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 .
Status of Claims
This action is in reply to the claims filed on 13 May 2025. Claims 1-20 are currently pending and have been examined.
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 1-20 are rejected under 35 USC § 101
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Claims 1-20 fall within one or more statutory categories. Claims 1-10 fall within the category of a process. Claims 11-20 fall within the category of a machine.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1-20 recite an abstract idea. Representative claim 1 recites:
analyzing [an input time series]; and
performing an action responsive to the [analysis].
Therefore, the claim as a whole is directed to “treating a patient,” which is an abstract idea because it is a method of organizing human activity. “Treating a patient” is considered to be a method of organizing human activity because it is an example of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The broadest reasonable interpretation of the claim language includes the interaction between a healthcare provider and the patient they are treating.
Alternatively, the broadest reasonable interpretation of the claims include a mental process, because collecting and analyzing data is a concept capable of being performed in the human mind (e.g. an observation, evaluation, judgment, opinion).
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s):
encoding input time series data using a pre-trained encoder;
mapping the encoded time series to a format suitable for a large language model (LLM) using an alignment model;
analyzing the mapped, encoded time series using the LLM to generate a text output.
The additional elements individually or in combination do not integrate the exception into a practical application. These additional element merely amount to reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). 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. Claim 1 is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claim 1 does not include additional elements, considered individually or in combination, 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 element(s), individually and in combination, amount to reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, claim 1 is ineligible.
Dependent claim 2 recites the method of claim 1, wherein:
training the alignment model using a self-supervised training process.
The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes 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)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 2 is ineligible.
Dependent claim 3 recites the method of claim 2, wherein:
the self-supervised training process includes adding noise to a training time series and training the alignment model to identify the training time series.
The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes 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)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 3 is ineligible.
Dependent claim 4 recites the method of claim 3, wherein:
training the alignment model to identify the training time series includes a selection between the training time series and at least one contrastive time series sample.
The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes 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)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 4 is ineligible.
Dependent claim 5 recites the method of claim 4, wherein:
training the alignment model includes maximizing a negative log-likelihood of selecting the training time series.
This element is directed to mathematical calculation, which is an abstract idea. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 5 is considered to be ineligible.
Dependent claim 6 recites the method of claim 1, wherein:
analyzing the mapped, encoded time series further includes adding a prompt that specifies a task for the LLM to perform.
The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes 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)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 6 is ineligible.
Dependent claim 7 recites the method of claim 1, wherein:
the input time series includes measurements taken of a patient's medical state.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 7 is considered to be ineligible.
Dependent claim 8 recites the method of claim 1, wherein:
the action includes changing or halting a treatment to the patient.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 8 is considered to be ineligible.
Dependent claim 9 recites the method of claim 7, wherein:
the text output is used to assist in medical decision making.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 9 is considered to be ineligible.
Dependent claim 10 recites the method of claim 1, wherein:
the alignment model is implemented as a machine learning model.
The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes 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)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 10 is ineligible.
Claims 11-20 are parallel in nature to claims 1-10. Accordingly claims 11-20 are rejected as being directed towards ineligible subject matter based upon the same analysis above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Anand et al. (U.S. 2025/0336523), hereinafter “Anand,” in view of Ho et al. (U.S. 2025/0049413), hereinafter “Ho.”
Regarding Claim 1, Anand discloses a computer-implemented method for time series analysis, comprising:
encoding input time series data … (See Anand [0046] receive a biomedical signal such as a ECG. [0059] use machine learning to extract biomedical feature. See also [0119].);
mapping the encoded time series to a format suitable for a large language model (LLM) using an alignment model (See Anand [0054] system can convert ECG data into a textual format to use as a prompt for LLM. [0059] use machine learning to extract biomedical feature. See also [0120].);
analyzing the mapped, encoded time series using the LLM to generate a text output (See Anand [0062] system can match biomedical feature to a diagnostic feature using generative model. [0073] use LLM to create a textual output. [0075] LLM can generate a comprehensive overview of diagnosis, including without limitation, potential causes, implications, associated conditions, suggested next steps for treatment, or further investigation. [0121] select a diagnostic hypothesis based on the biomedical feature. See also [0118].); and
performing an action responsive to the text output (See Anand [0068] system can generate a diagnostic hypothesis. [0070] diagnostic hypotheses containing related conditions, possible complications, and/or associated treatment options.).
Anand does not disclose:
[encoding input time series data] using a pre-trained encoder.
Ho teaches:
[encoding input time series data] using a pre-trained encoder (See Ho [0138] the system uses contrastive self-supervised learning to perform signal processing for PCG classification. [0057] the system is used on PCG time series data that uses different heart sounds. [0095]-[0096] training of the system includes the addition of Gaussian noise to the unlabeled PCG data.).
The system of Ho is applicable to the disclosure of Anand as they both share characteristics and capabilities, namely, they are directed to using machine learning to analyze ECG data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Anand to include the machine learning principles as taught by Ho. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Anand in order to improve the classification accuracy and robustness of the model (see Ho [0138]).
Regarding claim 2, Anand in view of Ho discloses the method of claim 1 as discussed above. Anand does not further disclose a method, comprising:
training the alignment model using a self-supervised training process.
Ho teaches:
training the alignment model using a self-supervised training process (See Ho [0138] the system uses contrastive self-supervised learning to perform signal processing for PCG classification. [0057] the system is used on PCG time series data that uses different heart sounds.).
The system of Ho is applicable to the disclosure of Anand as they both share characteristics and capabilities, namely, they are directed to using machine learning to analyze ECG data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Anand to include the machine learning principles as taught by Ho. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Anand in order to improve the classification accuracy and robustness of the model (see Ho [0138]).
Regarding claim 3, Anand in view of Ho discloses the method of claim 2 as discussed above. Anand does not further disclose a method, wherein:
the self-supervised training process includes adding noise to a training time series and training the alignment model to identify the training time series.
Ho teaches:
the self-supervised training process includes adding noise to a training time series and training the alignment model to identify the training time series (See Ho [0138] the system uses contrastive self-supervised learning to perform signal processing for PCG classification. [0057] the system is used on PCG time series data that uses different heart sounds. [0095]-[0096] training of the system includes the addition of Gaussian noise to the unlabeled PCG data.).
The system of Ho is applicable to the disclosure of Anand as they both share characteristics and capabilities, namely, they are directed to using machine learning to analyze ECG data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Anand to include the machine learning principles as taught by Ho. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Anand in order to improve the classification accuracy and robustness of the model (see Ho [0138]).
Regarding claim 4, Anand in view of Ho discloses the method of claim 3 as discussed above. Anand does not further disclose a method, wherein:
training the alignment model to identify the training time series includes a selection between the training time series and at least one contrastive time series sample.
Ho teaches:
training the alignment model to identify the training time series includes a selection between the training time series and at least one contrastive time series sample (See Ho [0138] the system uses contrastive self-supervised learning to perform signal processing for PCG classification. [0057] the system is used on PCG time series data that uses different heart sounds.).
The system of Ho is applicable to the disclosure of Anand as they both share characteristics and capabilities, namely, they are directed to using machine learning to analyze ECG data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Anand to include the machine learning principles as taught by Ho. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Anand in order to improve the classification accuracy and robustness of the model (see Ho [0138]).
Regarding claim 6, Anand in view of Ho discloses the method of claim 1 as discussed above. Anand further discloses a method, wherein:
analyzing the mapped, encoded time series further includes adding a prompt that specifies a task for the LLM to perform (See Anand [0054] A “query” for the purposes of the disclosure is a string of characters that poses a question. In some cases, such input may be received from a user device.).
Regarding claim 7, Anand in view of Ho discloses the method of claim 1 as discussed above. Anand further discloses a method, wherein:
the input time series includes measurements taken of a patient's medical state (See Anand [0046] receive a biomedical signal such as a ECG.).
Regarding claim 8, Anand in view of Ho discloses the method of claim 1 as discussed above. Anand further discloses a method, wherein:
the action includes changing or halting a treatment to the patient (See Anand [0068] system can generate a diagnostic hypothesis. [0070] diagnostic hypotheses containing related conditions, possible complications, and/or associated treatment options.).
Regarding claim 9, Anand in view of Ho discloses the method of claim 7 as discussed above. Anand further discloses a method, wherein:
the text output is used to assist in medical decision making (See Anand [0068] system can generate a diagnostic hypothesis. [0070] diagnostic hypotheses containing related conditions, possible complications, and/or associated treatment options.).
Regarding claim 10, Anand in view of Ho discloses the method of claim 1 as discussed above. Anand further discloses a method, wherein:
the alignment model is implemented as a machine learning model (See Anand [0054] system can convert EEG data into a textual format to use as a prompt for LLM. [0059] use machine learning to extract biomedical feature. See also [0120].).
Regarding claims 11-14 and 16-20, Anand in view of Ho discloses the method of claims 1-4 and 6-10 as discussed above. Claims 11-14 and 16-20 recite a system that performs a method that is substantially similar to the method of claims 1-4 and 6-10. According, claims 11-14 and 16-20 are rejected based on the same analysis
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Anand et al. (U.S. 2025/0336523), hereinafter “Anand,” in view of Ho et al. (U.S. 2025/0049413), hereinafter “Ho,” and further in view of Wexler et al. (U.S. 2021/0383925), hereinafter “Wexler.”
Regarding claim 5, Anand in view of Ho discloses the method of claim 4 as discussed above. Anand does not further disclose a method, wherein:
training the alignment model includes maximizing a negative log-likelihood of selecting the training time series.
Wexler teaches:
training the alignment model includes maximizing a negative log-likelihood of selecting the training time series (See Wexler [0034] state estimator is implemented as a machine learning model. [0086]-[0087] state estimator model can obtain the maximum likelihood estimation (MLE) using a negative log likelihood function.).
The system of Wexler is applicable to the disclosure of Anand in view of Ho as they both share characteristics and capabilities, namely, they are directed to using machine learning to analyze patient data, including time series of patient parameters. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Anand to include negative log-likelihood as taught by Wexler. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Anand in order to provide improved systems and methods for biomonitoring and/or providing personalized healthcare recommendations or information for the treatment (see Wexler [0004]).
Regarding claim 15, Anand in view of Ho and Wexler discloses the method of claim 5 as discussed above. Claim 15 recites a system that performs a method that is substantially similar to the method of claim 5. According, claim 15 is rejected based on the same analysis
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bade et al. (U.S. Patent No. 12,376,777) teaches a system and method for transforming electrocardiogram images for use in one or more machine learning models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN L HANKS whose telephone number is (571)270-5080. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at (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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/B.L.H./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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