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 .
Claim Objections
Claims 1 and 9 is objected to because of the following informalities: claim 1 contains the limitation “…, by the system,…” in lines 20 and 23, this should be “…, by the computing system,…” instead; claim 9 is dependent on itself in the application. For the purposes of a speedy prosecution, the examiner assumes this is a typo and claim 9 is supposed to be dependent on claim 8. Claim 13 is objected to because of the following informalities: “the plurality of attention scores represent” should be “the plurality of attention scores represents”. Appropriate correction is required.
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.
Claim 9 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 9 recites the limitation “the model” in line 2, “the plurality of attention scores for the plurality of data points of the primary time series” in line 2-3. However, if claim 9 is dependent on claim 8, as assumed by the examiner, the limitation would have sufficient antecedent basis. As written, there is insufficient antecedent basis for this limitation in the claim. Additionally, claim 9 recites the limitation “the determined correlation”. There is insufficient antecedent basis for this limitation in the claim. The closest antecedent basis for this limitation is determined information identifying correlation. For the purposes of a speedy prosecution, the examiner assumes this is what the limitation is intended to say.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in
dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 9 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 9 depends on itself and as such cannot further limit itself. However, assuming claim 9 depends on claim 8 instead, as assumed by the examiner, claim 9 would comply with the statutory requirements. Applicant may cancel the claim, amend the claim to place the claim in proper dependent form, rewrite the claim in independent form, or present a sufficient showing that the dependent claim complies with the statutory requirements.
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 1-7 and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidelines (“2019 PEG”).
Step 1: Independent claims 1 (A computer-implemented method comprising…), 15 (A system comprising…), and 18 (A non-transitory computer-readable medium storing instructions executable by a computer system that, when executed by one or more processors of the computer system, cause the one or more processors to perform operations comprising…) are directed towards a method, a system, and a manufacture respectively. Therefore, these claims, as well as their dependent claims are directed towards one of the four statutory categories (process, machine (i.e. system), manufacture, or composition of matter).
Claim 1
Step 2A, Prong 1: The claim recites, inter alia:
computing, …, a feature importance score for one or more features in the set of features;
This limitation recites a mathematical concept to calculate values for feature importance. See MPEP 2106.04(a)(2)(I)
selecting, …, a subset of features from the set of features based on the feature importance scores computed for the one or more features in the set of features;
This limitation recites a mental process to choose a feature based on previously calculated values. See MPEP 2106.04(a)(2)(III)
based on the selected subset of features, determining, …, a plurality of attention scores for a plurality of data points in the primary time series dataset;
This limitation recites a mathematical concept to calculate values for a relative importance of a plurality of data points.
predicting, …, an actual forecast for the particular time point based on the plurality of attention scores determined for the plurality of data points in the primary time series dataset;
This limitation recites a mental process to guess a result based on previously calculated values for attention.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
A computer-implemented method comprising
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
… by a computing system …
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
obtaining, by a computing system, a time series forecast request requesting a forecast for a particular time point, wherein the time series forecast request identifies a primary time series dataset to be used as a basis for generating the requested forecast and a set of features related to the primary time series dataset, wherein the primary time series dataset comprises multiple historical time series data points, each time series data point having a value and an associated time value;
This limitation represents an insignificant extra-solution activity of data gathering and selecting a particular type of data to be manipulated, being pre-solution activity, performed by a generic computing system. See MPEP 2106.05(g);
providing, by a computing system, the primary time series dataset and the set of features to a model to be used for generating the forecast, wherein the model is trained upon or fit to the time series data points in the primary time series dataset and the set of features related to the primary time series dataset;
This limitation represents an insignificant extra-solution activity of data gathering and selecting a particular type of data to be manipulated, being pre-solution activity, performed by a generic computing system. See MPEP 2106.05(g);
… using the model …
This limitation is recited at a high level of generality and recites use of a generic class of computer algorithms to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using a generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
and outputting, by a computing system, the actual forecast and explanation information associated with the actual forecast.
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A computer-implemented method comprising
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
… by a computing system …
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
obtaining, by a computing system, a time series forecast request requesting a forecast for a particular time point, wherein the time series forecast request identifies a primary time series dataset to be used as a basis for generating the requested forecast and a set of features related to the primary time series dataset, wherein the primary time series dataset comprises multiple historical time series data points, each time series data point having a value and an associated time value;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
providing, by a computing system, the primary time series dataset and the set of features to a model to be used for generating the forecast, wherein the model is trained upon or fit to the time series data points in the primary time series dataset and the set of features related to the primary time series dataset;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
… using the model …
This limitation is recited at a high level of generality and recites use of a generic class of computer algorithms to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using a generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
and outputting, by a computing system, the actual forecast and explanation information associated with the actual forecast.
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f).
Claim 2
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the set of features related to the primary time series dataset comprise a set of one or more dynamic features related to the primary time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the set of features related to the primary time series dataset comprise a set of one or more dynamic features related to the primary time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 3
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein a dynamic feature in the set of dynamic features is represented as an additional feature time series dataset related to the primary time series dataset, wherein each data point in a set of data points represented by the additional feature time series dataset comprises a value of a dynamic feature and an associated time value.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein a dynamic feature in the set of dynamic features is represented as an additional feature time series dataset related to the primary time series dataset, wherein each data point in a set of data points represented by the additional feature time series dataset comprises a value of a dynamic feature and an associated time value.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 4
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the datapoints in the set of datapoints represented by the additional feature time series dataset represent past covariate values of the dynamic feature related to the primary time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the datapoints in the set of datapoints represented by the additional feature time series dataset represent past covariate values of the dynamic feature related to the primary time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 5
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the datapoints in the set of datapoints represented by the additional feature time series dataset represent a combination of past covariate values and future covariate values of the dynamic feature related to the primary time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the datapoints in the set of datapoints represented by the additional feature time series dataset represent a combination of past covariate values and future covariate values of the dynamic feature related to the primary time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 6
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the primary time series dataset is an individual time series dataset comprised in a multiple time series dataset and the primary time series dataset represents a univariate time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the primary time series dataset is an individual time series dataset comprised in a multiple time series dataset and the primary time series dataset represents a univariate time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 7
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the primary time series dataset is an individual time series dataset comprised in a multiple time series dataset and the primary time series dataset represents a multivariate time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the primary time series dataset is an individual time series dataset comprised in a multiple time series dataset and the primary time series dataset represents a multivariate time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 10
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the feature importance score for a feature in the set of features is represented as a weight value, wherein the weight value represents a relevance of the feature for predicting the actual forecast for the particular time point of the primary time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the feature importance score for a feature in the set of features is represented as a weight value, wherein the weight value represents a relevance of the feature for predicting the actual forecast for the particular time point of the primary time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 11
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the explanation information associated with the actual forecast comprises information identifying the plurality of attention scores determined for the plurality of data points in the primary time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the explanation information associated with the actual forecast comprises information identifying the plurality of attention scores determined for the plurality of data points in the primary time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 12
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the explanation information associated with the actual forecast comprises information identifying the feature importance scores computed for one or more features in the set of features related to the primary time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the explanation information associated with the actual forecast comprises information identifying the feature importance scores computed for one or more features in the set of features related to the primary time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 13
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the plurality of attention scores represent information identifying an impact of the plurality of data points in the primary time series dataset for predicting the actual forecast for the particular time point of the primary time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the plurality of attention scores represent information identifying an impact of the plurality of data points in the primary time series dataset for predicting the actual forecast for the particular time point of the primary time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 14
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements;
wherein the set of features comprise a set of one or more static metadata features related to the primary time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the set of features comprise a set of one or more static metadata features related to the primary time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 15
Step 2A, Prong 1: The claim recites, inter alia:
computing, …, a feature importance score for one or more features in the set of features;
This limitation recites a mathematical concept to calculate values for feature importance. See MPEP 2106.04(a)(2)(I)
selecting, …, a subset of features from the set of features based on the feature importance scores computed for the one or more features in the set of features;
This limitation recites a mental process to choose a feature based on previously calculated values. See MPEP 2106.04(a)(2)(III)
based on the selected subset of features, determining, …, a plurality of attention scores for a plurality of data points in the primary time series dataset;
This limitation recites a mathematical concept to calculate values for a relative importance of a plurality of data points.
predicting, …, an actual forecast for the particular time point based on the plurality of attention scores determined for the plurality of data points in the primary time series dataset;
This limitation recites a mental process to guess a result based on previously calculated values for attention.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
A system comprising
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
a memory; and
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
one or more processors configured to perform processing, the processing comprising
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
obtaining a time series forecast request requesting a forecast for a particular time point, wherein the time series forecast request identifies a primary time series dataset to be used as a basis for generating the requested forecast and a set of features related to the primary time series dataset, wherein the primary time series dataset comprises multiple historical time series data points, each time series data point having a value and an associated time value;
This limitation represents an insignificant extra-solution activity of data gathering and selecting a particular type of data to be manipulated, being pre-solution activity, performed by a generic computing system. See MPEP 2106.05(g);
providing the primary time series dataset and the set of features to a model to be used for generating the forecast, wherein the model is trained upon or fit to the time series data points in the primary time series dataset and the set of features related to the primary time series dataset;
This limitation represents an insignificant extra-solution activity of data gathering and selecting a particular type of data to be manipulated, being pre-solution activity, performed by a generic computing system. See MPEP 2106.05(g);
… using the model …
This limitation is recited at a high level of generality and recites use of a generic class of computer algorithms to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using a generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
and outputting the actual forecast and explanation information associated with the actual forecast.
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A system comprising
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
a memory; and
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
one or more processors configured to perform processing, the processing comprising
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
obtaining a time series forecast request requesting a forecast for a particular time point, wherein the time series forecast request identifies a primary time series dataset to be used as a basis for generating the requested forecast and a set of features related to the primary time series dataset, wherein the primary time series dataset comprises multiple historical time series data points, each time series data point having a value and an associated time value;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
providing the primary time series dataset and the set of features to a model to be used for generating the forecast, wherein the model is trained upon or fit to the time series data points in the primary time series dataset and the set of features related to the primary time series dataset;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
… using the model …
This limitation is recited at a high level of generality and recites use of a generic class of computer algorithms to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using a generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
and outputting the actual forecast and explanation information associated with the actual forecast.
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f).
Claim 16
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the set of features related to the primary time series dataset comprise a set of one or more dynamic features related to the primary time series dataset and a set of one or more static metadata features related to the primary time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the set of features related to the primary time series dataset comprise a set of one or more dynamic features related to the primary time series dataset and a set of one or more static metadata features related to the primary time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 17
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the explanation information associated with the actual forecast comprises information identifying the plurality of attention scores determined for the plurality of data points in the primary time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the explanation information associated with the actual forecast comprises information identifying the plurality of attention scores determined for the plurality of data points in the primary time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 18
Step 2A, Prong 1: The claim recites, inter alia:
computing, …, a feature importance score for one or more features in the set of features;
This limitation recites a mathematical concept to calculate values for feature importance. See MPEP 2106.04(a)(2)(I)
selecting, …, a subset of features from the set of features based on the feature importance scores computed for the one or more features in the set of features;
This limitation recites a mental process to choose a feature based on previously calculated values. See MPEP 2106.04(a)(2)(III)
based on the selected subset of features, determining, …, a plurality of attention scores for a plurality of data points in the primary time series dataset;
This limitation recites a mathematical concept to calculate values for a relative importance of a plurality of data points.
predicting, …, an actual forecast for the particular time point based on the plurality of attention scores determined for the plurality of data points in the primary time series dataset;
This limitation recites a mental process to guess a result based on previously calculated values for attention.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
A non-transitory computer-readable medium storing instructions executable by a computer system that, when executed by one or more processors of the computer system, cause the one or more processors to perform operations comprising
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
obtaining a time series forecast request requesting a forecast for a particular time point, wherein the time series forecast request identifies a primary time series dataset to be used as a basis for generating the requested forecast and a set of features related to the primary time series dataset, wherein the primary time series dataset comprises multiple historical time series data points, each time series data point having a value and an associated time value;
This limitation represents an insignificant extra-solution activity of data gathering and selecting a particular type of data to be manipulated, being pre-solution activity, performed by a generic computing system. See MPEP 2106.05(g);
providing the primary time series dataset and the set of features to a model to be used for generating the forecast, wherein the model is trained upon or fit to the time series data points in the primary time series dataset and the set of features related to the primary time series dataset;
This limitation represents an insignificant extra-solution activity of data gathering and selecting a particular type of data to be manipulated, being pre-solution activity, performed by a generic computing system. See MPEP 2106.05(g);
… using the model …
This limitation is recited at a high level of generality and recites use of a generic class of computer algorithms to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using a generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
and outputting the actual forecast and explanation information associated with the actual forecast.
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory computer-readable medium storing instructions executable by a computer system that, when executed by one or more processors of the computer system, cause the one or more processors to perform operations comprising
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
obtaining a time series forecast request requesting a forecast for a particular time point, wherein the time series forecast request identifies a primary time series dataset to be used as a basis for generating the requested forecast and a set of features related to the primary time series dataset, wherein the primary time series dataset comprises multiple historical time series data points, each time series data point having a value and an associated time value;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
providing the primary time series dataset and the set of features to a model to be used for generating the forecast, wherein the model is trained upon or fit to the time series data points in the primary time series dataset and the set of features related to the primary time series dataset;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
… using the model …
This limitation is recited at a high level of generality and recites use of a generic class of computer algorithms to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using a generic class of computer algorithms in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
and outputting the actual forecast and explanation information associated with the actual forecast.
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f).
Claim 19
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein the set of features related to the primary time series dataset comprise a set of one or more dynamic features related to the primary time series dataset and a set of one or more static metadata features related to the primary time series dataset.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the set of features related to the primary time series dataset comprise a set of one or more dynamic features related to the primary time series dataset and a set of one or more static metadata features related to the primary time series dataset.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim 20
Step 2A, Prong 1: No additional abstract idea limitations.
Step 2A, Prong 2: The additional elements recited in the claim do not integrate the
judicial exception into a practical application.
Additional elements:
wherein a dynamic feature in the set of dynamic features is represented as an additional feature time series dataset related to the primary time series dataset, wherein each data point in a set of data points represented by the additional feature time series dataset comprises a value of a dynamic feature and an associated time value.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein a dynamic feature in the set of dynamic features is represented as an additional feature time series dataset related to the primary time series dataset, wherein each data point in a set of data points represented by the additional feature time series dataset comprises a value of a dynamic feature and an associated time value.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, and 7-20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by WO 2021126500 A1 by Lim et al., hereafter Lim.
Regarding claim 1, Lim teaches:
A computer-implemented method comprising:
obtaining, by a computing system, a time series forecast request requesting a forecast for a particular time point, (Paragraph [0134] “The memory 714 of the computing device 710 can store information accessible by the one or more processors 712, including instructions 716 that can be executed by the one or more processors 712 to cause the computing device 710 to perform operations for multi-horizon forecasting consistent with aspects of this disclosure.” Paragraph [0016] “...each RNN decoder configured to predict a short-term pattern for a respective future time period based on the encoder vectors, the static covariates, and time-varying known future input data.” Instructions to cause a computing device to perform operations are requests and the operations include predicting for a respective future time period based in part on time-varying input data so instructions are a time series forecast request requesting a forecast for a particular time point obtained by a computing system.) wherein the time series forecast request identifies a primary time series dataset to be used as a basis for generating the requested forecast and a set of features related to the primary time series dataset, (Paragraph [0049] “The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” The static covariates and time-dependent features are the set of features related to the primary time series dataset. The targets are the primary time series dataset to be used as a basis for generating the requested forecast.) wherein the primary time series dataset comprises multiple historical time series data points, each time series data point having a value and an associated time value; (Paragraph [0032] “Long-term temporal patterns can include patterns of characteristics observed across an entire analyzed time-window. A time-window as described herein refers to the period in the past and in the future from which the system can analyze data to generate a forecast.” Paragraph [0066] “In some implementations, the system 100 is configured to receive and process the input 105 for a plurality of different time-windows defined by the look-behind and look-ahead parameters.” Data analyzed to generate a forecast includes the targets which is the primary time series dataset. Processing a plurality of different time windows, where a time-window includes a reference to a period in the past, means that the targets (the primary time series dataset) comprise multiple historical time series data points. A variable that corresponds to an entity represented through a time-series data has a value from the entity, and an associated time value from the time-series data.)
providing, by the computing system, the primary time series dataset and the set of features to a model to be used for generating the forecast, wherein the model is trained upon or fit to the time series data points in the primary time series dataset and the set of features related to the primary time series dataset; (Fig. 1, Paragraph [0049] “The system 100 is configured to obtain input 105 and to generate forecasts 120 for different variables at different forecasting horizons. The system 100 can obtain the input 105 from a time-series database, or any source of time-series data. The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” Paragraph [0129] “The system 100 can be trained according to a variety of machine learning training techniques, for example using a model trainer configured to train the system 100. The system 100 can be trained according to a supervised learning technique on a training set of labeled time-series data.” The system referred to in this quote includes a model that generates forecasts. The system (the model) obtaining the input means that it was provided the targets(the primary time series dataset), the static covariates, and the time-dependent features (the set of features). The labeled time-series data includes the targets(the primary time series dataset), the static covariates, and the time-dependent features (the set of features). So, the model is trained upon a training set of the time series data points in the primary time series dataset and the set of features related to the primary time series dataset.)
computing, by the computer system, using the model, a feature importance score for one or more features in the set of features; (Paragraph [0084] “As described in more detail, below with reference to FIG. 5, the variable selection layer 230 can weigh different variables of the input 105 according to a learned measure of relevance” The values computed by weighing different variables according to a learned measure of relevance include a feature importance for the one or more features in the set of features.)
selecting, by the computer system, using the model, a subset of features from the set of features based on the feature importance scores computed for the one or more features in the set of features; (Paragraph [0084] “As described in more detail, below with reference to FIG. 5, the variable selection layer 230 can weigh different variables of the input 105 according to a learned measure of relevance” Paragraph [0113] “In addition, the variable selection layer 230 can also suppress or elevate different input variables under different use-cases or different types of time-series data.” The variable selection layer is selecting a subset of features from the set of features based on values computed from weighing different variables, these values include the feature importance scores computed for the one or more features in the set of features.)
based on the selected subset of features, determining, by the computer system, using the model, a plurality of attention scores for a plurality of data points in the primary time series dataset; (Fig. 2, Paragraph [0097] “The multi-head attention layer 262 can be implemented as a multi-head attention mechanism, in which different “heads” attend to different subspaces within ( Q , K, F) according to different weight values.” Different weight values, that affect which of different “heads” attend to different subspaces, are a plurality of attention scores for a plurality of data points in the primary time series dataset. The multi-head attention layer shown in Fig. 2, receives information from the variable selection layer so it is basing the different weight values (plurality of attention scores) on the selected subset of features selected by the variable selection layer.)
predicting, by the system, using the model, an actual forecast for the particular time point based on the plurality of attention scores determined for the plurality of data points in the primary time series dataset; and (Fig.1, Paragraph [0060] “For T forecasting horizons, the system 100 can generate forecasts 120A-T, each forecast corresponding to respective one or more time-steps from A to T.” Time-steps are particular time points. Fig. 1 shows the system, which includes an attention layer which would include the plurality of attention scores determined for a plurality of data points in the primary time series data set, predicting actual forecasts for time-steps(particular time points).)
outputting, by the system, the actual forecast and explanation information associated with the actual forecast. (Paragraph [0142] “As an example, the computing device 710 may include web servers capable of communicating with storage system 750 as well as computing devices 720, 730, and 740 via the network. For example, one or more of server computing devices 710 may use network 760 to transmit and present information, web applications, etc., on a display, such as displays 722 of the computing device 720.” Transmitting and presenting information includes outputting the actual forecast and explanation information associated with the actual forecast.)
Regarding claim 2, Lim teaches all of the material disclosed in claim 1, and additionally teaches:
wherein the set of features related to the primary time series dataset comprise a set of one or more dynamic features related to the primary time series dataset. (Paragraph [0049] “The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” Time-dependent features are a set of one or more dynamic features related to the primary time series dataset.)
Regarding claim 3, Lim teaches all of the material disclosed in claim 2, and additionally teaches:
wherein a dynamic feature in the set of dynamic features is represented as an additional feature time series dataset related to the primary time series dataset, wherein each data point in a set of data points represented by the additional feature time series dataset comprises a value of a dynamic feature and an associated time value. (Paragraph [0049] “The system 100 is configured to obtain input 105 and to generate forecasts 120 for different variables at different forecasting horizons. The system 100 can obtain the input 105 from a time-series database, or any source of time-series data. The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” Paragraph [0050] “Time-dependent features 130, on the other hand, are variables that do vary with time, i.e., from time- step to time-step. Time-dependent features include observed inputs 132 and known inputs 134 for a given time-step. The observed inputs 132 are variables that are measured at or before a time-step t, and the known inputs 134 are variables that vary for different time-steps after a time-step t, but that are known in advance. For example, if time-steps are measured in days, then an observed input for day t and for an entity retail store i can be revenue for the store on day t, and a known input can be the day of the week or calendar date on day t.” A time dependent feature is a dynamic feature. Each time dependent feature(dynamic feature) has an associated time value. The time dependent features(dynamic features) are not the same set, but are related to, as the targets(the primary time series dataset). So, the time dependent features(dynamic features) are an additional feature time series dataset related to the targets(the primary time series dataset).)
Regarding claim 4, Lim teaches all of the material disclosed in claim 3, and additionally teaches:
wherein the datapoints in the set of datapoints represented by the additional feature time series dataset represent past covariate values of the dynamic feature related to the primary time series dataset. (Paragraph [0050] “Time-dependent features include observed inputs 132 and known inputs 134 for a given time-step. The observed inputs 132 are variables that are measured at or before a time-step t, and the known inputs 134 are variables that vary for different time-steps after a time-step t, but that are known in advance.” Time-dependent features are dynamic features. Observed inputs represent past covariate values of the dynamic feature related to the primary time series dataset.)
Regarding claim 5, Lim teaches all of the material disclosed in claim 3, and additionally teaches:
wherein the datapoints in the set of datapoints represented by the additional feature time series dataset represent a combination of past covariate values and future covariate values of the dynamic feature related to the primary time series dataset. (Paragraph [0050] “Time-dependent features include observed inputs 132 and known inputs 134 for a given time-step. The observed inputs 132 are variables that are measured at or before a time-step t, and the known inputs 134 are variables that vary for different time-steps after a time-step t, but that are known in advance.” Observed inputs represent past covariate values of the dynamic feature related to the primary time series dataset. Known input represent future covariate values of the dynamic feature related to the primary time series dataset. The time-dependent features are dynamic features and include a combination of observed inputs and known inputs.)
Regarding claim 7, Lim teaches all of the material disclosed in claim 1, and additionally teaches:
wherein the primary time series dataset is an individual time series dataset comprised in a multiple time series dataset and the primary time series dataset represents a multivariate time series dataset. (Paragraph [0004] “Multi-horizon forecasting refers to prediction of different variables for more than one horizon.” Paragraph [0049] “FIG. 1 is a functional diagram of an example of a multi-horizon forecasting system 100. The system 100 is configured to obtain input 105 and to generate forecasts 120 for different variables at different forecasting horizons. The system 100 can obtain the input 105 from a time-series database, or any source of time-series data. The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” The input is a multiple time series dataset that includes the targets, the static covariates, and the time-dependent features. The primary time series dataset is the targets which is an individual time-series dataset comprised in the input (a multiple time series dataset). The targets are the basis for generating forecasts. As there are different variables, the targets(the primary time series dataset) are a multivariate dataset.)
Regrading claim 8, Lim teaches all of the material disclosed in claim 1, and additionally teaches:
generating, using the model, an encoded representation of the primary time series dataset; (Paragraph [0069] “An encoded vector refers to a vector (or in some implementations, a multi-dimensional array or tensor) storing a representation of input data in a format the sequence processing layer 110 and/or the attention processing layer 115 is configured to receive.” An encoded vector is an encoded representation and the input data includes the primary time series dataset.)
generating, using the model, an encoded representation of a feature time series dataset associated with a feature in the selected subset of features; (Paragraph [0069] “Referring to FIG. 3, the determining 320 can include generating 322, using one or more encoders in the sequence-to-sequence layer 220, encoder vectors based on static covariates and time- varying input data captured during respective past time-periods.” Static covariates and time-varying input data are included in a feature time series dataset. Thus, the encoder vectors are an encoded representation of a feature time series dataset associated with a feature in the selected subset of features.)
and determining, using the model, information identifying correlations between one or more data points of the encoded representation of the primary time series dataset and one or more data points of the feature time series dataset associated with the feature in the selected subset of features. (Paragraph [0072] “In time-series data, points or periods of significance are often identified in relation to values of variables representing other points near-in-time.” Paragraph [0096] “The interpretable multi-head multi-head attention layer 262 is a mechanism by which the system 100 learns long-term relationships between variables represented at different time-steps. The multi-head attention layer 262 as described in this specification can allow for greater interpretability of forecasts generated by the system 100.” Paragraph [0112] “As mentioned above, the techniques described in this specification can allow for improved interpretability of the relationship between time-series data and resultant forecasts generated by the system 100. Interpretability generally refers to identifying and understanding portions of the input data and how individual variables are patterned or inter-relate with each other.” A mechanism that learns long-term relationships between variables is in part based on those relationships. Long-term relationships between variables is interpreted as correlations. Improved interpretability includes information on identifying how individual variables inter-relate with each other. The inter-relation includes the correlation between one or more data points of the encoded representation of the targets (the primary time series dataset) and one or more data points or periods of significance of the time-dependent features(the feature time series dataset).)
Regarding claim 9, Lim teaches all of the material disclosed in claim 8, and additionally teaches:
determining, using the model, the plurality of attention scores for the plurality of data points of the primary time series dataset based on the determined correlations. (Paragraph [0096] “The interpretable multi-head multi-head attention layer 262 is a mechanism by which the system 100 learns long-term relationships between variables represented at different time-steps. The multi-head attention layer 262 as described in this specification can allow for greater interpretability of forecasts generated by the system 100.” Paragraph [0112] “As mentioned above, the techniques described in this specification can allow for improved interpretability of the relationship between time-series data and resultant forecasts generated by the system 100. Interpretability generally refers to identifying and understanding portions of the input data and how individual variables are patterned or inter-relate with each other.” A mechanism that learns long-term relationships between variables is in part based on those relationships. Long-term relationships between variables is interpreted as correlations. Greater interpretability includes information on identifying how individual variables inter-relate with each other. How individual variables inter-relate with each other is interpreted as correlation. Interpretable means it includes improved interpretability including being based on the determined information identifying correlations. Thus, the interpretable multi-head attention layer determines the plurality of attention scores for the plurality of data points of target variables (the primary time series dataset) based on the determined information identifying correlations.)
Regarding claim 10, Lim teaches all of the material disclosed in claim 1, and additionally teaches:
wherein the feature importance score for a feature in the set of features is represented as a weight value, wherein the weight value represents a relevance of the feature for predicting the actual forecast for the particular time point of the primary time series dataset. (Paragraph [0084] “As described in more detail, below with reference to FIG. 5, the variable selection layer 230 can weigh different variables of the input 105 according to a learned measure of relevance” Weighing different variables of the input according to a learned measure of relevance generates weight values representing a relevance of the feature for predicting the actual forecast for the particular time point of the primary time series dataset.)
Regarding claim 11, Lim teaches all of the material disclosed in claim 1, and additionally teaches:
wherein the explanation information associated with the actual forecast comprises information identifying the plurality of attention scores determined for the plurality of data points in the primary time series dataset. (Paragraph [0096] “The multi-head attention layer 262 as described in this specification can allow for greater interpretability of forecasts generated by the system 100.” Greater interpretability of forecasts means there is explanation information with information identifying the information from the multi-head attention layer. The multi-head attention layer includes a plurality of attention scores determined for a plurality of data points in the primary time series dataset.)
Regarding claim 12, Lim teaches all of the material disclosed in claim 1, and additionally teaches:
wherein the explanation information associated with the actual forecast comprises information identifying the feature importance scores computed for one or more features in the set of features related to the primary time series dataset. (Paragraph [0112] “The system 100 can generate forecasts in an interpretable way, meaning that the system 100 can provide insight into the relative importance or un-importance of certain input variables” The relative importance or un-importance of certain input variables is the feature importance scores computed for one or more features in the set of features related to the primary time series dataset. Providing insight into the feature importance scores includes comprising information identifying it.)
Regarding claim 13, Lim teaches all of the material disclosed in claim 1, and additionally teaches:
wherein the plurality of attention scores represent information identifying an impact of the plurality of data points in the primary time series dataset for predicting the actual forecast for the particular time point of the primary time series dataset. (Paragraph [0117] “In addition or alternatively, the system 100 can obtain the weights of the multi-head attention layer 262 to determine variable importance for different input variables in relation to the remote processing layer 115.” A plurality of data points is equivalent to a variable. Different input variables in relation to the remote processing layer is the primary time series dataset for predicting the actual forecast for the particular time point of the primary time series dataset. To determine variable importance is to represent impact of the plurality of data. The weights from the multi-head attention layer are a plurality of attention scores.)
Regarding claim 14, Lim teaches all of the material disclosed in claim 1, and additionally teaches:
wherein the set of features comprise a set of one or more static metadata features related to the primary time series dataset. (Paragraph [0050] “An entity can be represented at least partially through one or more static covariates. Static covariates are variables that are time-independent, i.e., do not vary in time from time-step to time-step.” Static covariates are a set of one or more static metadata features related to the primary time series dataset.)
Regarding claim 15, Lim teaches:
A system comprising:
a memory; and
one or more processors configured to perform processing the processing comprising: (Paragraph [0134] “The memory 714 of the computing device 710 can store information accessible by the one or more processors 712, including instructions 716 that can be executed by the one or more processors 712 to cause the computing device 710 to perform operations for multi-horizon forecasting consistent with aspects of this disclosure.” The one or more processors includes the computing device. Performing operations is performing processing.)
obtaining a time series forecast request requesting a forecast for a particular time point, (Paragraph [0134] “The memory 714 of the computing device 710 can store information accessible by the one or more processors 712, including instructions 716 that can be executed by the one or more processors 712 to cause the computing device 710 to perform operations for multi-horizon forecasting consistent with aspects of this disclosure.” Paragraph [0016] “...each RNN decoder configured to predict a short-term pattern for a respective future time period based on the encoder vectors, the static covariates, and time-varying known future input data.” Instructions to cause a computing device to perform operations are requests and the operations include predicting for a respective future time period based in part on time-varying input data so instructions are a time series forecast request requesting a forecast for a particular time point obtained by a processor.) wherein the time series forecast request identifies a primary time series dataset to be used as a basis for generating the requested forecast and a set of features related to the primary time series dataset, (Paragraph [0049] “The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” The static covariates and time-dependent features are the set of features related to the primary time series dataset. The targets are the primary time series dataset to be used as a basis for generating the requested forecast.) wherein the primary time series dataset comprises multiple historical time series data points, each time series data point having a value and an associated time value; (Paragraph [0032] “Long-term temporal patterns can include patterns of characteristics observed across an entire analyzed time-window. A time-window as described herein refers to the period in the past and in the future from which the system can analyze data to generate a forecast.” Paragraph [0066] “In some implementations, the system 100 is configured to receive and process the input 105 for a plurality of different time-windows defined by the look-behind and look-ahead parameters.” Data analyzed to generate a forecast includes the targets which is the primary time series dataset. Processing a plurality of different time windows, where a time-window includes a reference to a period in the past, means that the targets (the primary time series dataset) comprise multiple historical time series data points. A variable that corresponds to an entity represented through a time-series data has a value from the entity, and an associated time value from the time-series data.)
providing the primary time series dataset and the set of features to a model to be used for generating the forecast, wherein the model is trained upon or fit to the time series data points in the primary time series dataset and the set of features related to the primary time series dataset; (Fig. 1, Paragraph [0049] “The system 100 is configured to obtain input 105 and to generate forecasts 120 for different variables at different forecasting horizons. The system 100 can obtain the input 105 from a time-series database, or any source of time-series data. The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” Paragraph [0129] “The system 100 can be trained according to a variety of machine learning training techniques, for example using a model trainer configured to train the system 100. The system 100 can be trained according to a supervised learning technique on a training set of labeled time-series data.” The system referred to in this quote includes a model that generates forecasts. The system (the model) obtaining the input means that it was provided the targets(the primary time series dataset), the static covariates, and the time-dependent features (the set of features). The labeled time-series data includes the targets(the primary time series dataset), the static covariates, and the time-dependent features (the set of features). So, the model is trained upon a training set of the time series data points in the primary time series dataset and the set of features related to the primary time series dataset.)
computing, using the model, a feature importance score for one or more features in the set of features; (Paragraph [0084] “As described in more detail, below with reference to FIG. 5, the variable selection layer 230 can weigh different variables of the input 105 according to a learned measure of relevance” The values computed by weighing different variables according to a learned measure of relevance include a feature importance for the one or more features in the set of features.)
selecting, using the model, a subset of features from the set of features based on the feature importance scores computed for the one or more features in the set of features; (Paragraph [0084] “As described in more detail, below with reference to FIG. 5, the variable selection layer 230 can weigh different variables of the input 105 according to a learned measure of relevance” Paragraph [0113] “In addition, the variable selection layer 230 can also suppress or elevate different input variables under different use-cases or different types of time-series data.” The variable selection layer is selecting a subset of features from the set of features based on values computed from weighing different variables, these values include the feature importance scores computed for the one or more features in the set of features.)
based on the selected subset of features, determining, using the model, a plurality of attention scores for a plurality of data points in the primary time series dataset; (Fig. 2, Paragraph [0097] “The multi-head attention layer 262 can be implemented as a multi-head attention mechanism, in which different “heads” attend to different subspaces within ( Q , K, F) according to different weight values.” Different weight values, that affect which of different “heads” attend to different subspaces, are a plurality of attention scores for a plurality of data points in the primary time series dataset. The multi-head attention layer shown in Fig. 2, receives information from the variable selection layer so it is basing the different weight values (plurality of attention scores) on the selected subset of features selected by the variable selection layer.)
predicting, using the model, an actual forecast for the particular time point based on the plurality of attention scores determined for the plurality of data points in the primary time series dataset; and (Fig.1, Paragraph [0060] “For T forecasting horizons, the system 100 can generate forecasts 120A-T, each forecast corresponding to respective one or more time-steps from A to T.” Time-steps are particular time points. Fig. 1 shows the system, which includes an attention layer which would include the plurality of attention scores determined for a plurality of data points in the primary time series data set, predicting actual forecasts for time-steps(particular time points).)
outputting, by the system, the actual forecast and explanation information associated with the actual forecast. (Paragraph [0142] “As an example, the computing device 710 may include web servers capable of communicating with storage system 750 as well as computing devices 720, 730, and 740 via the network. For example, one or more of server computing devices 710 may use network 760 to transmit and present information, web applications, etc., on a display, such as displays 722 of the computing device 720.” Transmitting and presenting information includes outputting the actual forecast and explanation information associated with the actual forecast.)
Regarding claim 16, Lim teaches all of the material disclosed in claim 15, and additionally teaches:
wherein the set of features related to the primary time series dataset comprise a set of one or more dynamic features related to the primary time series dataset and a set of one or more static metadata features related to the primary time series dataset. (Paragraph [0049] “The system 100 is configured to obtain input 105 and to generate forecasts 120 for different variables at different forecasting horizons. The system 100 can obtain the input 105 from a time-series database, or any source of time-series data. The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” Time dependent feature are a set of one or more dynamic features related to the primary dataset. Static covariates are a set of one or more static metadata features related to the primary time series dataset.)
Regarding claim 17, Lim teaches all of the material disclosed in claim 15, and additionally teaches:
wherein the explanation information associated with the actual forecast comprises information identifying the plurality of attention scores determined for the plurality of data points in the primary time series dataset. (Paragraph [0096] “The multi-head attention layer 262 as described in this specification can allow for greater interpretability of forecasts generated by the system 100.” Greater interpretability of forecasts means there is explanation information with information identifying the information from the multi-head attention layer. The multi-head attention layer includes a plurality of attention scores determined for a plurality of data points in the primary time series dataset.)
Regarding claim 18, Lim teaches:
A non-transitory computer-readable medium storing instructions executable by a computer system that, when executed by one or more processors of the computer system, cause the one or more processors to perform operations comprising: (Paragraph [0134] “The memory 714 of the computing device 710 can store information accessible by the one or more processors 712, including instructions 716 that can be executed by the one or more processors 712 to cause the computing device 710 to perform operations for multi-horizon forecasting consistent with aspects of this disclosure.” The one or more processors includes the computing device.)
obtaining a time series forecast request requesting a forecast for a particular time point, (Paragraph [0134] “The memory 714 of the computing device 710 can store information accessible by the one or more processors 712, including instructions 716 that can be executed by the one or more processors 712 to cause the computing device 710 to perform operations for multi-horizon forecasting consistent with aspects of this disclosure.” Paragraph [0016] “...each RNN decoder configured to predict a short-term pattern for a respective future time period based on the encoder vectors, the static covariates, and time-varying known future input data.” Instructions to cause a computing device to perform operations are requests and the operations include predicting for a respective future time period based in part on time-varying input data so instructions are a time series forecast request requesting a forecast for a particular time point obtained by a processor.) wherein the time series forecast request identifies a primary time series dataset to be used as a basis for generating the requested forecast and a set of features related to the primary time series dataset, (Paragraph [0049] “The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” The static covariates and time-dependent features are the set of features related to the primary time series dataset. The targets are the primary time series dataset to be used as a basis for generating the requested forecast.) wherein the primary time series dataset comprises multiple historical time series data points, each time series data point having a value and an associated time value; (Paragraph [0032] “Long-term temporal patterns can include patterns of characteristics observed across an entire analyzed time-window. A time-window as described herein refers to the period in the past and in the future from which the system can analyze data to generate a forecast.” Paragraph [0066] “In some implementations, the system 100 is configured to receive and process the input 105 for a plurality of different time-windows defined by the look-behind and look-ahead parameters.” Data analyzed to generate a forecast includes the targets which is the primary time series dataset. Processing a plurality of different time windows, where a time-window includes a reference to a period in the past, means that the targets (the primary time series dataset) comprise multiple historical time series data points. A variable that corresponds to an entity represented through a time-series data has a value from the entity, and an associated time value from the time-series data.)
providing the primary time series dataset and the set of features to a model to be used for generating the forecast, wherein the model is trained upon or fit to the time series data points in the primary time series dataset and the set of features related to the primary time series dataset; (Fig. 1, Paragraph [0049] “The system 100 is configured to obtain input 105 and to generate forecasts 120 for different variables at different forecasting horizons. The system 100 can obtain the input 105 from a time-series database, or any source of time-series data. The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” Paragraph [0129] “The system 100 can be trained according to a variety of machine learning training techniques, for example using a model trainer configured to train the system 100. The system 100 can be trained according to a supervised learning technique on a training set of labeled time-series data.” The system referred to in this quote includes a model that generates forecasts. The system (the model) obtaining the input means that it was provided the targets(the primary time series dataset), the static covariates, and the time-dependent features (the set of features). The labeled time-series data includes the targets(the primary time series dataset), the static covariates, and the time-dependent features (the set of features). So, the model is trained upon a training set of the time series data points in the primary time series dataset and the set of features related to the primary time series dataset.)
computing, using the model, a feature importance score for one or more features in the set of features; (Paragraph [0084] “As described in more detail, below with reference to FIG. 5, the variable selection layer 230 can weigh different variables of the input 105 according to a learned measure of relevance” The values computed by weighing different variables according to a learned measure of relevance include a feature importance for the one or more features in the set of features.)
selecting, using the model, a subset of features from the set of features based on the feature importance scores computed for the one or more features in the set of features; (Paragraph [0084] “As described in more detail, below with reference to FIG. 5, the variable selection layer 230 can weigh different variables of the input 105 according to a learned measure of relevance” Paragraph [0113] “In addition, the variable selection layer 230 can also suppress or elevate different input variables under different use-cases or different types of time-series data.” The variable selection layer is selecting a subset of features from the set of features based on values computed from weighing different variables, these values include the feature importance scores computed for the one or more features in the set of features.)
based on the selected subset of features, determining, using the model, a plurality of attention scores for a plurality of data points in the primary time series dataset; (Fig. 2, Paragraph [0097] “The multi-head attention layer 262 can be implemented as a multi-head attention mechanism, in which different “heads” attend to different subspaces within ( Q , K, F) according to different weight values.” Different weight values, that affect which of different “heads” attend to different subspaces, are a plurality of attention scores for a plurality of data points in the primary time series dataset. The multi-head attention layer shown in Fig. 2, receives information from the variable selection layer so it is basing the different weight values (plurality of attention scores) on the selected subset of features selected by the variable selection layer.)
predicting, using the model, an actual forecast for the particular time point based on the plurality of attention scores determined for the plurality of data points in the primary time series dataset; and (Fig.1, Paragraph [0060] “For T forecasting horizons, the system 100 can generate forecasts 120A-T, each forecast corresponding to respective one or more time-steps from A to T.” Time-steps are particular time points. Fig. 1 shows the system, which includes an attention layer which would include the plurality of attention scores determined for a plurality of data points in the primary time series data set, predicting actual forecasts for time-steps(particular time points).)
outputting the actual forecast and explanation information associated with the actual forecast. (Paragraph [0142] “As an example, the computing device 710 may include web servers capable of communicating with storage system 750 as well as computing devices 720, 730, and 740 via the network. For example, one or more of server computing devices 710 may use network 760 to transmit and present information, web applications, etc., on a display, such as displays 722 of the computing device 720.” Transmitting and presenting information includes outputting the actual forecast and explanation information associated with the actual forecast.)
Regarding claim 19, Lim teaches all of the material disclosed in claim 18, and additionally teaches:
wherein the set of features related to the primary time series dataset comprise a set of one or more dynamic features related to the primary time series dataset and a set of one or more static metadata features related to the primary time series dataset. (Paragraph [0049] “The system 100 is configured to obtain input 105 and to generate forecasts 120 for different variables at different forecasting horizons. The system 100 can obtain the input 105 from a time-series database, or any source of time-series data. The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” Time dependent features are a set of one or more dynamic features related to the primary time series dataset. Static covariates are a set of one or more static metadata features related to the primary time series dataset.)
Regarding claim 20, Lim teaches all of the material disclosed in claim 19, and additionally teaches:
wherein a dynamic feature in the set of dynamic features is represented as an additional feature time series dataset related to the primary time series dataset, wherein each data point in a set of data points represented by the additional feature time series dataset comprises a value of a dynamic feature and an associated time value. (Paragraph [0049] “The system 100 is configured to obtain input 105 and to generate forecasts 120 for different variables at different forecasting horizons. The system 100 can obtain the input 105 from a time-series database, or any source of time-series data. The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” Paragraph [0050] “Time-dependent features 130, on the other hand, are variables that do vary with time, i.e., from time- step to time-step. Time-dependent features include observed inputs 132 and known inputs 134 for a given time-step. The observed inputs 132 are variables that are measured at or before a time-step t, and the known inputs 134 are variables that vary for different time-steps after a time-step t, but that are known in advance. For example, if time-steps are measured in days, then an observed input for day t and for an entity retail store i can be revenue for the store on day t, and a known input can be the day of the week or calendar date on day t.” A time dependent feature is a dynamic feature. Each time dependent feature(dynamic feature) has an associated time value. The time dependent features(dynamic features) are not the same set, but are related to, as the targets(the primary time series dataset). So, the time dependent features(dynamic features) are an additional feature time series dataset related to the targets(the primary time series dataset).)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Lim in view of The Comparison of PM2.5 forecasting methods in the form of multivariate and univariate time series based on Support Vector Machine and Genetic Algorithm by Chuentawat and Kan-ngan, hereafter Chuentawat.
Regarding claim 6, Lim teaches all of the material disclosed in claim 1, and additionally teaches:
wherein the primary time series dataset is an individual time series dataset comprised in a multiple time series dataset ((Lim) (Paragraph [0004] “Multi-horizon forecasting refers to prediction of different variables for more than one horizon.” (Lim) Paragraph [0049] “FIG. 1 is a functional diagram of an example of a multi-horizon forecasting system 100. The system 100 is configured to obtain input 105 and to generate forecasts 120 for different variables at different forecasting horizons. The system 100 can obtain the input 105 from a time-series database, or any source of time-series data. The input 105 includes different variables that each correspond to at least one entity represented through the time-series data. Specifically, the input 105 includes static covariates 125, time-dependent features 130, and targets 140 that each at least partially represent one or more entities.” The input is a multiple time series dataset that includes targets, static covariates, and time-dependent features. The primary time series dataset is the targets which is an individual time-series dataset comprised in the input (a multiple time series dataset).)
Lim does not explicitly disclose, but together with Chuentawat teaches:
the primary time series dataset represents a univariate time series dataset. ((Chuentawat) page 1 Introduction section paragraph 1 “This research focus on a study to generate a multivariate and a univariate forecasting time series model.”)
In addition, Lim suggests (The targets are the basis for generating forecasts.)
Chuentawat and Lim are analogous art as both deal with the subject of making time series forecasting models. In addition, Chuentawat teaches that univariate time series models have lower error than multivariate time series models. ((Chuentawat) page 3 table III. All of the RMSE and MAPE are lower for univariate time series than they are for the multivariate time series.) Also, Chuentawat teaches turning a data subset into both a multivariate time series dataset and a univariate time series dataset. ((Chuentawat) page 2 Fig. 3. Experimental procedure)
Thus, it would have been obvious a person having ordinary skill in the art before the effective filing date of the application to have substitute using both the univariate time series model and multivariate time series model constructed using the same data subset, as shown in Chuentawat, with the multivariate time series model in Lim, in order to have a model where the error is lower, as demonstrated in Chuentawat. This represents a simple substitution of one known element for another to obtain predictable results. The substitution would produce the predictable result that includes claim 6 of the instant application.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Patents and/or related publications are cited in the Notice of References Cited (Form PTO-892) attached to this action to further show the state of the art with respect to feature selection, time series, attention scores, correlation, covariates, static and dynamic features, and interpretable forecasts.
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D. H. L.
Examiner
Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
Prosecution Insights