DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-16 filed on 9/21/2023 have been reviewed and considered by this office action.
Information Disclosure Statement
The information disclosure statements filed on 9/21/2023 and 12/18/2024 have been reviewed and considered by this office action.
Drawings
The drawings filed on 9/21/2023 have been reviewed and are considered acceptable.
Specification
The specification filed on 9/21/2023 has been reviewed and is considered acceptable.
Claim Objections
Claim 13 is objected to because of the following informalities: Claim 13 recites the term “FAR table”, without properly defining the acronym “FAR”. Please amend the claims to include the appropriate definition for “FAR” prior to use in the claim. Claim 14 depends upon claim 13 and is objected to by virtue of dependency. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “model pool” in claim 15; “model selector” in claim 15; “hybrid model candidate creator” in claim 15; and “hybrid model selector” in claim 15.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more. Claim 1 recites, “selecting one of the plurality of hybrid model candidates as a hybrid model by comparing the plurality of hybrid model candidates.”, which analyzed under Step 2A Prong One, includes the act of selecting a model based upon a comparison which are actions that can reasonably be performed in the human mind and thus, fall within the, “Mental Process” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. Claim 1 further recites, “creating each of a plurality of hybrid model candidates that judge the categories, by selecting and combining two or more models from among the plurality of models pooled;”, which analyzed under Step 2A Prong Two, simply create new models by combining existing models, essentially adjusting values within the model without application and thus simply apply the use of the judicial exception (see MPEP 2106.05(f)). Claim 1 additionally recites, “pooling a plurality of models that predict categories of input data, at least one of the plurality of models being a model trained by machine learning;”, which analyzed under Step 2A Prong Two, adds insignificant extra solution activity in the form of mere data gathering (see MPEP 2106.05(g)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed under Step 2B, the additional elements merely amount to gathering hybrid model data and sending the data over a network. Analyzed under Berkheimer, the act of gathering and sending data over a network has been deemed as well-understood, routine, and conventional by the courts (see MPEP 2106.05(d)(II), “sending/receiving data over a network”).
Independent claims 15 and 16 are substantially similar to claim 1 and are thus rejected using the same rationale.
Claim 15 differs from claim 1 in that it includes the additional elements of, “a model selector”, “a model pool”, “a hybrid model candidate creator”, and “a hybrid model selector”, which generally recited represent merely generic computer components for implementing the abstract idea.
Similarly claim 16 recites, “a non-transitory computer-readable medium” and “a computer”, however, as generally recited merely represent generic computer components for implementing the abstract idea.
Dependent claims 2-14 are rejected under 35 U.S.C. 101 as being directed towards an abstract idea without significantly more. For instance, claims 3, 5, 9, and 13, each include instances excluding, selecting, and comparing of model data, which analyzed under Step 2A Prong One, includes limitations that can reasonable be performed in the human mind and thus fall within the, “Mental Processes” grouping of abstract ideas. Further, claims 4, 6, 7, 10-11, and 14, each includes instances of performing various calculations, which analyzed under Step 2A Prong Two, include instances of mathematical calculations and thus fall within the, “Mathematical Concepts” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. Claims 5-7 and 13-14, each includes limitations of gathering various data, which analyzed under Step 2A Prong Two, adds insignificant extra solution activity in the form of mere data gathering (see MPEP 2106.05(g)). Further, claims 2, 8, and 12, include descriptions as to the type of input data, disclose the output is part of an intermediate/final layer of a deep learning model, and disclose various details about hardware cost, which analyzed under Step 2A Prong Two, simply links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed under Step 2B, the additional elements merely amount to gathering hybrid model data and sending the data over a network. Analyzed under Berkheimer, the act of gathering and sending data over a network has been deemed as well-understood, routine, and conventional by the courts (see MPEP 2106.05(d)(II), “sending/receiving data over a network”).
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, 5-6, 8-11, and 15-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Daultani et al. (US PGPUB 20210406932).
Regarding Claims 1 and 15-16; Daultani teaches; A hybrid model creation method comprising: pooling a plurality of models that predict categories of input data, at least one of the plurality of models being a model trained by machine learning; (Daultani; at least paragraphs [0002] and [0013]; disclose selecting a plurality of parent machine learning models that have a higher fitness score compared to other models in a population)
creating each of a plurality of hybrid model candidates that judge the categories, by selecting and combining two or more models from among the plurality of models pooled; and (Daultani; at least paragraph [0013]; disclose generating a virtual model (i.e. hybrid model) by combining the output of a plurality of the selected parent models)
selecting one of the plurality of hybrid model candidates as a hybrid model by comparing the plurality of hybrid model candidates. (Daultani; at least paragraph [0013]; disclose providing the generated virtual model back to the population, and selecting a model among the models with the highest fitness score).
Regarding Claim 5; Daultani teaches; The hybrid model creation method according to claim 1, further comprising :before selecting the two or more models: obtaining a prediction accuracy of each of the plurality of models pooled by inputting a plurality of validation data sets into each of the plurality of models pooled and causing each of the plurality of models pooled to predict the categories of the plurality of validation sets; and excluding each model whose prediction accuracy is less than or equal to a threshold from the plurality of models pooled, wherein the two or more models are selected from the plurality of models remaining after excluding each model whose prediction accuracy is less than or equal to the threshold. (Daultani; at least paragraphs [0080]-[0088]).
Regarding Claim 6; Daultani teaches; The hybrid model creation method according to claim 1, further comprising: before selecting the two or more models: obtaining predictions of each of the plurality of models pooled by inputting a plurality of validation data sets into each of the plurality of models pooled and causing each of the plurality of models pooled to predict the categories of the plurality of validation sets; calculating correlations for all of the plurality of models pooled using the predictions; excluding each model whose correlation with all other models is stronger than a threshold from the plurality of models pooled, wherein the two or more models are selected from the plurality of models after excluding each model whose correlation with all other models is stronger than the threshold. (Daultani; at least paragraphs [0080]-[0088]).
Regarding Claim 8; Daultani teaches; The hybrid model creation method according to claim 6, wherein in a deep learning model, the prediction is an output of an intermediate layer or a final layer of the deep learning model. (Daultani; at least paragraph [0052])
Regarding Claim 9; Daultani teaches; The hybrid model creation method according to claim 1, wherein each of the plurality of hybrid model candidates is a machine learning model that: takes, as input, two or more outputs obtained by inputting a plurality of validation data sets into each of the two or more models selected to compose the hybrid model candidate and causing each of the two or more models to predict the categories of the plurality of validation data sets; and outputs judgments obtained by judging the categories of the plurality of validation data sets, and the one of the plurality of hybrid model candidates is selected as the hybrid model by comparing the judgments output by the plurality of hybrid model candidates. (Daultani; at least paragraphs [0013]-[0026]).
Regarding Claim 10; Daultani teaches; The hybrid model creation method according to claim 9, wherein comparing the plurality of hybrid model candidates includes: for each of the plurality of hybrid model candidates, calculating an importance of each of the two or more models selected to compose the hybrid model candidate from judgments output by the hybrid model candidate; and reporting each model whose calculated importance is below a preset threshold. (Daultani; at least paragraphs [0013]-[0026]).
Regarding Claim 11; Daultani teaches; The hybrid model creation method according to claim 9, wherein comparing the plurality of hybrid model candidates includes, for each of the plurality of hybrid model candidates, calculating an importance of each of the two or more models selected to compose the hybrid model candidate from judgments output by the hybrid model candidate, and the selecting includes selecting one of the plurality of hybrid model candidates, excluding each hybrid model candidate including a model whose calculated importance is below a preset threshold, as the hybrid model. (Daultani; at least paragraphs [0016]-[0017]).
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Daultani et al. (US PGPUB 20210406932) in view of Diao et al. (US PGPUB 20190362480).
Regarding Claim 2; Daultani appears to be silent on; The hybrid model creation method according to claim 1, wherein the input data is an inspection image of a manufactured product, and the categories include a category in which the manufactured product is non-defective and a category in which the manufactured product is defective.
However, Diao teaches; The hybrid model creation method according to claim 1, wherein the input data is an inspection image of a manufactured product, and the categories include a category in which the manufactured product is non-defective and a category in which the manufactured product is defective. (Diao; at least paragraphs [0045] and [0101]; disclose a manufacturing system and method which includes a plurality of models for detecting defects in manufactured products which could reasonably be applied to the hybrid model combining as taught by Daultani).
Daultani and Diao are analogous art because they are from the same field of endeavor or problem solving area of, model training and selection for control systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have incorporated the known method of using a plurality of models for detecting visual defects as taught by Diao with the known system of a model training and selection control system as taught by Daultani in order to improve production downtime and detection accuracy as taught by Diao (paragraph [0003]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Daultani et al. (US PGPUB 20210406932) in view of Li (CN 113407680).
Regarding Claim 7; Daultani teaches; The hybrid model creation method according to claim 1, further comprising: before creating the plurality of hybrid model candidates: obtaining predictions of each of a plurality of models pooled or selected, by inputting a plurality of validation data sets into each of the plurality of models pooled or selected and causing each of the plurality of models pooled or selected to predict the categories of the plurality of validation data sets; and (Daultani; at least paragraphs [0013]-[0026])
Daultani appears to be silent on; calculating correlations for all of the plurality of models pooled or selected using the predictions, wherein each of the plurality of hybrid model candidates is created by combining the two or more models selected so as not to include a combination of two models having a stronger correlation than a threshold.
However, Li teaches; calculating correlations for all of the plurality of models pooled or selected using the predictions, wherein each of the plurality of hybrid model candidates is created by combining the two or more models selected so as not to include a combination of two models having a stronger correlation than a threshold. (Li; at least pages 3 and 4; disclose a hybrid model creation system and method in which a plurality of correlation coefficients are calculated regarding the combination of a plurality of models and wherein models will not be combined depending the calculated correlation coefficient with respect to a threshold).
Daultani and Li are analogous art because they are from the same field of endeavor or problem solving area of, model training and selection for control systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have incorporated the known method of not combining models based upon a correlation coefficient as taught by Li with the known system of a model training and selection control system as taught by Daultani in order to improve efficiency when combining multiple models as taught by Li (Abstract).
Allowable Subject Matter
The office would first like to make note that each of the listed claims have an outstanding 101 rejection that must be overcome before being considered for allowance.
Claims 3-4 and 12-14 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 3 recites: “The hybrid model creation method according to claim 2, wherein the creating includes:
excluding each output value predicted to be defective due to being higher than a threshold from output values obtained by, for each of the plurality of hybrid model candidates, inputting a validation data set into each of the two or more models selected to compose the hybrid model candidate and causing each of the two or more models to predict the categories of the validation data set; and
creating the plurality of hybrid model candidates by machine learning using the output values excluding each output value higher than the threshold as input and using ground truth values of the validation data set corresponding to the output values used.”
The closest prior art of record is Daultani (US PGPUB 20210406932). Daultani discloses a hybrid model creation system and method in which a population of models is analyzed and then subsequently grouped based upon determined metrics. The models can be further combined based upon determined accuracies and the resulting outputs are combined to create a new hybrid model which is then subsequently reintroduced to the population creating a more refined hybrid model. However, Daultani is silent on, “excluding each output value predicted to be defective due to being higher than a threshold from output values obtained by, for each of the plurality of hybrid model candidates, inputting a validation data set into each of the two or more models selected to compose the hybrid model candidate and causing each of the two or more models to predict the categories of the validation data set; and
creating the plurality of hybrid model candidates by machine learning using the output values excluding each output value higher than the threshold as input and using ground truth values of the validation data set corresponding to the output values used.”
Claim 4 recites, “The hybrid model creation method according to claim 2, wherein the creating includes:
calculating a convex envelope from a plot of output values predicted to be defective from among output values obtained by, for each of the plurality of hybrid model candidates, inputting a plurality of validation data sets into each of the two or more models selected to compose the hybrid model candidate and causing each of the two or more models to predict the categories of the plurality of validation data sets;
excluding each output value included in the convex envelope from the output values obtained, except vertices of the convex envelope; and
creating the plurality of hybrid model candidates by machine learning using the output values obtained, excluding each output value included in the convex envelope except the vertices of the convex envelope, as input, and using ground truth values of the plurality of validation data sets corresponding to the output values used.”
The closest prior art of record is Daultani (US PGPUB 20210406932). Daultani discloses a hybrid model creation system and method in which a population of models is analyzed and then subsequently grouped based upon determined metrics. The models can be further combined based upon determined accuracies and the resulting outputs are combined to create a new hybrid model which is then subsequently reintroduced to the population creating a more refined hybrid model. However, Daultani is silent on, “calculating a convex envelope from a plot of output values predicted to be defective from among output values obtained by, for each of the plurality of hybrid model candidates, inputting a plurality of validation data sets into each of the two or more models selected to compose the hybrid model candidate and causing each of the two or more models to predict the categories of the plurality of validation data sets;
excluding each output value included in the convex envelope from the output values obtained, except vertices of the convex envelope; and
creating the plurality of hybrid model candidates by machine learning using the output values obtained, excluding each output value included in the convex envelope except the vertices of the convex envelope, as input, and using ground truth values of the plurality of validation data sets corresponding to the output values used.”
Claim 12 recites, “The hybrid model creation method according to claim 9, further comprising:
obtaining, for each of the plurality of models selected to create the plurality of hybrid model candidates, a processing time required by the model to predict the categories of the validation data set after inputting the validation data set into the model; and
defining, for each of the plurality of models, based on the processing times obtained, a value of the processing time of the model relative to a sum of the processing times of all of the plurality of models as a hardware cost, wherein
the creating includes adding, in each of the plurality of hybrid model candidates, a regularization term to a loss function of the machine learning model of the hybrid model candidate, the loss function taking into account the hardware cost of each of the two or more models selected to compose the hybrid model candidate.”
The closest prior art of record is Daultani (US PGPUB 20210406932). Daultani discloses a hybrid model creation system and method in which a population of models is analyzed and then subsequently grouped based upon determined metrics. The models can be further combined based upon determined accuracies and the resulting outputs are combined to create a new hybrid model which is then subsequently reintroduced to the population creating a more refined hybrid model. However, Daultani is silent on, “obtaining, for each of the plurality of models selected to create the plurality of hybrid model candidates, a processing time required by the model to predict the categories of the validation data set after inputting the validation data set into the model; and
defining, for each of the plurality of models, based on the processing times obtained, a value of the processing time of the model relative to a sum of the processing times of all of the plurality of models as a hardware cost, wherein
the creating includes adding, in each of the plurality of hybrid model candidates, a regularization term to a loss function of the machine learning model of the hybrid model candidate, the loss function taking into account the hardware cost of each of the two or more models selected to compose the hybrid model candidate.”
Claim 13 recites, “The hybrid model creation method according to claim 2, further comprising:
creating, for each of a plurality of models selected for creating the plurality of hybrid model candidates, a FAR table of miss rates obtained using a variable threshold, based on a distribution of output values obtained by inputting, as the input data, data that indicates defective in a validation data set into the model and causing the model to predict the categories of the input data, wherein comparing the plurality of hybrid model candidates includes:
for each of the two or more models selected to compose each of the plurality of hybrid model candidates, looking up an output value obtained by inputting a data sample included in the validation data set into the model and causing the model to predict the category of the data sample in the FAR table to obtain a first FAR value of the model corresponding to the data sample;
obtaining a second FAR value of the hybrid model candidate corresponding to the two or more models by multiplying the first FAR values obtained for the two or more models; and
comparing the plurality of hybrid model candidates using judgments based on the second FAR values of the plurality of hybrid model candidates as judgments output by the plurality of hybrid model candidates in response to inputting the data sample, wherein the data sample is judged to be non-defective when the second FAR value is lower than a preset threshold.”
The closest prior art of record is Daultani (US PGPUB 20210406932). Daultani discloses a hybrid model creation system and method in which a population of models is analyzed and then subsequently grouped based upon determined metrics. The models can be further combined based upon determined accuracies and the resulting outputs are combined to create a new hybrid model which is then subsequently reintroduced to the population creating a more refined hybrid model. However, Daultani is silent on, “creating, for each of a plurality of models selected for creating the plurality of hybrid model candidates, a FAR table of miss rates obtained using a variable threshold, based on a distribution of output values obtained by inputting, as the input data, data that indicates defective in a validation data set into the model and causing the model to predict the categories of the input data, wherein comparing the plurality of hybrid model candidates includes:
for each of the two or more models selected to compose each of the plurality of hybrid model candidates, looking up an output value obtained by inputting a data sample included in the validation data set into the model and causing the model to predict the category of the data sample in the FAR table to obtain a first FAR value of the model corresponding to the data sample;
obtaining a second FAR value of the hybrid model candidate corresponding to the two or more models by multiplying the first FAR values obtained for the two or more models; and
comparing the plurality of hybrid model candidates using judgments based on the second FAR values of the plurality of hybrid model candidates as judgments output by the plurality of hybrid model candidates in response to inputting the data sample, wherein the data sample is judged to be non-defective when the second FAR value is lower than a preset threshold.”
Claim 14 is dependent upon claim 13 and would be allowable if wholly incorporated into identified allowable claim 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lin et al. (US Patent 8,370,280): disclose a system and method for processing feature vectors of one or more predictive models, determining a compatibility between the plurality of models, and combining a plurality of predictive models based upon determined compatibility.
Fukui et al. (US PGPUB 20220246302): disclose a system and method for employing ensemble modeling techniques in which a plurality of models are combined to form hybrid models based upon determined criteria.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER W CARTER whose telephone number is (469)295-9262. The examiner can normally be reached 9-6:30.
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/CHRISTOPHER W CARTER/Examiner, Art Unit 2117