is implemented in software (comprised of computer-readable and/or computer-executable instructions) stored in computer-readable medium 108 and accessible by processor 110 for execution of the instructions that embody the operations of predictive model selection application 122 …”), the method comprising: [[[the method of claim 1]]].
Regarding claim 2, the rejection of claim 1 is incorporated, and Ma further teaches: wherein determining the target interface configuration based on the test results corresponding to the plurality of model interface configuration sets comprises: obtaining a first evaluation identifier and obtaining a first evaluation rule based on the first evaluation identifier, wherein the first evaluation rule comprises an item to be evaluated (Ma, e.g., ¶115, “a twelfth indicator may be received that indicates an assessment criterion method used to estimate a quality of or a goodness of fit of each predictive model to paired values of the explanatory variable X and the response variable Y using an assessment sample … For example, the eleventh indicator indicates a name of an assessment criterion method …”); and
obtaining an evaluation result corresponding to each model interface configuration set, by evaluating the item to be evaluated in the test result corresponding to each model interface configuration set based on the first evaluation rule (Ma, e.g., ¶123, “the trained predictive model is validated … a validation criterion value or values is computed based on the objective function selected in operation 220.” See also, e.g., ¶125, “parameters that describe each validated predictive model and the computed validation criterion value(s) may be stored … in association with the hyperparameters used to train the validated predictive model”); and
determining the target interface configuration based on the evaluation results corresponding to the plurality of model interface configuration sets (Ma, e.g., ¶127, “a trained predictive model of the selected predictive type model having the best validation criterion value(s) is determined from the stored validation criterion value(s) …” See also, e.g., ¶128, “selected best predictive model of the selected predictive type model is scored using each observation parameter … and the associated hyperparameters and other characteristics of the selected best predictive model to compute an assessment criterion value based on the assessment criterion selected in operation 222 …”).
Regarding claim 3, the rejection of claim 2 is incorporated, and Ma further teaches: wherein determining the target interface configuration based on the evaluation results corresponding to the plurality of model interface configuration sets comprises: searching for a new interface configuration in the search space based on the evaluation results corresponding to the plurality of model interface configuration sets (Ma, e.g., ¶73, “factorization machine predictive type model … stochastic gradient descent (SGD) algorithm … SGD algorithm proceeds until a maximum number of iterations is reached … specifying the one or more hyperparameters … include a number of factors, a learning step size, a maximum number of iterations …”);
obtaining a test result corresponding to the new interface configuration by using the test data set (Ma, e.g., ¶123, “trained predictive model is validated using each observation vector read from the selected validation sample … and a validation criterion value or values is computed …” See also, e.g., ¶125, “the parameters that describe each validated predictive model and the computed validation criterion value(s) may be stored … in association with the hyperparameters used to train the validated predictive model.”);
obtaining an evaluation result corresponding to the new interface configuration, by evaluating the item to be evaluated in the test result corresponding to the new interface configuration based on the first evaluation rule (Ma, e.g., ¶123, “the trained predictive model is validated … a validation criterion value or values is computed based on the objective function selected in operation 220.” See also, e.g., ¶125, “parameters that describe each validated predictive model and the computed validation criterion value(s) may be stored … in association with the hyperparameters used to train the validated predictive model”); and
continuing to search for another new interface configuration in the search space based on the plurality of model interface configuration sets, the evaluation results corresponding to the plurality of model interface configuration sets, the test result corresponding to the new interface configuration and the evaluation result corresponding to the new interface configuration, until a preset number of interface configurations are searched, and selecting the target interface configuration from the preset number of interface configurations based on evaluation results corresponding to the preset number of interface configurations (Ma, e.g., ¶126, “a determination is made concerning whether there is another train/validate cycle to process based on the number of train/validate cycles k … and a current iteration number of operation 228 …” See also, e.g., ¶¶147-149, “a determination is made concerning whether there is another predictive type model of the plurality of predictive type models to process. When there is another predictive type model to process, processing continues in operation 226 to select and process the next predictive type mode of the plurality of predictive type models …”).
Claims 13-14 are rejected for the additional reasons given in the rejections of claims 2-3 above.
Regarding claim 6, the rejection of claim 2 is incorporated, and Ma further teaches: wherein determining the target interface configuration based on the evaluation results corresponding to the plurality of model interface configuration sets comprises: determining a goal of recommending the model interface configuration that matches the item to be evaluated (Ma, e.g., ¶¶84-85, “tenth indicator further may include an indicator of values for one or more optimization parameters used to determine when training/validation of the predictive model type is complete … an indicator of an objective function … name of an objective function … specifies a measure of model error as a measure of predictive model performance …” See also, e.g., ¶115, “a twelfth indicator may be received that indicates an assessment criterion method used to estimate a quality or goodness of fit of each predictive model … used to select a champion predictive model from the plurality of predictive models …”); and
determining the target interface configuration from the plurality of model interface configuration sets based on the goal of recommending the model interface configuration, and the evaluation results corresponding to the plurality of model interface configuration sets (Ma, e.g., ¶127, “a trained predictive model of the selected predictive type model having the best validation criterion value(s) is determined from the stored validation criterion value(s) …” See also, e.g., ¶128, “selected best predictive model of the selected predictive type model is scored … to compute an assessment criterion value based on the assessment criterion selected in operation 222 …”).
Claim 17 is rejected for the additional reasons given in the rejection of claim 6 above.
Regarding claim 8, the rejection of claim 1 is incorporated, and Ma further teaches: wherein obtaining the test result corresponding to each model interface configuration set, by using the test data set to test the large model called based on each model interface configuration set comprises: obtaining an interface function corresponding to each model interface configuration set by transforming first interface call information corresponding to the candidate model interface in each model interface configuration set, wherein the first interface call information comprises a value of an input parameter, and a method of parsing an output parameter (Ma, e.g., ¶123, “in operation 232, the trained predictive model is validated using each observation vector read from the selected validation sample … and a validation criterion or values is computed based on the objective function selected in operation 220.” See also, e.g., the code example of ¶124, which includes an indicator of input data, a call to the model for validation, and a method for interpreting the output in order to generate a score); and
obtaining an interface call output result by calling the interface function corresponding to each model interface configuration set and using the test data set to test the large model called based on each model interface configuration set; and parsing a model output result from the interface call output result based on the method of parsing the output parameter; and obtaining the test result corresponding to each model interface configuration set based on the model output result (Ma, e.g., ¶123, “in operation 232, the trained predictive model is validated using each observation vector read from the selected validation sample … and a validation criterion or values is computed based on the objective function selected in operation 220.” See also, e.g., the code example of ¶124, which includes an indicator of input data, a call to the model for validation, and a method for interpreting the output in order to generate a score).
Claim 19 is rejected for the additional reasons given in the rejection of claim 8 above.
Regarding claim 11, the rejection of claim 1 is incorporated, and Ma further teaches: obtaining second evaluation information to be registered, wherein the second evaluation information comprises a second evaluation identifier and a second evaluation rule (Ma, e.g., ¶84, “tenth indicator further may include an indicator of values for one or more optimization parameters … a maximum number of configuration evaluations … maximum number of configurations …” See also, e.g., ¶85, “tenth indicator further may include an indicator of an objective function … indicates a name of an objective function …” See also, e.g., ¶115, “a twelfth indicator may be received that indicates an assessment criterion method used to estimate a quality or goodness of fit of each predictive value … A default value for the assessment criterion method may further be stored, for example, in computer-readable medium 108 …”); and
storing the second evaluation information into a second database in case that the second evaluation identifier does not exist in the second database; and updating an evaluation rule corresponding to the second evaluation identifier in the second database by using the second evaluation rule in case that the second evaluation identifier exists in the second database (Ma, e.g., ¶85, “tenth indicator further may include an indicator of an objective function … indicates a name of an objective function …” See also, e.g., ¶115, “a twelfth indicator may be received that indicates an assessment criterion method used to estimate a quality or goodness of fit of each predictive value … A default value for the assessment criterion method may further be stored, for example, in computer-readable medium 108 …” Examiner’s note: the user may enter a plurality of evaluation information / identifiers, which may alternatively be stored into the database, or one or more default values may be stored in the database).
Claim Rejections - 35 USC § 103
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.
Claims 4, 7, 15 and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Ma in view of Grida Ben Yahya et al., U.S. 2025/0088946 A1 (“Grida Ben Yahya”).
Regarding claim 4, the rejection of claim 3 is incorporated, and Ma further teaches: wherein searching for the new interface configuration in the search space based on the evaluation results corresponding to the plurality of model interface configuration sets comprises: obtaining a first output requirement for the model interface configuration, wherein the first output requirement comprises at least one of: a goal of recommending the model interface configuration (Ma, e.g., ¶58, “a user may execute predictive model selection application 122 …” See also, e.g., ¶60, “a second indicator may be received that indicates response variable Y …” and, e.g., ¶85, “tenth indicator further may include an indicator of an objective function … name of an objective function … specifies a measure of model error as a measure of predictive model performance …”), a requirement for a number of model interface configurations (Ma, e.g., ¶84, “tenth indicator may include an indicator of values for one or more optimization parameters used to determine when training/validation of the predictive type model is complete. For example, a maximum number of configuration evaluations”), a first condition that the model interface configuration satisfies (Ma, e.g., ¶115, “a twelfth indicator may be received that indicates an assessment criterion method used to estimate a quality of or a goodness of fit of each predictive model to paired values of the explanatory variable X and the response variable Y …”), and a format requirement for the model interface configuration (Ma, e.g., ¶¶60-61, “second indicator may be received that indicates response variable Y … third indicator may be received that indicates one or more explanatory variables …” See also, e.g., ¶¶69-70, “tenth indicator of a plurality of predictive type models may be received … an eleventh indicator may include specified values for one or more of the hyperparameters … associated with each of the plurality of predictive type models …”); …
the evaluation results corresponding to the plurality of model interface configuration sets, the search space, and the first output requirement (Ma, e.g., ¶123, “trained predictive model is validated using each observation vector read from the selected validation sample … and a validation criterion value or values is computed …” See also, e.g., ¶125, “the parameters that describe each validated predictive model and the computed validation criterion value(s) may be stored … in association with the hyperparameters used to train the validated predictive model.” See also, e.g., ¶128, “selected best predictive model of the selected predictive type model is scored using each observation vector read from assessment dataset 1502 and the associated hyperparameters and other characteristics of the selected best predictive model to compute an assessment criterion value based on the assessment criterion selected in operation 222 …”).
Ma does not more particularly teach generating first prompt information based on the plurality of model interface configuration sets and obtaining the new interface configuration by inputting the first prompt information into the large model for processing. However, Grida Ben Yahya does teach: generating first prompt information based on the plurality of model interface configuration sets (Grida Ben Yahya, e.g., ¶135, “AI assistant service 427 receives a subsequent prompt … with a specific intent to modify the deployment configuration …”) … ; and
obtaining the new interface configuration by inputting the first prompt information into the large model for processing (Grida Ben Yahya, e.g., ¶135, “AI assistant service 427 using the AI language model generates a modification to the deployment configuration according to the intent expressed in the subsequent prompt”) for the purpose of assisting a user in generating or modifying network function configurations for deployment readiness (Grida Ben Yahya, e.g., ¶¶130-144).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for machine learning model prediction and selection as taught by Ma to provide for generating first prompt information based on the plurality of model interface configuration sets and obtaining the new interface configuration by inputting the first prompt information into the large model for processing because the disclosure of Grida Ben Yahya shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for artificial intelligence-assisted network configuration optimization to provide for generating first prompt information based on the plurality of model interface configuration sets and obtaining the new interface configuration by inputting the first prompt information into the large model for processing for the purpose of assisting a user in generating or modifying network function configurations for deployment readiness (Grida Ben Yahya, Id.).
Claim 15 is rejected for the additional reasons given in the rejection of claim 4 above.
Regarding claim 7, the rejection of claim 1 is incorporated, and Ma further teaches: wherein obtaining the plurality of model interface configuration sets based on the search space comprises: obtaining a second output requirement for the model interface configuration, wherein the second output requirement for the model interface configuration comprises at least one of: a second condition that the model interface configuration satisfies, a requirement for a number of model interface configurations, and a format requirement for the model interface configuration (Ma, e.g., ¶84, “tenth indicator may include an indicator of values for one or more optimization parameters used to determine when training/validation of the predictive type model is complete. For example, a maximum number of configuration evaluations”).
Ma does not more particularly teach generating second prompt information based on the search space and second output requirement, and obtaining the plurality of model interface configuration sets by inputting the second prompt information into the large model for processing. However, Grida Ben Yahya does teach: generating second prompt information based on the search space and the second output requirement (Grida Ben Yahya, e.g., ¶135, “AI assistant service 427 receives a subsequent prompt … with a specific intent to modify the deployment configuration …”); and
obtaining the plurality of model interface configuration sets by inputting the second prompt information into the large model for processing (Grida Ben Yahya, e.g., ¶135, “AI assistant service 427 using the AI language model generates a modification to the deployment configuration according to the intent expressed in the subsequent prompt”) for the purpose of assisting a user in generating or modifying network function configurations for deployment readiness (Grida Ben Yahya, e.g., ¶¶130-144).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for machine learning model prediction and selection as taught by Ma to provide for generating first prompt information based on the plurality of model interface configuration sets and obtaining the new interface configuration by inputting the first prompt information into the large model for processing because the disclosure of Grida Ben Yahya shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for artificial intelligence-assisted network configuration optimization to provide for generating first prompt information based on the plurality of model interface configuration sets and obtaining the new interface configuration by inputting the first prompt information into the large model for processing for the purpose of assisting a user in generating or modifying network function configurations for deployment readiness (Grida Ben Yahya, Id.).
Claim 18 is rejected for the additional reasons given in the rejection of claim 7 above.
Claims 5 and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over Ma in view of Langford, Michael, U.S. 2025/0156730 A1 (“Langford”).
Regarding claim 5, the rejection of claim 3 is incorporated, and Ma further teaches: wherein the evaluation result comprises an evaluation score for the item to be evaluated (Ma, e.g., ¶128, “selected best predictive model of the selected predictive type model is scored using each observation vector … and the associated hyperparameters and other characteristics of the selected best predictive model to compute an assessment criterion value based on the assessment criterion …”), and
searching for the new interface configuration in the search space based on the evaluation results corresponding to the plurality of model interface configuration sets comprises: … searching for the new interface configuration in the search space by using the large model based on the [] evaluation scores corresponding to the plurality of model interface configuration sets (Ma, e.g., ¶¶147-149, “a determination is made concerning whether there is another predictive type model … to process. When there is another predictive type model to process, processing continues in operation 226 to select and process the next predictive type model … When there is not another predictive type model to process, processing continues in an operation 248. In operation 248, a trained predictive model of the plurality of predictive type models having the best assessment criterion value is determined …”).
Ma does not more particularly teach obtaining a transformed evaluation score by performing data transformation on the evaluation score corresponding to each model interface configuration set with a method for power transformation. However, Langford does teach: obtaining a transformed evaluation score by performing data transformation on the evaluation score corresponding to each model interface configuration set with a method for power transformation (Langford, e.g., ¶75, “Another example of numerical feature transformation is a Box-Cox transformation … comprise a family of power transformations that can be applied to make data more normally distributed …”) for the purpose of reducing the dimensionality of data to be used in configuring one or more machine learning models based on one or more parameters or configurations of the machine learning model being trained or configured (Langford, e.g., ¶¶74-82).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for machine learning model prediction and selection as taught by Ma to provide for obtaining a transformed evaluation score by performing data transformation on the evaluation score corresponding to each model interface configuration set with a method for power transformation because the disclosure of Langford shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for reducing dataset dimensionality in machine learning model generation to provide for obtaining a transformed evaluation score by performing data transformation on the evaluation score corresponding to each model interface configuration set with a method for power transformation for the purpose of reducing the dimensionality of data to be used in configuring one or more machine learning models based on one or more parameters or configurations of the machine learning model being trained or configured (Langford, Id.).
Claim 16 is rejected for the additional reasons given in the rejection of claim 5 above.
Claims 9-10 are rejected under 35 U.S.C. § 103 as being unpatentable over Ma in view of Perez, Ricardo Martinez, U.S. 2004/0172620 A1 (“Perez”).
Regarding claim 9, the rejection of claim 1 is incorporated, but Ma does not more particularly teach obtaining second and verifying interface call information to be registered which comprises an interface identifier, determining whether the interface identifier exists in a database, and if not, storing the second interface call information, and if so, updating interface call information corresponding to the identifier. However, Perez does teach: obtaining second interface call information to be registered, and verifying the second interface call information, wherein the second interface call information comprises an interface identifier (Perez, e.g., ¶23, “Framework API 24 registers the Framework native code 21 of a Framework native function into the registration database 48 and assigns the native function an entry identification … application 18 calls a register function …” See also, e.g., ¶29, “After the J2ME application 18 performs the instantiation process 300 and the registration process 400, the Framework API 24 will be stored in the memory 14 … will include the registration database 48 having therein one or more entry identifications …”); and
determining whether the interface identifier exists in a first database in case that the verifying of the second interface call information passes; and storing the second interface call information into the first database in case that the interface identifier does not exist in the first database; and updating interface call information corresponding to the interface identifier in the first database by using the second interface call information in case that the interface identifier exists in the first database (Perez, e.g., ¶23, “Framework API 24 registers the Framework native code 21 of a Framework native function into the registration database 48 and assigns the native function an entry identification … application 18 calls a register function …” See also, e.g., ¶30, “After passing the entry identification … searches for the Framework native code 21 associated with this entry identification in the registration database … determined whether the Framework native code 21 associated with this entry identification was successfully found. If the Framework native code 21 was not found, at 508 the execution process 500 fails”) for the purpose of enabling the registration of and access to various function interface functionalities through a database that may be populated or updated based on a registration request (Perez, e.g., ¶¶19-31).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for machine learning model prediction and selection as taught by Ma to provide for obtaining second and verifying interface call information to be registered which comprises an interface identifier, determining whether the interface identifier exists in a database, and if not, storing the second interface call information, and if so, updating interface call information corresponding to the identifier because the disclosure of Perez shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for interface call and function code registration and updating to provide for obtaining second and verifying interface call information to be registered which comprises an interface identifier, determining whether the interface identifier exists in a database, and if not, storing the second interface call information, and if so, updating interface call information corresponding to the identifier for the purpose of enabling the registration of and access to various function interface functionalities through a database that may be populated or updated based on a registration request (Perez, Id.).
Regarding claim 10, the rejection of claim 9 is incorporated, and Perez further teaches: generating registration state information of the second interface call information based on a registration result of the second interface call information, wherein the registration state information comprises any one of: a registration success, an update success, a registration failure, or an update failure (Perez, e.g., ¶23, “Framework API 24 registers the Framework native code 21 of a Framework native function into the registration database 48 and assigns the native function an entry identification … application 18 calls a register function …” See also, e.g., ¶30, “After passing the entry identification … searches for the Framework native code 21 associated with this entry identification in the registration database … determined whether the Framework native code 21 associated with this entry identification was successfully found. If the Framework native code 21 was not found, at 508 the execution process 500 fails”).
Conclusion
Examiner has identified particular references contained in the prior art of record within the body of this action for the convenience of Applicant. Although the citations made are representative of the teachings in the art and are applied to the specific limitations within the enumerated claims, the teaching of the cited art as a whole is not limited to the cited passages. Other passages and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art and/or disclosed by Examiner.
Examiner respectfully requests that, in response to this Office Action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
When responding to this Office Action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 C.F.R. 1.111(c).
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Any inquiry concerning this communication or earlier communication from Examiner should be directed to Andrew M. Lyons, whose telephone number is (571) 270-3529, and whose fax number is (571) 270-4529. The examiner can normally be reached Monday to Friday from 10:00 AM to 6:00 PM ET. If attempts to reach Examiner by telephone are unsuccessful, Examiner’s supervisor, Wei Mui, can be reached at (571) 272-3708. Information regarding the status of an application may be obtained from the Patent Center system. For more information about the Patent Center system, see https://www.uspto.gov/patents/apply/patent-center. If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call (800) 786-9199 (in USA or Canada) or (571) 272-1000.
/Andrew M. Lyons/Primary Examiner, Art Unit 2191
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