DETAILED ACTION
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 .
Response to Amendment
In light of the amendments, the claims remain rejected under 35 U.S.C. 101.
In light of the amendments, the previous 35 U.S.C. 103 rejections have been withdrawn.
Notice to Applicant
In the amendment dated 01/22/2025, the following has occurred: claims 1-2 and 11-12 have been amended; claims 3-5, 8-9, 13-15, and 18-19 have remained unchanged; claims 6-7, 10, 16-17, and 20 have been canceled; and no new claims have been added.
Claims 1-5, 8-9, 11-15, and 18-19 are pending.
Effective Filing Date: 01/04/2024
Response to Arguments
35 U.S.C. 101 Rejections:
Step 2A, Prong One:
Applicant argues that the claims do not include an abstract idea classified under mathematical concepts. Applicant point to Thales Visionix, Inc. and states that the presence of mathematical calculations do not render the claims abstract. Applicant further states that the claims are directed to a specific and inventive application of processing techniques that utilize mathematical tools to achieve a technical improvement. Examiner however respectfully disagrees. Thales Visionix, Inc. recites a system of 3 physical components where one component is able to determine orientation using an unconventional arrangement of 2 sensors. The present claims however recite an abstract idea involving mathematical concepts with no unconventional arrangement of components.
Step 2A, Prong Two:
Applicant argues that claim 1 recites a specific and structured method for improving the functionality of a machine-learning-based diagnostic system through iterative ECG validation. Under Prong Two it is assessed whether there are additional elements or not in the claims and if these additional elements integrate with the abstract idea to form a practical application. The claims are deemed to have additional elements which either provide insignificant extra-solution activity or they take generic computing components and “apply it” to the abstract idea. Applicant’s statement of improving the functioning of a machine learning system by iterative retraining does not consider the nature of machine learning. Machine learning is iterative and to say that retraining of a model is a technical improvement does not consider this.
Example 47 of the 101 examples provided by the USPTO describes training of a model. Applicant points towards claims 1 and 3 of that example and states that the claims are similar to claim 3 as the claims improve the functioning of a computer. Examiner however respectfully disagrees as this statement is not supported with reasoning as to what this technical improvement is. Applicant states that the system addresses the technical problem of maintaining diagnostic accuracy in a scalable, automated way, however the usage of machine learning to provide the solution is done so in an “apply it” manner as machine learning is both automated and provides consistency based on its training.
Applicant further states that the claims includes a digital-to-analog conversion circuit (though the claim language does not say this verbatim). The conversion circuit is viewed as a circuit with a description, and this circuit is viewed as a generic component that is being applied to an abstract idea including converting data from one format to another.
Step 2B:
Applicant states that the claims recite significantly more than the alleged judicial exception. Applicant cites a few different court cases and asserts that the computing processor of claim 1 is not generic. Examiner however respectfully disagrees based on the current claim language. For example, the claim recites using a processor and that the processor performs steps. This is written in an “apply it” manner. Furthermore, paragraphs [0088] – [0089] of the specification outline that the processor is generic.
35 U.S.C. 103 Rejections:
Applicant argues with respect to the previous 103 rejections. These rejections have been withdrawn in view of the amendments to the claims.
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 1-5, 8-9, 11-15, and 18-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-5 and 8-9 are drawn to an apparatus and claims 11-15 and 18-19 are drawn to a method, each of which is within the four statutory categories. Claims 1-5, 8-9, 11-15, and 18-19 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites an apparatus for generating an electrocardiogram (ECG) verification set, wherein the apparatus comprises:
a) at least a processor, and
b) a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
1) receive digital ECG data,
2) convert the digital ECG data into analog ECG data using c) a conversion circuit,
3) generate an ECG validation set, diagnostic training data, and a test set by dividing the analog ECG data utilizing d) a validation machine learning model, wherein generating the ECG validation set further comprises:
3a) receiving validation training data, wherein the validation training data comprises inputs of analog ECG data correlated to outputs of ECG validation sets,
3b) training, iteratively, the validation machine learning model using the validation training data, wherein training the validation machine learning model includes retraining the validation machine learning model with feedback from previous iterations of the validation machine learning model, and
3c) generating the ECG validation set using the trained validation machine learning model, and
4) validate e) a diagnostic machine learning model as a function of the ECG validation set, wherein validating the diagnostic machine learning model comprises:
4a) iteratively training the diagnostic machine learning model using diagnostic training data,
4b) inputting the ECG validation set into the diagnostic machine learning model,
4c) generating performance data associated with the diagnostic machine learning model based on the ECG validation set, wherein generating the performance data comprises performing an error analysis on the diagnostic machine learning model, wherein performing the error analysis comprises:
4c1) generating a confusion matrix as a function of an output of the diagnostic machine learning model; and
4c2) identifying one or more error patterns associated with the confusion matrix;
4c3) generating an error report as a function of the error analysis, wherein the error report comprises a representation of the confusion matrix and data for enhancing the diagnostic machine learning model based on identified challenges;
4d) comparing the performance data to a validation threshold,
4e) retraining the diagnostic machine learning model as a function of the comparison of the performance data to the validation threshold, wherein retraining the diagnostic machine learning model comprises incorporating additional ECG data in the diagnostic training data, and
4f) accepting the diagnostic machine learning model as a function of the test set, and
5) generate diagnostic data as a function of accepting the diagnostic machine learning model, wherein the diagnostic data comprises at least an abnormality.
Claim 1 recites, in part, performing the steps of 2) convert the digital ECG data into analog ECG data, 3) generate an ECG validation set, diagnostic training data, and a test set by dividing the analog ECG data utilizing a validation model, wherein generating the ECG validation set further comprises: 3b) training, iteratively, the validation model using the validation training data, wherein training the validation model includes retraining the validation model with feedback from previous iterations of the validation model, and 3c) generating the ECG validation set using the trained validation model, and 4) validate a diagnostic model as a function of the ECG validation set, wherein validating the diagnostic model comprises: 4a) iteratively training the diagnostic model using diagnostic training data, 4b) inputting the ECG validation set into the diagnostic model, 4c) generating performance data associated with the diagnostic model based on the ECG validation set, wherein generating the performance data comprises performing an error analysis on the diagnostic machine learning model, wherein performing the error analysis comprises: 4c1) generating a confusion matrix as a function of an output of the diagnostic machine learning model, 4c2) identifying one or more error patterns associated with the confusion matrix, and 4c3) generating an error report as a function of the error analysis, wherein the error report comprises a representation of the confusion matrix and data for enhancing the diagnostic machine learning model based on identified challenges, 4d) comparing the performance data to a validation threshold, 4e) retraining the diagnostic model as a function of the comparison of the performance data to the validation threshold, wherein retraining the diagnostic model comprises incorporating additional ECG data in the diagnostic training data, and 4f) accepting the diagnostic model as a function of the test set, and 5) generate diagnostic data as a function of accepting the diagnostic model, wherein the diagnostic data comprises at least an abnormality. These steps correspond to Mathematical Concepts. Independent claim 11 recites similar limitations and is also directed to an abstract idea under the same analysis.
Depending claims 2-5, 8-9, 12-15, and 18-19 include all of the limitations of claims 1 and 11, and therefore likewise incorporate the above described abstract idea. Depending claims 3 and 13 add the additional step of “generate normalized ECG data as a function of the conversion of the digital ECG data into the analog ECG data”; claims 4 and 14 add the additional step of “calculate one or more confidence intervals associated with the conversion of the digital ECG data into the analog ECG data”; claims 8 and 18 add the additional step of “iteratively retrain the diagnostic machine learning model as a function of the performance data failing to meet the validation threshold”; and claims 9 and 19 recite the additional step of “anonymize the digital ECG data using an anonymization process”. Additionally, the limitations of depending claims 2, 5, 12, and 15 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 2-5, 8-9, 12-15, and 18-19 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 11 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – a) at least a processor, b) a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to perform functions, c) a conversion circuit, d) a validation machine learning model, and e) a diagnostic machine learning model to perform the claimed steps.
The claims also include the additional element steps of 1) “receive digital ECG data” and 3a) “receiving validation training data, wherein the validation training data comprises inputs of analog ECG data correlated to outputs of ECG validation sets.”
The a) at least a processor, b) memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor, and the c) conversion circuit to perform functions in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification, paragraphs [0088] – [0089] where the processor and memory are generic components and paragraph [0065] where the circuitry is generic, see MPEP 2106.05(f)).
Further, the d) validation machine learning model and e) diagnostic machine learning model in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that it amount to no more than mere instructions to apply the exception using generic computer components, see MPEP 2106.05(f).
Lastly, the additional element steps of 1) “receive digital ECG data” and 3a) “receiving validation training data, wherein the validation training data comprises inputs of analog ECG data correlated to outputs of ECG validation sets” adds insignificant extra-solution activity to the abstract idea which amounts to mere data gathering, see MPEP 2106.05(g).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
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. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a) at least a processor, b) a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to perform functions, c) a conversion circuit, d) a validation machine learning model, and e) a diagnostic machine learning model to perform the claimed steps in addition to the additional element steps of 1) “receive digital ECG data” and 3a) “receiving validation training data, wherein the validation training data comprises inputs of analog ECG data correlated to outputs of ECG validation sets” amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity) and mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain mathematical steps. Specifically, MPEP 2106.05(d) and MPEP 2106.05(f) recite that the following limitations are not significantly more:
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); and
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
The additional element steps of 1) “receive digital ECG data” and 3a) “receiving validation training data, wherein the validation training data comprises inputs of analog ECG data correlated to outputs of ECG validation sets” in these steps add insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives ECG data and training data, and transmits the data to a model(s) over a network, for example the Internet.
Lastly, the current invention generates data utilizing a) at least a processor, b) a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to perform functions, c) a conversion circuit, d) a validation machine learning model, and e) a diagnostic machine learning model, thus these computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Mere instructions to apply an exception using generic computer components or insignificant extra-solution activity in the form of WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1-5, 8-9, 11-15, and 18-19 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached on 571-270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684