DETAILED ACTION
Notice of 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 .
Response to Amendment
Applicant’s amendment and remarks dated 6/24/2026 have been considered. Claim 3 has been cancelled. Claims 1-2 and 4-17 are pending.
35 U.S.C. 112(f) Interpretation. Claims 1-12 no longer contain any claim limitations that are being interpreted under 35 U.S.C. 112(f) in view of Applicant’s claim amendments.
Response to Arguments
On page 11 of Applicant’s 6/24/2026 Amendment and remarks, Applicant asserts that no new matter has been added by the present amendments.
The examiner agrees that the amendments to the independent claims are supported by at least original claim 3 and paras. 0044-0048 and Fig. 5 of the disclosure.
On page 11 of Applicant’s 6/24/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, Applicant argues:
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The examiner agrees that reciting “one or more processors and a memory” are not mental steps. However, those elements are generic computer components that are addressed under Step 2A, Prong 2 and Step 2B.
The examiner respectfully disagrees that the claims now require a “specific machine-learning training architecture.” As amended, the claims merely recite additional mental processes (as previously explained with respect to claim 3) and no specific machine-learning training architecture is actually claimed.
On pages 11-12 of Applicant’s 6/24/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, Prong 1, Applicant argues:
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The examiner respectfully disagrees. Notably, Applicant has not rebutted any of the examples provided in the detailed rejections about how each of the mental processes identified by the examiner could be performed in the human mind, or using pencil and paper. Simple latent vectors (e.g., vectors having 2 dimensions) can be written on paper and used to predict next states and probabilities, and also to calculate distances between latent vectors.
On page 12 of Applicant’s 6/24/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, Prong 2, Applicant argues:
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The examiner respectfully disagrees. The concept of non-deterministic time-series future state prediction is itself a mental process and not a technical field. A human can mentally view time-series data and make predictions about a future state, especially when time-series values are periodic. The use of a machine learning model and generic computer hardware (processors, memory) might make such predictions more quickly and efficiently, but are not an improvement to any actual technology. At most, the machine learning model and generic computer hardware are improvements to the abstract ideas themselves.
On page 12 of Applicant’s 6/24/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2B Applicant argues:
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The examiner respectfully disagrees. These are merely mental steps that are part of the judicial exception and therefore do not supply “significantly more” than the mental processes themselves.
On page 13 of Applicant’s 6/24/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 102 and 103, Applicant argues that all such rejections should be withdrawn because Applicant has incorporated, into each of the independent claims, subject matter previously found to be allowable.
The examiner agrees. All rejections under 35 U.S.C. 102 and 103 are hereby withdrawn.
Claim Objections
Claims 1, 6-9, and 11-12 objected to because of the following informalities:
In claim 1, in the 3rd line from the bottom, “vector” should read “vector;” to add a semicolon.
In claims 6-9, the examiner suggests amending “the at least one processor” to recite “the one or more processors” to be consistent with the claim language of claim 1.
In claims 11-12, the examiner suggests amending “the at least one processor” to recite “the one or more processors” to be consistent with the claim language of claim 10.
Appropriate correction is required.
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 1-2 and 4-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Step 1 of the Alice/Mayo framework, Claims 1-2 and 4-12 are directed to an apparatus (a machine) and Claims 13-17 are directed to a method (a process), which each fall within one of the four statutory categories of inventions.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “processors”, “memory”, “instructions”, “machine learning model”).
generate raw data by removing or replacing an outlier and a missing value in time series data; (under the broadest reasonable interpretation, a human such as a data scientist can view lab data on paper, and delete outliers (e.g., above a certain threshold) and fill in missing values (e.g., using simple interpolation like a moving average))
generate preprocessed time series data by converting the raw data into one integrated data; (under the broadest reasonable interpretation, a human such as a data scientist can view lab data on paper and take features and create a matrix on paper (such as in Fig. 3 of the instant application))
generate preprocessed learning data by clustering the preprocessed time series data depending on a similarity; (under the broadest reasonable interpretation, a human such as a data scientist can cluster matrices based on feature similarities)
generate group learning data by grouping the input data, similar data included in the same first cluster as the input data, and dissimilar data included in the different second cluster from the input data; (under the broadest reasonable interpretation, a human such as a data scientist can group together data including (1) the input data, (2) similar data included in the same first cluster as the input data, such as determined features data for that cluster, and (3) dissimilar data for a different second cluster from the input data, such as determined features data for that cluster)
calculate an input latent vector, a similar latent vector, and a dissimilar latent vector, respectively, representing changes over time in the input data, the similar data, and the dissimilar data; (under the broadest reasonable interpretation, a human such as a data scientist can calculate derivatives of feature vectors for each of the input vector, similar data, and dissimilar data)
use the input latent vector to predict values associated with possible next states of the input data and probabilities of reaching each of the possible next states; and (under the broadest reasonable interpretation, a human such as a data scientist can use the calculated input latent vector to predict a possible next state and a probability thereof, such as by predicting 2 equally likely outcomes at 50% probability each)
calculate a first similarity distance between the input latent vector and the similar latent vector and a second similarity distance between the input latent vector and the dissimilar latent vector. (under the broadest reasonable interpretation, a human such as a data scientist can calculate similarities between the vectors, such as by using a Euclidean distance, or a cosine similarity metric)
... for predicting a future state of the time series data at an arbitrary time point. (under the broadest reasonable interpretation, a human such as a data scientist can view time series data, and predict at an arbitrary point in time, what such future state of the time series data may be at some arbitrary point in time, such as by observing an ordinary sine wave, and based on its cycles, predicting the state at a particular future point of time)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements (e.g., “processors”, “memory”, “instructions”, “machine learning model”) which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “one or more processors”, “memory storing instructions that, when executed by the one or more processors, cause the one or more processors to” limitations, such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional elements of processors and memory. These additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components (processors and memory). Accordingly, 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 (See MPEP 2106.05(f)).
Regarding the “receive each data of the preprocessed learning data as input data” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “to train a prediction model such that a first similarity between a first future state predicted using the input data and a second future state predicted using data included in a same first cluster as the input data increases and such that a second similarity between the first future state and a third future state predicted using data included in a different second cluster from the input data decreases” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic model training. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic model training). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “wherein the prediction model is a machine learning model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a generic machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a generic machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements (e.g., “processors”, “memory”, “instructions”, “machine learning model”) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “one or more processors”, “memory storing instructions that, when executed by the one or more processors, cause the one or more processors to” limitations, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “receive each data of the preprocessed learning data as input data” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “to train a prediction model such that a first similarity between a first future state predicted using the input data and a second future state predicted using data included in a same first cluster as the input data increases and such that a second similarity between the first future state and a third future state predicted using data included in a different second cluster from the input data decreases” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “wherein the prediction model is a machine learning model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception.
Regarding Claim 2
Step 2A, Prong 1
generate the preprocessed time series data by adding a time interval between time points of the raw data as a next time point (under the broadest reasonable interpretation, a human such as a data scientist can interpolate data by adding a new time interval between time points of the raw data, such as for sine(t), and points t = 2 and t=4, interpolating a value for t=3)
Step 2A, Prong 2
Regarding the “the one or more processors” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “the one or more processors” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 4
Step 2A, Prong 1
generate the group learning data by changing the similar data to other similar data or changing the dissimilar data to different dissimilar data, with respect to the same input data (under the broadest reasonable interpretation, a human can do this by changing the features selected from the first cluster as similar data, and the features selected from the second cluster as dissimilar data)
Step 2A, Prong 2
Regarding the “the one or more processors” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “while training the prediction model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic training using a software module. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a generic software module to perform generic machine learning training). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “the one or more processors” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “while training the prediction model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 5
Step 2A, Prong 1
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(under the broadest reasonable interpretation, this is a particular mathematical calculation that a human can perform mentally; this is also a judicial exception under the mathematical concepts category)
Step 2A, Prong 2
Regarding the “the one or more processors” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “the one or more processors” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 6
Step 2A, Prong 2
Regarding the “wherein the at least one processor randomly calculates the change rate estimation function and the diffusion degree estimation function using a deep learning network, and wherein the deep learning network is one of a CNN (Convolutional Neural Network), an RNN (Recurrent Neural Network), or a BNN (Bayesian Neural Network)” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic types of neural networks. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a generic neural network). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “wherein the at least one processor randomly calculates the change rate estimation function and the diffusion degree estimation function using a deep learning network, and wherein the deep learning network is one of a CNN (Convolutional Neural Network), an RNN (Recurrent Neural Network), or a BNN (Bayesian Neural Network)” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 7
Step 2A, Prong 1
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(under the broadest reasonable interpretation, this is a particular mathematical calculation that a human can perform mentally; this is also a judicial exception under the mathematical concepts category)
Step 2A, Prong 2
Regarding the “the at least one processor” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “the at least one processor” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 8
Step 2A, Prong 2
Regarding the “wherein the at least one processor converts values associated with the possible next states into the corresponding probabilities” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished (e.g., any “fully connected layer” can do such a conversion), or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “wherein the at least one processor converts values associated with the possible next states into the corresponding probabilities” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 9
Step 2A, Prong 1
calculates a loss function depending on equation 3 based on the first similarity distance and the second similarity distance
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(under the broadest reasonable interpretation, this is a particular mathematical calculation that a human can perform mentally; this is also a judicial exception under the mathematical concepts category)
Step 2A, Prong 2
Regarding the “the at least one processor” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “trains the prediction model such that the loss function decreases” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic machine learning training. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic machine learning training). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “the at least one processor” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “trains the prediction model such that the loss function decreases” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 10
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 10 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “processors”, “memory”, “instructions”, “machine learning model”).
generate raw data by removing or replacing an outlier and a missing value of time series data, (under the broadest reasonable interpretation, a human such as a data scientist can view lab data on paper, and delete outliers (e.g., above a certain threshold) and fill in missing values (e.g., using simple interpolation like a moving average))
convert the raw data into one integrated data to generate preprocessed time series data; (under the broadest reasonable interpretation, a human such as a data scientist can view lab data on paper, and take features and create a matrix on paper (such as in Fig. 3 of the instant application))
generate a prediction result corresponding to a future state at an arbitrary next time point in time..., based on the preprocessed time series data, event data indicating an additional state not included in the preprocessed time series data, and next time point data indicating the arbitrary next time point (under the broadest reasonable interpretation, a human such as a data scientist can view time series data, related event data, and an arbitrary next point in time, and predict at the arbitrary point in time, what such future state of the time series data may be at the arbitrary point in time, such as by observing an ordinary sine wave, and based on its cycles, predicting the state at a particular future point of time)
... for predicting a future state of the time series data at an arbitrary time point. (under the broadest reasonable interpretation, a human such as a data scientist can view time series data, and predict at an arbitrary point in time, what such future state of the time series data may be at some arbitrary point in time, such as by observing an ordinary sine wave, and based on its cycles, predicting the state at a particular future point of time)
generating group learning data by grouping input data, similar data included in a same first cluster as the input data, and dissimilar data included in a different second cluster from the input data; (under the broadest reasonable interpretation, a human such as a data scientist can group together data including (1) the input data, (2) similar data included in the same first cluster as the input data, such as determined features data for that cluster, and (3) dissimilar data for a different second cluster from the input data, such as determined features data for that cluster)
calculating an input latent vector, a similar latent vector, and a dissimilar latent vector, respectively, representing changes over time in the input data, the similar data, and the dissimilar data; (under the broadest reasonable interpretation, a human such as a data scientist can calculate derivatives of feature vectors for each of the input vector, similar data, and dissimilar data)
using the input latent vector to predict values associated with possible next states of the input data and probabilities of reaching each of the possible next states; and (under the broadest reasonable interpretation, a human such as a data scientist can use the calculated input latent vector to predict a possible next state and a probability thereof, such as by predicting 2 equally likely outcomes at 50% probability each)
calculating a first similarity distance between the input latent vector and the similar latent vector and a second similarity distance between the input latent vector and the dissimilar latent vector. (under the broadest reasonable interpretation, a human such as a data scientist can calculate similarities between the vectors, such as by using a Euclidean distance, or a cosine similarity metric)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements (e.g., “processors”, “memory”, “instructions”, “machine learning model”) which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “one or more processors”, “memory storing instructions that, when executed by the one or more processors, cause the one or more processors to” limitations, such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional elements of processors and memory. These additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components (processors and memory). Accordingly, 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 (See MPEP 2106.05(f)).
Regarding the “through a prediction model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a generic prediction model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic modules). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “wherein the prediction model is a machine learning model for predicting a future state of the time series data at an arbitrary time point” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a generic machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a generic machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “wherein the prediction model has been trained using training operations including” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of training a generic machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (training a generic machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements (e.g., “processors”, “memory”, “instructions”, “machine learning model”) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “one or more processors”, “memory storing instructions that, when executed by the one or more processors, cause the one or more processors to” limitations, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “through a prediction model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “wherein the prediction model is a machine learning model for predicting a future state of the time series data at an arbitrary time point” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “wherein the prediction model has been trained using training operations including” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception.
Regarding Claim 11
Step 2A, Prong 1
generates the preprocessed time series data by adding a time interval between time points of the raw data as a next time point (under the broadest reasonable interpretation, a human such as a data scientist can interpolate data by adding a new time interval between time points of the raw data, such as for sine(t), and points t = 2 and t=4, interpolating a value for t=3)
Step 2A, Prong 2
Regarding the “wherein the at least one processor” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “wherein the at least one processor” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 12
Step 2A, Prong 1
generates prediction data by adding the event data to the preprocessed time series data and setting a next time point of the event data as the next time point data; (under the broadest reasonable interpretation, a human such as a data scientist can add event data to the previously preprocessed time series data and mentally select a next time point of the event data as the next time point data)
Step 2A, Prong 2
Regarding the “wherein the at least one processor” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “applies the prediction model to the prediction data and to output a value associated with a future state at a time point corresponding to the next time point data and a probability of reaching the future state” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic training of a model to have a particular output. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic model training). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “wherein the at least one processor” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “applies the prediction model to the prediction data and to output a value associated with a future state at a time point corresponding to the next time point data and a probability of reaching the future state” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 13
Claim 13 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “prediction model”).
generating raw data by removing or replacing an outlier and a missing value in time series data; (under the broadest reasonable interpretation, a human such as a data scientist can view lab data on paper, and delete outliers (e.g., above a certain threshold), fill in missing values (e.g., using simple interpolation like a moving average), etc.)
generating preprocessed time series data by converting the raw data into one integrated data; (under the broadest reasonable interpretation, a human such as a data scientist can view lab data on paper, and take features and create a matrix on paper (such as in Fig. 3 of the instant application))
generating preprocessed learning data by clustering the preprocessed time series data depending on a similarity; (under the broadest reasonable interpretation, a human such as a data scientist can cluster matrices based on feature similarities)
generating group learning data by grouping the input data, similar data included in a same first cluster as the input data, and dissimilar data included in a different second cluster from the input data; (under the broadest reasonable interpretation, a human such as a data scientist can group together data including (1) the input data, (2) similar data included in the same first cluster as the input data, such as determined features data for that cluster, and (3) dissimilar data for a different second cluster from the input data, such as determined features data for that cluster)
calculating an input latent vector, a similar latent vector, and a dissimilar latent vector, respectively, representing changes over time in the input data, the similar data, and the dissimilar data; (under the broadest reasonable interpretation, a human such as a data scientist can calculate derivatives of feature vectors for each of the input vector, similar data, and dissimilar data)
predicting values associated with possible next states of the input data and probabilities of reaching each of the possible next states using the input latent vector; and (under the broadest reasonable interpretation, a human such as a data scientist can use the calculated input latent vector to predict a possible next state and a probability thereof, such as by predicting 2 equally likely outcomes at 50% probability each)
calculating a first similarity distance between the input latent vector and the similar latent vector and a second similarity distance between the input latent vector and the dissimilar latent vector (under the broadest reasonable interpretation, a human such as a data scientist can calculate similarities between the vectors, such as by using a Euclidean distance, or a cosine similarity metric)
generating a prediction result corresponding to a future state at an arbitrary next time point of the time series data received from a user (under the broadest reasonable interpretation, a human such as a data scientist can view time series data, and predict at an arbitrary point in time selected by a user, what such future state of the time series data may be at the arbitrary point in time, such as by observing an ordinary sine wave, and based on its cycles, predicting the state at a particular future point of time selected by a user)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
Regarding the “receiving each data of the preprocessed learning data as input data” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “training a prediction model such that a first similarity between a first future state predicted using the input data and a second future state predicted using the similar data increases and such that a second similarity between the first future state and a third future state predicted using the dissimilar data decreases” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generically training a model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic model training). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “through the prediction model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a generic prediction model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a generic model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
Regarding the “receiving each data of the preprocessed learning data as input data” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “training a prediction model such that a first similarity between a first future state predicted using the input data and a second future state predicted using the similar data increases and such that a second similarity between the first future state and a third future state predicted using the dissimilar data decreases” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “through the prediction model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception.
Regarding Claim 14
Step 2A, Prong 1
PNG
media_image10.png
442
602
media_image10.png
Greyscale
(under the broadest reasonable interpretation, this is a particular mathematical calculation that a human can perform mentally; this is also a judicial exception under the mathematical concepts category)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 15
Step 2A, Prong 1
predicting values associated with possible next states of the input data and probabilities of reaching each of the possible next states, by using the input latent vector (under the broadest reasonable interpretation, a human such as a data scientist can predict next states and probabilities of each, by using an input latent vector, such as for simple and predictable time series data like a basic sine wave)
wherein the loss function is calculated according to Equation 2
PNG
media_image11.png
184
594
media_image11.png
Greyscale
(under the broadest reasonable interpretation, this is a particular mathematical calculation that a human can perform mentally; this is also a judicial exception under the mathematical concepts category)
Step 2A, Prong 2
Regarding the “training the prediction model such that a loss function based on the values associated with the possible next states and the probabilities decreases” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic model training. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic model training). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “training the prediction model such that a loss function based on the values associated with the possible next states and the probabilities decreases” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 16
Step 2A, Prong 1
calculating a first similarity distance between the input latent vector and the similar latent vector and a second similarity distance between the input latent vector and the dissimilar latent vector; (under the broadest reasonable interpretation, a human such as a data scientist can calculate similarities between the vectors, such as by using a Euclidean distance, or a cosine similarity metric)
wherein the loss function is calculated according to Equation 3
PNG
media_image12.png
220
588
media_image12.png
Greyscale
(under the broadest reasonable interpretation, this is a particular mathematical calculation that a human can perform mentally; this is also a judicial exception under the mathematical concepts category)
Step 2A, Prong 2
Regarding the “training the prediction model such that a loss function based on the first similarity distance and the second similarity distance decreases” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic model training. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic model training). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “training the prediction model such that a loss function based on the first similarity distance and the second similarity distance decreases” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 17
Step 2A, Prong 1
generating second preprocessed time series data by removing or replacing an outlier and a missing value of time series data received from the user and converting the processed time series data into one integrated data; (under the broadest reasonable interpretation, a human such as a data scientist can view lab data on paper, and delete outliers (e.g., above a certain threshold), fill in missing values (e.g., using simple interpolation like a moving average), and take features and create a matrix on paper (such as in Fig. 3 of the instant application))
generating prediction data by adding the event data to the second preprocessed time series data and setting a next time point of the event data as the next time point data; (under the broadest reasonable interpretation, a human such as a data scientist can view time series data and add related time data to such time series data, and mentally set a next point in time)
Step 2A, Prong 2
Regarding the “receiving the second preprocessed time series data, event data indicating an additional state not included in the second preprocessed time series data, and next time point data indicating the arbitrary next time point” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “outputting a value associated with a future state at a time point corresponding to the next time point data and a probability of reaching the future state, by applying the prediction model to the prediction data” limitation, such limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Step 2B
Regarding the “receiving the second preprocessed time series data, event data indicating an additional state not included in the second preprocessed time series data, and next time point data indicating the arbitrary next time point” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “outputting a value associated with a future state at a time point corresponding to the next time point data and a probability of reaching the future state, by applying the prediction model to the prediction data” limitation, this limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
Allowable Subject Matter
Claims 1-2 and 4-17 would be allowed over the prior art if the rejections under 35 U.S.C. 101 are overcome.
The following is an examiner’s statement of reasons for allowance (provided that the rejections under 35 U.S.C. 101 are overcome):
Independent claims 1, 10, and 13 are allowable over the prior art, provided that the rejections under 35 U.S.C. 101 are overcome, because none of the references of record either alone or in combination fairly disclose or suggest the combination of limitations specified in the independent claims, including at least:
calculate an input latent vector, a similar latent vector, and a dissimilar latent vector, respectively, representing changes over time in the input data, the similar data, and the dissimilar data;
calculate a first similarity distance between the input latent vector and the similar latent vector and a second similarity distance between the input latent vector and the dissimilar latent vector.
The closest prior art of record discloses:
Gittler, Thomas, et al. "International Conference on Advanced and Competitive Manufacturing Technologies milling tool wear prediction using unsupervised machine learning." The International Journal of Advanced Manufacturing Technology 117.7 (2021): 2213-2226, hereinafter referenced as GITTLER, teaches techniques, implemented using software, for performing data preprocessing, and building and actually testing a software model. (p. 2215, section 1). GITTLER discloses a prediction model for predicting a future state (VB) of the time series data, over the entire degradation span, so any arbitrary point of time within the degradation span can be predicted. (GITTLER, p. 2216, section 2.3, p. 2222, section 4.1 and p. 2225, section 4.7).
US 20190294524 A1, hereinafter referenced as GUPTA discloses that over time, a point can transition from being more similar (based on distance) from a first cluster to a second cluster. (para. 0064).
US 20210224918 A1, hereinafter referenced as HEYRANI-NOBARI, teaches tracking cluster behavior according to differences in values between cluster vectors at different points in time. (para. 0075).
However, the examiner has found that the distinct feature of the Applicant's claimed invention over the prior art is the explicit claiming of the aforementioned limitations in combination with all the other limitations as specified in the independent claims. In particular, one of ordinary skill in the art would not have been motivated to modify GITTLER, GUPTA, and HEYRANI-NOBARI further to calculate changes over time, for a specific input latent vector, a similar latent vector (for similar data included in the recited “same first cluster”), and a dissimilar latent vector (for dissimilar data included in the recited “different second cluster”), and then calculate distances between such change-over-time vectors, without the hindsight aid of Applicant’s disclosure. Therefore, because the prior art of record does not anticipate nor make obvious the limitations of the independent claims, such claims would be allowed provided that the rejections under 35 U.S.C. 101 are overcome.
Dependent claims 2, 4-9, 11-12, and 14-17 would be allowed for depending from an allowed independent base claim, provided that the rejections under 35 U.S.C. 101 are overcome.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL C. LEE/Examiner, Art Unit 2128
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