DETAILED ACTION
This office action is responsive to the Request for Reconsideration-After Non-Final Rejection filed 4/29/2025. The application contains claims 1, 5-17, all examined and rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
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.
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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 5-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1, 16 and 17 are each directed to a statutory category, it recites a series of steps which appears to be directed to an abstract idea (mental process, mathematical concept).
Claims 1-5-17 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) 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. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. 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 amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include process, manufacture, and machine as in independent Claim 1, 16, and 17, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to which is akin to Mental Process and Mathematical Concept (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
“setting a first weight for a first data sample, of a plurality of data samples of learning data, based on a relationship between time information of the first data sample and time information of prediction data: setting a second weight for a second data sample of the plurality of data samples based on a relationship between time information of the second data sample and the time information of the prediction data (Mental process, observation, evaluation and judgment) (Mental process, observation, evaluation and judgment, Mathematical Concept).
The claim recites additional elements as
“An information processing method” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
“wherein the time information of the prediction data is closer to the time information of the first data sample than the time information of the second data sample, and the set first weight is larger than the set second weight” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h))
“training a first prediction model based on at least one of the first data sample, the second data sample, the first weight of the first data sample, the second weight of the second data sample, or the prediction data, wherein the first prediction model is trained for predictive analysis” (This limitation is directed to training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)).
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
“An information processing method” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
“wherein the time information of the prediction data is closer to the time information of the first data sample than the time information of the second data sample, and the set first weight is larger than the set second weight” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h))
“training a first prediction model based on at least one of the first data sample, the second data sample, the first weight of the first data sample, the second weight of the second data sample, or the prediction data, wherein the first prediction model is trained for predictive analysis” (This limitation is directed to training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)).
In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Claim 16 recites a device comprising “a central processing unit (CPU)” configured to perform the same method as set forth in claim 1, the added element of “a central processing unit (CPU)” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Claim 16 is therefore rejected according to the same findings and rationale as provided above.
Claim 17 recites a system comprising “A non-transitory computer-readable medium having stored thereon, computer-executable instructions that, when executed by a computer” configured to perform the same method as set forth in claim 1, the added element of “A non-transitory computer-readable medium having stored thereon, computer-executable instructions that, when executed by a computer” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Claim 17 is therefore rejected according to the same findings and rationale as provided above.
Independent claims 16 and 17 are the same analogy and rejected using similar analysis as claim 1.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 5 disclose “training a plurality of prediction models (This limitation is directed to training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)), wherein the learning data includes a plurality of pieces of partial data in a plurality of ranges of the learning data, a first range of the plurality of ranges is different from a second range of the plurality of ranges, the plurality of pieces of partial data is associated with at least one data sample of the plurality of data samples, the training of the plurality of prediction models is based on each piece of partial data of the plurality of pieces of partial data and the plurality of prediction models includes the first prediction model (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); generating virtual prediction data based on at least one piece of partial data of the plurality of pieces of partial data (Mental process); calculating prediction accuracy of each prediction model of the plurality of prediction models based on the virtual prediction data (Mental process, Mathematical concept); and setting, among the plurality of ranges, a third range of the learning data for the training of the first prediction model, wherein the setting of the third range of the learning data is based on the prediction accuracy of each prediction model of the plurality of prediction models (Mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 6 disclose “the plurality of ranges of the learning data corresponds to a plurality of periods of the learning data, a first period of the plurality of periods is different from a second period of the plurality of periods, the training of each prediction model of the plurality of prediction models is based on each piece of partial data of the plurality of pieces of partial data associated with the plurality of periods of the learning data (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), the information processing method further comprises setting, among the plurality of periods, a third period of the learning data for the training of the first prediction model, and the setting of the third period of the learning data is based on of the prediction accuracy of each prediction model of the plurality of prediction models (Mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 7 disclose “dividing the learning data into a plurality of pieces of partial data (Mental process), wherein the plurality of pieces of partial data is associated with at least one data sample of the plurality of data samples (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); calculating a degree of similarity between each piece of partial data of the plurality of pieces of partial data and the prediction data (Mental process, Mathematical method); setting a third weight for each piece of partial data of the plurality of pieces of partial data based on the degree of similarity(Mental process) and training the first prediction model based on each piece of partial data of the plurality of pieces of partial data and the third weight of each piece of partial data of the plurality of pieces of partial data (This limitation is directed to training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 8 disclose “the plurality of pieces of partial data is associated with a plurality of periods of the learning data, and a first period of the plurality of periods is different from a second period of the plurality of periods (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 9 disclose “generating the learning data based on the prediction data (Mental process); and training the first prediction model based on the generated learning data (This limitation is directed to training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 10 disclose “setting, based on the prediction data, a feature amount for the learning data (Mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 11 disclose “selecting, based on a degree of similarity between the learning data and the prediction data one of a first learning method or a second learning method (Mental process), wherein the first learning method utilizes both the learning data and the prediction data, the second learning method utilizes based on the learning data, and the training of the first prediction model is based on the selected one of the first learning method or the second learning method (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 12 disclose “selecting one of a first learning method or a second learning method (Mental process), wherein the first learning method utilizes both the learning data and the prediction data, the second learning method utilizes based on the learning data, the second learning method utilizes the learning data, the learning data includes a plurality of pieces of partial data in a plurality of ranges of the learning data, a first range of the plurality of ranges is different from a second range of the plurality of ranges, the plurality of pieces of partial data is associated with at least one data sample of the plurality of data samples, the selection of the one of the first learning method or the second learning method is based on a degree of similarity between the plurality of pieces of partial data, and the training of the first prediction model is based on the selected one of the first learning method or the second learning method (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 13 disclose “the plurality of ranges of the learning data corresponds to a plurality of periods of the learning data, a first period of the plurality of periods is different from a second period of the plurality of periods, and the selection of the one of the first learning method or the second learning method is based on a time-series change in the degree of similarity between the plurality of pieces of partial data associated with the plurality of periods of the learning data (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 14 disclose “training a second prediction model based on a first learning method (This limitation is directed to training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)), wherein the first learning method utilizes both the learning data and the prediction data, training a third prediction model based on a second learning method (This limitation is directed to training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)), wherein the second learning method utilizes the learning data, and a part of the learning data corresponds to virtual prediction data; calculating, based on the virtual prediction data, each of a prediction accuracy of the second prediction model and a prediction accuracy of the third prediction model (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); selecting one of the first learning method or the second learning method based on the prediction accuracy of the second first prediction model and the prediction accuracy of the third prediction model (Mental process), wherein the training of the first prediction model is based on the selected one of the first learning method or the second learning method (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). . It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 15 disclose “selecting the one of the first learning method or the second learning method, based on a time associated with the training of the second prediction model and a time associated with the training of the third prediction model (Mental process), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
For at least these reasons, the claimed inventions of each of dependent claims 2-18,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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, 9-10, 16-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jain et al. [US 2019/0362027 A1, hereinafter Jain].
With regard to Claim 1,
Jain teach information processing method. comprising:
setting a first weight for a first data sample, of a plurality of data samples of learning data, based on a relationship between time information of the first data sample and time information of prediction data (¶5, “computing weighted moving average (WMA) of the data points sampled for the first time period, wherein a new data point is given a higher weight compared to an earlier data point”);
setting a second weight for a second data sample of the plurality of data samples based on a relationship between time information of the second data sample and the time information of the prediction data (¶5, “computing weighted moving average (WMA) of the data points sampled for the first time period, wherein a new data point is given a higher weight compared to an earlier data point”), wherein
the time information of the prediction data is closer to the time information of the first data sample than the time information of the second data sample (¶5, “computing weighted moving average (WMA) of the data points sampled for the first time period, wherein a new data point is given a higher weight compared to an earlier data point”), and
the set first weight is larger than the set second weight (¶5, “computing weighted moving average (WMA) of the data points sampled for the first time period, wherein a new data point is given a higher weight compared to an earlier data point”); and
training a first prediction model based on at least one of the first data sample, the second data sample, the first weight of the first data sample, the second weight of the second data sample, or the of prediction data, wherein the first prediction model is trained for predictive analysis (¶5, “obtaining feedback from the at least one agricultural area by a re-training module, the feedback comprising the crop identifier and confirmation or correction of the predicted severity level of the one or more diseases; and updating the pre-trained disease prediction model based on the obtained feedback”, ¶10, “execute the pre-trained prediction model to predict severity level of the one or more disease”).
With regard to Claim 9,
Jain teach the information processing method according to claim 1, further comprising:
generating the learning data based on the prediction data (¶5, “obtaining feedback from the at least one agricultural area by a re-training module, the feedback comprising the crop identifier and confirmation or correction of the predicted severity level of the one or more diseases; and updating the pre-trained disease prediction model based on the obtained feedback”, ¶43, “The feedback is merged with a training dataset and used as a whole to update the pre-trained disease prediction model at step 212”); and
training the first prediction model based on the generated learning data (¶5, “obtaining feedback from the at least one agricultural area by a re-training module, the feedback comprising the crop identifier and confirmation or correction of the predicted severity level of the one or more diseases; and updating the pre-trained disease prediction model based on the obtained feedback”, ¶43, “The feedback is merged with a training dataset and used as a whole to update the pre-trained disease prediction model at step 212”).
With regard to Claim 10,
Jain teach the information processing method according to claim 9, further comprising setting, based on the prediction data a feature amount for the learning data (¶42, “the severity level is ‘normal’ and the sampling period is set to say 15 min. If the label changes to favorable and the change is identified as a valid change, then severity level is ‘exceptional’ and the sampling period may be reduced to say 5 min”, ¶11, “execute the sampling rate controller to increase the current sampling rate more when the predicted severity level is extreme as compared to the predicted severity level being exceptional “).
With regard to Claim 16,
Claim 16 is similar in scope to claim 1; therefore it is rejected under similar rationale. Jain further teach central processing learning unit (CPU) configured (Jain, ¶6, “provided a system comprising: one or more data storage devices operatively coupled to the one or more processors and configured to store instructions configured for execution by the one or more processors”, ¶¶10-11, ¶25).
With regard to Claim 17,
Claim 17 is similar in scope to claim 1; therefore it is rejected under similar rationale. Jain further teach A non-transitory computer-readable medium having stored thereon, computer-executable instructions that, when executed by a computer, cause the computer to perform processing of execute operations (Jain, ¶49, “provided a system comprising: one or more data storage devices operatively coupled to the one or more processors and configured to store instructions configured for execution by the one or more processors”, ¶51, “A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein”, claim 13).
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.
Claims 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. [US 2019/0362027 A1, hereinafter Jain] in view of Achin [US 2018/0046926 A1, hereinafter Achin].
With regard to Claim 5,
Jain teach information processing method according to claim 1.
Jain does not explicitly teach training a plurality of prediction models, herein the learning data includes a plurality of pieces of partial data in a plurality of ranges of the learning data, a first range of the plurality of ranges is different from a second range of the plurality of ranges, the plurality of pieces of partial data is associated with at least one data sample of the plurality of data samples, and the plurality of prediction models includes the first prediction model; generating virtual prediction data based on at least one piece of partial data of the plurality of pieces of partial data; calculating prediction accuracy of each prediction model of the plurality of prediction models based on the virtual prediction data; and setting, among the plurality of ranges, a third range of the learning data for the training of the first prediction model, wherein the setting of the third range of the learning data is based on the prediction accuracy of each prediction model of the plurality of prediction models.
Achin teach training a plurality of prediction models (¶27, “fitting the predictive model to the second training data to obtain a second fitted model”, ¶352, “fitting a model with sliding training and validation windows”), wherein the learning data includes a plurality of pieces of partial data in a plurality of ranges of the learning data (¶15, “obtaining time-series data including one or more data sets, wherein each data set includes a plurality of observations”, ¶347, “the engine 110 can divide up the dataset into a consistent series of training and validation ranges”),
a first range of the plurality of ranges is different from a second range of the plurality of ranges (¶18, “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”, ¶28, “the first subset of observations corresponds to a sliding training window covering a first range of training times and each observation included in the first subset is associated with a time within the first range of training times, the third subset of observations corresponds to the sliding training window covering a second range of training times and each observation included in the third subset is associated with a time within the second range of training times, and an earliest time in the first range of training times is earlier than an earliest time in the second range of training times”),
the plurality of pieces of partial data is associated with at least one data sample of the plurality of data samples (¶28, “each observation included in the first subset is associated with a time within the first range of training times”),
the training of the plurality of prediction models is based on each piece of partial data of the plurality of pieces of partial data (¶27, “(j) fitting the predictive model to the second training data to obtain a second fitted model; and (k) testing the second fitted model on the second testing data”), and
the plurality of prediction models includes the first prediction model(¶27, “In some embodiments, the training data are first training data, the testing data are first testing data, the fitted model is a first fitted model, and performing the cross-validation of the predictive model includes: (i) generating second training data and second testing data from the time-series data, wherein the second training data include a third subset of the observations of at least one of the data sets, and wherein the second testing data include a fourth subset of the observations of at least one of the data sets; (j) fitting the predictive model to the second training data to obtain a second fitted model”);
generating virtual prediction data based on at least one piece of partial data of the plurality of pieces of partial data (¶112, “the results (actual or expected) of applying the predictive modeling technique represented by the template to one or more prediction problems and/or datasets. The results of applying a predictive modeling technique to a prediction problem or dataset may include, without limitation, the accuracy with which predictive models generated by the predictive modeling technique predict the target(s) of the prediction problem or dataset”, ¶38, “generating one or more predictions by applying the fitted model to second time-series data representing one or more instances of the prediction problem”, ¶15, “testing the fitted model on the testing data”);
calculating prediction accuracy of each prediction model of the plurality of prediction models based on the virtual prediction data (¶112, “ the accuracy with which predictive models generated by the predictive modeling technique predict the target(s) “, ¶45, “determining a first respective accuracy score of each of the fitted predictive models, wherein the first accuracy score of each fitted model represents an accuracy with which the fitted model predicts one or more outcomes”, ¶352, “the sensitivity analysis on demand when a user requests it. A sensitivity analysis may include fitting a model with sliding training and validation windows, then measuring how the model accuracy varies with the points included in the windows”); and
setting, among the plurality of ranges, a third range of the learning data for the training of the first prediction model (¶354, “users may refit a model on training data whose last observation is the last observation of the holdout window. The choice of first observation to use in this re-fitting may depend on the size (e.g., optimal size) of the training window calculated during sensitivity analysis”), wherein the setting of the third range of the learning data is based on the prediction accuracy of each prediction model of the plurality of prediction models (¶354, “The choice of first observation to use in this re-fitting may depend on the size (e.g., optimal size) of the training window calculated during sensitivity analysis”, ¶352, “measuring how the model accuracy varies with the points included in the windows”, ¶49, “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”).
Jain and Achin are analogous art to the claimed invention because they are from a similar field of endeavor of predictive data analytic. Thus, 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 Jain resulting in resolutions as disclosed by Achin with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Jain as described above to anticipate problems or opportunities by combining operations data describing what happened in the past with evaluation data describing subsequent values of performance metrics to build predictive models to make decisions, adjust processes, or take other actions. Also, to build a predictive model that can provide more accurate forecasts (Achin, ¶7). This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 6,
Jain-Achin teach the information processing method according to claim 5, wherein
the plurality of ranges of the learning data corresponds to a plurality of periods of the learning data (Achin ¶29, “partitioning the time-series data into a plurality of partitions includes assigning each of the data sets to a corresponding partition. In some embodiments, partitioning the time-series data into a plurality of partitions includes temporally partitioning the time-series data, wherein each of the partitions corresponds to a respective portion of a time period associated with the time-series data”),
a first period of the plurality of periods is different from a second period of the plurality of periods (Achin ¶349, “validation windows measured in weeks may use a first fraction starting 4 weeks before the end of the training window, a second fraction starting 8 weeks before”, ¶28, ”an earliest time in the first range of testing times is earlier than an earliest time in the second range of testing times”),
the training of each prediction model of the plurality of prediction models is based on each piece of partial data of the plurality of pieces of partial data associated with the plurality of different periods of the learning data (Achin ¶349, “the engine 110 may iterate through the dataset, training each model on a small fraction of the training window”, ¶352, “A sensitivity analysis may include fitting a model with sliding training and validation windows”, ¶27, “fitting the predictive model to the second training data to obtain a second fitted model”, ¶28, “the first subset of observations corresponds to a sliding training window covering a first range of training times and each observation included in the first subset is associated with a time within the first range of training times”),
the information processing method further comprises setting, among the plurality of periods, a third period of the learning data to be used for the training of the first prediction model (Achin ¶354, “users may refit a model on training data whose last observation is the last observation of the holdout window. The choice of first observation to use in this re-fitting may depend on the size (e.g., optimal size) of the training window calculated during sensitivity analysis”), and
the setting of the third period of the learning data is based on the prediction accuracy of each prediction model of the plurality of prediction models (Achin ¶354, “users may refit a model on training data whose last observation is the last observation of the holdout window. The choice of first observation to use in this re-fitting may depend on the size (e.g., optimal size) of the training window calculated during sensitivity analysis”, ¶352, “measuring how the model accuracy varies with the points included in the windows”). The same motivation to combine for claim 5 equally applies for current claim.
With regard to Claim 7,
Jain teach the information processing method according to claim 1, further comprising:
calculating a degree of similarity between each piece of partial data of the plurality of pieces of partial data and the prediction data (¶5, “computing weighted moving average (WMA) of the data points sampled for the first time period, wherein a new data point is given a higher weight compared to an earlier data point”);
setting a third weight for each piece of partial data of the plurality of pieces of partial data based on the degree of similarity (¶5, “computing weighted moving average (WMA) of the data points sampled for the first time period, wherein a new data point is given a higher weight compared to an earlier data point”); and
training the first prediction model based on each piece of partial data of the plurality of pieces of partial data and the third weight of each piece of partial data of the plurality of pieces of partial data (¶5, “analyzing change in label of the sampled parameters over a first time period, by an analyzer, to identify a valid change in label … adapting the current sampling rate”).
Jain does not explicitly teach dividing the learning data into a plurality of pieces of partial data, wherein the plurality of pieces of partial data is associated with at least one data sample of the plurality of data samples.
Achin teach dividing the learning data into a plurality of pieces of partial data, wherein the plurality of pieces of partial data is associated with at least one data sample of the plurality of data samples (¶191, “At step 416 of method 400, the user instructs the exploration engine 110 to begin the search for modeling solutions in either manual mode or automatic mode. In automatic mode, the exploration engine 110 partitions the dataset (step 418) using a default sampling algorithm and prioritizes the modeling techniques”, ¶347, “divide up the dataset into a consistent series of training and validation ranges”).
Jain and Achin are analogous art to the claimed invention because they are from a similar field of endeavor of predictive data analytic. Thus, 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 Jain resulting in resolutions as disclosed by Achin with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Jain as described above to anticipate problems or opportunities by combining operations data describing what happened in the past with evaluation data describing subsequent values of performance metrics to build predictive models to make decisions, adjust processes, or take other actions. Also, to build a predictive model that can provide more accurate forecasts (Achin, ¶7). This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 8,
Jain-Achin teach the information processing method according to claim 7, wherein plurality of pieces of partial data is associated with a plurality of different periods of the learning data (Achin ¶29, “partitioning the time-series data into a plurality of partitions includes assigning each of the data sets to a corresponding partition. In some embodiments, partitioning the time-series data into a plurality of partitions includes temporally partitioning the time-series data, wherein each of the partitions corresponds to a respective portion of a time period associated with the time-series data”, ¶347, “the engine 110 can divide up the dataset into a consistent series of training and validation ranges”), and
a first period of the plurality of periods is different from a second period of the plurality of periods (Achin ¶349, “validation windows measured in weeks may use a first fraction starting 4 weeks before the end of the training window, a second fraction starting 8 weeks before”, ¶28, “the first subset of observations corresponds to a sliding training window covering a first range of training times and each observation included in the first subset is associated with a time within the first range of training times, the third subset of observations corresponds to the sliding training window covering a second range of training times and each observation included in the third subset is associated with a time within the second range of training times, and an earliest time in the first range of training times is earlier than an earliest time in the second range of training times”). The same motivation to combine for claim 7 equally applies for current claim.
Claims 11, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. [US 2019/0362027 A1, hereinafter Jain] in view of Paul [US 2020/0116522 A1, hereinafter Paul].
With regard to Claim 11,
Jain teach the information processing method according to claim 1.
Jain does not explicitly teach selecting, based on a degree of similarity between the learning data and the prediction data one of a first learning method or a second learning method , wherein the first learning method utilizes both the learning data and the prediction data , the second learning method utilizes based on the learning data , and the training of the first prediction model is based on the selected one of the first learning method or the second learning method.
Paul teach selecting, based on a degree of similarity between the learning data and the prediction data (¶47, “the concept drift detector 13 detects whether incrementally-arriving training data has largely changed from prior training data”, ¶62, “the concept drift detector 13 determines that a concept drift has occurred, and hence issues an instruction to the model-group learner/updater 3 to reset model learning”) one of a first learning method or a second learning method (¶62, “the concept drift detector 13 determines that a concept drift has occurred, and hence issues an instruction to the model-group learner/updater 3 to reset model learning”, ¶¶37-41, “perform model updating with any one of a plurality of systems”), wherein the first learning method utilizes both the learning data and the prediction data (¶36, “The model updater 10 may update the plurality of candidate models based on at least either of new sensor data which has been determined to be normal or abnormal by the knowledge of an expert and new sensor data which has been determined to be normal or abnormal based on an anomaly detection model”, ¶38, “In the first system, the model updater 10 collects all incrementally-arriving training data and newly learns each candidate model using all techniques selected by the model creator 8, at a timing at which the training data incrementally arrive”), the second learning method utilizes based on the learning data (¶62, “learn the models created by all techniques, using the training data 5 only”), and
the training of the first prediction model is based on the selected one of the first learning method or the second learning method (¶62, “The model-group learner/updater 3 receives the model-learning reset instruction from the concept drift detector 13 to learn the models created by all techniques, using the training data 5 only and calculates decision accuracies”).
Jain and Paul are analogous art to the claimed invention because they are from a similar field of endeavor of machine learning for IOT sensors data streams. Thus, 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 Jain resulting in resolutions as disclosed by Paul with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Jain as described above to provide a dynamic model architecture that continuously update instead of a usage of a pretrained model that degrades over time (Paul, ¶4, “Moreover, 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”). This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 14,
Jain teach the information processing method according to claim 1.
Jain does not explicitly teach training a second prediction model based on a first learning method, wherein the first learning method utilizes both the learning data and the prediction data; training a third prediction model based on a second learning method, wherein the second learning method utilizes the learning data, and a part of the learning data corresponds to virtual prediction data; calculating, based on the virtual prediction data, each of a prediction accuracy of the second prediction model and a prediction accuracy of the third prediction model ; and selecting one of the first learning method or the second learning method based on the prediction accuracy of the second first prediction model and the prediction accuracy of the third prediction model , wherein the training of the first prediction model is based on the selected one of the first learning method or the second learning method.
Paul teach training a second prediction model based on a first learning method, wherein the first learning method utilizes both the learning data and the prediction data (¶85, “In the supervised learning, the model creator 8 uses training data composed of normal data and abnormal data to create a plurality of candidate models {B1(t1), B2(t1), B3(t1), B4(t1), B5(t1)} “;
training a third prediction model based on a second learning method, wherein the second learning method utilizes the learning data, and a part of the learning data corresponds to virtual prediction data (¶85, “the unsupervised learning, the model creator 8 uses training data composed of normal data only to create a plurality of candidate models {A1(t1), A2(t1), A3(t1), A4(t1)}”);
calculating, based on the virtual prediction data, each of a prediction accuracy of the second prediction model and a prediction accuracy of the third prediction model (¶85, “The accuracy calculator 9 calculates decision accuracies of these candidate models, which are {0.7, 0.9, 0.8, 0.6} in this example. In the supervised learning, the model creator 8 uses training data composed of normal data and abnormal data to create a plurality of candidate models {B1(t1), B2(t1), B3(t1), B4(t1), B5(t1)} with a plurality of techniques {B1, B2, B3, B4, B5}. The decision accuracies of these candidate models are {0.4, 0.3, 0.5, 0.9. 0.2}”); and
selecting one of the first learning method or the second learning method based on the prediction accuracy of the second first prediction model and the prediction accuracy of the third prediction model (¶89, “The candidate-model group selector 21 uses, for example, an average decision accuracy to select candidate model groups. The average decision accuracy of the unsupervised model group is 0.725, whereas the average decision accuracy of the supervised model group is 0.54. Therefore, the candidate-model group selector 21 selects the unsupervised model groups “), wherein the training of the first prediction model is based on the selected one of the first learning method or the second learning method (¶90, “The applied-model group selector 22 selects applied model groups {A1(t2), A2(t2), A4(t1)} for creating an applied model (metamodel) by which a high decision accuracy can be obtained, from the selected candidate model groups “).
Jain and Paul are analogous art to the claimed invention because they are from a similar field of endeavor of machine learning for IOT sensors data streams. Thus, 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 Jain resulting in resolutions as disclosed by Paul with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Jain as described above to provide a dynamic model architecture that continuously update instead of a usage of a pretrained model that degrades over time (Paul, ¶4, “Moreover, 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”). This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. [US 2019/0362027 A1, hereinafter Jain] in view of Paul [US 2020/0116522 A1, hereinafter Paul] in view of Achin [US 2018/0046926 A1, hereinafter Achin].
With regard to Claim 12,
Jain teach the information processing method according to claim 1.
Jain does not explicitly teach selecting one of a first learning method or a second learning method , wherein the first learning method utilizes both the learning data and the prediction data , the second learning method utilizes based on the learning data , the second learning method utilizes the learning data, the selection of the one of the first learning method or the second learning method is based on a degree of similarity between the plurality of pieces of partial data, and the training of the first prediction model is based on the selected one of the first learning method or the second learning method.
Paul teach selecting one of a first learning method or a second learning method (¶62, “the concept drift detector 13 determines that a concept drift has occurred, and hence issues an instruction to the model-group learner/updater 3 to reset model learning”, ¶¶37-41, “perform model updating with any one of a plurality of systems”), wherein the first learning method utilizes both the learning data and the prediction data (¶36, “The model updater 10 may update the plurality of candidate models based on at least either of new sensor data which has been determined to be normal or abnormal by the knowledge of an expert and new sensor data which has been determined to be normal or abnormal based on an anomaly detection model”, ¶38, “In the first system, the model updater 10 collects all incrementally-arriving training data and newly learns each candidate model using all techniques selected by the model creator 8, at a timing at which the training data incrementally arrive”), the second learning method utilizes based on the learning data (¶62, “learn the models created by all techniques, using the training data 5 only”), the second learning method utilizes the learning data (¶62, “learn the models created by all techniques, using the training data 5 only”),
the selection of the one of the first learning method or the second learning method is based on a degree of similarity between the plurality of pieces of partial data, and the training of the first prediction model is based on the selected one of the first learning method or the second learning method (¶47, “the concept drift detector 13 detects whether incrementally-arriving training data has largely changed from prior training data”, ¶62, “the concept drift detector 13 determines that a concept drift has occurred, and hence issues an instruction to the model group learner/updater 3 to reset model learning”, ¶62, “learn the models created by all techniques, using the training data 5 only”).
Jain and Paul are analogous art to the claimed invention because they are from a similar field of endeavor of machine learning for IOT sensors data streams. Thus, 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 Jain resulting in resolutions as disclosed by Paul with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Jain as described above to provide a dynamic model architecture that continuously update instead of a usage of a pretrained model that degrades over time (Paul, ¶4, “Moreover, 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”). This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Jain-Paul does not explicitly teach the learning data includes a plurality of pieces of partial data in a plurality of ranges of the learning data, a first range of the plurality of ranges is different from a second range of the plurality of ranges, the plurality of pieces of partial data is associated with at least one data sample of the plurality of data samples.
Achin teach the learning data includes a plurality of pieces of partial data in a plurality of ranges of the learning data, a first range of the plurality of ranges is different from a second range of the plurality of ranges, the plurality of pieces of partial data is associated with at least one data sample of the plurality of data samples (¶28, “the first subset of observations corresponds to a sliding training window covering a first range of training times and each observation included in the first subset is associated with a time within the first range of training times, the third subset of observations corresponds to the sliding training window covering a second range of training times and each observation included in the third subset is associated with a time within the second range of training times, and an earliest time in the first range of training times is earlier than an earliest time in the second range of training times”).
Jain-Paul and Achin are analogous art to the claimed invention because they are from a similar field of endeavor of predictive data analytic. Thus, 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 Jain-Paul resulting in resolutions as disclosed by Achin with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Jain-Paul as described above to anticipate problems or opportunities by combining operations data describing what happened in the past with evaluation data describing subsequent values of performance metrics to build predictive models to make decisions, adjust processes, or take other actions. Also, to build a predictive model that can provide more accurate forecasts (Achin, ¶7). This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 13,
Jain-Paul-Achin teach the information processing method according to claim 12, wherein the plurality of ranges of the learning data corresponds to a plurality of periods of the learning data, a first period of the plurality of periods is different from a second period of the plurality of periods (Achin, ¶29, “partitioning the time-series data into a plurality of partitions … each of the partitions corresponds to a respective portion of a time period associated with the time-series data”, ¶28, “… an earliest time in the first range of training times is earlier than an earliest time in the second range of training times”), and
the selection of the one of the first learning method or the second learning method is based on a time-series change in the degree of similarity between the plurality of pieces of partial data associated with the plurality of periods of the learning data (Paul, ¶61, “The decision accuracies of these candidate models are {0.6, 0.4, 0.5, 0.7. 0.2}”, ¶62, “Since, compared with the decision accuracies at time t4, the decision accuracies of all candidate models are lowered, the concept drift detector 13 determines that a concept drift has occurred, and hence issues an instruction to the model-group learner/updater 3 to reset model learning”, ¶48, “supplies, at time t4, training data 4 composed of normal data 4 and abnormal data 4, and incrementally supplies, at time t5, training data 5 composed of normal data 5 and abnormal data 5, to the preprocessor 2”).
Claims 15 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. [US 2019/0362027 A1, hereinafter Jain] in view of Paul [US 2020/0116522 A1, hereinafter Paul] in view of Pham et al. [US 20200012917 A1, hereinafter Pham].
With regard to Claim 15,
Jain-Paul teach the information processing method according to claim 14,further comprising selecting the one of the first learning method or the second learning method (¶85, “In the supervised learning, the model creator 8 uses training data composed of normal data and abnormal data to create a plurality of candidate models {B1(t1), B2(t1), B3(t1), B4(t1), B5(t1)} “, ¶85, “the unsupervised learning, the model creator 8 uses training data composed of normal data only to create a plurality of candidate models {A1(t1), A2(t1), A3(t1), A4(t1)}”). The same motivation to combine for claim 14 equally applies for current claim.
Jain-Paul does not explicitly teach based on a time associated with the training of the second prediction model and a time associated with the training of the third prediction model.
Pham teach selecting the one of the first learning method or the second learning method (¶159, “At step 1614, a candidate neural network model is selected based on one or more selection criteria”, ¶171, “At step 1714, a candidate new model may be selected based on the comparison at step 1712 and one or more selection criteria”, ¶47, “ the data model for the machine learning application can be generated without directly using the actual data. As the actual data may include sensitive information, and generating the data model may require distribution and/or review of training data, the use of the synthetic data can protect the privacy”, ¶55, “The training dataset can include actual training data, in some aspects. The training dataset can include synthetic training data, in some aspects. In some embodiments, dataset generator 103 can be configured to generate synthetic data from sample values”) based on a time associated with the training of the second prediction model and a time associated with the training of the third prediction model (¶158, “ Model output at step 1612 may include a log file, a run time, a number of epochs”, ¶159, “The model selection criteria may comprise a desired performance metric of the transformed model (e.g., an accuracy score of the neural network model, a model run time …”, claim 13, ¶170, “At step 1712, candidate new models may be applied to the input dataset and the results are compared to the legacy model output. In some embodiments, the comparison may include at least one of an accuracy score, a model run time”).
Jain-Paul and Pham are analogous art to the claimed invention because they are from a similar field of endeavor of predictive data analytic. Thus, 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 Jain-Paul resulting in resolutions as disclosed by Pham with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Jain-Paul as described above to reduce infrastructure cost as shorter training times mean less time spent on compute resources. Also, for easier deployment and maintenance as models with shorter training times are often simpler in architecture, which can make them easier to deploy, maintain, and update. This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Response to Amendment
Examiner notes that the office did not receive a certified English translation of the foreign application.
Examiner respectfully withdraw the 35 USC 112(b) based on the amendments.
Applicant argue that the claims do not include an abstract idea.
Examiner respectfully disagrees, calculating and assigning weigh to data is both a Mental process and mathematical concept.
Applicant argue that the claims are integrated into a practical application as it disclose an improvement to technology.
Examiner respectfully disagrees, the argued limitations are part of the abstract idea and the additional elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, this judicial exception is not integrated into a practical application.
Applicant argue that the claims as a whole amounts to significantly more than the alleged abstract idea.
Examiner respectfully disagrees, the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves. Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts. Further, note that the limitations, in the instant claims, are done by the generically recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Applicant’s arguments with respect to claims 1, and 16-17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
As to the remaining dependent claims, applicant argue that they are allowable due to their respective direct and indirect dependencies upon one of the aforementioned Independent claims. The examiner respectfully disagrees, Independent claims were not allowable as stated in the paragraph above in this “Response to Arguments” section in this office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 2017/0061329 filed by Kobayashi et al. that disclose training predictive models using progressive sampling See at least ¶¶4-7
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references 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 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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