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 .
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a process type claim. Claim 11 is a machine type claim. Therefore, claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“for each ECG measurement of the training data set: determining using a statistical estimation technique, an estimated analyte level at a time of the ECG measurement based on the corresponding set of analyte measurements;” A user mentally or with pencil and paper can determine an analyte level at the time of the ECG measurement based on corresponding analyte measurements.
“determining whether the estimated analyte level at the time of the ECG measurement meets a certainty threshold;” A user mentally or with pencil and paper can determine whether the analyte level at the time of the ECG measurement meets a certain threshold.
“and in response to determining that the estimated analyte level at the time of the ECG measurement meets a certainty threshold, labeling the ECG measurement based on the estimated analyte level at the time of the ECG measurement;” A user mentally or with pencil and paper can label ECG measurements that are based on analyte levels at the time the ECG was recorded.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“providing a training data set comprising a plurality of electrocardiogram (ECG) measurements and a corresponding set of analyte measurements for each of the plurality of ECG measurements;” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
“training, using each of the plurality of ECG measurements that are labeled, a machine learning model to predict a level of an analyte based on ECG data” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations beyond a generic, off the shelf ML model)
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“providing a training data set comprising a plurality of electrocardiogram (ECG) measurements and a corresponding set of analyte measurements for each of the plurality of ECG measurements;” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving step is well-understood, routine, conventional activity is supported under Berkheimer).
“training, using each of the plurality of ECG measurements that are labeled, a machine learning model to predict a level of an analyte based on ECG data” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations beyond a generic, off the shelf ML model.
As per claims 2-8 and 10, these claims contain additional mental steps of determining and labeling analyte levels, analyte measurements, and ECG measurements and are rejected for similar reasons to claim 1.
As per claim 9, this claim generally links ML model of claim 1 to a convolutional neural network, a recurrent neural network or a combination of both and is rejected for similar reasons to claim 1.
As per claim 11,
2A Prong 1:
“for each ECG measurement of the training data set: determining using a statistical estimation technique, an estimated analyte level at a time of the ECG measurement based on the corresponding set of analyte measurements;” A user mentally or with pencil and paper can determine an analyte level at the time of the ECG measurement based on corresponding analyte measurements.
“determining whether the estimated analyte level at the time of the ECG measurement meets a certainty threshold;” A user mentally or with pencil and paper can determine whether the analyte level at the time of the ECG measurement meets a certain threshold.
“and in response to determining that the estimated analyte level at the time of the ECG measurement meets a certainty threshold, labeling the ECG measurement based on the estimated analyte level at the time of the ECG measurement;” A user mentally or with pencil and paper can label ECG measurements that are based on analyte levels at the time the ECG was recorded.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“a memory”, “a processing device operatively coupled to the memory” (mere instructions to apply the exception using a generic computer component)
“providing a training data set comprising a plurality of electrocardiogram (ECG) measurements and a corresponding set of analyte measurements for each of the plurality of ECG measurements;” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
“training, using each of the plurality of ECG measurements that are labeled, a machine learning model to predict a level of an analyte based on ECG data” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations beyond a generic, off the shelf ML model)
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“providing a training data set comprising a plurality of electrocardiogram (ECG) measurements and a corresponding set of analyte measurements for each of the plurality of ECG measurements;” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving step is well-understood, routine, conventional activity is supported under Berkheimer).
“training, using each of the plurality of ECG measurements that are labeled, a machine learning model to predict a level of an analyte based on ECG data” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations beyond a generic, off the shelf ML model.
As per claims 12-18 and 20, these claims contain additional mental steps of determining and labeling analyte levels, analyte measurements, and ECG measurements and are rejected for similar reasons to claim 11.
As per claim 19, this claim generally links ML model of claim 1 to a convolutional neural network, a recurrent neural network or a combination of both and is rejected for similar reasons to claim 11.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 1 of U.S. Patent 11915825 (reference application) in view of PGPUB 20180350468 (Friedman et al.). The claims of the instant application and the claims of the reference patent are compared in the table below.
With regard to Claim 11,
Instant Application 18407346
U.S. Patent 11915825
A system comprising:
a memory; and
a processing device operatively coupled to the memory, the processing device to: provide a training data set comprising a plurality of electrocardiogram (ECG) measurements and a corresponding set of analyte measurements for each of the plurality of ECG measurements;
for each ECG measurement of the training data set: determine using a statistical estimation technique, an estimated analyte level at a time of the ECG measurement based on the corresponding set of analyte measurements;
determine whether the estimated analyte level at the time of the ECG measurement meets a certainty threshold;
and in response to determining that the estimated analyte level at the time of the ECG measurement meets a certainty threshold, label the ECG measurement based on the estimated analyte level at the time of the ECG measurement; and train, using each of the plurality of ECG measurements that are labeled, a machine learning model to predict a level of an analyte based on ECG data.
A system for non-invasively predicting a level of an analyte comprising:
an electrocardiogram sensor; and
a processing device operatively coupled to the electrocardiogram sensor, wherein the processing device is to: analyze electrocardiogram training data comprising an electrocardiogram of each of a plurality of subjects and one or more measured analyte levels associated with each electrocardiogram to determine an associated label for each electrocardiogram of the electrocardiogram training data, the associated label for each electrocardiogram of the electrocardiogram training data based on a regression line fitted to the one or more measured analyte levels associated with each electrocardiogram, wherein the associated label is determined from the regression line at a time of measurement of the associated electrocardiogram;
train a machine learning model by: analyzing each electrocardiogram of the electrocardiogram training data to generate an output; and comparing the output generated for each electrocardiogram of the electrocardiogram training data to the associated label for each electrocardiogram of the electrocardiogram training data to update the machine learning model using backpropagation, wherein one or more weight matrices of the machine learning model are adjusted based on a confidence interval associated with the associated label for each electrocardiogram of the electrocardiogram training data;
receive electrocardiogram data of a subject from the electrocardiogram sensor; and
apply the machine learning model to the received electrocardiogram data to determine an indication of a measured analyte level of the subject based on the electrocardiogram data
Claim 11
Claim 11 of the reference patent recites all of the limitations of claim 1 of the instant application except “a memory” and “determine whether the estimated analyte level at the time of the ECG measurement meets a certainty threshold.” However, Friedman et al. teaches both a memory (Friedman, paragraph 0127, “computing device 2300 includes a processor 2302, memory…”) and giving each beat a quality score and comparing it to a specific threshold (Friedman, paragraph 0083-0084, “In some implementations, the computing system generates histograms of feature values from multiple beats over a period of time… The system may compare the quality score for each beat to a static or dynamic threshold score…”).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate memory and a way to give a quality score to a beat in order to compare it to a static or dynamic threshold, as disclosed in Friedman et al, within the method of claim 1 of the reference patent, to give reliable analyte assessments to patients(Friedman, paragraph 0005, “…other techniques are described herein by which processed ECG signals, or other types of electrogram data, can be used to determine levels of analytes (e.g., potassium) in a patient with clinically meaningful resolution”). This would allow a patient to be alerted to their potassium value and allow for interventions (Friedman, paragraph 0005, “These techniques may thus allow for unobtrusive remote monitored potassium assessment. By enabling both accurate potassium value ascertainment and trend detection, alerts can be issued and interventions initiated, thereby improving clinical outcomes”).
Claims 1-10 and 12-20 are rejected similarly.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7, 9-17, 19, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Friedman et al, U.S. PG PUB (US 20180350468 A1) published November 23, 2016.
With regard to independent claim 1,
Friedman teaches “A method comprising: providing a training data set comprising a plurality of electrocardiogram (ECG) measurements and a corresponding set of analyte measurements for each of the plurality of ECG measurements;” (Paragraph 0008; EN: This denotes obtaining data from ECG results, where the results contain a plurality of beats. Each beat can indicate a level of analyte once the results have gone through statistical analysis). “for each ECG measurement of the training data set: determining using a statistical estimation technique, an estimated analyte level at a time of the ECG measurement based on the corresponding set of analyte measurements;” (Paragraph 0083; EN: This denotes that the computing system can generate histograms of feature values from multiple beats over a period of time to facilitate a statistical analysis of beats from recorded ECG data). “determining whether the estimated analyte level at the time of the ECG measurement meets a certainty threshold;” (Paragraph 0083-0084; EN: This denotes that the computing systems can compare the quality score for each beat of the ECG results to a static or dynamic threshold score). “and in response to determining that the estimated analyte level at the time of the ECG measurement meets a certainty threshold, labeling the ECG measurement based on the estimated analyte level at the time of the ECG measurement;” (Paragraph 0091; EN: This denotes that the various models will classify ECG segments to different analyte levels or bins as low, normal, or high. See also Paragraph 0084, 0093 and Fig. 20: showing that low quality beats are discarded. Further, high quality ones are used to train models.) “and training, using each of the plurality of ECG measurements that are labeled, a machine learning model to predict a level of an analyte based on ECG data” (Paragraph 0091; EN: This denotes that the analyte estimation model that is personalized or population-based can predict analyte levels based on ECG features).
With regard to dependent claim 2,
Friedman teaches “The method of claim 1, wherein determining the estimated analyte level at the time of the ECG measurement comprises: determining a regression line that fits to each analyte measurement of the corresponding set of analyte measurements;” (Paragraph 0071-0073; EN: This denotes that a computing system can utilize both a linear filtering technique and a non-linear filtering technique. These techniques can be used to detect artifacts that need to be discarded or retained based on a specific score and they can depict a progression of ECG features over time, along with indications of calculated analyte levels, measured analyte levels, or both at corresponding times). “and determining the estimated analyte level at the time of the ECG measurement based on the regression line” (Paragraph 0071-0073; EN: This denotes that the computing system can depict a progression of ECG features over time, along with indications of calculated analyte levels, measured analyte levels, or both at corresponding times)
With regard to dependent claim 3,
Friedman teaches “The method of claim 1, further comprising: for each ECG measurement of the training data: determining a statistical measure of a fit of the regression line to each of the corresponding analyte measurements of the ECG measurement;” (Paragraph 0085; EN: This denotes that quality score of a beat can be computed as an r-squared value). “and determining, based on the statistical measure, a confidence interval of the estimated analyte level at the time of the ECG measurement” (Paragraph 0093-0094; EN: This denotes that a computing system can update a population-based analyte estimation model based on if the measured analyte values are outliers that fall outside of a statistically acceptable range of values. All while the model will learn from new data and may evolve over time to generate predicted analyte levels based on ECG features with greater statistical confidence).
With regard to dependent claim 4,
Friedman teaches “The method of claim 3, wherein determining whether the estimated analyte level at the time of the ECG measurement meets the certainty threshold comprises: comparing the confidence interval of the estimated analyte level at a time of the ECG measurement to the certainty threshold” (Paragraph 0093; EN: This denotes that a personalized or population-based analyte estimation model can display measured analyte levels, T-wave right slope/Sqrt(Tamp) value from an ECG at or near the time of the analyte measurement, and the statistical confidence of estimated analyte levels as a confidence bounds).
With regard to dependent claim 5,
Friedman teaches “The method of claim 4, wherein the statistical measure is an r-squared value” (Paragraph 0085; EN: This denotes that quality score of a beat can be computed as an r-squared value)
With regard to dependent claim 6,
Friedman teaches “The method of claim 1, wherein labeling the ECG measurement based on the estimated analyte level at the time of the ECG measurement comprises: labeling the ECG measurement as one of a low analyte level, a medium analyte level and a high analyte level based on the estimated analyte level at the time of the ECG measurement” (Paragraph 0091; EN: This denotes that the various models will classify ECG segments to different analyte levels or bins as low, normal, or high).
With regard to dependent claim 7,
Friedman teaches “The method of claim 1, wherein labeling the ECG measurement based on the estimated analyte level at the time of the ECG measurement comprises: labeling the ECG measurement with the estimated analyte level at the time of the ECG measurement” (Paragraph 0091; EN: This denotes that the various models will classify ECG segments to different analyte levels or bins as low, normal, or high)
With regard to dependent claim 9,
Friedman teaches “The method of claim 1, wherein the machine learning model is one of a convolutional neural network, a recurrent neural network and a combination of a convolutional neural network and a recurrent neural network” (Paragraph 0091; EN: This denotes that the ML model can be a deep convolutional neural network).
With regard to dependent claim 10,
Friedman teaches “The method of claim 1, wherein the analyte is one of potassium, magnesium and calcium” (Paragraph 0063; EN: This denotes quantifying the concentration of analytes in a patient's blood, which can be potassium).
With regard to independent claim 11,
Friedman teaches “A system comprising: a memory;” (Paragraph 0127; EN: This denotes a computing device utilizing memory). “and a processing device operatively coupled to the memory,” (Paragraph 0127; EN: This denotes a computing device utilizing memory). The rest of this claim is similar in scope to claim 1 and is rejected under a similar rationale.
With regard to dependent claim 12,
This claim is similar in scope to claim 2 and is rejected under a similar rationale.
With regard to dependent claim 13,
This claim is similar in scope to claim 3 and is rejected under a similar rationale.
With regard to dependent claim 14,
This claim is similar in scope to claim 4 and is rejected under a similar rationale.
With regard to dependent claim 15,
This claim is similar in scope to claim 5 and is rejected under a similar rationale.
With regard to dependent claim 16,
This claim is similar in scope to claim 6 and is rejected under a similar rationale.
With regard to dependent claim 17,
This claim is similar in scope to claim 7 and is rejected under a similar rationale.
With regard to dependent claim 19,
This claim is similar in scope to claim 9 and is rejected under a similar rationale.
With regard to dependent claim 20,
This claim is similar in scope to claim 10 and is rejected under a similar rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Friedman et al, U.S. PG PUB (US 20180350468 A1) published November 23, 2016 in view of Zhan et al, NPL (SAR Automatic Target Recognition Based on Deep Convolutional Neural Networks), published June 28, 2016.
With regard to dependent claim 8,
Friedman teaches “The method of claim 3, wherein training the machine learning model comprises: analyzing, using the machine learning model, each of the plurality of ECG measurements that are labeled to generate a corresponding output;” (Paragraph 0093; EN: This denotes that the personalized or population-based analyte estimation model can generate predicted analyte levels based on ECG features with greater statistical confidence). “and comparing the corresponding output for each of the plurality of ECG measurements that are labeled to the associated label for each of the plurality of ECG measurements that are labeled to update the machine learning model using backpropagation” (Paragraph 0091-0094; EN: This denotes that both the personalized or population-based analyte estimation model, which can be a deep convolutional neural network that utilizes backpropagation, and the computing system can update the model based on additional information). “wherein … of the machine learning model are adjusted based on the confidence interval associated with each of the plurality of ECG measurements that are labeled” (Paragraph 0093-0094; EN: This denotes that a computing system can update a population-based analyte estimation model if the measured analyte values are outliers that fall outside of a statistically acceptable range of values. All while the model will learn from new data and may evolve over time to generate predicted analyte levels based on ECG features with greater statistical confidence).
However, Friedman fails to explicitly disclose “one or more weight matrices”.
Zhan teaches “… one or more weight matrices…” (Pg. 3-4, Section: Back-propagation Details; EN: This denotes a weight matrix update for each training sample used by the output layer of a deep CNN).
Friedman and Zhan are considered to be analogous art to the claimed invention due to the fact that they both disclose the use of deep convolutional neural networks. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the use of deep convolutional neural networks for measuring ECGs and analyte levels with the design of deep neural network architecture to achieve high recognition accuracy for the target classification tasks of Zhan. One would be motivated to do so to improve the accuracy reliability of analyte assessment gained from ECGs.
With regard to dependent claim 18,
This claim is similar in scope to claim 8 and is rejected under a similar rationale.
Conclusion
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/SETH CAPRIANO-UMARI SHELTON/ Examiner, Art Unit 2141
/MATTHEW ELL/ Supervisory Patent Examiner, Art Unit 2141
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