DETAILED ACTION
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 .
2. This action is responsive to the application filed on 01/03/2024. Claims 1-20 are pending in the present application.
Information Disclosure Statement
4. As required by MPEP 609 (c), the Applicants’ submission of the Information Disclosure Statement(s) filed on 01/03/2024 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
Claim Rejections - 35 USC § 101
5. 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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-20 falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1,
At step 2A, prong 1, Does the claim recite a judicial exception?
a training component that trains one or more multivariate time series forecasting models (This step involves creating mathematical prediction forecasting based on multivariate data that falls within mathematical concepts category of abstract ideas.); and
a prediction component that forecasts long-horizon action trajectories for a set of state variables over a defined range of time (This step involves creating mathematical algorithm and statistical modeling that falls within mathematical concepts category of abstract ideas.)
The claim recites a judicial exception, a mathematical operations, predictive modeling and data analysis applied in the field of machine learning. The claim recites mathematical relationships, data manipulation using math and data analysis calculations which falls within the “Mathematical Concepts” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application?
No, As shown above with respect to integration of the abstract idea into a practical application, the additional element of:
A system, comprising: a processor that executes computer-executable components stored in a non-transitory computer-readable memory, the computer-executable components comprising the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f)))
A computer-implemented method, comprising (claim 10), (the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f)))
A computer-readable recording medium with a stored program, the stored program (claim 11) the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f));
A computer program product comprising a non-transitory computer-readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to (claim 20) (the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f)))
The additional elements as disclosed above alone or in combination do not
integrate the judicial exception into practical application as they are generic computer functions in combination with limitations that are generally linking the use of the judicial exception to a particular technological environment or field of use that are implemented to perform the disclosed abstract idea above. Thus, the claim is directed towards the abstract idea.
Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
No, As shown above with respect to integration of the abstract idea into a practical application, the additional element of:
A system, comprising: a processor that executes computer-executable components stored in a non-transitory computer-readable memory, the computer-executable components comprising (see MPEP 2106.05(f));
A computer-implemented method, comprising (claim 10) the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f)))
A computer-readable recording medium with a stored program, the stored program (claim 11) the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f));
A computer program product comprising a non-transitory computer-readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to (claim 20) the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f));
Thus, the claims are not patent eligible. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Neither can insignificant extra-solution activity. All of these additional elements as generically claimed are thus considered well-understood, routine, and conventional. Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, the claim is not patent eligible.
The dependent claims respectively recite a judicial exception in limitations of: “wherein the training component simulates action trajectories to obtain a response from a global forecasting model.” (claims 2/11), “wherein the response from the global forecasting model is used as a target trajectory to train a state-based action response model.” (claims 3/12), “an analysis component that linearizes the state- based action response model.” (claims 4/13), “wherein the analysis component employs the state-based action response model to compute trajectory approximations of the long-horizon action trajectories.”(claims 5/14), “wherein the training component linearizes constraints on correlated control variables with piece-wise linear equations.”(claims 6/15/20), “an encoding component that reformulates the state-based action response model as a mixed-integer linear program.”(claims 7/16), “wherein the prediction component employs the reformulated state- based action response model in optimization formulation to generate a multi-step set point recommendation.”(claims 8/17), “wherein the training component trains the state-based action response model by perturbing future data on a set of control variables.” (claim 9/18). These additional limitations (in claims 2-9,11-18 and 20) also constitute concepts performed in the human mind which fall within the “Mental Processes” groupings of abstract ideas.
This judicial exception is not integrated into a practical application. Additional elements “computer readable medium comprising: computer program code (in claims 2-9,11-18 and 20), all amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of non-transitory computer readable medium comprising: computer program code are again insignificant extra-solution activity steps that cannot provide an inventive concept. All of these additional elements as generically claimed are considered well-understood, routine, and conventional.
Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, all of the dependent claims are also not patent eligible.
Examiner Comments
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 103
6. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Krishna (US 20230385612 B1, 2023-11-30) in view of Fairbank (US 10417556 B1, 2019-09-17)
Regarding independent Claim 1,
Krishna teaches a system, comprising: a processor that executes computer-executable components stored in a non-transitory computer-readable memory (see Krishna: Fig.1, [0020], device 100 can include a processor 102 and/or memory 104 configured to execute or store instructions or other parameters related to providing an operating system 106, which can execute one or more applications or processes), the computer-executable components comprising:
a training component that trains one or more multivariate time series forecasting models (see Krishna: Fig.4, [0044], “at action 404, the future time interval can be provided as input to a model for the timeseries data set. In an example, forecasting component 110, e.g., in conjunction with processor 102, memory 104, operating system 106, etc., can provide the future time interval (or some indication thereof) as input to the model (e.g., ML model 120) for the timeseries data series”… [0031], “causal convolution component 114, e.g., in conjunction with processor 102, memory 104, operating system 106, etc., can generate, for the timeseries data input, the short range output from the causal convolution process that is based on timeseries data inputs associated with timestamps that are within a threshold time before the timestamp of the timeseries data input. In an example, causal convolution component 114 can perform this causal convolution process for each of multiple timeseries data inputs in the timeseries data set (e.g., where the timeseries data inputs have at least one other timeseries data input in the timeseries data set with an earlier timestamp”).
As shown above, Krishna teaches multivariate time-series forecasting models and generating long-horizon forecast using a machine learning forecasting architecture. However, Krishna does not expressly disclose forecasting long-horizon action trajectories for a set of state variables.
Krishna does not explicitly teach computer-executable components comprising:
a prediction component that forecasts long-horizon action trajectories for a set of state variables over a defined range of time.
However, Fairbank teaches the computer-executable components comprising
a prediction component that forecasts long-horizon action trajectories for a set of state variables over a defined range of time (see Fairbank: Fig.25, Col.25, Ln. 40-54, “At 2508, the plurality of predictions can be provided to a reinforcement learning model configured to generate a plurality of predicted outcomes, wherein the reinforcement learning model varies a plurality of parameters to simulate conditions for at least a first entity and a second entity, and an artificial intelligence agent simulates actions performed by one or more of the first entity and second entity, the plurality of data predictions comprising a parameter for the simulation. For example, the first entity can be a merchant, the second entity can be a financial entity that provides or administers a loan to or for the merchant or that purchases invoices from the merchant (e.g., lender and/or factor), and the generated input data and the training data can be transactions for the merchant.”)
Because both Krishna and Fairbank are in the same/similar field of endeavor of forecasting and prediction machine learning system, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Krishna to include the system that forecasts long-horizon action trajectories for a set of state variables over a defined range of time, as taught by Fairbank. After modification of Krishna, the neural network based multivariate time-series forecasting model that generate long range forecasts from historical temporal data can incorporate Fairbank’s machine learning generated future predictions within reinforcement learning framework to evaluate future system behavior and future actions over multiple future time periods. One would have been motivated to make such a combination to provide improved prediction of future system behavior to support better long-term decision making by enabling forecasted future states guide future events. (see Fairbank: Abstract)
Regarding Claim 2,
As shown above, Krishna and Fairbank teaches all the limitations of claim 1. Krishna further teaches the system wherein:
the training component simulates action trajectories to obtain a response from a global forecasting model (see Krishna: Fig.2, [0035], “generating the long range output at action 210, optionally at action 212, the short range outputs from the causal convolution process can be gated for non-linear activation after each causal convolution layer.”)
Regarding Claim 3,
As shown above, Krishna and Fairbank teaches all the limitations of claim 2. Krishna further teaches the system wherein:
the response from the global forecasting model is used as a target trajectory to train a state-based action response model (see Krishna: Fig.2, [0038], “in conjunction with processor 102, memory 104, operating system 106, etc., can provide the model (e.g., ML model 120) for the timeseries data set based at least in part on the long range outputs for each of the timeseries data inputs. Accordingly, the ML model 120 can be trained as described above to model short range dependencies and long range dependencies, which can result in learning true underlying distributions.”)
Regarding Claim 4,
As shown above, Krishna and Fairbank teaches all the limitations of claim 3. Krishna further teaches the system further comprising:
an analysis component that linearizes the state- based action response model (see Krishna: Fig.2, [0036], “normalizing flow component 118 can perform the above processes multiple times. This can include providing (or stacking) multiple layers, as described, where each layer can perform the causal convolution process and the transformer process, and the output of one layer can be used as input to the next layer for a configured number of layers.”)
Regarding Claim 5,
As shown above, Krishna and Fairbank teaches all the limitations of claim 4. Krishna further teaches the system wherein:
the analysis component employs the state-based action response model to compute trajectory approximations of the long-horizon action trajectories model (see Krishna: Fig.2, [0038], “a model for the timeseries data set can be provided based at least in part on the long range outputs for each of the timeseries data inputs. In an example, forecasting component 110, or one or more components thereof, e.g., in conjunction with processor 102, memory 104, operating system 106, etc., can provide the model (e.g., ML model 120) for the timeseries data set based at least in part on the long range outputs for each of the timeseries data input.”)
Regarding Claim 6,
As shown above, Krishna and Fairbank teaches all the limitations of claim 1. Krishna further teaches the system wherein:
the training component linearizes constraints on correlated control variables with piece-wise linear equations (see Krishna: Fig.3, [0039], “he outputs of the causal convolution block 304 can be provided to a highway network block 306 (e.g., the gating process provided by transformer component 116) to provide non-linear dependencies across timestamps. The outputs of the highway network block 306 can be provided to a transformer block 308 (e.g., the transformer process provided by transformer component 116). The outputs 310 of the transformer block 308 can be provided to a next layer in the affine autoregressive flow or as output to the ML model (e.g., ML model 120).”)
Regarding Claim 7,
As shown above, Krishna and Commons teaches all the limitations of claim 3. Krishna further teaches the system further comprising:
an encoding component that reformulates the state-based action response model as a mixed-integer linear program (see Krishna Fig.3, [0041], “The transformer block 308 can process longer range dependencies, which is shown where transformer process 320 in transformer block 308 considers all previous outputs of causal convolution block 304 (or as possibly gated by the highway network block 306). The highway network block 306 can act as a bridge between the transformer block 308 and the causal convolution block 304.”)
Regarding Claim 8,
As shown above, Krishna and Fairbank teaches all the limitations of claim 7. Fairbank further teaches the system wherein:
the prediction component employs the reformulated state- based action response model in optimization formulation to generate a multi-step set point recommendation (see Fairbank: Fig.25, Col.31, Ln. 40-45, “At 2508, the plurality of predictions can be provided to a reinforcement learning model configured to generate a plurality of predicted outcomes, wherein the reinforcement learning model varies a plurality of parameters to simulate conditions for at least a first entity and a second entity, and an artificial intelligence agent simulates actions performed by one or more of the first entity and second entity, the plurality of data predictions comprising a parameter for the simulation. ”)
See motivation to combine Krishna and Fairbank claim 1 above.
Regarding Claim 9,
As shown above, Krishna and Fairbank teaches all the limitations of claim 3. Fairbank further teaches the system wherein:
the training component trains the state-based action response model by perturbing future data on a set of control variable (see Fairbank: Fig.25, Col.31, Ln. 16-27, “At 2502, a trained neural network that is configured to generate a plurality of predictions for a plurality of periods of time in the future based on input data can be received, where the neural network is trained using training data that includes time series data segmented into a plurality of windows. For example, the trained neural network can be a variational auto-encoder that includes an encoder that encodes the generated input data and a decoder that decodes output data to generate the plurality of data predictions. In some embodiments, the training data can be time series data segmented into windows that can be a predetermined number of days, such as a windows size.”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Krishna to include the system that the trains the state-based action response model by perturbing future data on a set of control variable, as taught by Fairbank. After modification of Krishna, the neural network based multivariate time-series forecasting model that generate long range forecasts from historical temporal data can incorporate Fairbank’s machine learning generated future predictions within reinforcement learning framework to evaluate future system behavior and future actions over multiple future time periods. One would have been motivated to make such a combination to provide improved prediction of future system behavior to support better long-term decision making by enabling forecasted future states guide future events. (see Fairbank: Abstract)
Regarding independent Claim 10,
Claim 10 is a computer-implemented method claim and has similar/same claim limitations as Claim 1 and is rejected under the same rationale
Regarding Claims 11-18,
Claim 11-18 are a computer-implemented method claim and has similar/same claim limitations as Claim 2-9 and are rejected under the same rationale.
Regarding independent Claim 19,
Claim 19 is a computer program product and has similar/same claim limitations as Claim 1 and is rejected under the same rationale.
Regarding Claim 20,
Claim 20 is a computer program product and has similar/same claim limitations as Claim 6 and is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
NUMBER:
INVENTOR-INFORMATION:
TITLE / DESCRIPTION
US 20240037419 A1
MUKHOPADHYAY; Sabyasachi
Title: ACCURACY OF MULTIVARIATE APPROACH FOR TIME-SERIES BASED FORECASTING
Description: time-series based forecasting is a technique for predicting future events by analyzing past trends, based on the assumption that future trends will be similar to historical trends. Often, forecasting involves using a machine learning model that processes historical data to predict future values.
US 20230376734 A1
Liu; Chenghao
Title: SYSTEMS AND METHODS FOR TIME SERIES FORECASTING
Description: The embodiments relate generally to time series forecasting, and more specifically to systems and methods for learning latent causal dynamics in time series forecasting.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm.
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/Zelalem Shalu/Examiner, Art Unit 2145
/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
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