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 .
Status of Claims
Claims 1-17 are pending and are examined herein.
Claim 16 rejected under 35 USC 112(a) as failing to comply with the enablement requirement.
Claim 5 and 6 rejected under 35 USC 112(b) as being indefinite.
Claims 16 and 17 rejected under 35 USC 101 because the claimed invention is directed to non-statutory subject matter (not one of the four statutory categories).
Claims 2-15 rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
Claims 1, 2, 5, 6, 9, 10, 16, and 17 rejected under 35 USC 102.
Claims 3, 4, 7, 8, and 11-15 rejected under 35 USC 103.
Priority
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Information Disclosure Statement
The attached information disclosure statement (IDS) submitted on 05/09/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the attached information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 16 rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claim purports to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claim.
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.
Claim 5 and 6 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 5 recites the limitation "the prediction models" found in all 3 actions that “the information processing system” preforms. The first instance of the limitation is found within the first 25 words of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 dependent on Claim 5 does not resolve the issue of indefiniteness and is rejected with the same rationale.
Claim Rejections - 35 USC § 101 – Non-Statutory Category
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 16 and 17 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Claim 16, the claim does not fall within at least one of the four categories of patent eligible subject matter because a learning unit could be virtual and therefore not be tangible. See MPEP 2106.03 for more information.
Regarding Claim 17, the claim does not fall within at least one of the four categories of patent eligible subject matter because it is a computer program per se. See MPEP 2106.03 for more information.
Claim Rejections - 35 USC § 101 - Abstract Idea
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 2-15 rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis
According to the first part of the analysis, in the instant case Claims 2-15 are directed to a method; consequently, these claims fall within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2 Analysis (Combined Step 2A Prong 1-2 and Step 2B Analysis)
Claim 2 includes the following recitation of an abstract idea:
sets a weight for each of data samples included in the learning data on a basis of a relationship with the prediction data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 2 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
(inherited from the claim in which it depends) an information processing system including one or more information processing devices, training of a prediction model, on a basis of prediction data used for predictive analysis using the prediction model and learning data (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
the information processing system (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
performs the training of the prediction model on a basis of each of the data samples and the weight for each of the data sample (This falls under mere instructions to apply general training of said model. See MPEP 2106.05(f).)
Claim 2 does not reflect an improvement to computer technology or any other
technology.
Claim 3 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 3 includes the following recitation of an additional abstract idea:
sets the weight on a basis of a difference of a predetermined attribute between the data sample and the prediction data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 3 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
the information processing system (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 3 does not reflect an improvement to computer technology or any other
technology.
Claim 4 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 4 includes the following recitation of an additional abstract idea:
the attribute sets the weight on a basis of a temporal difference between the data sample and the prediction data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components. In addition, calculating the temporal difference falls under a mathematical concept.)
Claim 4 recites no further additional elements which, considered individually and as an
ordered combination with the additional elements from the claim upon which it depends, that
integrate the abstract idea into a practical application or amount to significantly more than the
abstract ideas.
Claim 4 does not reflect an improvement to computer technology or any other
technology.
Claim 5 includes the following recitation of an abstract idea:
calculates prediction accuracy of each of the prediction models (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components. In addition, the calculation falls under a mathematical concept.)
sets a range of the learning data to be used for the training of the prediction model on a basis of the prediction accuracy of each of the prediction models (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 5 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
(inherited from the claim in which it depends) an information processing system including one or more information processing devices, training of a prediction model, on a basis of prediction data used for predictive analysis using the prediction model and learning data (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
by using a part of the learning data as virtual prediction data (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
the information processing system performs training of a plurality of the prediction models on a basis of each of a plurality of pieces of partial data in different ranges of the learning data (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
Claim 5 does not reflect an improvement to computer technology or any other
technology.
Claim 6 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 6 includes the following recitation of an additional abstract idea:
sets a period of the learning data to be used for the training of the prediction model on a basis of the prediction accuracy of each of the prediction models (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 6 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
the information processing system performs the training of each of the prediction models on a basis of each of a plurality of pieces of the partial data of different periods of the learning data (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
Claim 6 does not reflect an improvement to computer technology or any other
technology.
Claim 7 includes the following recitation of an abstract idea:
divides the learning data into a plurality of pieces of partial data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
calculates a degree of similarity between each piece of the partial data and the prediction data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components. In addition, the calculation could fall under a mathematical concept.)
sets a weight for each piece of the partial data on a basis of the degree of similarity (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 7 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
(inherited from the claim in which it depends) an information processing system including one or more information processing devices, training of a prediction model, on a basis of prediction data used for predictive analysis using the prediction model and learning data (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
the information processing system (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
performs the training of the prediction model on a basis of each piece of the partial data and the weight for each of the partial data (This falls under mere instructions to apply general training of said model. See MPEP 2106.05(f).)
Claim 7 does not reflect an improvement to computer technology or any other
technology.
Claim 8 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 8 includes the following recitation of an additional abstract idea:
divides the learning data into a plurality of pieces of the partial data of different periods (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 8 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
the information processing system (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 8 does not reflect an improvement to computer technology or any other
technology.
Claim 9 includes the following recitation of an abstract idea:
generates the learning data on a basis of the prediction data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 9 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
(inherited from the claim in which it depends) an information processing system including one or more information processing devices, training of a prediction model, on a basis of prediction data used for predictive analysis using the prediction model and learning data (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
the information processing system (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
performs the training of the prediction model on a basis of the generated learning data (This falls under mere instructions to apply general training of said model. See MPEP 2106.05(f).)
Claim 9 does not reflect an improvement to computer technology or any other
technology.
Claim 10 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 10 includes the following recitation of an additional abstract idea:
sets a feature amount to be used for the learning data on a basis of the prediction data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 10 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
the information processing system (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 10 does not reflect an improvement to computer technology or any other
technology.
Claim 11 includes the following recitation of an abstract idea:
selects a learning method based on the learning data and the prediction data or a learning method based on the learning data on a basis of a degree of similarity between the learning data and the prediction data to perform the training of the prediction model (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 11 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
(inherited from the claim in which it depends) an information processing system including one or more information processing devices, training of a prediction model, on a basis of prediction data used for predictive analysis using the prediction model and learning data (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
the information processing system (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 11 does not reflect an improvement to computer technology or any other
technology.
Claim 12 includes the following recitation of an abstract idea:
selects a learning method based on the learning data and the prediction data or a learning method based on the learning data on a basis of a degree of similarity between a plurality of pieces of partial data in different ranges of the learning data to perform the training of the prediction model (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 12 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
(inherited from the claim in which it depends) an information processing system including one or more information processing devices, training of a prediction model, on a basis of prediction data used for predictive analysis using the prediction model and learning data (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
the information processing system (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 12 does not reflect an improvement to computer technology or any other
technology.
Claim 13 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 13 includes the following recitation of an additional abstract idea:
selects the learning method on a basis of a time-series change in degree of similarity between a plurality of pieces of the partial data of different periods of the learning data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 13 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
the information processing system (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 13 does not reflect an improvement to computer technology or any other
technology.
Claim 14 includes the following recitation of an abstract idea:
calculates prediction accuracy of a first prediction model by a learning method based on the learning data and the prediction data as well as prediction accuracy of a second prediction model by a learning method based only on the learning data by using a part of the learning data as virtual prediction data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components. In addition, the calculation could fall under a mathematical concept.)
selects the learning method on a basis of the prediction accuracy of the first prediction model and the prediction accuracy of the second prediction model to perform the training of the prediction model (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 14 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
the information processing system (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 14 does not reflect an improvement to computer technology or any other
technology.
Claim 15 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 15 includes the following recitation of an additional abstract idea:
selects the learning method on an additional basis of a time required for training of the first prediction model and a time required for training of the second prediction model (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 15 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
the information processing system (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 15 does not reflect an improvement to computer technology or any other
technology.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 5, 6, 9, 10, 16, and 17 rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by “Achin” (US 20180046926 A1, Systems for Time-Series Predictive Data Analytics, And Related Methods and Apparatus).
Regarding Claim 1, Achin teaches
performing…training of a prediction model, on a basis of prediction data used for predictive analysis using the prediction model and learning data (Achin, Abstract recites that the invention includes “generating training data and testing data from the time-series data; fitting a predictive model to the training data; and testing the fitted model on the testing data”. Fitting a predictive model is understood to involve training the model with the data. The model is predictively analyzed through testing the model on untrained data originating from the dataset. Achin, Field of Invention also recites “The present disclosure relates generally to systems and techniques for time-series predictive data analysis.” The training data that the model is being fitted to in Achin is the learning data found in the claim. Likewise, the testing data used by the model to gather analysis is the prediction data found in the claim.)
by an information processing system including one or more information processing devices (Achin, Paragraph [0016] states “Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.”)
Claim 1 anticipated by Achin before the effective filing date of the claimed invention.
Regarding Claim 2, the rejection of Claim 1 is incorporated herein. Furthermore, Achin teaches
the information processing system sets a weight for each of data samples included in the learning data on a basis of a relationship with the prediction data (Achin, Paragraph [0049] recites “In some embodiments, calculating the weighted combination of the model-specific predictive values includes assigning respective weights to the model-specific predictive values, wherein the weight assigned to a particular model-specific predictive value corresponding to a particular fitted predictive model increases as the first accuracy score of the fitted predictive model increases”. From the recitation, the weights are being assigned on a per value basis and based on a relationship, the accuracy, of how well the model is being fitted to the dataset, where both the learning and testing data originates from.)
performs the training of the prediction model on a basis of each of the data samples and the weight for each of the data sample (Achin, Paragraph [0049] in context of when to assign the weights recites “as the first accuracy score of the fitted predictive model increases”. This indicates that the model is continuing training (fitted) and the weights are continuing changing to increase the accuracy of the model as a whole. Moreover, the fitting of the model to the data samples is described in Achin, Abstract, “generating training data and testing data from the time-series data; fitting a predictive model to the training data; and testing the fitted model on the testing data”.)
Claim 2 anticipated by Achin before the effective filing date of the claimed invention.
Regarding Claim 5, the rejection of Claim 1 is incorporated herein. Furthermore, Achin teaches
the information processing system performs training of a plurality of the prediction models on a basis of each of a plurality of pieces of partial data in different ranges of the learning data (Achin, Figure 9 shows in Elements 920 that a time interval is determined for a dataset, “Determine a time interval of the time-series data”; Element 950 and 980 that subsets are generated from the dataset for training and testing, “Generate training data from the time-series data” and “Generate testing data from the time-series data”; and Element 970 and 980 that a model gets fitted and tested on a dataset that is associated for a certain time interval, “Fit a predictive model to the training data” and “Test the fitted model on the testing data”. Achin, Figure 10 Element 1010 expands this idea from one model to many, “including fitting associated predictive models to an initial dataset representing an initial prediction problem.” Achin, Paragraph [0018] proceeds to recite, “for each of the data sets, determining a respective time interval of the data set; and determining that the time intervals of at least two of the data sets are different.” Lastly, Achin, Paragraph [0015] states “obtaining timeseries data including one or more data sets, wherein each data set includes a plurality of observations”.)
calculates prediction accuracy of each of the prediction models by using a part of the learning data as virtual prediction data (Achin, Paragraph [0112] recites how the accuracy is calculated, “the accuracy with which predictive models generated by the predictive modeling technique predict the target(s) of the prediction problem or dataset.” Achin, Figure 10 Elements 1020 and 1040 also state that an accuracy score is determined for each model.)
sets a range of the learning data to be used for the training of the prediction model on a basis of the prediction accuracy of each of the prediction models (Achin, Paragraph [0353] recites “the user may further explore the effect of different training data windows on model performance.” Paragraph [0341] repeatedly states that a window is a “a time range of data”. Additionally, Paragraph [0354] recites “After the user has reviewed the holdout results (e.g., at step 450 of the method 400), the user may refit any subset of the models using any combination of data from the training, validation, and holdout windows.” The user can change the range of data by changing the 'window’ as the data starts as time-series data seen in Figure 9 with Element 910. Time-series data is understood to be an ordered list of data based on time which means taking a period of time would also be taking a range of this data. The user does this to any train model based on the accuracy and re-trains ‘refits’ the particular model with the new window.)
Claim 5 anticipated by Achin before the effective filing date of the claimed invention.
Regarding Claim 6, the rejection of Claim 5 is incorporated herein. Furthermore, Achin teaches
the information processing system performs the training of each of the prediction models on a basis of each of a plurality of pieces of the partial data of different periods of the learning data (Achin, Figure 9 shows in Elements 920 that a time interval is determined for a dataset, “Determine a time interval of the time-series data”; Element 950 and 980 that subsets are generated from the dataset for training and testing, “Generate training data from the time-series data” and “Generate testing data from the time-series data”; and Element 970 and 980 that a model gets fitted and tested on a dataset that is associated for a certain time interval, “Fit a predictive model to the training data” and “Test the fitted model on the testing data”. Achin, Figure 10 Element 1010 expands this idea from one model to many, “including fitting associated predictive models to an initial dataset representing an initial prediction problem.” Achin, Paragraph [0018] proceeds to recite, “for each of the data sets, determining a respective time interval of the data set; and determining that the time intervals of at least two of the data sets are different.” Lastly, Achin, Paragraph [0015] states “obtaining timeseries data including one or more data sets, wherein each data set includes a plurality of observations”.)
sets a period of the learning data to be used for the training of the prediction model on a basis of the prediction accuracy of each of the prediction models (Achin, Paragraph [0353] recites “the user may further explore the effect of different training data windows on model performance.” Paragraph [0341] repeatedly states that a window is a “a time range of data”. Additionally, Paragraph [0354] recites “After the user has reviewed the holdout results (e.g., at step 450 of the method 400), the user may refit any subset of the models using any combination of data from the training, validation, and holdout windows.” The user can change the period of data by changing the 'window’ of any trained model. This is performed based on the accuracy, and the user re-trains ‘refits’ the particular model with this new window.)
Claim 6 anticipated by Achin before the effective filing date of the claimed invention.
Regarding Claim 9, the rejection of Claim 1 is incorporated herein. Furthermore, Achin teaches
the information processing system generates the learning data on a basis of the prediction data (Achin, Figure 9, Element 950 states “Generate training data from the time-series data.” The learning data as stated by the claim is understood to be the training data in Achin while the prediction data as stated by the claim is the dataset itself in Achin.)
performs the training of the prediction model on a basis of the generated learning data (Achin, Figure 9, Element 970 recites “Fit a predictive model to the training data”. The training data being generated before in the Element 950. Fitting a model to data is understood as performing training of the model with the data.)
Claim 9 anticipated by Achin before the effective filing date of the claimed invention.
Regarding Claim 10, the rejection of Claim 9 is incorporated herein. Furthermore, Achin teaches
the information processing system sets a feature amount to be used for the learning data on a basis of the prediction data (Achin, Figure 9 Element 930 recites “Identify one or more variables of the time-series data as targets, and identify zero or more other variables as features”. The training data originates from the same time-series data where these features are defined as recited in Element 950, “Generate training data from the time-series data”. Because Element 930 appears as a step before Element 950, the generated training data is associated with the defined features.)
Claim 10 anticipated by Achin before the effective filing date of the claimed invention.
Regarding Claim 16, Achin teaches
performs training of a prediction model, on a basis of prediction data used for predictive analysis using the prediction model and learning data (Achin, Abstract recites that the invention includes “generating training data and testing data from the time-series data; fitting a predictive model to the training data; and testing the fitted model on the testing data”. Fitting a predictive model is understood to involve training the model with the data. The model is predictively analyzed through testing the model on untrained data originating from the dataset. Achin, Field of Invention also recites “The present disclosure relates generally to systems and techniques for time-series predictive data analysis.” The training data that the model is being fitted to in Achin is the learning data found in the claim. Likewise, the testing data used by the model to gather analysis is the prediction data found in the claim.)
An information processing device comprising: a learning unit (Achin, Paragraph [0016] states “Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.” A learning unit is generic and could be one of the computer systems performing the action in Achin.)
Claim 16 anticipated by Achin before the effective filing date of the claimed invention.
Regarding Claim 17, Achin teaches
performing training of a prediction model, on a basis of prediction data used for predictive analysis using the prediction model and learning data (Achin, Abstract recites that the invention includes “generating training data and testing data from the time-series data; fitting a predictive model to the training data; and testing the fitted model on the testing data”. Fitting a predictive model is understood to involve training the model with the data. The model is predictively analyzed through testing the model on untrained data originating from the dataset. Achin, Field of Invention also recites “The present disclosure relates generally to systems and techniques for time-series predictive data analysis.” The training data that the model is being fitted to in Achin is the learning data found in the claim. Likewise, the testing data used by the model to gather analysis is the prediction data found in the claim.)
A program for causing a computer to perform processing (Achin, Paragraph [0016] states “Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.” Achin explicitly states the usage of computer programs store on computer readable medium that contain the necessary steps to perform the stated actions.)
Claim 17 anticipated by Achin before the effective filing date of the claimed invention.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3 and 4 rejected under 35 U.S.C. 103 as being unpatentable over “Achin” (US 20180046926 A1, Systems for Time-Series Predictive Data Analytics, And Related Methods and Apparatus) in view of “lcalem” (https://lcalem.github.io/blog/2018/10/31/sutton-chap06-td#64-sarsa-on-policy-td-control, Sutton & Barto summary chap 06 - Temporal Difference Learning).
Claims 7 and 8 rejected under 35 U.S.C. 103 as being unpatentable over “Achin” (US 20180046926 A1, Systems for Time-Series Predictive Data Analytics, And Related Methods and Apparatus) in view of “Jiangtao” (Translated CN 109472700 A, Stock price prediction method, server, and storage medium).
Claims 11-15 rejected under 35 U.S.C. 103 as being unpatentable over “Achin” (US 20180046926 A1, Systems for Time-Series Predictive Data Analytics, And Related Methods and Apparatus) in view of “Toshiba” (US 20200116522 A1, Anomaly Detection Apparatus and Anomaly Detection Method).
Regarding Claim 3, the 102 rejection of Claim 2 is incorporated herein. Achin does not appear to explicitly teach
the information processing system sets the weight on a basis of a difference of a predetermined attribute between the data sample and the prediction data
However, lcalem, directed to analogous art (specifically in the mathematical side of predictive learning), teaches
the information processing system sets the weight on a basis of a difference of a predetermined attribute between the data sample and the prediction data (lcalem presents a predictive learning method called Temporal Difference Learning that combines dynamic programming with “learn from experience without knowing the model”. Icalem recites in 6.1. TD Prediction an algorithm in 6.2 for setting the weight at each step which is showcased in Figure 6.1. Icalem in context of the shown algorithm in 6.1. TD Prediction recites “The quantity in brackets in 6.2 is a sort of error, measuring the difference between the estimated value of V(St) and the better estimate Rt+1 + γV(St+1).” The algorithm uses a predetermined attribute as the next step ‘V(St+1)’ is not known at the time of the calculation and is only an estimate. Thus, the weight is assigned based on the difference of the data sample given and our predicted future data sample.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Achin in view of lcalem to use the specific predictive learning algorithm, temporal difference learning. The motivation for doing so would be that Achin does not specify a particular algorithm: Achin, Paragraph [0100] states “The exploration engine 110 may implement a search technique ( or "modeling methodology") for efficiently exploring the predictive modeling search space ( e.g., potential combinations of pre-processing steps, modeling algorithms, and post-processing steps).” Thus, said person would need to search for an algorithm to recreate the art. Additionally, lcalem, explicitly recites the advantages in 6.2. Advantages of TD prediction Methods. These methods include “they do not require a model of the environment”, “online, fully incremental updates”, and “TD Temporal Difference methods usually converge faster than MC Monte Carlo methods”. In summary, these advantages allow Temporal Difference learning to be more flexible to incoming data i.e. being more predictive and giving an answer to new data. Said person choosing an algorithm would be more likely to choose the Temporal Difference learning method found in lcalem due to the strong benefits as listed in lcalem.
Regarding Claim 4, the rejection of Claim 3 is incorporated herein. Furthermore, Achin does not appear to explicitly teach
the attribute sets the weight on a basis of a temporal difference between the data sample and the prediction data
However, lcalem, directed to analogous art (specifically in the mathematical area of predictive learning), teaches
the attribute sets the weight on a basis of a temporal difference between the data sample and the prediction data (lcalem presents a predictive learning method called Temporal Difference Learning that combines dynamic programming with “learn from experience without knowing the model”. Icalem recites in 6.1. TD Prediction an algorithm in 6.2 for setting the weight at each step which is showcased in Figure 6.1. Icalem in context of the shown algorithm in 6.1. TD Prediction recites “The quantity in brackets in 6.2 is a sort of error, measuring the difference between the estimated value of V(St) and the better estimate Rt+1 + γV(St+1).” The algorithm is predetermined as the next step ‘V(St+1)’ is not known at the time of the calculation and is only an estimate. Thus, the algorithm teaches a temporal difference between the data sample given and our predicted future data sample.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Achin in view of lcalem as described above with respect to claim 3.
Regarding Claim 7, the 102 rejection of Claim 1 is incorporated herein. Achin teaches
the information processing system divides the learning data into a plurality of pieces of partial data (Achin, Paragraph [0015] and [0042] both recite “obtaining time-series data including one or more data sets, wherein each data set includes a plurality of observations”. Additionally, Achin, Background states “The observations are generally partitioned into at least one "training" dataset and at least one "test" dataset.” It can be seen that this background knowledge is applied in Figure 9 with Element 950, “Generate training data…include first subset of observations” and Element 960, “Generate testing data…include a second subset of observations.”)
calculates a degree of similarity between each piece of the partial data and the prediction data (Achin, Paragraph [0121] states “Such tools may determine the similarity between two prediction problems based on the data indicative of the characteristics of the prediction problems”. Achin, Paragraph [0121] clarifies that the similarity can be labeled to a variety of degrees, “Such tools may express the similarity between two prediction problems as a score (e.g., on a predetermined scale), a classification (e.g., "highly similar", "somewhat similar", "somewhat dissimilar", "highly dissimilar"), a binary determination ( e.g., "similar" or "not similar"), etc.” Data “indicative of the characteristics of the prediction problems” would contain the two subsets (training and testing) that the model gets fitted to. If the data found in the testing is substantially different to the data found in the training, then this would suffice as meeting the limitations of Achin while also meeting the claimed limitations.)
Achin does not appear to explicitly teach
sets a weight for each piece of the partial data on a basis of the degree of similarity
performs the training of the prediction model on a basis of each piece of the partial data and the weight for each of the partial data
However, Jiangtao, directed to analogous art (specifically teaching a predictive method), teaches
sets a weight for each piece of the partial data on a basis of the degree of similarity (Jiangtao, Abstract recites “According to the time sequencing of the historical stock data, time weighting is assigned to the pretreated historical stock data, the time weighting is positively correlated with the time sequencing” and “by the way that the historical stock data closer with the time point of price expectation result are assigned with bigger weight,”. A degree of similarity falls under positively correlating, and assigning heavier weights to data closer together is describing a relationship based on similarity. It is understood that weighting in general would be for individual data pieces. This is emphasized to be true due to the weighting being correlated with time sequencing and individual data pieces having time points. Jiangtao, Description states “In this way, being assigned different by according to the time point of each historical stock data and the distance of newest current point in time.”)
performs the training of the prediction model on a basis of each piece of the partial data and the weight for each of the partial data (Jiangtao, Abstract recites “Based on the deep neural network model constructed in advance, the price expectation result of the target stock is obtained according to the historical stock data after imparting time weighting.” Before any interaction of the model takes place, time weighting occurs for the input to the model. It is understood that training happens after this process and that the model could be refitted to a particular set of weights.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Achin in view of Jiangtao to weight the data inputs of the prediction model based on a similarity relationship. The motivation for doing so would be that Achin already classifies datasets and models on similarity: Achin, Paragraph [0115] recites “Such tools may express the similarity between two predictive modeling techniques as a score ( e.g., on a predetermined scale), a classification (e.g., "highly similar", "somewhat similar", "somewhat dissimilar", "highly dissimilar"), a binary determination (e.g., "similar" or "not similar"), etc.”. Additionally, Jiangtao states “The main purpose of the present invention is to provide a kind of prediction technique of stock price, server and computer-readable deposit Storage media improves the accuracy rate to Prediction of Stock Price.” Said person looking to improve Achin in a general way or specifically with the accuracy rate would look further into how the training is performed and apply the similar already-disclosed methods (i.e. the similarity rating). Thus, Jiangtao would not only be found but preferred due to the commonality of basing things on similarity.
Regarding Claim 8, the rejection of Claim 7 is incorporated herein. Furthermore, Achin teaches
the information processing system divides the learning data into a plurality of pieces of the partial data of different periods (Achin, Paragraph [0042] recites “obtaining time-series data including one or more data sets, wherein each data set includes a plurality of observations, wherein each observation includes (1) an indication of a time associated with the observation.” If each data set includes many pieces of data ‘observations’ associated to a particular time, then each data set would also be associated to a time period. It is understood that each ‘observation’ would be different, and a reason for including multiple data sets is to cover different periods.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Achin in view of Jiangtao as described above with respect to claim 7.
Regarding Claim 11, the 102 rejection of Claim 1 is incorporated herein. Achin teaches
on a basis of a degree of similarity between the learning data and the prediction data to perform the training of the prediction model (Achin, Paragraph [0121] states “Such tools may determine the similarity between two prediction problems based on the data indicative of the characteristics of the prediction problems”. Achin, Paragraph [0121] clarifies that the similarity can be labeled to a variety of degrees, “Such tools may express the similarity between two prediction problems as a score (e.g., on a predetermined scale), a classification (e.g., "highly similar", "somewhat similar", "somewhat dissimilar", "highly dissimilar"), a binary determination ( e.g., "similar" or "not similar"), etc.” Data “indicative of the characteristics of the prediction problems” would contain the two subsets (training and testing) that the model gets fitted to. If the data found in the testing is substantially different to the data found in the training, then this would suffice as meeting the limitations of Achin while also meeting the claimed limitations.)
Achin does not appear to explicitly teach
the information processing system selects a learning method based on the learning data and the prediction data or a learning method based on the learning data
However, Toshiba, directed to analogous art (specifically in the area of using multiple predictive methods), teaches
the information processing system selects a learning method based on the learning data and the prediction data or a learning method based on the learning data (Toshiba, Figure 6, Element S13 states to “Acquire Technique List” immediately after acquiring the training data. Particular methods are selected, and we can see that being recorded for each model in Toshiba, Figure 15A and 15B. It is understood that selecting the technique for learning would be based on the data acquired as the technique list is received in the step before Element S13.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Achin in view of Toshiba to include a step of selecting a learning method from list. The motivation for doing so would be that Achin does not specify a particular algorithm: Achin, Paragraph [0100] states “The exploration engine 110 may implement a search technique ( or "modeling methodology") for efficiently exploring the predictive modeling search space ( e.g., potential combinations of pre-processing steps, modeling algorithms, and post-processing steps).” Thus, said person would need to search for an algorithm to recreate the art. It is unclear how many method(s) or how to choose a certain method for learning. Toshiba, Background recites “since abnormal data have a tendency to increase with passage of time, it is not always desirable to continuously use an anomaly detection model created in an initial state with a higher ratio of normal data.” Having multiple methods to select from after acquiring the data allows for predicting “a large amount of normal data and a small amount of abnormal data” (Toshiba, Background). In general, it is a way to make a predictive model more flexible with the data it generates results for.
Regarding Claim 12, the 102 rejection of Claim 1 is incorporated herein. Achin teaches
a plurality of pieces of partial data in different ranges of the learning data (Achin, Paragraph [0042] recites “obtaining time-series data including one or more data sets, wherein each data set includes a plurality of observations, wherein each observation includes (1) an indication of a time associated with the observation.” If each data set includes many pieces of data ‘observations’ associated to a particular time, then each data set would also be associated to a time period. It is understood that each ‘observation’ would be different, and a reason for including multiple data sets is to cover different periods.)
on a basis of a degree of similarity between the learning data and the prediction data to perform the training of the prediction model (Achin, Paragraph [0121] states “Such tools may determine the similarity between two prediction problems based on the data indicative of the characteristics of the prediction problems”. Achin, Paragraph [0121] clarifies that the similarity can be labeled to a variety of degrees, “Such tools may express the similarity between two prediction problems as a score (e.g., on a predetermined scale), a classification (e.g., "highly similar", "somewhat similar", "somewhat dissimilar", "highly dissimilar"), a binary determination ( e.g., "similar" or "not similar"), etc.” Data “indicative of the characteristics of the prediction problems” would contain the two subsets (training and testing) that the model gets fitted to. If the data found in the testing is substantially different to the data found in the training, then this would suffice as meeting the limitations of Achin while also meeting the claimed limitations.)
Achin does not appear to explicitly teach
the information processing system selects a learning method based on the learning data and the prediction data or a learning method based on the learning data
However, Toshiba, directed to analogous art (specifically in the area of using multiple predictive methods), teaches
the information processing system selects a learning method based on the learning data and the prediction data or a learning method based on the learning data (Toshiba, Figure 6, Element S13 states to “Acquire Technique List” immediately after acquiring the training data. Particular methods are selected, and we can see that being recorded for each model in Toshiba, Figure 15A and 15B. It is understood that selecting the technique for learning would be based on the data acquired as the technique list is received in the step before Element S13.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Achin in view of Toshiba as described above with respect to claim 11.
Regarding Claim 13, the rejection of Claim 12 is incorporated herein. Achin teaches
in degree of similarity between data pieces (Achin, Paragraph [0121] states “Such tools may determine the similarity between two prediction problems based on the data indicative of the characteristics of the prediction problems”. Achin, Paragraph [0121] clarifies that the similarity can be labeled to a variety of degrees, “Such tools may express the similarity between two prediction problems as a score (e.g., on a predetermined scale), a classification (e.g., "highly similar", "somewhat similar", "somewhat dissimilar", "highly dissimilar"), a binary determination ( e.g., "similar" or "not similar"), etc.” Data “indicative of the characteristics of the prediction problems” would contain the two subsets (training and testing) that the model gets fitted to. If the data found in the testing is substantially different to the data found in the training, then this would suffice as meeting the limitations of Achin while also meeting the claimed limitations.)
a plurality of pieces of partial data in different periods of the learning data (Achin, Paragraph [0042] recites “obtaining time-series data including one or more data sets, wherein each data set includes a plurality of observations, wherein each observation includes (1) an indication of a time associated with the observation.” If each data set includes many pieces of data ‘observations’ associated to a particular time, then each data set would also be associated to a time period. It is understood that each ‘observation’ would be different, and a reason for including multiple data sets is to cover different periods.)
Achin does not appear to explicitly teach
the information processing system selects the learning method on a basis of a time-series change
However, Toshiba, directed to analogous art (specifically in the area of using multiple predictive methods), teaches
the information processing system selects the learning method on a basis of a time-series change (Toshiba, Figure 6, Element S13 states to “Acquire Technique List” immediately after acquiring the training data. Particular methods are selected, and we can see that being recorded for each model in Toshiba, Figure 15A and 15B. It is understood that selecting the technique for learning would be based on the data acquired as the technique list is received in the step before Element S13. Toshiba, Figure 6, Element S16 “apply new training data to candidate models”, however if concept drift occurs (Element S17) which is known to involve checking if the new data is in the time-series scope of the currently trained data, then the model is reset (Element S18), initialized, and fully retrained using said data (Element S19). It is understood a new learning method is selected if the model is reset, initialized, and retrained as that was a step involved in Element S13 and S14.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Achin in view of Toshiba as described above with respect to claim 11.
Regarding Claim 14, the 102 rejection of Claim 1 is incorporated herein. Achin teaches
the information processing system calculates prediction accuracy of a first prediction model by a learning method based on the learning data and the prediction data as well as prediction accuracy of a second prediction model by a learning method based only on the learning data by using a part of the learning data as virtual prediction data (Achin, Figure 10, Element 1020 recites “Determine a first accuracy score for each fitted predictive model, representing an accuracy with which the fitted model predicts an outcome of the initial prediction problem.” The dataset is shuffled to produce a modified dataset in Element 1030. Element 1040 then recites “Determine a second accuracy score for each fitted predictive model, representing an accuracy with which the fitted model predicts an outcome of the modified prediction problem.” To summarize, there are two sets that contain one or more models that get assigned an accuracy score that is based on the dataset pertaining to the problem. Achin, Figure 9 goes into more detail about how each model is trained. Element 950 and Element 960 both generate a training and testing dataset for the model to be trained on. However, it is possible that after the dataset gets shuffled for the second set of models (to generate the second accuracy) that all the testing data is used is now within the training data as listed in Element 940 in Figure 9 for the second group of models. Thus, none of the testing data used for the first set would be found in the training of the second set arriving at the claimed limitations. Due to the fact that Achin, Figure 9, Element 960 states “Generate testing data from the time-series data”, it is reasonable to assume this could be virtual data.)
Achin does not appear to explicitly teach
selects the learning method on a basis of the prediction accuracy of the first prediction model and the prediction accuracy of the second prediction model to perform the training of the prediction model
However, Toshiba, directed to analogous art (specifically in the area of using multiple predictive methods), teaches
selects the learning method on a basis of the prediction accuracy of the first prediction model and the prediction accuracy of the second prediction model to perform the training of the prediction model (Toshiba, Figure 7 lists both a ‘Past Model Group’ and a ‘Current Model Group’ under ‘Selected Candidate Model Group’. Both of these model subgroups have the particular ‘Technique’ or learning method tied to an ‘Accuracy’ value. It can also be seen that there is a ‘Selection’ column listing whether or not the model technique is selected. These selected models then appear under ‘Selected Applied Model Group’ if they have the check mark. It can be inferred that the ‘Accuracy’ value from the ‘Selected Candidate Model Group’ is considered when making the selection to move the certain technique to the ‘Selected Applied Model Group.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Achin in view of Toshiba as described above with respect to claim 11.
Regarding Claim 15, the rejection of Claim 14 is incorporated herein. Achin teaches
on an additional basis of a time required for training of the first prediction model and a time required for training of the second prediction model (Achin, Paragraph [0079] recites “In some embodiments, the actions of the method further include determining a disparity between the amount of computational resources used by the fitted first-order model and the amount of computational resources used by the fitted second-order model.” Time wasted using the model is a resource used by any model and thus be compared against the two models. Furthermore, Time could be a metric and be factored into scoring the models: Achin, Paragraph [0181] states “In some embodiments, exploration engine 110 also prompts the user to identify the metric of model performance to be used for scoring the models (e.g., the metric of model performance to be optimized, in the sense of statistical optimization techniques, by the statistical learning algorithm implemented by exploration engine 110”)
Achin does not appear to explicitly teach
the information processing system selects the learning method
However, Toshiba, directed to analogous art (specifically in the area of using multiple predictive methods), teaches
the information processing system selects the learning method (Toshiba, Figure 6, Element S13 states to “Acquire Technique List” immediately after acquiring the training data. Particular methods are selected, and we can see that being recorded for each model in Toshiba, Figure 15A and 15B. It is understood that selecting the technique for learning would be based on the data acquired as the technique list is received in the step before Element S13.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Achin in view of Toshiba as described above with respect to claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: “Kobayashi” (US 20170061329 A1, Machine Learning Management Apparatus and Method)
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/JUSTIN C PRESLEY/Examiner, Art Unit 2121
/MARKUS A. VASQUEZ/Primary Examiner, Art Unit 2121
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