Office Action 2: Notice of Allowance (2025-02-24)

CLAIMS:
18599435 LISTING OF CLAIMS

This listing of claims will replace all prior versions, and listings, of claims in the application:

1\. (Currently amended) An apparatus for generating a quality diagnostic of analog ECG (electrocardiogram) data, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive an image of ECG data; extract a plurality of ECG parameters from the image of ECG data; convert the image of the ECG data to one or more digitized ECG signals; validate the one or more digitized ECG signals, wherein validating comprises: classifying the one or more digitized ECG signals to a plurality of preliminary ECG parameters; and determining an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters, wherein: the accuracy status further comprises a variable indicating accuracy of the plurality of preliminary ECG parameters; determining an accuracy status comprises using a scoring algorithm using a threshold to determine the accuracy status, wherein the accuracy status comprises one of a pass status and a fail status, and wherein the apparatus is configured to generate the threshold by: training a threshold classifier with training data correlating a plurality of annotations indicating abnormalities to a plurality of thresholds; inputting the analog ECG data into the threshold classifier; and outputting, by the threshold classifier, the threshold; and generate a quality diagnostic of the image of ECG data based on validation of the one or more digitized ECG signals, wherein the quality diagnostic represents a degree of quality of the image of ECG data.
2\. (Currently amended) The apparatus of claim 1, wherein the ECG data comprises an ECG printout comprising one or more ECG signals and the plurality of ECG parameters.
3\. (Currently amended) The apparatus of claim 1, further comprising a machine vision system, wherein the memory contains instructions further configuring the at least a processor to: capture the \[\[an \]\]image of the ECG data; and extract the plurality of ECG parameters from the image.
4\. (Original) The apparatus of claim 3, wherein: the machine vision system comprises an optical character recognition reader; and the memory contains instructions further configuring the at least a processor to extract the plurality of ECG parameters from the image using the optical character recognition reader.
5\. (Original) The apparatus of claim 1, wherein classifying the plurality of digitized ECG signals comprises: training a validation classifier using training data comprising a plurality of digitized ECG signals correlated to a plurality of textbook validators of ECG parameters; inputting the plurality of digitized ECG signals into the validation classifier; and outputting, by the validation classifier, the plurality of preliminary ECG parameters.
6\. (Original) The apparatus of claim 1, wherein determining an accuracy status comprises using a scoring algorithm using a threshold to determine the accuracy status, wherein the accuracy status comprises one of a pass status and a fail status.
7\. (Original) The apparatus of claim 6, wherein the memory contains instructions further configuring the at least a processor to determine the threshold by: training a threshold classifier with training data correlating a plurality of annotations indicating abnormalities to a plurality of thresholds; inputting the analog ECG data into the threshold classifier; and outputting, by the threshold classifier, the threshold.
8\. (Original) The apparatus of claim 1, wherein the quality diagnostic comprises an auditory representation comprising sounds to indicate a pass status or a fail status regarding an overall quality of the analog ECG data.
9\. (Original) The apparatus of claim 1, wherein generating the quality diagnostic comprises generating a visual representation regarding the quality status of the analog ECG data by: training a diagnostic classifier with training data correlating analog ECG data to a plurality of flags; inputting the analog ECG data into the diagnostic classifier; and outputting, by the diagnostic classifier, one or more flags indicating an issue with the analog ECG data.
10\. (Original) The apparatus of claim 1, wherein validating the digitized ECG signals comprises receiving ECG verification data from a user comprising a threshold for a scoring algorithm.
11\. (Currently amended) A method for generating a quality diagnostic of analog ECG (electrocardiogram) data, wherein the method comprises: receiving, by at least a processor, an image of ECG data; extracting, by the at least a processor, a plurality of ECG parameters from the image of ECG data; converting, by the at least a processor, the image of ECG data to one or more digitized ECG signals; validating, by the at least a processor, the digitized ECG signals, wherein validating comprises: classifying, by the at least a processor, the plurality of digitized ECG signals to a plurality of preliminary ECG parameters; and determining, by the at least a processor, an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters, wherein: the accuracy status further comprises a variable indicating accuracy of the plurality of preliminary ECG parameters; determining an accuracy status comprises using a scoring algorithm using a threshold to determine the accuracy status, wherein the accuracy status comprises one of a pass status and a fail status, and wherein generating the threshold further comprises: training a threshold classifier with training data correlating a plurality of annotations indicating abnormalities to a plurality of thresholds; inputting the analog ECG data into the threshold classifier; and outputting, by the threshold classifier, the threshold; and generating, by the at least a processor, a quality diagnostic of the image of ECG data based on validation of the digitized ECG signals, wherein the quality diagnostic represents a degree of quality of the image of ECG data.
12\. (Original) The method of claim 11, wherein the ECG data comprises an ECG printout comprising one or more ECG signals and the plurality of ECG parameters.
13\. (Currently amended) The method of claim 11, wherein receiving the ECG data comprises: capturing, using a machine vision system, the \[\[an \]\]image of the ECG data; and extracting, using the machine vision system, the plurality of ECG parameters from the image.
14\. (Original) The method of claim 13, wherein extracting, using the machine vision system, the plurality of ECG parameters further comprises using an using an optical character recognition reader.
15\. (Original) The method of claim 11, wherein classifying the plurality of digitized ECG signals comprises: training a validation classifier using training data comprising a plurality of digitized ECG signals correlated to a plurality of textbook validators of ECG parameters; inputting the plurality of digitized ECG signals into the validation classifier; and outputting, by the validation classifier, the plurality of preliminary ECG parameters.
16\. (Original) The method of claim 11, wherein determining an accuracy status comprises using a scoring algorithm using a threshold to determine the accuracy status, wherein the accuracy status comprises one of a pass status and a fail status.
17\. (Original) The method of claim 16, wherein using the scoring algorithm comprises determining the threshold by: training a threshold classifier with training data correlating a plurality of annotations indicating abnormalities to a plurality of thresholds; inputting the analog ECG data into the threshold classifier; and outputting, by the threshold classifier, the threshold.
18\. (Original) The method of claim 11, wherein the quality diagnostic comprises an auditory representation comprising sounds to indicate a pass status or a fail status regarding an overall quality of the analog ECG data.
19\. (Original) The method of claim 11, wherein generating the quality diagnostic comprises generating a visual representation regarding the quality status of the analog ECG data by: training a diagnostic classifier with training data correlating analog ECG data to a plurality of flags; inputting the analog ECG data into the diagnostic classifier; and outputting, by the diagnostic classifier, one or more flags indicating an issue with the analog ECG data.
20\. (Original) The method of claim 11, wherein validating the digitized ECG signals comprises receiving ECG verification data from user comprising a threshold for a scoring algorithm.

OFFICE ACTION:
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 .
Allowable Subject Matter
Claims 1-20 are allowed.
The following is an examiner’s statement of reasons for allowance:
None of the prior art made of record teaches determining an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters, wherein:
the accuracy status further comprises a variable indicating accuracy of the plurality of preliminary ECG parameters;
determining an accuracy status comprises using a scoring algorithm using a threshold to determine the accuracy status, wherein the accuracy status comprises one of a pass status and a fail status, and wherein the apparatus is configured to generate the threshold by:
training a threshold classifier with training data correlating a plurality of annotations indicating abnormalities to a plurality of thresholds;
inputting the analog ECG data into the threshold classifier; and
outputting, by the threshold classifier, the threshold; and
generating a quality diagnostic of the image of ECG data based on validation of the one or more digitized ECG signals, wherein the quality diagnostic represents a degree of quality of the image of ECG data.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

APPLICANT REBUTTAL: REMARKS
Claims 1–20 are pending, with claims 1 and 11 being independent. Claims 1–3, 11, and 13 are amended.  No claims are canceled in this paper.  No new claims are added in this paper.
In taking the foregoing actions, Applicant respectfully submits Applicant is not necessarily conceding in this application that these claims, as originally presented, are not patentable over the references cited by the U.S. Patent and Trademark Office (USPTO), as pertinent ones of the present claim amendments and cancellations are only for facilitating expeditious prosecution.  Applicant respectfully reserves the right to pursue the canceled claims and/or other claims in one or more continuing applications.
Applicant-Initiated Interview Summary
A telephonic interview regarding the instant matter was conducted on July 26, 2024, between Examiner Nguyen and Applicant’s representative, Micah T. Drayton, 69,906.  Patentability under 35 U.S.C. § 103 was discussed.   Applicant thanks the Examiner for his time and consideration in conducting the interview.  An agreement was reached that claim 1 as amended, and as specifying receipt of ECG data in image form, overcomes the references of record in the instant Office Action.
Rejection of claims under 35 U.S.C. § 103
Brockway/Taha
Claims 1, 6, 10, 11, 16, and 20 stand rejected under 35 U.S.C. § 103 as allegedly unpatentable over US 2013/0289424 to Brockway, et al. (hereinafter “Brockway”) in view of US 6304772 to Taha, et al. (hereinafter “Taha”).  Applicant respectfully submits that, as agreed to in the above-referenced interview, claims 1, 6, 10, 11, 16, and 20 at least as amended patentably distinguish Brockway and Taha, alone and in combination.
Claim 1 as amended recites:
An apparatus for generating a quality diagnostic of analog ECG (electrocardiogram) data, wherein the apparatus comprises:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive an image of ECG data;
extract a plurality of ECG parameters from the image of ECG data;
convert the image of the ECG data to one or more digitized ECG signals;
validate the one or more digitized ECG signals, wherein validating comprises:
classifying the one or more digitized ECG signals to a plurality of preliminary ECG parameters; and
determining an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters, wherein:
the accuracy status further comprises a variable indicating accuracy of the plurality of preliminary ECG parameters;
determining an accuracy status comprises using a scoring algorithm using a threshold to determine the accuracy status, wherein the accuracy status comprises one of a pass status and a fail status, and wherein the apparatus is configured to generate the threshold by:
training a threshold classifier with training data correlating a plurality of annotations indicating abnormalities to a plurality of thresholds;
inputting the analog ECG data into the threshold classifier; and
outputting, by the threshold classifier, the threshold; and
generate a quality diagnostic of the image of ECG data based on validation of the one or more digitized ECG signals, wherein the quality diagnostic represents a degree of quality of the image of ECG data.
Applicant respectfully submits that, as agreed to in the above-referenced interview, Brockway does not teach, suggest, or motivate using a threshold generated by a threshold classifier to determine an accuracy status of an ECG image. Therefore, and for at least this reason, Applicant respectfully submits that claim 1 as amended patentably distinguishes Brockway.
Taha does not cure the above-referenced deficiencies of Brockway.  As agreed to in the above-referenced interview, Taha does not teach, suggest, or motivate using a threshold generated by a threshold classifier to determine an accuracy status of an ECG image. Therefore, and for at least this reason, Applicant respectfully submits that claim 1 as amended patentably distinguishes Brockway and Taha, alone and in combination.
Claim 11 contains substantially the same limitations as claim 1. Therefore, and for at least this reason, Applicant respectfully submits that claim 11 as amended patentably distinguishes Brockway and Taha, alone and in combination.
Each of claims 6, 10, 11, 16, and 20 depends, directly or indirectly, from claim 1 or claim 11.  As noted above, claim 1 is patentably distinguishable over Brockway and Taha, alone or in combination, for at least the reasons discussed above.  Accordingly, Applicant respectfully submits that each of claims 6, 10, 11, 16, and 20 is patentably distinguishable over Brockway and Taha, alone or in combination, for at least the reasons discussed above.
Brockway/Taha/Lyman
Claims 2-5 and 12-15 stand rejected under 35 U.S.C. § 103 as allegedly unpatentable over Brockway and Taha, in further view of US 2020/0160980 to Lyman, et al. (hereinafter “Lyman”).  Applicant respectfully submits that, as agreed to in the above-referenced interview, claims 2-5 and 12-15 at least as amended patentably distinguish Brockway, Taha, and Lyman alone and in combination.
Each of claims 2-5 and 12-15 depends, directly or indirectly, from claim 1 or claim 11.  As noted above, claim 1 is patentably distinguishable over Brockway and Taha, alone or in combination, for at least the reasons discussed above.  Accordingly, Applicant respectfully submits that each of claims 2-5 and 12-15 is patentably distinguishable over Brockway and Taha, alone or in combination, for at least the reasons discussed above.
Lyman does not cure the above-referenced deficiencies of Brockway.  As agreed to in the above-referenced interview, Lyman does not teach, suggest, or motivate using a threshold generated by a threshold classifier to determine an accuracy status of an ECG image. Therefore, and for at least this reason, Applicant respectfully submits that each of claims 2-5 and 12-15 patentably distinguishes Brockway, Taha, and Lyman alone and in combination.
Brockway/Taha/Sullivan
Claims 7 and 17 stand rejected under 35 U.S.C. § 103 as allegedly unpatentable over Brockway and Taha, in further view of US 2018/0116537 to Sullivan, et al. (hereinafter “Sullivan”).  Applicant respectfully submits that, as agreed to in the above-referenced interview, claims 7 and 17 at least as amended patentably distinguish Brockway, Taha, and Sullivan alone and in combination.
Each of claims 7 and 17 depends, directly or indirectly, from claim 1 or claim 11.  As noted above, claim 1 is patentably distinguishable over Brockway and Taha, alone or in combination, for at least the reasons discussed above.  Accordingly, Applicant respectfully submits that each of claims 7 and 17 is patentably distinguishable over Brockway and Taha, alone or in combination, for at least the reasons discussed above.
Sullivan does not cure the above-referenced deficiencies of Brockway.  As agreed to in the above-referenced interview, Sullivan does not teach, suggest, or motivate using a threshold generated by a threshold classifier to determine an accuracy status of an ECG image. Therefore, and for at least this reason, Applicant respectfully submits that claims 7 and 17 as amended patentably distinguish Brockway, Taha, and Sullivan alone and in combination.
Accordingly, Applicant respectfully submits that all claims in the application are in a condition for allowance and respectfully solicits the prompt issuance of a Notice of Allowance.  If any issues remain, the Examiner is encouraged to call the undersigned practitioner at the number listed below.
Respectfully submitted,