Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/01/2026 has been entered.
Status of the Claims
Claims 1-2, 5-6, 7, 11-13, 16, 18, and 20 have been amended. Claims 1-18 and 20 are currently pending and have been considered by the Examiner.
Claim Objections
Claim 6 is objected to because of the following informalities: On page 5, line 15, the limitation “the replace values” should recite “the replaced values”. 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 1-18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-5 recite a method, claims 6-11 recite a product comprising a computer (a product), and claims 12-18 and 20 recite a system comprising a processor set (a system). A method, a product, and a system are each one of the four statutory categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1: Generating, based on the irregular time series data X, replaced values (x̃t) by replacing the missing values in the xt with imputed values using an imputation is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
The limitation of:
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is a mathematical calculation.
Generating, as a regular time series data, time-aligned reconstructed data (x̂t) from the irregular time series data X, wherein the generating of the time-aligned reconstructed data (x̂t) comprises multiplying the replaced values x̃t by the
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is a mathematical calculation.
Updating hidden states of the recurrent layers of the RNN based on the inputting of the time-aligned reconstructed data (x̂t) and previous hidden states of the recurrent layers is a mathematical calculation. Specification paragraphs [0055]-[0057] disclose a formula for updating the hidden states.
Predicting one or more labels by utilizing the updated hidden states, the weight matrices of the RNN, and the biases of the RNN is a mathematical calculation. Specification paragraphs [0058]-[0060] disclose a formula for predicting labels.
Utilizing a cross entropy loss to optimize the predicted one or more labels against a true label from a sample from the irregular time series data is a mathematical calculation. Specification paragraphs [0061]-[0062] disclose a formula for a cross-entropy loss. The claim recites abstract ideas.
Step 2A Prong 2: Training a recurrent neural network (RNN) for classifying irregular time series data that are data observed on time intervals having different lengths and missing values amounts to mere instructions to apply an abstract idea using a generic computer under MPEP 2106.05(f).
The limitation of:
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amounts to an insignificant extra-solution activity under MPEP 2106.05(g).
Inputting the time-aligned reconstructed data (x̂t) to recurrent layers of the RNN amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: Training a recurrent neural network (RNN) for classifying irregular time series data that are data observed on time intervals having different lengths and missing values amounts to mere instructions to apply an abstract idea using a generic computer under MPEP 2106.05(f).
The limitation of:
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is analogous to receiving data over a network or retrieving information from memory, which the courts have recognized as well-understood, routine, convention activities under MPEP 2106.05(d)(II).
Inputting the time-aligned reconstructed data (x̂t) to recurrent layers of the RNN amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 2 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas from claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The RNN includes a long short-term memory (LSTM) amounts to mere instructions to apply an abstract idea using a generic computer under MPEP 2106.05(f) and a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 3 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas from claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The RNN includes gated recurrent units (GRUs) amounts to mere instructions to apply an abstract idea using a generic computer under MPEP 2106.05(f) and a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 4 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas from claim 1 are incorporated. The imputed values are derived from a weighted mean and an empirical mean of a variable before the t-th timestep observation is a mathematical calculation. A weighted mean is a mathematical calculation, so performing an imputation by using the weighted mean is also a mathematical calculation. The first equation in specification paragraph [0028] disclose an equation for deriving imputed values from an empirical mean, and [0029] explains the variables used in the equation.
Step 2A Prong 2 and Step 2B: The claim does not recite additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 1.
Step 2A Prong 1: An updated hidden state (ht) of the updated hidden states in a recurrent layer of the recurrent layers is given as
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, where ht-1 is a previous state of the previous hidden states, and RNNCell is the recurrent layer is a mathematical calculation.
Step 2A Prong 2 and Step 2B: The claim does not recite additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 6 recites a product which implements the same features as the method of claim 1 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claims 7-8 and 10-11 each recites a product which implements the same features as the method of claims 2-5, respectively, and are therefore rejected for at least the same reasons.
Claim 9 incorporates the rejection of claim 6.
Step 2A Prong 1: The abstract ideas from claim 6 are incorporated.
Step 2A Prong 2 and Step 2B: The regular time series data for input to the recurrent layers of the RNN includes healthcare data amounts to a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 12
Step 2A Prong 1: Generating replaced values (x̃t) based on the irregular time series data, wherein the generating of replaced values (x̃t) comprises performing imputation to the missing values by using a weighted mean and an empirical mean of a value of a last observation from the irregular time series data is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper, and it is a mathematical calculation. A weighted mean is a mathematical calculation, so performing an imputation by using the weighted mean is also a mathematical calculation. The first equation in specification paragraph [0028] disclose an equation for deriving imputed values from an empirical mean, and [0029] explains the variables used in the equation.
Transforming, via time-aligned reconstruction, inputs including the replaced values (x̃t) to time-aligned representations and obtaining time-aligned reconstructed data (x̂t), wherein
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is a mathematical calculation. Instant specification paragraph [0031] discloses time-aligned reconstruction, and paragraphs [0032]-[0035] disclose equations for time-aligned reconstruction.
Updating hidden states of the recurrent layers of the RNN based on the inputting of the time-aligned reconstructed data (x̂t) and previous hidden states of the recurrent layers is a mathematical calculation. Specification paragraphs [0055]-[0057] disclose a formula for updating the hidden states.
Predicting one or more labels by utilizing the updated hidden states, the weight matrices of the RNN, and the biases of the RNN is a mathematical calculation. Specification paragraphs [0058]-[0060] disclose a formula for predicting labels.
Utilizing a cross entropy loss to optimize the predicted one or more labels against a true label from a sample from the irregular time series data is a mathematical calculation. Specification paragraphs [0061]-[0062] disclose a formula for a cross-entropy loss. The claim recites abstract ideas.
Step 2A Prong 2 and Step 2B: A system comprising: a processor set, one or more computer-readable storage media, and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Training a recurrent neural network (RNN) for classifying irregular time series data that are data observed on time intervals having different lengths and missing values amounts to mere instructions to apply an abstract idea using a generic computer under MPEP 2106.05(f).
Inputting the time-aligned reconstructed data (x̂t) to recurrent layers of the RNN amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea. The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claims 13-14 each recites a system which implements the same features as the method of claims 2-3, respectively, and are therefore rejected for at least the same reasons.
Claim 15 incorporates the rejection of claim 12.
Step 2A Prong 1: The abstract ideas from claim 12 are incorporated.
Step 2A Prong 2 and Step 2B: The regular time series data for input to the RNN includes healthcare data amounts to a field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 16 incorporates the rejection of claim 12.
Step 2A Prong 1: The abstract ideas from claim 12 are incorporated. In the time-aligned reconstruction, a time interval of each input of the inputs is rescaled is a mathematical calculation. The equations in instant specification paragraphs [0033]-[0034] and explanation in [0035] discloses calculations for this limitation. The rescaled time interval is
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.
Step 2A Prong 2 and Step 2B: The claim does not recite additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 17 incorporates the rejection of claim 16.
Step 2A Prong 1: The abstract ideas from claim 16 are incorporated. The rescaling is performed with scale parameters and a logarithmic transformation is a mathematical calculation. The equation in instant specification paragraph [0034] and explanation in [0035] disclose calculations for this limitation.
Step 2A Prong 2 and Step 2B: The claim does not recite additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 18 incorporates the rejection of claim 17.
Step 2A Prong 1: The abstract ideas from claim 17 are incorporated. The rescaled time intervals are multiplied to an input xt, where xt is a D-dimensional feature vector is a mathematical calculation. The equation in instant specification paragraph [0033] and explanation in [0035] discloses calculations for this limitation.
Step 2A Prong 2 and Step 2B: The claim does not recite additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 20 recites a system which implements the same features as the method of claim 5 and is therefore rejected for at least the same reasons.
Response to Arguments
The following is the Examiner’s response to Applicant’s arguments filed 05/01/2026.
Applicant’s First Argument: On page 11, the Applicant submits that rescaling time interval data to handle irregular time data as regular time data and using the regular time data to update the hidden layers of a Recurrent Neural Networks does not involve a mathematical concept but rather applies a mathematical concept. The claims are not directed to the alleged abstract ideas.
Examiner’s Response: Applicants arguments have been fully considered but they are not persuasive. MPEP 2106.04(a)(2) subsection (I)(B) states, “A claim that recites a numerical formula or equation will be considered as falling within the ‘mathematical concepts’ grouping.” MPEP 2106.04(a)(2) subsection (I)(C) states, “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the ‘mathematical concepts’ grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation.”
Pending claim 1 recites the limitation of
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Rescaling the time interval data comprises calculating a mathematical operation. Claim 1 explicitly recites a mathematical formula
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and it explicitly recites an act of calculating using mathematical methods to determine a variable or number. Therefore, claim 1 recites a mathematical calculation.
Applicant’s Second Argument: On pages 12-13, Applicant argues the claimed invention enables the RNN to process irregular time series data as regular time series data, which is a specific improvement to how the RNN operations. The Applicant submits that similar to Ex Parte Desjardins, the present claims provide specific technical solutions that improve the operations of the neural network. The claimed time-aligned reconstruction is precisely such an improvement that changes how the RNN processes irregular time series data.
On page 14, Applicant argues the specific ordered combination of steps recited by claim 1 constitutes a particular way of improving RNN training for irregular time series data. This is not merely applying mathematics on a generic computer, it is a specific technical solution that improves how the RNN itself operates by enabling it to handle irregular time series data as regular time series data. Applicant explains these benefits further on pages 15-16.
Examiner’s Response: Applicants arguments have been fully considered but they are not persuasive. The specific ordered combination of steps recited by claim 1 amounts to transforming an irregular time series data into time-aligned reconstructed data, and then training an RNN in a generic manner. In Step 2A Prong 1, the data imputation step in lines 13-14 is a mental process. The data transformation steps from lines 16 to page 3, line 6 are mathematical calculations. The updating, predicting, and optimizing steps on page 3, lines 11-18 are mathematical calculations based on specification paragraphs [0055]-[0062]. These limitations cannot provide technical improvements because an improvement to the abstract idea itself is not an improvement in technology, as explained in MPEP 2106.05(a)(II). In Step 2A Prong 2, the training limitation in claim 1, lines 2-4 is recited at a high level of generality. It lacks specific technical improvements such as improvements to a training algorithm or improvements to the architecture of the RNN. Thus, the training amounts to mere instructions to apply the abstract ideas. Similarly, inputting the time-aligned reconstructed data to recurrent layers of the RNN amounts to mere instructions to apply the abstract ideas.
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are generic computer functions as disclosed that are implemented to perform the abstract ideas disclosed above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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