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 1-14 are presented in the case.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on PCT application PCT/JP2021/026334 filed 07/13/2021.
Information Disclosure Statement
The information disclosure statements submitted on 12/29/2023 and 12/27/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claims 3-4 and 6-7 are objected to because of the following informalities:
Claim 3, line 4-5 recite the phrase “generate search information that associates the feature” which should be “generate search information that associates the generated feature”
Claim 4, line 3 recites the phrase “a linear sum of explanatory variable” which should be “a weighted linear sum of explanatory variables”
Claim 7, line 2 recites the phrase “wherein the processor” which should be “wherein the one or more processors”
Claim 7, line 4-5 recite the phrase “on candidate optimization solver” which should be “on a candidate optimization solver”
For the informalities above and wherever else they may occur appropriate correction is required.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: “Generating Search Information for Optimization Problems”.
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 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 of this title, 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.
Claims 1, 3 6-7, 10-11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over REZGUI et al. (US 20210018881 A1) hereinafter Rezgui in view of Johnson et al. (US 20200151029 A1) hereinafter Johnson.
As to independent claim 1, Rezgui teaches an information generation device comprising: [system with devices ¶201-203]
a memory storing instructions; and [memory with instructions ¶203]
one or more processors configured to execute the instructions to: [processor ¶203]
accept input of first data indicating an optimization problem including an objective function and a constraint, and
[receives input from a user including an optimization objective (function ¶107), semantic problem ¶5, variables and constraints ¶99 "capture domain and user requirements, and to infer variables and constraints based on the requirements. Ontology server 216 then sends the variables and constraints to prediction server 212"]
second data indicating a feature of the optimization problem; and [features in natural language format ¶7 "an optimization scenario comprising one or more optimization features in a natural language format"]
[[generate search information that]] associates the first data with the second data. [an ontology server receives (registers) generated meta-data from constructed models usable to select/associates problems, models, features in the future ¶97, ¶139 "resulting prediction model is then saved and the metadata (e.g. performance of the prediction model, time taken to develop the model, its hyper-parameters, etc.) is then stored in the ontology server. The meta-data is then used in future to select a most appropriate prediction model."]
Rezgui does not specifically teach generate search information that associates the first data with the second data.
However, Johnson teaches generate search information that associates the first data with the second data. [name input (search information) used by the API to identify the record with data in a database ¶61, ¶65 " name input for identifying the optimization work request via the intelligent API"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model building disclosed by Rezgui by incorporating the generate search information that associates the first data with the second data disclosed by Johnson because both techniques address the same field of machine learning and by incorporating Johnson into Rezgui improves optimization to enable efficient and effecting evaluations of models [Johnson ¶5]
As to dependent claim 3, the rejection of claim 1 is incorporated. Rezgui and Johnson further teach wherein the processor is configured to execute the instructions to:
generate a feature of the optimization problem; and [Rezgui variables, time-lag from created models ¶97]
generate search information that associates the feature of the generated optimization problem with the first data. [Rezgui registers the data as a record in the server ¶97]
As to dependent claim 6, the rejection of claim 3 is incorporated. Rezgui and Johnson further teach wherein the processor is configured to execute the instructions to: present the generated feature to be recommended to the user and allow the user to specify the feature; and
[Rezgui user interface with recommendations to select algorithm and features ¶163-166 " user interface allows a user to select variables 1402 (e.g., environmental and control variables in this example) for running a simulation"]
generate search information including the feature specified by the user in the second data. [Rezgui registers data for user ¶97, ¶139]
As to dependent claim 7, the rejection of claim 1 is incorporated. Rezgui and Johnson further teach wherein the processor is configured to execute the instructions to: accept input of information on candidate optimization solver for solving the optimization problem; and [Rezgui user interface with recommendations to select algorithm ¶163-166]
generate search information including the information on the optimization solver. [Rezgui registers data for user ¶97, ¶139]
As to dependent claim 10, the rejection of claim 1 is incorporated. Rezgui and Johnson further teach wherein the processor is configured to execute the instructions to register the generated search information in a database. [Rezgui registers data for user ¶97, ¶139], [Johnson database ¶51]
As to independent claim 11, Rezgui teaches an information generation method comprising:
accepting input of first data indicating an optimization problem including an objective function and a constraint, and [receives input from a user including an optimization objective (function ¶107), semantic problem ¶5, variables and constraints ¶99 "capture domain and user requirements, and to infer variables and constraints based on the requirements. Ontology server 216 then sends the variables and constraints to prediction server 212"]
second data indicating a feature of the optimization problem; and [features in natural language format ¶7 "an optimization scenario comprising one or more optimization features in a natural language format"]
[[generating search information that]] associates the first data with the second data. [an ontology server receives (registers) generated meta-data from constructed models usable to select/associates problems, models, features in the future ¶97, ¶139 "resulting prediction model is then saved and the metadata (e.g. performance of the prediction model, time taken to develop the model, its hyper-parameters, etc.) is then stored in the ontology server. The meta-data is then used in future to select a most appropriate prediction model."]
Rezgui does not specifically teach generating search information that associates the first data with the second data.
However, Johnson teaches generating search information that associates the first data with the second data. [name input (search information) used by the API to identify the record with data in a database ¶61, ¶65 " name input for identifying the optimization work request via the intelligent API"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model building disclosed by Rezgui by incorporating the generate search information that associates the first data with the second data disclosed by Johnson because both techniques address the same field of machine learning and by incorporating Johnson into Rezgui improves optimization to enable efficient and effecting evaluations of models [Johnson ¶5].
As to independent claim 13, Rezgui teaches a non-transitory computer readable information recording medium storing an information generation program, when executed by a processor, that performs a method for: [memory, processor and instructions ¶203]
accepting input of first data indicating an optimization problem including an objective function and a constraint, and [receives input from a user including an optimization objective (function ¶107), semantic problem ¶5, variables and constraints ¶99 "capture domain and user requirements, and to infer variables and constraints based on the requirements. Ontology server 216 then sends the variables and constraints to prediction server 212"]
second data indicating a feature of the optimization problem; and [features in natural language format ¶7 "an optimization scenario comprising one or more optimization features in a natural language format"]
[[generating search information that]] associates the first data with the second data. [an ontology server receives (registers) generated meta-data from constructed models usable to select/associates problems, models, features in the future ¶97, ¶139 "resulting prediction model is then saved and the metadata (e.g. performance of the prediction model, time taken to develop the model, its hyper-parameters, etc.) is then stored in the ontology server. The meta-data is then used in future to select a most appropriate prediction model."]
Rezgui does not specifically teach generating search information that associates the first data with the second data.
However, Johnson teaches generating search information that associates the first data with the second data. [name input (search information) used by the API to identify the record with data in a database ¶61, ¶65 " name input for identifying the optimization work request via the intelligent API"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model building disclosed by Rezgui by incorporating the generating search information that associates the first data with the second data disclosed by Johnson because both techniques address the same field of machine learning and by incorporating Johnson into Rezgui improves optimization to enable efficient and effecting evaluations of models [Johnson ¶5].
Claims 2, 5, 8-9 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Rezgui in view of Johnson, as applied in the rejection of claim 1, 3, 11 and 13 above, and further in view of Kimura et al. (US 20190272465 A1) hereinafter Kimura.
As to dependent claim 2, Rezgui and Johnson teach the method of claim 1 above that is incorporated,
Rezgui and Johnson do not specifically teach wherein the processor is configured to execute the instructions to generate the search information that associates the first data including the objective function learned using target person's decision-making history data, with the second data.
However, Kimura teaches wherein the processor is configured to execute the instructions to generate the search information that associates the first data including the objective function learned using target person's decision-making history data, with the second data. [function for model learned from expert demonstrations (history) ¶33-34 " reinforcement learning system 110 learns a reward function appropriate for the environment 102 by using the expert demonstrations that are actually performed by the expert 104"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model optimizing disclosed by Rezgui and Johnson by incorporating the wherein the processor is configured to execute the instructions to generate the search information that associates the first data including the objective function learned using target person's decision-making history data, with the second data disclosed by Kimura because all techniques address the same field of machine learning and by incorporating Kimura into Rezgui and Johnson alleviates use of extra computational resources saving time while providing optimal learning [Kimura ¶3-4].
As to dependent claim 5, Rezgui and Johnson teach the method of claim 3 above that is incorporated,
Rezgui and Johnson do not specifically teach generate information indicating a user who was a basis for generating training data used to learn the objective function as the feature of the optimization problem.
However, Kimura teaches generate information indicating a user who was a basis for generating training data used to learn the objective function as the feature of the optimization problem. [human experts ¶32 with training data (demonstrations) ¶32]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model optimizing disclosed by Rezgui and Johnson by incorporating the generate information indicating a user who was a basis for generating training data used to learn the objective function as the feature of the optimization problem disclosed by Kimura because all techniques address the same field of machine learning and by incorporating Kimura into Rezgui and Johnson alleviates use of extra computational resources saving time while providing optimal learning [Kimura ¶3-4].
As to dependent claim 8, Rezgui and Johnson teach the method of claim 1 above that is incorporated,
Rezgui and Johnson further teach generate search information that associates the first data including the generated objective function with the second data. [Rezgui registers data for user ¶97, ¶139]
Rezgui and Johnson do not specifically teach generate an objective function for an optimization problem by inverse reinforcement learning using target person's decision-making history data; and
However, Kimura teaches generate an objective function for an optimization problem by inverse reinforcement learning using target person's decision-making history data; [Inverse reinforcement learning for function using human expert data ¶33]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model optimizing disclosed by Rezgui and Johnson by incorporating the generate an objective function for an optimization problem by inverse reinforcement learning using target person's decision-making history data; disclosed by Kimura because all techniques address the same field of machine learning and by incorporating Kimura into Rezgui and Johnson alleviates use of extra computational resources saving time while providing optimal learning [Kimura ¶3-4].
As to dependent claim 9, the rejection of claim 8 is incorporated. Rezgui, Johnson and Kimura further teach wherein the processor is configured to execute the instructions to accept input of a prediction model and [Rezgui user interface with recommendations to select algorithm ¶163-166] generate the objective function using a prediction result of the accepted prediction model as an explanatory variable. [Kimura uses results (predicted state) as explanatory variable (similarity) for learning/reward ¶5, ¶79 "reward signal can be estimated as a function of the similarity measure between the predicted next state and one actually observed by the agent as similar to the embodiment with the generative model"]
As to dependent claim 12, Rezgui and Johnson teach the method of claim 11 above that is incorporated,
Rezgui and Johnson do not specifically teach generating the search information that associates the first data including the objective function learned using target person's decision-making history data, with the second data by the computer.
However, Kimura teaches generating the search information that associates the first data including the objective function learned using target person's decision-making history data, with the second data by the computer. [function for model learned from expert demonstrations (history) ¶33-34 " reinforcement learning system 110 learns a reward function appropriate for the environment 102 by using the expert demonstrations that are actually performed by the expert 104"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model optimizing disclosed by Rezgui and Johnson by incorporating the generating the search information that associates the first data including the objective function learned using target person's decision-making history data, with the second data by the computer disclosed by Kimura because all techniques address the same field of machine learning and by incorporating Kimura into Rezgui and Johnson alleviates use of extra computational resources saving time while providing optimal learning [Kimura ¶3-4].
As to dependent claim 14, Rezgui and Johnson teach the method of claim 13 above that is incorporated,
Rezgui and Johnson do not specifically teach wherein the search information that associates the first data including the objective function learned using target person's decision-making history data, with the second data is generated.
However, Kimura teaches wherein the search information that associates the first data including the objective function learned using target person's decision-making history data, with the second data is generated. [function for model learned from expert demonstrations (history) ¶33-34 " reinforcement learning system 110 learns a reward function appropriate for the environment 102 by using the expert demonstrations that are actually performed by the expert 104"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model optimizing disclosed by Rezgui and Johnson by incorporating the wherein the search information that associates the first data including the objective function learned using target person's decision-making history data, with the second data is generated disclosed by Kimura because all techniques address the same field of machine learning and by incorporating Kimura into Rezgui and Johnson alleviates use of extra computational resources saving time while providing optimal learning [Kimura ¶3-4].
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Rezgui in view of Johnson, as applied in the rejection of claim 3 above, and further in view of Wen et al. (US10828775 B2) hereinafter Wen.
As to dependent claim 4, Rezgui and Johnson teach the method of claim 3 above that is incorporated,
Rezgui and Johnson do not specifically teach the objective function is expressed as a linear sum of an explanatory variable, and the processor is configured to execute the instructions to generate the feature of the optimization problem according to weights of the explanatory variable included in the objective function.
However, Wen teaches the objective function is expressed as a linear sum of an explanatory variable, and [functions with sum of variables Col. 3 ln. 20-60]
the processor is configured to execute the instructions to generate the feature of the optimization problem according to weights of the explanatory variable included in the objective function. [weighted variables Col. 3 ln. 20-60 " w.sub.x Weighting coefficient reflecting the relative importance of accuracy w.sub.u Weighting coefficient penalizing big changes in manipulated variables"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model optimizing disclosed by Rezgui and Johnson by incorporating the objective function is expressed as a linear sum of an explanatory variable, and the processor is configured to execute the instructions to generate the feature of the optimization problem according to weights of the explanatory variable included in the objective function disclosed by Wen because all techniques address the same field of machine learning and by incorporating Wen into Rezgui and Johnson saves time and reduces error and time in building models [Wen Col. 1 ln. 21-46]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
ROUSIS et al. (US 20230260058 A1) teaches optimization problems and solutions stored in a database (see ¶104)
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEAU SPRATT whose telephone number is (571)272-9919. The examiner can normally be reached M-F 8:30-5 PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 5712127212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEAU D SPRATT/Primary Examiner, Art Unit 2143
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