DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This non-final office action is in response to the application filed 23 December 2022.
Claims 1-9 are pending. Claims 1 and 8-9 are independent claims.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 23 December 2022 and 4 November 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The examiner accepts the drawings filed 23 December 2022.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 3, the term “robustly train a forecast effect model” in line 2 is a relative term which renders the claim indefinite. The term “robustly train a forecast effect model” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For the purpose of examination, the examiner will interpret the term “robustly train a forecast effect model” as though it recites “train a forecast effect model.”
With respect to claim 4, the claim recites “perform the training based on an evaluation function that gives a lower evaluation when a first relation and a second relation do not much (lines 2-4; emphasis added).” It is unclear how two relations may “much,” and the examiner cannot determine what is intended by the claim.
For the purpose of examination, the examiner will interpret the limitation as though it recites “perform the training based on an evaluation function that gives a lower evaluation when a first relation and a second relation do not match.”
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 USC 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; MPEP 2106.03). If the claim falls within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed toward a judicial exception (Step 2A; MPEP 2106.04). This step is broken into two prongs.
The first prong (Step 2A, Prong 1) determines 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 at Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2; MPEP 2106.04). The second prong (Step 2A, Prong 2) determines whether the claims integrate the judicial exception into a practical application. If the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determine whether the claim is a patent-eligible exception (Step 2B; MPEP 2106.05).
If an abstract idea is present int the claim, in order to recite statutory subject matter, 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 amounts to significantly more than the abstract idea itself (see: 2019 PEG).
Step 1:
According to Step 1 of the two Step analysis, claims 1-7 are directed toward a device (machine). Claim 8 is directed toward a method (process). Claim 9 is directed toward a non-transitory computer readable medium (manufacture). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 1, the claim recites:
indicating a relationship between: the forecast value; and the actual value when the forecast value is disclosed (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to indicate that the forecast value and the actual value constitute a relationship)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
a memory configured to store instructions
a processor configured to execute instructions
These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Further, the claim recites the additional element:
acquire learning data including: a forecast value; and an actual value when the forecast value is disclosed
This additional element amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recite the additional element:
train a model… by using the learning data
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
a memory configured to store instructions
a processor configured to execute instructions
These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Further, the claim recites the additional element:
acquire learning data including: a forecast value; and an actual value when the forecast value is disclosed
This additional element amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recite the additional element:
train a model… by using the learning data
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 2:
With respect to dependent claim 2, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 2, the claim recites:
calculate… a forecast value at which the forecast value and the actual value are estimated to match when a forecast is disclosed (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to calculate a forecast value)
Claim 3:
With respect to dependent claim 3, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 2:
The claim discloses the following additional elements:
robustly train a forecast effect model when a distribution of forecast values included in learning data is in a dependent relationship with a conditional parameter or is biased in comparison with a uniform distribution,
the forecast effect model being for forecast value calculation with respect to the dependency on the condition parameter value or the bias in the distribution of the forecast values
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
robustly train a forecast effect model when a distribution of forecast values included in learning data is in a dependent relationship with a conditional parameter or is biased in comparison with a uniform distribution,
the forecast effect model being for forecast value calculation with respect to the dependency on the condition parameter value or the bias in the distribution of the forecast values
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 4:
With respect to dependent claim 4, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 4, the claim recites:
an evaluation function that gives a lower evaluation when a first relation and a second relation do not match, the first relation indicating a magnitude correlation of an actual value when a certain forecast value is disclosed and an estimated value of the actual value when the certain forecast value is disclosed, the second relation indicating a magnitude correlation of the actual value when the certain forecast value is disclosed and the certain forecast value (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to determine whether a first relation, indicating a magnitude correlation of an actual value or estimated value, and a second relation, indicating a magnitude correlation of an actual value, match and assign an evaluation value)
Claim 5:
With respect to dependent claim 5, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 5, the claim recites:
an evaluation function that gives a lower evaluation when a first distance is equal to or greater than a second distance between arbitrary two forecast values, the first distance indicating a distance between an action value estimated when one of the arbitrary two forecast values is disclosed and an actual value estimated when the other one of the arbitrary two forecast values is disclosed (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to if a first distance, which indicates a distance between an forecast value and an actual value, is greater than or equal to a second distance)
Step 2A, Prong 2:
The claim discloses the following additional elements:
train a model indicating a relationship between forecast values and actual values
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
train a model indicating a relationship between forecast values and actual values
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 6:
With respect to dependent claim 6, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 6, the claim recites:
indicating a relationship between: the forecast value for the first point; and the actual value at the second point when the forecast value for the first point is disclosed (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to indicate that the forecast value and the actual value constitute a relationship)
Step 2A, Prong 2:
The claim discloses the following additional elements:
Further, the claim recites the additional element:
acquire learning data including: a forecast value for a first point; and an actual value at a second point when the forecast value for the first point is disclosed
This additional element amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recite the additional element:
train a model… by using the learning data
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
a memory configured to store instructions
a processor configured to execute instructions
These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Further, the claim recites the additional element:
acquire learning data including: a forecast value for a first point; and an actual value at a second point when the forecast value for the first point is disclosed
This additional element amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recite the additional element:
train a model… by using the learning data
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 7:
With respect to dependent claim 7, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 7, the claim recites:
indicating a relationship between: the forecast value for the first time; and the actual value at the second time when the forecast value for the first time is disclosed (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to indicate that the forecast value and the actual value constitute a relationship)
Step 2A, Prong 2:
The claim discloses the following additional elements:
Further, the claim recites the additional element:
acquire learning data including: a forecast value for a first time; and an actual value at a second time when the forecast value for the first time is disclosed
This additional element amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recite the additional element:
train a model… by using the learning data
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
a memory configured to store instructions
a processor configured to execute instructions
These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Further, the claim recites the additional element:
acquire learning data including: a forecast value for a first time; and an actual value at a second time when the forecast value for the first time is disclosed
This additional element amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recite the additional element:
train a model… by using the learning data
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 8:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 8, the claim recites:
indicating a relationship between: the forecast value; and the actual value when the forecast value is disclosed (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to indicate that the forecast value and the actual value constitute a relationship)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
acquire learning data including: a forecast value; and an actual value when the forecast value is disclosed
This additional element amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recite the additional element:
train a model… by using the learning data
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
acquire learning data including: a forecast value; and an actual value when the forecast value is disclosed
This additional element amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recite the additional element:
train a model… by using the learning data
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 9:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 9, the claim recites:
indicating a relationship between: the forecast value; and the actual value when the forecast value is disclosed (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to indicate that the forecast value and the actual value constitute a relationship)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
a non-transitory computer readable recording medium that stores a program
These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Further, the claim recites the additional element:
acquire learning data including: a forecast value; and an actual value when the forecast value is disclosed
This additional element amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recite the additional element:
train a model… by using the learning data
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
a non-transitory computer readable recording medium that stores a program
These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Further, the claim recites the additional element:
acquire learning data including: a forecast value; and an actual value when the forecast value is disclosed
This additional element amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recite the additional element:
train a model… by using the learning data
The additional element of training a model using the learning data is recited at a high-level of generality, with no detail of the training process, and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, and 6-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Morgan et al. (US 11170391, patented 9 November 2021, hereafter Morgan).
As per independent claim 1, Morgan discloses a forecast support device, comprising:
a memory configured to store instructions (Figure 2, item 208; column 3, lines 46-60)
a processor configured to execute instructions (Figure 2, item 202; column 3, line 61- column 4, line 7) to:
acquire learning data including: a forecast value (column 5, lines 39-43) and an actual value when the forecast value is disclosed (Figure 4, item 402 and 408; column 4, lines 62-67: Here, a forecast model is trained using actual received data. Further, forecast values are obtained by the applying the model to generate forecast data)
train a model indicating a relationship between: the forecast value and the actual value when the forecast value is disclosed by using the learning data (Figure 6; column 8, line 54- column 9, line 20: Here, the trained forecast model is trained using values using past performance and component models. This includes training the model using the component model that was used to generate forecast values)
As per dependent claim 3, Morgan discloses the limitations similar to those in claim 1, and the same rejection is incorporated herein. Morgan discloses wherein the processor is configured to execute the instructions to, robustly train a forecast effect model when a distribution of forecast values included in learning data is in a dependent relationship with a conditional parameter value (column 8, line 54- column 9, line 20) or is biased in comparison with a uniform distribution, the forecast effect model being for forecast value calculation with respect to the dependency on the condition parameter value (Figure 6; column 8, line 54- column 9, line 20: Here, a forecast is generated based upon a plurality of parameters, including a time period, a location, and other attributes. A plurality of component models are selected, based on past performance to generate a prediction and these predictions are evaluated on a continuous bases as new data is received. This causes updating of the model and potentially selecting additional/different component models).
As per dependent claim 6, Morgan discloses wherein the processor is configured to execute the instructions to:
acquire learning data including: a forecast value for the first point (column 8, line 54- column 9, line 20) and an actual value at a second point when the forecast value for the first point is disclosed (column 8, line 54- column 9, line 20: Here, a forecast is generated based upon a plurality of parameters, including a time period, a location, and other attributes. The examiner interprets a location as being a “point.” A plurality of component models are selected, based on past performance to generate a prediction and these predictions are evaluated on a continuous bases as new data is received. This causes updating of the model and potentially selecting additional/different component models)
train a model indicating a relationship between: the forecast value for the first point, and the actual value at the second point when the forecast value for the first point is disclosed (column 8, line 54- column 9, line 20: Here, a plurality of component models are selected, based on past performance to generate a prediction and these predictions are evaluated on a continuous bases as new data is received. This causes updating of the model and potentially selecting additional/different component models)
As per dependent claim 7, Morgan discloses wherein the processor is configured to execute the instructions to:
acquire learning data including: a forecast value for the first time (column 8, line 54- column 9, line 20) and an actual value at a second time when the forecast value for the first time is disclosed (column 8, line 54- column 9, line 20: Here, a forecast is generated based upon a plurality of parameters, including a time period, a location, and other attributes. The examiner interprets a time period as being a “time.” A plurality of component models are selected, based on past performance to generate a prediction and these predictions are evaluated on a continuous bases as new data is received. This causes updating of the model and potentially selecting additional/different component models)
train a model indicating a relationship between: the forecast value for the first time, and the actual value at the second time when the forecast value for the first time is disclosed (column 8, line 54- column 9, line 20: Here, a plurality of component models are selected, based on past performance to generate a prediction and these predictions are evaluated on a continuous bases as new data is received. This causes updating of the model and potentially selecting additional/different component models)
With respect to claim 8, the claim discloses the limitations substantially similar to those in claim 1. The analysis of claim 1 is incorporated herein by reference.
With respect to claim 9, the claim discloses the limitations substantially similar to those in claim 1. The analysis of claim 1 is incorporated herein by reference.
Further, Morgan discloses a non-transitory computer readable recording medium that stores a program (column 4, lines 8-20).
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 2 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Morgan and further in view of Lowry et al. (US 2012/0123994, published 17 May 2012).
As per dependent claim 2, Morgan discloses the limitations similar to those in claim 1, and the same rejection is incorporated herein. Morgan discloses a trained model having forecast values (column 5, lines 39-43) and actual values (column 4, lines 62-67). Morgan fails to specifically disclose calculate a value at which the forecast value and an actual value are estimated to match when a forecast is disclosed.
However, Lowry, which is analogous to the claimed invention because it is directed toward analyzing data quality of forecast values and actual values, discloses calculating a value at with a forecast value and an actual value are estimated to match when a forecast is disclosed (paragraph 0052: Here, a normalized quality score is calculated by comparing the actual value and the forecast value. The quality of the score is determined based upon the how closely the actual and forecasted values match). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Lowry with Morgan, with a reasonable expectation of success, as it would have allowed for determining the quality of the forecast based upon analyzing the data to determine a quality score (Lowry: paragraph 0052).
As per dependent claim 4, Morgan discloses the limitations similar to those in claim 1, and the same rejection is incorporated herein. Morgan discloses performing training based on an evaluation function based on forecasted values (column 8, line 54- column 9, line 20: Here, the trained forecast model is trained using values using past performance and component models. This includes training the model using the component model that was used to generate forecast values) and testing the forecasted values based on a validation set (column 17, line 62- column 18, line 6).
Morgan fails to specifically disclose a function that gives a lower evaluation when a first relation and a second relation do not match, the first relation indicating a magnitude correlation of an actual value when a certain forecast value is disclosed and an estimated value of the actual value when the certain forecast value is disclosed, the second relation indicating a magnitude correlation of the actual value when the certain forecast value is disclosed and the certain forecast value.
However, Lowry, which is analogous to the claimed invention because it is directed toward analyzing data quality, discloses a function that gives a lower evaluation when a first relation and a second relation do not match, the first relation indicating a magnitude correlation of an actual value when a certain forecast value is disclosed and an estimated value of the actual value when the certain forecast value is disclosed, the second relation indicating a magnitude correlation of the actual value when the certain forecast value is disclosed and the certain forecast value (Figure 9; paragraphs 0052 and 0074: Here, a lower control limit (LCL) is calculated when the actual value and forecast value do not match. The lower control limit gives a lower evaluation calculated by subtracting the maximum variance value from the forecasted value (Figure 9, item 902). This LCL defines an accepted magnitude correlation between the actual value and the forecasted value). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Lowry with Morgan, with a reasonable expectation of success, as it would have allowed for defining a lower limit for the quality metric associated with a forecast (Lowry: paragraph 0052).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Morgan and further in view of Liao (US 8280757, patented 2 October 2012).
As per dependent claim 5, Morgan discloses the limitations similar to those in claim 1, and the same rejection is incorporated herein. Morgan discloses training a model indicating a relationship between forecast values and actual values (column 8, line 54- column 9, line 20: Here, the trained forecast model is trained using values using past performance and component models. This includes training the model using the component model that was used to generate forecast values) and testing the forecasted values based on a validation set (column 17, line 62- column 18, line 6).
Morgan fails to specifically disclose using an evaluation function that gives a lower evaluation when a first distance is equal to or greater than a second distance between arbitrary two forecast values, the first distance indicating a distance between an actual value estimated when one of the arbitrary two forecast values is disclosed and an actual value estimated when the other one of the arbitrary two forecast values is disclosed.
However, Liao, which is analogous to the claimed invention because it is directed toward evaluating a forecasting model, discloses using an evaluation function that gives a lower evaluation when a first distance is equal to or greater than a second distance between arbitrary two forecast values, the first distance indicating a distance between an actual value estimated when one of the arbitrary two forecast values is disclosed and an actual value estimated when the other one of the arbitrary two forecast values is disclosed (column 3, line 56- column 4, line 5; column 5, lines 8-37: Here, a distance between forecast error interval and the predetermined benchmark is determined. This includes comparing two different forecast error intervals based upon distance and adjusting the target forecasts based upon the distance). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Liao with Morgan, with a reasonable expectation of success, as it would have allowed for adjusting the forecast value based upon comparing the distance to benchmarks as a way to improve forecasts (Liao: column 5, line 17-37).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hyndman (Measuring forecast accuracy): Discloses generating a forecast model based upon historical data and measuring accuracy of the comparison based upon actual data (Section 1)
Lin et al. (Improving Deep Learning for Forecasting Accuracy in Financial Data): Discloses using deep learning forecast models to improve predictions based upon analyzing historical financial data and comparing analytical results to the actual data to improve the model (Abstract)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128