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 1-19 are pending.
Claim Objections
Claims 15-19 are objected to because of the following informalities: Claim 14 is a “storage devices” claim, and claims 15-19 depend on claim 14. However, claims 15-19 recite “[T]he data structure of claim”, which is not consistent with the recited “storage devices” of claim 14. 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:
“information” specifying” in claims 14-19.
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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 11/886,963. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-19 of U.S. Patent No. 11/886,963 teach every limitation of claims 1-19 of the instant application.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a non-statutory subject matter. The claims 1-6 recite “one or more instances of computer-readable media” which typically covers both forms of non-transitory tangible medium and transitory propagating signals per se under the broadest reasonable interpretation of the claims. The specification of the application does not preclude the computer-readable media construed as transitory tangible medium and transitory propagating signals per se. The claims drawn to such computer-readable media that cover both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § 101 by adding the limitation non-transitory to the claims (emphasis added).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the method comprising" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-6 depend on claim 1. Therefore, claims 2-6 are also rejected under the same rationale set forth in claim 1.
Claim 2 recites “the machine learning model repository” in line 3. There is insufficient antecedent basis for this limitation in the claim.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bowers et al. (Bower), US Patent Application Publication No. US 2016/0300156A1 and further in view of Walters et al. (Walters), US Patent Application Publication NO. US 2020/0302234 A1.
As to independent claim 1, Bowers discloses one or more instances of computer-readable media collectively having contents configured to cause a computing device to obtain a plurality of machine learning models and optimize the plurality of machine learning models, none of the instances of computer-readable media constituting a transitory propagating signal per se (Bowers, Abstract: a machine learner platform can implement a model tracking service to track one or more machine learning models for one or more application services; paragraph [0086]: software or firmware for use in implementing the techniques introduced here may be stored on a machine-readable storage medium and may be executed by one or more general-purpose or special-purpose programable microprocessors), the method comprising:
obtaining a plurality of machine learning models (Bowers, paragraph [0020]: the model tracking service can record the latent model in a model tracker database 118, which can store one or more machine learning models 120), including obtaining a description of each machine learning model of the plurality of machine learning models (Bowers, paragraph [0020]: the model tracking service can record the training configurations used to generate the latent model in the model tracker database 118. The model tracking service can store a version history of the latent model in the model tracker database 118, wherein tracking the version history can include tracking one or more modifications from a previous machine learning model to a subsequent machine learning model); and
for each machine learning model of the plurality of machine learning models:
identifying a hardware target of the machine learning model (paragraph [0021]: a model evaluation engine can perform offline testing of the latent model and compute evaluative metrics 124 based on the offline testing results 126. The evaluative metrics can be one or more binary values (e.g., “validation criteria failed” or “validation criteria passed”), discrete values (e.g., a discrete score between 0 and 10), or continuous (e.g., a percentage of the expected results that the latent model is able to reproduce by running a validation dataset or a percentage of overlapping results between the latent model and the production copy). For example, the offline testing can be a comparison of results of running the same test dataset through the latent model and the production copy. The offline testing can be a comparison of the results of running a validation dataset through the latent model and the expected results corresponding to the validation dataset (e.g., a set of crucial test cases or a set of random input data with known results). The offline testing can produce other performance-related scoring when running the latent model (e.g., memory consumption, equal error rate, consistency rating, variance of results, false positive rates, false negative rates, etc.). The evaluative metrics from the offline testing can also be stored in the model tracker database 118);
retrieving optimization result data from a repository of optimization result data based on the identified hardware target, the retrieved optimization result data reflecting a level of consumption of hardware resources resulting from changing one or more machine learning models (Bowers, paragraph [0019]: the model trainer engine 112 schedules automatic recurring training sessions to improve and optimize a latent model that can potentially replace the production model 106);
obtaining additional optimization result data by changing the machine learning model based on the hardware target and the retrieved optimization result data, the additional optimization result data including an indication of the identified hardware target (Bowers, paragraph [0024]: the machine learning interface enables the developer/analyst users of a latent model to update/push the latent model into production via an updater engine 134. In other embodiments, the machine learner system 100 or a developer/analyst user can set a threshold constraint (e.g., one or more threshold values along one or more metric dimensions) in the model evaluation engine 122 or the update engine 134 such that when the evaluative metrics 124 satisfy the threshold constraint, the latent model is automatically pushed into production by the updater engine 134. The update/push action can trigger the updater engine 134 to replace the production model 106 with the latent model. In response to this update, the updater engine 134 can again make a production copy of the new production model); and
storing the additional optimization result data within the repository of optimization result data (Bowers, paragraph [0020]: the model tracking service can record the latent model in a model tracker database 118, which can store one or more machine learning models).
Bowers, however, does not disclose identifying a model type of the machine learning model based on the description of the machine learning model and retrieving optimization result data based on the model type.
In the same field of endeavor, Walters discloses system and method for efficient generation of machine-learning models including generating a plurality of primary models of different model types (Abstract). Walters further discloses optimization system 105 may select a group of machine learning predictive model types to generate sample models. With the goal of providing estimates for future models, optimization system 105 may determine a group of selected model types for optimizations (Walters, paragraph [0101]). Walters further discloses optimization system 105 would generate sample models for different category data profiles and also different model types, preparing to create correlations based on category data profiles and target model types that allows optimization systems 105 to estimate minimum data requirements (Walters, paragraph [0102]).
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 of Bowers to incorporate identifying a model type of the machine learning model based on the description of the machine learning model and retrieving optimization result data based on the model type, as taught by Walters. Walters suggest using optimization system for classifications tasks (Walters, paragraph [0101]).
As to dependent claim 2, Bowers discloses accessing a model repository, the machine learning model repository including a second plurality of machine learning models, wherein the obtained plurality of machine learning models includes at least a subset of the second plurality of machine learning models (Bowers, paragraph [0020].
As to dependent claim 3, Bowers discloses periodically accessing a plurality of model repositories, each model repository including one or more machine learning models (Bowers, paragraphs [0019],[0020]); and
for each model repository of the plurality of model repositories:
obtaining a second plurality of machine learning models included in the
model repository, wherein the obtained plurality of machine learning models includes at least a subset of the second plurality of machine learning models (Bowers, paragraphs [0020], [0028]).
As to dependent claim 4, Bowers discloses wherein at least one machine learning model of the plurality of machine learning models has been previously trained (Bowers, Abstract).
As to dependent claim 5, Bowers discloses wherein changing the machine learning model includes changing at least one variable weight used by the machine learning model (Bowers, paragraph [0018]).
As to dependent claim 6, Bowers discloses for each machine learning model of the plurality of machine learning models:
identify a second hardware target of the machine learning model (Bowers, paragraph [0021]);
retrieve second optimization result data from a repository of optimization
result data (Bowers, paragraphs [0011], [0019]);
obtain second additional optimization result data by optimizing the machine learning model based on the second hardware target and the retrieved second optimization result data (Bowers, paragraph [0019]); and
store the second additional optimization result data within the repository of
optimization result data (Bowers, paragraph [0020]).
As to independent claim 7, Bowers discloses a system to obtain a plurality of machine learning models and optimize the plurality of machine learning models, the system comprising:
an optimization result data repository configured to store optimization logs
containing optimization result data for each machine learning model of a plurality of machine learning models (Bowers, paragraphs [0011], [0019]: a machine learner system can implement a model tracker service to assist enterprise users in training and evaluating one or more “latent” models that may potentially replace production models, the machine learner system improves the workflow of building and optimizing machine learning models by providing tracking service and model tracking interface; paragraph [0020]: store one or more machine learning models and record the training configurations), the optimization result data indicating a hardware target of a respective machine learning model (paragraph [0021]: a model evaluation engine can perform offline testing of the latent model and compute evaluative metrics 124 based on the offline testing results 126. The evaluative metrics can be one or more binary values (e.g., “validation criteria failed” or “validation criteria passed”), discrete values (e.g., a discrete score between 0 and 10), or continuous (e.g., a percentage of the expected results that the latent model is able to reproduce by running a validation dataset or a percentage of overlapping results between the latent model and the production copy). For example, the offline testing can be a comparison of results of running the same test dataset through the latent model and the production copy. The offline testing can be a comparison of the results of running a validation dataset through the latent model and the expected results corresponding to the validation dataset (e.g., a set of crucial test cases or a set of random input data with known results). The offline testing can produce other performance-related scoring when running the latent model (e.g., memory consumption, equal error rate, consistency rating, variance of results, false positive rates, false negative rates, etc.). The evaluative metrics from the offline testing can also be stored in the model tracker database 118); and
a computing device configured to:
obtain an indication of one or more machine learning models (Bowers, paragraph [0020]: the model tracking service can record the latent model in a model tracker database 118, which can store one or more machine learning models 120); and
for each machine learning model:
identify a hardware target of the machine learning model (Bowers, paragraph [0021]: the offline testing can produce other performance-related scoring when running the latent model (e.g., memory consumption, equal error rate, consistency rating, variance of results, false positive rates, false negative rates, etc.);
retrieve optimization result data from the repository based on the identified hardware target (Bowers, paragraph [0019]: the model trainer engine 112 schedules automatic recurring training sessions to improve and optimize a latent model that can potentially replace the production model 106);
obtain second optimization result data by changing the machine
learning model for optimized operation on hardware specified by the identified hardware target (Bowers, paragraph [0024]: the machine learning interface enables the developer/analyst users of a latent model to update/push the latent model into production via an updater engine 134. In other embodiments, the machine learner system 100 or a developer/analyst user can set a threshold constraint (e.g., one or more threshold values along one or more metric dimensions) in the model evaluation engine 122 or the update engine 134 such that when the evaluative metrics 124 satisfy the threshold constraint, the latent model is automatically pushed into production by the updater engine 134. The update/push action can trigger the updater engine 134 to replace the production model 106 with the latent model. In response to this update, the updater engine 134 can again make a production copy of the new production model); and
store the second optimization result data in the optimization result
data repository (Bowers, paragraph [0020]: the model tracking service can record the latent model in a model tracker database 118, which can store one or more machine learning models).
Bowers, however, does not disclose identifying a model type of the machine learning model based on the description of the machine learning model and retrieving optimization result data based on the model type.
In the same field of endeavor, Walters discloses system and method for efficient generation of machine-learning models including generating a plurality of primary models of different model types (Abstract). Walters further discloses optimization system 105 may select a group of machine learning predictive model types to generate sample models. With the goal of providing estimates for future models, optimization system 105 may determine a group of selected model types for optimizations (Walters, paragraph [0101]). Walters further discloses optimization system 105 would generate sample models for different category data profiles and also different model types, preparing to create correlations based on category data profiles and target model types that allows optimization systems 105 to estimate minimum data requirements (Walters, paragraph [0102]).
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 of Bowers to incorporate identifying a model type of the machine learning model based on the description of the machine learning model and retrieving optimization result data based on the model type, as taught by Walters. Walters suggest using optimization system for classifications tasks (Walters, paragraph [0101]).
As to dependent claim 8, Bowers discloses wherein the computing device obtains the indication of one or more machine learning models from at least one repository including a plurality of machine learning models (Bowers, paragraph [0020]).
As to dependent claim 9, Bowers discloses wherein the computing device is further configured to periodically access the at least one repository to obtain the indication of one or more machine learning models (Bowers, paragraphs [0019],[0020]).
As to dependent claim 10, Bowers discloses wherein changing the machine learning model includes altering the code used by the machine learning model (Bowers, paragraphs [0018],[0020]).
As to dependent claim 11, Bowers discloses wherein at least one machine learning model of the one or more of machine learning models has been trained (Bowers, paragraph [0012]).
As to dependent claim 12, Bowers discloses wherein changing the machine learning model includes altering variable weights used by the machine learning model (Bowers, paragraph [0018]).
As to dependent claim 13, Bowers discloses wherein the computing device is further configured to:
for each machine learning model:
identify a second hardware target of the machine learning model (Bowers, paragraph [0021]);
retrieve third optimization result data from the repository (Bowers, paragraphs [0011], [0019]);
obtain fourth optimization result data regarding the machine learning model by changing the machine learning model based on the retrieved third optimization result data and the second hardware target (Bowers, paragraph [0019]); and
store the fourth optimization result data in the optimization result data repository (Bowers, paragraph [0020]).
As to independent claim 14, Bowers discloses one or more storage devices collectively storing a machine learning model retrieval and optimization result data structure, none of the storage devices constituting a transitory propagating signal per se, the data structure comprising:
information specifying a hardware target of the machine learning model (paragraph [0021]: a model evaluation engine can perform offline testing of the latent model and compute evaluative metrics 124 based on the offline testing results 126. The evaluative metrics can be one or more binary values (e.g., “validation criteria failed” or “validation criteria passed”), discrete values (e.g., a discrete score between 0 and 10), or continuous (e.g., a percentage of the expected results that the latent model is able to reproduce by running a validation dataset or a percentage of overlapping results between the latent model and the production copy). For example, the offline testing can be a comparison of results of running the same test dataset through the latent model and the production copy. The offline testing can be a comparison of the results of running a validation dataset through the latent model and the expected results corresponding to the validation dataset (e.g., a set of crucial test cases or a set of random input data with known results). The offline testing can produce other performance-related scoring when running the latent model (e.g., memory consumption, equal error rate, consistency rating, variance of results, false positive rates, false negative rates, etc.). The evaluative metrics from the offline testing can also be stored in the model tracker database 118);
information specifying optimization result data, the optimization result data being
associated with one or more hardware targets (Bowers, paragraph [0019]: the model trainer engine 112 schedules automatic recurring training sessions to improve and optimize a latent model that can potentially replace the production model 106); and
information specifying second optimization result data, the information specifying the second optimization result data is obtained by using the optimization result data and the hardware target to change the machine learning model (Bowers, paragraph [0024]: the machine learning interface enables the developer/analyst users of a latent model to update/push the latent model into production via an updater engine 134. In other embodiments, the machine learner system 100 or a developer/analyst user can set a threshold constraint (e.g., one or more threshold values along one or more metric dimensions) in the model evaluation engine 122 or the update engine 134 such that when the evaluative metrics 124 satisfy the threshold constraint, the latent model is automatically pushed into production by the updater engine 134. The update/push action can trigger the updater engine 134 to replace the production model 106 with the latent model. In response to this update, the updater engine 134 can again make a production copy of the new production model).
Bowers, however, does not disclose identifying a model type of the machine learning model based on the description of the machine learning model and retrieving optimization result data based on the model type.
In the same field of endeavor, Walters discloses system and method for efficient generation of machine-learning models including generating a plurality of primary models of different model types (Abstract). Walters further discloses optimization system 105 may select a group of machine learning predictive model types to generate sample models. With the goal of providing estimates for future models, optimization system 105 may determine a group of selected model types for optimizations (Walters, paragraph [0101]). Walters further discloses optimization system 105 would generate sample models for different category data profiles and also different model types, preparing to create correlations based on category data profiles and target model types that allows optimization systems 105 to estimate minimum data requirements (Walters, paragraph [0102]).
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 of Bowers to incorporate identifying a model type of the machine learning model based on the description of the machine learning model and retrieving optimization result data based on the model type, as taught by Walters. Walters suggest using optimization system for classifications tasks (Walters, paragraph [0101]).
As to dependent claim 15, Bowers discloses wherein the information specifying a machine learning model further comprises information specifying a repository including machine learning models from which the machine learning model was obtained (Bowers, paragraph [0020]).
As to dependent claim 16, Bowers discloses wherein using the information specifying optimization result data and the hardware target to change the machine learning model includes altering the code used by the machine learning model (Bowers, paragraphs [0018],[0020]).
As to dependent claim 17, Bowers discloses information specifying training data for the machine learning model (Bowers, paragraph [0012]).
As to dependent claim 18, Bowers discloses wherein using the information specifying optimization result data and the hardware target to change the machine learning model includes altering the weights of variables used by the machine learning model (Bowers, paragraph [0018]).
As to dependent claim 19, Bowers discloses wherein the information specifying a hardware target further comprises information specifying multiple hardware targets (Bowers, paragraph [0021]).
Conclusion
Any inquiry concerning this communication should be directed to CHAU T NGUYEN at telephone number (571)272-4092. The examiner can normally be reached on M-F from 8am to 5pm (PT).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula, can be reached at telephone number 5712724128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
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