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 .
Claim Status: Claims 1-20 are pending; Claims 9 and 10 have been withdrawn from consideration as they are directed to non-elected species.
Examiner notes: Even though claims 2-5, 12, and 17-20 are not rejected under prior art, they are not indicated as allowable in light of the pending 35 U.S.C. 101 rejections.
Response to Arguments
Applicant's arguments filed on September 10, 2025 have been fully considered but they are not persuasive.
Regarding 35 U.S.C. 102(a)(1) rejection, Applicant made an argument that “[t]he Office Action has not shown that Alam discloses, expressly or inherently, each and every element recited in independent claim 1 as currently presented.”
This argument has been considered but is not persuasive.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Regarding 35 U.S.C. 101 rejection, 1) Applicant made an argument that operations recited in independent claims 1 and 17 are not an abstract idea because they are too complex to be practically performed entirely in the human mind. 2) Applicant further argued that even if independent claims 1 and 17 would recite an abstract idea (which Applicant denies), each of these claims as currently presented, as a whole, is not directed to a judicial exception, because the claims as a whole would integrate the abstract idea into a practical application. 3) Applicant argued that the Office Action fails to comply with examination procedure as stated in MPEP 2106.05 that Examiners should examine each claim for eligibility separately, based on the particular elements recited therein.
These arguments have been considered but are not persuasive.
In response to argument 1), the operations in claims 1 and 17 can be performed in human mind or by hand. And if computers or other machinery merely as a tool performs an existing process, use of a computer or other machinery in its ordinary capacity for economic or other tasks or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. MPEP 2106.05.
In response to argument 2), Examiner would like to note that section MPEP 2106.05 II. states that “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Please also see MPEP 2106.05(f).
On page 8 of the Remarks, Applicant stated that the claims as currently presented improve the technical field of monitoring vitals of a subject and vital signs monitoring systems and referred to para. [0016] of their specification.
[0016] Accuracy of vital signs monitoring systems depend on the quality of the measurements by the sensors. Crude techniques are applied to discard measurements which have a low signal-to-noise ratio or are saturated. A more accurate and flexible technique enables more measurements to be kept, more meaningful signal quality information to be extracted, more accurate vital signs extraction and more systems to readily embed signal quality assessment in the signal processing pipeline. Improvements include preprocessing of the signal that is independent of variations of underlying hardware, use of features with low computational complexity and high predictive power, cross-channel feature extraction, application of a trained machine learning model, and flexible translation of signal quality classification information into a continuous metric for signal quality.
However, MPEP 2106.04(d)(1) states that “if the specification sets forth an improvement in technology or a technical field, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement, i.e., the claim includes the components or steps of the invention that provide the improvement described in the specification”.
The claims do not reflect the disclosed improvement in the Remarks and para. [0016], because they do not require a vital sign monitor or any kind of monitoring. All claims are drawn to “a method for signal quality assessment” on a signal that may have been acquired at any point in time in the past (for example, days or years), where the assessment does not need to function as a vital sign monitoring. The method as claimed is not tied to any particular technological device but instead can be carried out with any computer at any time on merely stored files.
As not being tied to a technological field or device, the claims further fail to reflect any improvement to the function of a computer itself. In contrast to the Desjardins case (In view of “Advance notice of change to MPEP in light of Ex Parte Desjardins” dated December 5, 2025; On page 2, please see the revision of MPEP 2106.04(d), subsection III), the claims do not recite any components in the machine learning model itself that provide the asserted improvements. The generic claim limitation of “applying a machine learning model” lacks specificity that provides the asserted improvements.
In response to argument 3), each dependent claim incorporates the limitations of its dependencies and the Examiner had considered each claim separately that no additional limitation added by dependent claim to the independent claim overcame 101. Indicated dependent claims merely recite abstract idea and no additional elements that are sufficient to amount to significantly more than the judicial exception.
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 1-8, 11-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) a method for signal quality assessment.
To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.04.
The instant claims are evaluated according to such analysis.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes, Claim 1 is directed to a method.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the judicial exception relied upon by the instantly claimed invention is an abstract idea, and the limitation that sets forth or describes the abstract idea is: “extracting first features from samples representative of a first photoplethysmogram signal over a period of time; generating signal quality classification information by applying a machine learning model to the first features, with the signal quality classification information corresponding to multiple defined classes of signal quality; outputting signal quality classification information; and transforming, based on an attribute of a vital sign extraction algorithm, the signal quality classification information to a signal quality index representing a quality of the first photoplethysmogram signal” in claim 1.
The reason that the limitations are considered an abstract idea is because they are directed to mental processes (observation, evaluation, judgment, opinion). The above steps can be performed in the mind or by hand.
The 2019 revised § 101 guidance makes clear that the "mental process" category of abstract ideas does not only apply to steps actually carried out mentally; it also applies to the types of processes that could be carried out mentally, but are instead carried out using generic processing/collection technology.
Please see the following analogous type of data manipulations that courts have found to be abstract ideas (all taken from MPEP 2106.04):
collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1351-52, 119 USPQ2d 1739, 1740 (Fed. Cir. 2016);
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim(s) does not recite additional elements.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, the claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, the claim is not patent eligible.
With regards to the instantly rejected dependent claims 2-8, 11-13, these claims when analyzed as a whole are also held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to a judicial exception and/or do not add significantly more to the judicial exception. Therefore, the claim(s) is/are not patent eligible.
Claims 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) a method for signal quality assessment.
To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.04.
The instant claims are evaluated according to such analysis.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes, Claim 17 is directed to a method.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the judicial exception relied upon by the instantly claimed invention is an abstract idea, and the limitation that sets forth or describes the abstract idea is: “extracting first features from first samples representative of a photoplethysmogram signal over a period of time; extracting one or more second features from second samples representative of a motion sensor signal over the period of time; extracting one or more cross-channel features from the first samples and the second samples, wherein the one or more cross-channel features include a sample correlation coefficient; generating signal quality information by applying a machine learning model to the first features, the one or more second features, and the one or more cross-channel features, with the signal quality classification information corresponding to multiple defined classes of signal quality; outputting signal quality classification; and generating based on the signal quality classification information and an attribute of a vital sign extraction algorithm, a signal quality index representing a quality of the photoplethysmogram signal.”.
The reason that the limitations are considered an abstract idea is because they are directed to mental processes (observation, evaluation, judgment, opinion). The above steps can be performed in the mind.
The 2019 revised § 101 guidance makes clear that the "mental process" category of abstract ideas does not only apply to steps actually carried out mentally; it also applies to the types of processes that could be carried out mentally, but are instead carried out using generic processing/collection technology.
Please see the following analogous type of data manipulations that courts have found to be abstract ideas (all taken from MPEP 2106.04):
collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1351-52, 119 USPQ2d 1739, 1740 (Fed. Cir. 2016);
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim(s) does not recite additional elements.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, the claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, the claim is not patent eligible.
With regards to the instantly rejected dependent claims 18-20, these claims when analyzed as a whole are also held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to a judicial exception and/or do not add significantly more to the judicial exception. Therefore, the claim(s) is/are not patent eligible.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 6-8, 11, and 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Alam et al. (US 2019/0133533A1).
Re Claim 1, Alam discloses a method for signal quality assessment, comprising:
extracting first features from samples representative of a first photoplethysmogram signal over a period of time (fig. 9, para. [0080], During the testing stage, the testing data containing PPG signals is obtained from a testing device. The testing data containing the PPG signals is pre-processed at 912, wherein during the pre-processing stage at 914 the PPG signal is split into a first plurality of signal samples. The signal samples passing the SSC check are utilized for feature extraction at 920. Various SQIs have been disclosed that are utilized as features. In an embodiment, a optimal set of features selected during the training stage are considered as testing features during the testing stage. The testing features are utilized for prediction of the signal samples as noisy or clean at 922, as is described with reference to FIG. 3 previously; para. [0042], the system 202 utilizes a set of Signal Quality Indices (SQIs) to represent the noise characteristics of the PPG waveform/signal.);
generating signal quality classification information by applying a machine learning model to the first features, with the signal quality classification information corresponding to multiple defined classes of signal quality (para. [0042], the system 202 utilizes a set of Signal Quality Indices (SQIs) to represent the noise characteristics of the PPG waveform/signal. The SQIs are presented to a Random Forest classifier to discriminate between clean and noisy signals, thereby assessing the quality of the PPG signal; para. [0060]-[0066], plurality of features including SQI1 to SQI10; para. [0067], The system 300 identifies, based on the set of features, each of the first PPG signal sample of the set of PPG signal samples as one of a noisy signal sample and a clean signal sample. In an embodiment, the system 300 utilizes a plurality of Random Forest (RF) models created during the training phase for identifying the first PPG signal samples as clean or noisy signal samples. The Random Forest model gives the output posterior probabilities which is in the range of 0 to 1. The system 300 classifies the signal with threshold 0.5. Signal with posterior probabilities greater than 0.5 are considered clean);
outputting signal quality classification information (para. [0042], The SQIs are presented to a Random Forest classifier to discriminate between clean and noisy signals, thereby assessing the quality of the PPG signal; para. [0067], The Random Forest model gives the output posterior probabilities which is in the range of 0 to 1); and
transforming, based on an attribute of a vital sign extraction algorithm (para. [0059], During the training phase, the training model determines the set of features from amongst the plurality of features, where the plurality of features represents PPG based SQIs that can be potentially used for quality assessment of PPG signals), the signal quality classification information to a signal quality index representing a quality of the first photoplethysmogram signal (para. [0067], The system 300 identifies, based on the set of features, each of the first PPG signal sample of the set of PPG signal samples as one of a noisy signal sample and a clean signal sample. In an embodiment, the system 300 utilizes a plurality of Random Forest (RF) models created during the training phase for identifying the first PPG signal samples as clean or noisy signal samples. The Random Forest model gives the output posterior probabilities which is in the range of 0 to 1. The system 300 classifies the signal with threshold 0.5. Signal with posterior probabilities greater than 0.5 are considered clean).
Re Claim 6, Alam discloses that the signal quality classification information includes a score associated with each classification (para. [0067], The system 300 identifies, based on the set of features, each of the first PPG signal sample of the set of PPG signal samples as one of a noisy signal sample and a clean signal sample. In an embodiment, the system 300 utilizes a plurality of Random Forest (RF) models created during the training phase for identifying the first PPG signal samples as clean or noisy signal samples. The Random Forest model gives the output posterior probabilities which is in the range of 0 to 1. The system 300 classifies the signal with threshold 0.5. Signal with posterior probabilities greater than 0.5 are considered clean).
Re Claim 7, Alam discloses that the signal quality classification information includes a probability associated with each classification (para. [0067], The system 300 identifies, based on the set of features, each of the first PPG signal sample of the set of PPG signal samples as one of a noisy signal sample and a clean signal sample. In an embodiment, the system 300 utilizes a plurality of Random Forest (RF) models created during the training phase for identifying the first PPG signal samples as clean or noisy signal samples. The Random Forest model gives the output posterior probabilities which is in the range of 0 to 1. The system 300 classifies the signal with threshold 0.5. Signal with posterior probabilities greater than 0.5 are considered clean).
Re Claim 8, Alam discloses that the signal quality index is a value within a bounded range (para. [0067], The Random Forest model gives the output posterior probabilities which is in the range of 0 to 1. The system 300 classifies the signal with threshold 0.5. Signal with posterior probabilities greater than 0.5 are considered clean).
Re Claim 11, Alam disclose that transforming the signal quality classification information to the signal quality index comprises: assigning a probability as the signal quality index, wherein the probability is associated with one of the multiple defined classes of signal quality in the signal quality classification information (para. [0067], The system 300 identifies, based on the set of features, each of the first PPG signal sample of the set of PPG signal samples as one of a noisy signal sample and a clean signal sample. In an embodiment, the system 300 utilizes a plurality of Random Forest (RF) models created during the training phase for identifying the first PPG signal samples as clean or noisy signal samples. The Random Forest model gives the output posterior probabilities which is in the range of 0 to 1. The system 300 classifies the signal with threshold 0.5. Signal with posterior probabilities greater than 0.5 are considered clean).
Re Claim 13, Alam discloses that the machine learning model comprises a neural network based model (para. [0029], [0099], neural network).
Allowable Subject Matter
Claims 14-16 are allowed.
Reasons for Allowance
The following is a statement of reasons for the indication of allowable subject matter:
Re Claim 14, the closest prior art is Alam et al. (US 2019/0133533A1), which discloses a vital sign monitoring device, comprising:
a buffer to store samples representative of a photoplethysmogram signals over a period of time that includes at least two cardiac cycles (para. [0047], memory; para. [0050], sensor data may include PPG signal samples, para. [0053], [0057], 40 beats (cardiac cycles));
a processor, when executing a set of instructions stored on non-transitory computer readable medium, configured to (para. [0048], hardware processor):
process the samples (para. [0079], preprocess; para. [0084], bandpass filtering);
extract features from the preprocessed processed samples (para. [0059], The system 300 extracts a set of features from the first set of PPG signal samples. The set of features represents an optimal set of features derived from a pre-stored training data by a training model during a training phase. During the training phase, the training model determines the set of features from amongst the plurality of features, where the plurality of features represents PPG based SQIs that can be potentially used for quality assessment of PPG signals; para. [0060]-[0066], plurality of features including SQI1 to SQI10);
apply a machine learning model using the features as input (para. [0042], the system 202 utilizes a set of Signal Quality Indices (SQIs) to represent the noise characteristics of the PPG waveform/signal. The SQIs are presented to a Random Forest classifier to discriminate between clean and noisy signals, thereby assessing the quality of the PPG signal, para. [0067]; para. [0060]-[0066], plurality of features including SQI1 to SQI10);
generate signal quality classification information based on the machine learning model (para. [0067], The system 300 identifies, based on the set of features, each of the first PPG signal sample of the set of PPG signal samples as one of a noisy signal sample and a clean signal sample. In an embodiment, the system 300 utilizes a plurality of Random Forest (RF) models created during the training phase for identifying the first PPG signal samples as clean or noisy signal samples. The Random Forest model gives the output posterior probabilities which is in the range of 0 to 1. The system 300 classifies the signal with threshold 0.5. Signal with posterior probabilities greater than 0.5 are considered clean).
Alam is silent regarding extract vital sign information based on the samples if the signal quality classification information indicates sufficient quality; and not extract vital sign information based on the samples if the signal quality classification information does not indicate sufficient quality.
Alam discloses the system removing the PPG signal samples failing the signal sufficiency check (SSC) in para. [0071], fig. 8, step 806 and fig. 9, step 916, but such step is before applying a ML model using features to generate signal quality classification (step 910 in fig. 9, step 810 in fig. 8). Therefore, it doesn’t meet the claim limitation of extract vital sign information based on the samples if the signal quality classification information indicates sufficient quality; and not extract vital sign information based on the samples if the signal quality classification information does not indicate sufficient quality.
Claim 14 and claims dependent thereon in the instant application have not been rejected using prior art because no references, or reasonable combination thereof, could be found which disclose, or suggest, in combination with other limitations of the claim, a vital sign monitoring device, comprising, a processor configured to: extract vital sign information based on the samples if the signal quality classification information indicates sufficient quality; and not extract vital sign information based on the samples if the signal quality classification information does not indicate sufficient quality.
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 VYNN V HUH whose telephone number is (571)272-4684. The examiner can normally be reached Monday to Friday from 9 am to 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at (571) 270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL W KAHELIN/Primary Examiner, Art Unit 3792
/V.V.H./
Vynn Huh, February 9, 2026
Examiner, Art Unit 3792