DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 11/2/2020 and the Remarks filed on 5/7/2025.
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, 3-6, 8-16, 18, and 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 Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a device; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
encoding the exogenous factor data per series and time point of the input multivariate time series data into a smaller number of shared/global underlying temporal patterns and non-linear combinations of the input time series data to improve speed and power consumption of processing by the computing device: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of encoding exogenous factor data into patterns and non-linear combinations of input time series data, which is performable through mathematical computation. The plain meaning of encoding exogenous factor data into temporal patterns and non-linear combinations in view of the specification is performing a mathematical operation to result in temporal patterns and non-linear combinations of the input time series data. Note that the element “to improve speed and power consumption of processing by the computing device” is not given patentable weight because this element is an intended result and the claim does not provide steps that clearly indicate how the speed and power consumption are improved.
cleaning and de-noising, …, the smaller number of shared/global underlying temporal patterns and non-linear combinations, to improve a speed of forecasting of the computing device: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of cleaning and de-noising data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, paragraphs [0044] and [0064] broadly state that the time series data combinations are cleaned or de-noised without providing more details as to what that means. A plain meaning of cleaning or de-noising data is removing unwanted data, which can be performed in the human mind. Further note that the element “to improve a speed of forecasting of the computing device” is not given patentable weight because this element is an intended result and the claim does not provide steps that clearly indicate how the speed of forecasting is improved.
encoding the multivariate time series data and: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of encoding an input of data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
performing a non-linear mapping of the encoded multivariate time series data to a lower-dimensional latent space to enhance forecasting accuracy of the computing device: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of performing a non-linear mapping of encoded data to a latent space, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Further note that the element “to enhance forecasting accuracy of the computing device” is not given patentable weight because this element is an intended result and the claim does not provide steps that clearly indicate how the accuracy of forecasting is improved.
predicting next values in time of the encoded multivariate time series data in the lower dimensional latent space: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of predicting next values in time series data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
mapping the predicted next values and a random noise back to an input space to expedite the computing device providing a predictive distribution sample for a next time points of the multivariate time series data: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of mapping predicted values and noise to provide a predictive distribution sample, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Further note that the element “to expedite the computing device providing a predictive distribution sample” is not given patentable weight because this element is an intended result and the claim does not provide steps that clearly indicate how the provisioning of the sample is expedited.
training a neural network deep learning model to compute time series modeling and the one or more time series forecasts, wherein training the neural network deep learning model comprises jointly training an encoder neural network, a temporal predictor network, and a decoder neural network, at a same time through a use of a stochastic gradient descent and a back-propagation to compute a gradient at each update step: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of jointly training an encoder neural network, a temporal predictor network, and a decoder neural network using stochastic gradient descent and a back-propagation algorithms. The plain meaning of using backpropagation and stochastic gradient descent in view of the specification are optimization algorithms, which compute neural network parameters using a series of mathematical calculations.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Specifically, the additional elements consist of “a processor; a memory coupled to the processor, the memory storing instructions to cause the processor to perform acts comprising”, “receiving an input time series data and exogenous factor data from an external data source”, “by an encoder neural network”, “outputting one or more time series forecasts based on the predictive distribution sample”.
The additional elements “by an encoder neural network” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished or how the encoder neural network is used to clean and de-noise the patterns. The additional elements of “a processor; a memory coupled to the processor, the memory storing instructions to cause the processor to perform acts comprising” amount to invoking computers or other machinery merely as a tool to perform existing processes or judicial exceptions. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer (see MPEP § 2106.05(f)).
The additional elements of “receiving an input time series data and exogenous factor data from an external data source” and “outputting one or more time series forecasts based on the predictive distribution sample” amount to insignificant extra-solution activities (see MPEP § 2106.05(g)).
Thus, the claim does not recite any additional elements that integrate the abstract idea into a practical application, and the claim is thus directed to the abstract idea
Step 2B: The claim does not contain significantly more than the judicial exceptions.
The additional elements “by an encoder neural network” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished or how the encoder neural network is used to clean and de-noise the patterns. The additional elements of “a processor; a memory coupled to the processor, the memory storing instructions to cause the processor to perform acts comprising” amount to invoking computers or other machinery merely as a tool to perform existing processes or judicial exceptions. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer (see MPEP § 2106.05(f)).
The additional elements of “receiving an input time series data and exogenous factor data from an external data source” and “outputting one or more time series forecasts based on the predictive distribution sample” amount to insignificant extra-solution activities (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”, and “Presenting offers and gathering statistics”).
As such, the claim is ineligible.
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 and Step 2B: The claim recites the additional element of “wherein the training of the deep learning model is unsupervised”, which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Nothing in the claim integrates the abstract idea into a practical application, nor does it provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 and Step 2B: The claim recites the additional element of “wherein the deep learning model comprises an end-to-end deep learning model trained using a stochastic gradient descent”, which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Nothing in the claim integrates the abstract idea into a practical application, nor does it provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
encode an input of a multivariate time series data: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of encoding input, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
predict next values in time from the encoded multivariate time series data received from the encoder network: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of predicting next values from encoded data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
map the predicted next values from the temporal predictor network to an input space: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of mapping predicted values to an input space, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional elements of “the encoder neural network”, “the temporal predictor network”, and “the decoder neural network” amount to invoking computers or other machinery merely as a tool to perform existing processes or judicial exceptions. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer (see MPEP § 2106.05(f)). As such, the claim is ineligible.
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
generate random noise that is input to the decoder neural network: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating random noise, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
map a combination of the random noise and latent space values back to the input space: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of mapping noise an latent space values to an input space, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “a noise generator” and “a decoder neural network” amount to invoking computers or other machinery merely as a tool to perform existing processes or judicial exceptions. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer (see MPEP § 2106.05(f)). As such, the claim is ineligible.
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 and Step 2B: The claim recites the additional element of “wherein the input multivariate time series data and the exogenous factor data is arranged as a 3D array, with a third dimension corresponding to features of the exogenous factor data”, which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Nothing in the claim integrates the abstract idea into a practical application, nor does it provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Claim 9
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 and Step 2B: The claim recites the additional element of “wherein the encoder neural network comprises a temporal auto-encoder”, which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Nothing in the claim integrates the abstract idea into a practical application, nor does it provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Claim 10
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 and Step 2B: The claim recites the additional element of “wherein the encoder neural network comprises a probabilistic temporal auto-encoder”, which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Nothing in the claim integrates the abstract idea into a practical application, nor does it provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Claim 11
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 and Step 2B: The claim recites the additional element of “wherein temporal patterns output by the temporal auto-encoder have a relation to input multivariate time series data”, which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Nothing in the claim integrates the abstract idea into a practical application, nor does it provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Claim 12
Step 1: The claim recites a method of improving speed and power consumption in multivariate time-series modeling and forecasting; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
forming the input multivariate time series data and an exogenous factor data as a 3D array, with a third dimension corresponding to features of the exogenous factor data into a smaller number of shared/global underlying temporal patterns and non-linear combinations of the input time series data to improve a speed and power consumption of processing of a computing device: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of forming time series data and exogenous factor data as an array, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Note that the element “to improve speed and power consumption of processing by the computing device” is not given patentable weight because this element is an intended result and the claim does not provide steps that clearly indicate how the speed and power consumption are improved.
cleaning and de-noising, …, the smaller number of shared/global underlying temporal patterns and non-linear combinations, to improve a speed of forecasting of the computing device: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of cleaning and de-noising data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, paragraphs [0044] and [0064] broadly state that the time series data combinations are cleaned or de-noised without providing more details as to what that means. A plain meaning of cleaning or de-noising data is removing unwanted data, which can be performed in the human mind. Further note that the element “to improve a speed of forecasting of the computing device” is not given patentable weight because this element is an intended result and the claim does not provide steps that clearly indicate how the speed of forecasting is improved.
encoding the multivariate time series data and: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of encoding an input of data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
mapping the encoded multivariate time series data to a lower-dimensional latent space: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of performing a non-linear mapping of encoded data to a latent space, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
predicting next values in time of the encoded multivariate time series data in the lower dimensional latent space to enhance forecasting accuracy of the computing device: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of predicting next values in time series data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Further note that the element “to enhance forecasting accuracy of the computing device” is not given patentable weight because this element is an intended result and the claim does not provide steps that clearly indicate how the accuracy of forecasting is improved.
mapping the predicted next values and a random noise back to an input space to expedite the computing device providing a predictive distribution sample for a next time points of the multivariate time series data: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of mapping predicted values and noise to provide a predictive distribution sample, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Further note that the element “to expedite the computing device providing a predictive distribution sample” is not given patentable weight because this element is an intended result and the claim does not provide steps that clearly indicate how the provisioning of the sample is expedited.
training a neural network deep learning model to compute time series modeling and the one or more time series forecasts, wherein training the neural network deep learning model comprises jointly training an encoder neural network, a temporal predictor network, and a decoder neural network, at a same time through a use of a stochastic gradient descent and a back-propagation to compute a gradient at each update step: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of jointly training an encoder neural network, a temporal predictor network, and a decoder neural network using stochastic gradient descent and a back-propagation algorithms. The plain meaning of these terms in view of the specification are optimization algorithms, which compute neural network parameters using a series of mathematical calculations.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Specifically, the additional elements consist of “receiving an input time series data and exogenous factor data from an external data source” and “outputting one or more time series forecasts based on the predictive distribution sample”.
The additional elements of “receiving an input time series data and exogenous factor data from an external data source” and “outputting one or more time series forecasts based on the predictive distribution sample” amount to insignificant extra-solution activities (see MPEP § 2106.05(g)).
Thus, the claim does not recite any additional elements that integrate the abstract idea into a practical application, and the claim is thus directed to the abstract idea
Step 2B: The claim does not contain significantly more than the judicial exceptions.
The additional elements of “receiving an input time series data and exogenous factor data from an external data source” and “outputting one or more time series forecasts based on the predictive distribution sample” amount to insignificant extra-solution activities (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”, and “Presenting offers and gathering statistics”).
As such, the claim is ineligible.
Claim 13
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
encoding of the multivariate time series data is performed by temporal auto-encoding: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of encoding data using temporal auto-encoding, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: This claim does not recite any additional elements that integrate the judicial exceptions into a practical application or provide significantly more than the judicial exceptions. As such, the claim is ineligible.
Claim 14
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
wherein the encoding the multivariate time series data is performed by probabilistic temporal auto-encoding: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of encoding data using probabilistic temporal auto-encoding, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: This claim does not recite any additional elements that integrate the judicial exceptions into a practical application or provide significantly more than the judicial exceptions. As such, the claim is ineligible.
Claim 15
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 and Step 2B: The claim recites the additional element of “wherein temporal patterns output by the temporal auto-encoder have a relation to auto- encoded input multivariate time series data”, which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Nothing in the claim integrates the abstract idea into a practical application, nor does it provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Claim 16
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 and Step 2B: The claim recites the additional element of “wherein the mapping of the encoded multivariate time series data to a lower-dimensional latent space comprises a non-linear mapping”, which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Nothing in the claim integrates the abstract idea into a practical application, nor does it provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Claim 18
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claim from which it depends.
Step 2A Prong 2 and Step 2B: The claim recites the additional element of “providing an end-to-end deep learning mode”, which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). The claim recites the additional element of “training the end-to-end deep learning model using a stochastic gradient descent”, which recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished or how the neural network is trained using SGD. Thus, this additional element is recited in a manner that represents no more than mere instructions to apply the judicial exceptions on a computer (see MPEP § 2106.05(f)). As such, the claim is ineligible.
Claim 20
Claims 20 recite a non-transitory computer-readable storage medium (step 1: a manufacture) using a computer device and program code to perform the steps of claim 1 which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and is thus rejected for the same reasons set forth in the rejection of claim 1.
Response to Arguments
Applicant’s arguments and amendments, filed on 5/7/2025, with respect to the 35 USC § 101 rejection of claims 1, 3-6, 8-16, 18, and 20 have been fully considered and are not persuasive.
Beginning on page 9 of the remarks, filed on 5/7/2025, Applicant argues that claim 1 is unlike claim 2 of Example 47 because claim 1 of the present application puts a limit on how the data is received, explains how the solution of claim 1 operates, explains the output, provides a technical improvement, and improves machine learning efficiency. Examiner respectfully disagrees.
Claim 2 of Example 47 was incorporated into the 101 analysis because it demonstrates that a neural network training limitation can be broadly and reasonably interpreted to be directed towards an abstract idea in the form of a mathematical concept. If a claim positively recites the training of a neural network using a particular algorithm, such as backpropagation, then the BRI of that limitation is directed towards an abstract idea in the form of a mathematical concept. The “training” limitation of claim 1, as amended, was identified as an abstract idea in the form of a mathematical concept because it recites the training of a neural network or neural network components through the use of specific mathematical algorithms, namely stochastic gradient descent and backpropagation. When given their broadest reasonable interpretation in light of the background, the backpropagation algorithm and gradient descent algorithms are mathematical calculations. The last limitation of claim 1 of the present application requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the neural network and therefore encompasses mathematical concepts. Paragraphs [0043, 0050, and 0066] do not demonstrate that the joint training of the encoder, decoder, and temporal predictor network, as claimed, provide a technical improvement. The joint training of these elements is performed though well-known mathematical concepts.
Applicant next argues that “claim 1 does not recite (a) any mathematical concepts, (b) a method of organizing human activity, or (c) a mental process that can be performed by a pen and paper. Rather, the claimed synergy of the claimed system is that of the operation of a machine, namely a computing device that is specifically configured to providing time series modeling and forecasting by processing a vast amount of data. For example, claim 1 does not specifically recite a mathematical concept … The Office Action does not explain, nor is it readily apparent, how a human mind could reasonably or practically improve a speed and power consumption of processing by the computing device by encoding an exogenous factor data per series and time point of an input multivariate time series data, let alone into a smaller number of shared/global underlying temporal patterns temporal patterns and non-linear combinations of the input time series data. The Office Action also does not explain how a human mind can improve a speed of forecasting of the computing device by cleaning and de-noising, by an encoder neural network, the smaller number of shared/global underlying temporal patterns and non-linear combinations? How can a human mind possibly, let alone practically, enhance forecasting accuracy of the computing device by encoding the multivariate time series data and perform a non-linear mapping of the encoded multivariate time series data to a lower-dimensional latent space?”. Examiner respectfully disagrees.
The “encoding”, “cleaning”, “predicting”, and “mapping” limitations of claim 1 were identified as abstract ideas in the form of mental processes and the “encoding” and “training” limitations were identified as abstract ideas in the form of mathematical concepts. Applicant has failed to dispute these characterizations of abstract ideas using any salient evidence from the specification or claim language. Paragraph [0003] discloses that “manual heuristic approaches lack the flexibility to capture the cross- series effects, and such approaches are not scalable. There are cross- product effects that can occur in demand forecasting with thousands to even billions of product-location combinations”, but this statement does not support the idea that any claim language in particular and as claimed is not a mental process or a mathematical concept. The specification as originally filed supports the idea that these limitations are mental processes in at least paragraphs [0050-0052 and [0055] and in figures 1, 2, 3, and 5B. The “improving” and “enhancing” elements of independent claim 1 also do not bear any patentable weight. These additional aspects of the abstract ideas provide only a result-oriented solution (enhancing accuracy or improving power consumption) and lack details as to how the improvements or enhancements are performed. The “encoding”, “cleaning”, “predicting”, and “mapping” limitations of claim 1 are broadly directed towards mental processes that can be performed in the human mind, and any alleged improvements or enhancements from performing these mental processes are not detailed in the claim language. The claims do not make it clear as to how to determine and relative to what standard these improvements or enhancements are measured.
Applicant then proceeds to argue that “the claim as a whole integrates the recited judicial exception into a practical application of the exception … Technical Implementation Over Abstract Ideas … Specific Machine Learning Enhancements … Demonstrable Practical Applications … the solution of claim trains a neural network deep learning model to compute a time series modeling and the one or more time series forecasts. Further, claim 1 increases a computational efficiency of the processor by encoding an input of a multivariate time series data and performing a non-linear mapping of the encoded multivariate time series data to a lower-dimensional latent space …claim 1 increases a computational efficiency of the processor by encoding an input of a multivariate time series data and performing a non-linear mapping of the encoded multivariate time series data to a lower-dimensional latent space … the additional elements of claim 1, particularly when viewed as a whole, integrate the method to a practical application, namely that of providing a more computationally efficient and more accurate time series modeling and forecasting ”, and cites to paragraphs [0042] and [0043] for evidence of the improvement. Examiner respectfully disagrees.
Assuming that the claimed encoding does increase some sort of computational efficiency, the mental process of encoding data alone cannot reflect an improvement in technology. Abstract ideas alone do not reflect an improvement in technology, integrate the claim into a practical application, or provide significantly more. Applicant points to paragraphs [0042] and [0043] and [0050-0052] of the originally filed specification to demonstrate a practical application for the claim, but it is not clear in the claim language where the practical application is reflected. The cited improvement is an improvement to an abstract idea rather than an improvement to a computer or technological field. Additional elements in the claim language beyond the identified abstract ideas can integrate the abstract ideas into a practical application or provide significantly more than the abstract ideas. Applicant has failed to identify any additional elements in the claim language beyond the identified abstract ideas that integrate the abstract ideas into a practical application or provide significantly more than the abstract ideas. Further, the amendments to the independent claims make it clear that the alleged improvements are reflected in the abstract ideas of the claims, namely the “encoding”, “cleaning”, and “mapping” limitations of the independent claims.
Applicant next argues “assuming, arguendo, that the claims are directed to an abstract idea and not integrated into a practical application, which Applicants have disputed above, claim 1 still recites patent-eligible subject matter because it recites a combination of features that, when viewed as a whole, amount to "significantly more" than the alleged abstract idea, at least because: (i) the claim includes improvements to another technology or technical field, (ii) the claimed solution is necessarily rooted in computer technology to overcome a problem arising in the realm of computerized time series modeling and forecasting, and (iii) the claim adds at least one specific limitation beyond what is well-understood, routine, and conventional”. Examiner respectfully disagrees.
As stated above, although Applicant points to paragraphs [0042] and [0043] and [0050-0052] of the originally filed specification to demonstrate a technical improvement, it is not clear in the claim language where the practical application is reflected. The cited improvement is an improvement to an abstract idea rather than an improvement to a computer or technological field. A claimed solution that is “necessarily rooted in computer technology” does not make a claim per se eligible in view of 101. The independent claims of the present application are directed towards abstract ideas in the form of mental processes (see the “encoding”, “cleaning”, “mapping”, and “predicting” limitations) or mathematical concepts (“encoding” and “training” limitations) and there are no additional elements in the claim language that integrate the abstract ideas into a practical application or provide significantly more than the abstract ideas.
The additional elements in claim 1 consist of a processor; a memory coupled to the processor, the memory storing instructions to cause the processor to perform acts comprising”, “receiving an input time series data and exogenous factor data from an external data source”, “by an encoder neural network”, “outputting one or more time series forecasts based on the predictive distribution sample”.
The additional elements “by an encoder neural network” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished or how the encoder neural network is used to clean and de-noise the patterns. The additional elements of “a processor; a memory coupled to the processor, the memory storing instructions to cause the processor to perform acts comprising” amount to invoking computers or other machinery merely as a tool to perform existing processes or judicial exceptions. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer (see MPEP § 2106.05(f)).
The additional elements of “receiving an input time series data and exogenous factor data from an external data source” and “outputting one or more time series forecasts based on the predictive distribution sample” amount to insignificant extra-solution activities (see MPEP § 2106.05(g)) and are well-understood, routine, conventional activities (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”, and “Presenting offers and gathering statistics”).
Applicant has failed to provide evidence or arguments from the claim language or specification that demonstrates that these elements are not well-understood, routine, conventional elements. The limitation “training a neural network deep learning model to compute time series modeling and the one or more time series forecasts, wherein training the neural network deep learning model comprises jointly training an encoder neural network, a temporal predictor network, and a decoder neural network, at a same time through a use of a stochastic gradient descent and a back-propagation to compute a gradient at each update step” is not an additional element in the claim language. This limitation is directed towards the abstract idea of joint training using a mathematical concept (backpropagation). Any alleged improvement in technology from this claim limitation is an improvement to an abstract idea of joint training using backpropagation.
In summary, any alleged technical improvement in the independent claims of the present invention appear to be reflected in the abstract ideas of the independent claims. Claiming an alleged improvement when connected to an abstract idea does not make a claim eligible in view of 35 USC § 101. The alleged technical improvements do not appear to be reflected in the additional elements of the claims beyond the abstract ideas because these additional elements are well-understood, routine, conventional elements.
Applicant last argues that “Even if a limitation is identified as a judicial exception (e.g., a mathematical concept) at Step 2A, Prong 1, that does not preclude it from being considered as part of the "significantly more" inquiry under Step 2B … Here, the Office Action cannot simply sidestep the factual inquiry by labeling the limitation as a mathematical concept under Step 2A, and then declining to assess it under Step 2B. If the feature contributes materially to the claimed solution-as it does here-then evidence is required to support the conclusion that it is "well-understood, routine, and conventional." Claim 1 is allowable for this additional reason”.
Examiner followed the procedure for determining subject matter eligibility per the guidance in § 2106.04 of the MPEP. The independent claims of the present Application recite an abstract idea (Step 2A, Prong 1), and does not recite additional elements that integrate the judicial exception into a practical application (Step 2A, Prong 2). At Step 2B, the Examiner evaluated the additional elements in the claim language individually and in combination and concluded that these additional elements do not amount to significantly more than the judicial exceptions of the claim, per the guidance in §2106.05 of the MPEP.
As such, Applicant’s arguments are not persuasive, and the 35 USC § 101 rejection of the pending claims STANDS.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BRENT JOHNSTON HOOVER/Examiner, Art Unit 2127
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