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 .
Status of the Claims
Claims 1, 6, and 12 have been amended. Claims 5, 9, 11, 15, and 19-20 have been canceled. Claims 1-4, 6-8, 10, 12-14, and 16-18 are currently pending and have been considered by the Examiner.
Claim Objections
Claims 1, 6, and 12 are objected to because of the following informalities: In claim 1 on page 3, the limitation in lines 16-18 from “time intervals” to “the threshold risk” appears to be grammatically incomplete. The limitation currently recites, “time intervals… indicates the risk of the severe health condition lower than the threshold risk”. Since the subject of “indicates” is “time intervals”, Examiner suggests changing this to recite (for example) “time intervals… indicate the risk… to be lower than the threshold risk.” Claims 6 and 12 recite the same minor informality.
In claim 12, line 6, the term “training” should recite “train”. The first term of each remaining step should be corrected in the same way. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4, 6-8, 10, 12-14, and 16-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “severe” in claim 1 on page 3, lines 15 and 17 is a relative term which renders the claim indefinite. The term “severe” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term “severe” is a subjective term under MPEP 2173.05(b) subsection IV because the claims and specification do not supply an objective standard for determining if a health condition is severe or not severe. Examiner treats “severe health condition” as “health condition.”
Claims 2-4 are rejected for failing to cure the deficiencies of claim 1.
Claim 6 recites the same indefinite limitations as the method of claim 1 and is therefore rejected for at least the same reasons.
Claims 7-8 and 10 are rejected for failing to cure the deficiencies of claim 6.
Claim 12 recites the same indefinite limitations as the method of claim 1 and is therefore rejected for at least the same reasons.
Claims 13-14 and 16-18 are rejected for failing to cure the deficiencies of claim 12.
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 1-4, 6-8, 10, 12-14, and 16-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-4 recite a method, claims 6-8 and 10 recite a product comprising a computer, and claims 12-14 and 16-18 recite a system comprising a processor set. 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, wherein 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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, and wherein ht-1 is a previous state of the previous hidden states, and RNNCell is the recurrent layer is a mathematical calculation.
Predicting one or more labels by utilizing the updated hidden states, the weight matrices of the RNN, and the biases of the RNN, wherein the one or more labels identify a risk of the patient of a severe health condition to be lower than a threshold risk, wherein time intervals whose length between observations of the patient exceeds a threshold indicates the risk of the severe health condition lower than the threshold risk, and wherein the time-aligned reconstructed data incorporates the time intervals into the updated hidden states to increase values of the updated hidden states, such that the RNN identifies the patient as being at lower than the threshold risk is a mathematical calculation. Specification paragraphs [0058]-[0060] disclose a formula for predicting labels. The limitations “to increase values of the updated hidden states, such that the RNN identifies the patient as being at lower than the threshold risk” are intended results of predicting labels. The claim does not explain how values of the updated hidden states are increased, nor how increasing the values would cause the RNN to identify patients that are below the threshold risk.
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, the irregular time series data comprising healthcare data of a patient from electronic health records amounts to mere instructions to apply an abstract idea using a generic computer under MPEP 2106.05(f). Healthcare data of a patient amounts to a mere field of use and technological environment under MPEP 2106.05(h).
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 and a field of use 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, the irregular time series data comprising healthcare data of a patient from electronic health records amounts to mere instructions to apply an abstract idea using a generic computer under MPEP 2106.05(f). Healthcare data of a patient amounts to a mere field of use and technological environment under MPEP 2106.05(h).
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 and a field of use 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 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 each recites a product which implements the same features as the method of claims 2-4, respectively, and are therefore rejected for at least the same reasons.
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, wherein 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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, and wherein ht-1 is a previous state of the previous hidden states, and RNNCell is the recurrent layer is a mathematical calculation.
Predicting one or more labels by utilizing the updated hidden states, the weight matrices of the RNN, and the biases of the RNN, wherein the one or more labels identify a risk of the patient of a severe health condition to be lower than a threshold risk, wherein time intervals whose length between observations of the patient exceeds a threshold indicates the risk of the severe health condition lower than the threshold risk, and wherein the time-aligned reconstructed data incorporates the time intervals into the updated hidden states to increase values of the updated hidden states, such that the RNN identifies the patient as being at lower than the threshold risk is a mathematical calculation. Specification paragraphs [0058]-[0060] disclose a formula for predicting labels. The limitations “to increase values of the updated hidden states, such that the RNN identifies the patient as being at lower than the threshold risk” are intended results of predicting labels. The claim does not explain how values of the updated hidden states are increased, nor how increasing the values would cause the RNN to identify patients that are below the threshold risk.
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, the irregular time series data comprising healthcare data of a patient from electronic health records amounts to mere instructions to apply an abstract idea using a generic computer under MPEP 2106.05(f). Healthcare data of a patient amounts to a mere field of use and technological environment under MPEP 2106.05(h).
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 in combination with a field of use 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 in combination with a field of use 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 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 the 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 the 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 the 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.
Response to Arguments
Below is the Examiner’s response to the Applicant’s arguments filed on 08/07/2026.
Applicant’s Arguments Under 35 U.S.C. 101: On page 11 of the remarks, the Applicant submits that regarding Step 2A Prong 1, rescaling time interval data to handle irregular time data as regular time data, for using the regular time data to update the hidden layers of a Recurrent Neural Network in order to predict health conditions of patients does not involve a mathematical concept but rather applies a mathematical concept.
On pages 11-12 of the remarks, the Applicant submits that regarding Step 2A Prong 2, the alleged abstract idea is integrated into a practical implementation. The Applicant argues that similar to Example 39, limitations of claim 1 reflects improvements: the time-aligned reconstruction, as recited by claim 1, transforms inputs (not hidden states) by a multiplying step. The Applicant argues that this enables the RNN to process irregular time series data as regular time series data, which is a specific improvement to how the RNN operations.
On page 12 of the remarks, the Applicant states that inputting the time-aligned reconstructed data (x̂t) to recurrent layers of the RNN is further used to update hidden states which constitutes a particular way of improving RNN training for irregular time series data. The Applicant argues that this improves how the RNN itself operates by enabling it to handle irregular time series data as regular time series data.
On page 14, the Applicant argues that conventional methods cannot take into account variable time intervals in patient data records to predict a patient’s risk of a severe health condition or mortality as the conventional methods fail to increase the values of hidden states in the RNN. The Applicant argues the claimed invention overcomes this by incorporating long time intervals into the hidden states of the neural network by updating values of the hidden states of the RNN, where the updated hidden states affect how the RNN predicts health conditions of the patient. The Applicant argues the features of claim 1 add specific limitations that are not well-understood, routine, conventional activity in the field.
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. First, 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.”
In Step 2A Prong 1, the step of 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, this limitation recites a mathematical calculation.
The step of 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.
The step of 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, wherein 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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, and wherein ht-1 is a previous state of the previous hidden states, and RNNCell is the recurrent layer is a mathematical calculation.
The step of predicting one or more labels by utilizing the updated hidden states, the weight matrices of the RNN, and the biases of the RNN, wherein the one or more labels identify a risk of the patient of a severe health condition to be lower than a threshold risk, wherein time intervals whose length between observations of the patient exceeds a threshold indicates the risk of the severe health condition lower than the threshold risk, and wherein the time-aligned reconstructed data incorporates the time intervals into the updated hidden states to increase values of the updated hidden states, such that the RNN identifies the patient as being at lower than the threshold risk is a mathematical calculation. Specification paragraphs [0058]-[0060] disclose a formula for predicting labels. The limitations “to increase values of the updated hidden states, such that the RNN identifies the patient as being at lower than the threshold risk” are intended results of predicting labels. The claim does not explain how values of the updated hidden states are increased, nor how increasing the values would cause the RNN to identify patients that are below the threshold risk. Therefore, rescaling time interval data to handle irregular time data as regular time data recites a mathematical calculation. It is noted that a judicial exception alone cannot provide the improvement, and that an improvement in the abstract idea itself is not an improvement in technology. See MPEP 2106.05(a), (a)(II).
The claim in Example 39 is not similar to pending claim 1. The claim in Example 39 is patent eligible because it does not recite any judicial exceptions in Step 2A Prong 1. However, pending claim 1 recites at least one mathematical calculation abstract idea in Step 2A Prong 1, and analysis of pending claim 1 proceeds to Step 2A Prong 2 after identifying every judicial exception.
Regarding the final paragraph on page 12, the Examiner respectfully disagrees with the Applicant that transforming the input data via the time-aligned reconstruction recites an improvement. First, it is unclear which pending claim limitation or portion of the specification discloses the features in quotation marks. These features appear to relate to claim 1, lines 14-15 and on page 3, lines 1-4. These features include performing multiplication to generate time-aligned reconstructed data, and then inputting the time-aligned reconstructed data to a neural network. Performing multiplication to generate time-aligned reconstructed data is a mathematical calculation. In Step 2A Prong 2, inputting the time-aligned reconstructed data to a neural network amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The time-aligned reconstructed data is regular and generic data. Inputting regular and generic time series data to a neural network is not an improvement to the operation of the neural network. Any technical improvement in these steps is directed to a multiplication operation which transforms irregular time series data into regular, generic time series data. Performing multiplication does not amount to an improvement in technology because an improvement to the mathematical calculation itself is not an improvement in technology
Examiner respectfully disagrees that updating hidden states of the recurrent layers of the RNN constitutes a particular way of improving RNN training for irregular time series data. Using time-aligned reconstructed data, having regular and complete generic data, to train a neural network amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). Training a neural network with generic time series data is not an improvement to the training. Any technical improvement in this steps is directed to the multiplication operation which transforms irregular time series data into regular, generic time series data.
In Step 2A Prong 1, the features of incorporating long time intervals into the hidden states of the neural network, where the updated hidden states affect how the RNN predicts health conditions of the patient, is part of a mathematical calculation of claim 1 on lines 13-21 because they recite intended results as explained above.
In Step 2A Prong 2, the features of healthcare data of a patient from electronic health records are part of the training limitation in claim 1, lines 2-5 and recite instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(h). These features do not provide an improvement in technology because training a neural network is a generic instruction to apply abstract ideas on a generic computer. The healthcare data of a patient from electronic health records is a mere field of use under MPEP 2106.05(h).
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 and a field of use that are implemented to perform the abstract ideas disclosed above.
Examiner respectfully disagrees with the Applicant’s argument in the final paragraph on page 14 of the remarks. At least the limitation in claim 1, lines 6-13 that recites obtaining the irregular time series data 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). Therefore, this limitation is a well-understood, routine, convention activity in the 101 analysis for claim 1 in Step 2B.
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 and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 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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