DETAILED ACTION
Note: The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s arguments filed in the reply on July 17, 2026 were received and fully considered. Claims 1, 31, and 32 were amended. Claims 33 and 34 are new. Please see corresponding rejection headings and response to arguments section below for more detail.
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, 3-15, 17-22, 25-27, and 29-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Regarding claim 1, the claim recites a computer-implemented method for predicting a presence of a cardiorespiratory disease in a user, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The following limitations set forth a judicial exception:
“...a machine learning model… obtaining a single breath waveform from the capnogram, wherein obtaining the single breath waveform comprises: identifying at least one anomalous breath waveform of plurality of breath waveforms; excluding the at least one anomalous breath waveform from the plurality of breath waveforms; normalizing a duration of the plurality of breath waveforms to produce a plurality of normalized breath waveforms; and selecting the single breath waveform from the plurality of normalized breath waveforms; determining one or more transition points of the single breath waveform, wherein the one or more transition points comprise: a delta transition point between an expiratory baseline and an expiratory upstroke, a gamma transition point between an inspiratory downstroke and an inspiratory baseline, and an alpha transition point between the expiratory upstroke and an expiratory plateau; extracting features of the single breath waveform using the one or more transition points; and wherein determining the one or more transition points comprises: identifying a hump artefact in the single breath waveform and, when there is a hump artefact, accounting for the hump artefact during the determining of the one or more transition points; inputting the extracted features of the single breath waveform to the machine learning model; determining by the machine learning model from the extracted features of the single breath waveform a classification of the capnogram…”
These limitations describe a mathematical calculation. When given their broadest reasonable interpretation in light of the specification, the limitations identified above including the recited machine learning algorithm are mathematical calculations. Moreover, the plain meaning a machine learning algorithm is a series of mathematical calculations. See also 2024 AI SME Update, which held a similar claim construction was not patent eligible (see claim 2 of example 47, using a trained artificial neural network to analyze anomalies on input data was not patent eligible). The 2024 AI SME Update also sets forth that a trained machine learning model/engine amounts to a mental process (claim 2 of example 47). As such, the limitations also describe a mental process as the skilled artisan is capable of performing the recited limitations and making a mental assessment thereafter. Examiner also notes that nothing from the claims suggest that the limitations cannot be practically performed by a human (using the simplest form of machine learning), or using simple pen/paper.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, integrates the identified judicial exception into a practical application.
For this part of the 101 analysis, the following additional limitations are considered:
“...obtaining a capnogram from a user, wherein the capnogram comprises a plurality of breath waveforms…the classification indicating whether the user has a cardiorespiratory disease.”
These additional limitations do not integrate the judicial exception into a practical application. Rather, the additional limitations are each recited at a high level of generality such that it amounts to insignificant extra-solution activity, i.e., mere data gathering steps necessary to perform the identified judicial exception do not integrate claims into a practical application. See MPEP 2106.05(g).
The additional limitations also do not add significantly more to the identified judicial exception because they pertain to obtaining data from conventional sensor (capnogram).
Independent claims 31, 32, and 34 recite mirrored limitations are also not patent eligible for substantially similar reasons. While new claim 34 also recites new structural limitations (an airflow region… an emitter… a detector… such that the light emitted by the emitted passes through the air flow region to the detection”), the additional limitations do not integrate the claims into a practical application as they are recited at a high level of generality. Moreover, it is widely known in prior capnometry systems to utilize the recited structural features for purposes of obtaining capnogram data. See prior art applied in previous office action for example teachings.
Dependent claims 3-15, 17-27, 29, 30, and 33 are also not patent eligible for the following reasons:
Claim 3 recites “wherein the plurality of breath waveforms represent a single respiratory cycle; and the obtaining the single breath waveform comprises splitting the capnogram into a plurality of capnogram sections, wherein each capnogram section represents a single breath waveform corresponding to the single respiratory cycle,” which also fail to integrate the claims into a practical application as they merely further limit the abstract idea.
Claim 4 recites “wherein the one or more transition points further comprise a beta transition point between the expiratory plateau and the inspiratory downstroke”, which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 5 recites “wherein determining the one or more transition points comprises determining a derivative of the single breath waveform; the derivative of the single breath waveform is a first order differential of the single breath waveform”, which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 6 recites “wherein the method further comprises: using the first order differential of the single breath waveform to determine whether the single breath waveform is an anomalous single breath waveform; and, when the single breath waveform is an anomalous single breath waveform, rejecting the single breath waveform,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 7 recites “wherein determining the first order differential of the single breath waveform comprises applying a time-based smoothing filter to the single breath waveform,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 8 recites “wherein identifying the hump artefact comprises: performing peak detection to identify local minima of the single breath waveform; identifying prominent minima from the local minima; identifying a maximum value of the single breath waveform and/or determining a beta transition point; dividing the single breath waveform into a first section not including the maximum value of the single breath waveform, and a second section including the maximum value of the single breath waveform and/or the beta transition point; when at least one prominent minimum is identified, searching for hump artefact(s) in the first section of the single breath waveform; and/or when no prominent minima are identified, using the first order differential of the single breath waveform to search for hump artefact(s) in the first section of the single breath waveform,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 9 recites “wherein determining the one or more transition points comprises determining a beta transition point, wherein determining the beta transition point comprises: performing peak detection to identify local maxima of the single breath waveform; identifying prominent maxima from the local maxima; when only a single prominent maximum is identified, determining this as the beta transition point; and, when a plurality of prominent maxima are identified, determining the most prominent maximum and defining this as the beta transition point,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 10 recites “wherein determining the one or more transition points comprises determining the delta transition point, wherein determining the delta transition point comprises: determining a first point in time at which a first order differential of the single breath waveform is above a delta threshold; and defining the first point as the delta transition point,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 11 recites “wherein determining the one or more transition points comprises determining the gamma transition point, wherein determining the gamma transition point comprises: identifying a minimum value of a first order differential of the single breath waveform; and, defining the gamma transition point as a first point in time after the minimum value at which the first order differential of the single breath waveform is higher than a gamma threshold,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 12 recites “wherein determining the one or more transition points comprises determining the alpha transition point, wherein determining the alpha transition point comprises: identifying a maximum value of a first order differential of the single breath waveform; identifying the maximum value of the single breath waveform and/or determining the beta transition point; and defining the alpha transition point as a first point in time after the maximum value of the first order differential, between the maximum value of the first order differential and the maximum value of the single breath waveform and/or the beta transition point, at which the first order differential of the single breath waveform is less than an alpha threshold; determining the alpha transition point further comprises: when no point between the maximum value of the first order differential of the single breath waveform and the maximum value of the single breath waveform is less than the alpha threshold, or when no point between the maximum value of the first order differential of the single breath waveform and the beta transition point is less than the alpha threshold, increasing the alpha threshold,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 13 recites “wherein determining the one or more transition points comprises determining an alpha transition point, wherein determining the alpha transition point comprises: calculating a line between the delta transition point and a maximum value of the breath waveform, or calculating a line between the delta transition point and the beta transition point; and, defining the alpha transition point based on the distance between the single breath waveform and the calculated line,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 14 recites “wherein determining the one or more transition points comprises: applying a trained machine learning model to a set of discrete samples of the single breath waveform, the single breath waveform representing a whole breath, wherein the trained machine learning model is configured to classify each sample into one of a plurality of output classes, each class representing a region of the single breath waveform, and wherein the trained machine learning model is trained by: obtaining a label associated with each discrete sample of a plurality of single breath waveforms, each single breath waveform being represented by a set of samples representing a whole breath and each label indicating which of a plurality of output classes that sample corresponds to; and, training a machine learning model on the labels and the samples to learn to classify a sample of a set of samples representing a whole breath into a class of the plurality of output classes,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 15 recites “extracting features from a plurality of single breath waveforms recorded from a same user; determining a variability of the extracted features; wherein the variability of the extracted features is also used to apply a trained machine learning model to classify the capnogram, or wherein the variability of the extracted features is also used to train the machine learning model to create a classifying function; the plurality of breath waveforms are recorded from the same user over a time period comprising two or more days”, which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 17 recites “wherein the machine learning model is further configured to output an indication of an importance of an extracted feature that led to the classification of the capnogram, wherein the importance of the extracted feature is a measure of how much the extracted feature contributed to the classification of the capnogram,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 18 recites “wherein extracting features of the single breath waveform using the transition points comprises determining an angle of the transition points,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 19 recites “wherein determining the angle of the transition points comprises: fitting a first linear function and a second linear function to adjacent phases on either side of the transition point and measuring an angle between the first and second linear functions; and/or fitting a third linear function to the expiratory upstroke or the inspiratory downstroke and measuring an angle between the third linear function and a horizontal,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 20 recites “wherein extracting features of the single breath waveform using the transition points comprises fitting a quadratic function to the expiratory plateau, and determining a coefficient of the quadratic function,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 21 recites “wherein extracting features of the single breath waveform using the transition points comprises fitting a hyperbolic tangent function to the expiratory upstroke and/or the inspiratory downstroke, and determining a coefficient of the hyperbolic tangent function,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 22 recites “wherein the classification of the capnogram comprises a probability value corresponding to a severity of a respiratory disease; the machine learning model is trained based on capnograms labelled with a respective severity of a respiratory disease,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 25 recites “ wherein the machine learning model is trained and configured to predict a likelihood that the capnogram is associated with a cardio respiratory disease; the machine learning model is configured to output the likelihood that the capnogram is associated with the cardiorespiratory disease,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 26 recites “wherein the machine learning model is trained using labels belonging to a class representing that a training capnogram is associated with a cardiorespiratory disease; the labels further belong to a plurality of classes, each class representing that the training capnogram is associated with a cardiorespiratory disease, each class corresponding to a respective disease; the labels further belong to a class representing that the training capnogram is not associated with a cardiorespiratory disease; and the cardiorespiratory disease is selected from a group comprising: COPD, Asthma, Asthma-COPD Overlap Syndrome (ACOS), small airways disease, chronic bronchitis subtype of COPD and emphysema subtype of COPD,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 27 recites “stratifying the output by comparing the likelihood to a set of threshold values, each strata representing a risk of the capnogram being associated with the cardiorespiratory disease,” which also fail to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 29 recites “an apparatus configured to perform the method of claim 1”, which fails to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 30 recites “a computer readable medium comprising instructions which, when executed by a processor, cause the processor to perform the method of claim 1,” which fails to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Claim 33 recites “wherein the steps of obtaining a capnogram from the user, obtaining a single breath waveform from the capnogram, determining one or more transition points of the single breath waveform, extracting features of the single breath waveform using the one or more transition points, inputting the extracted features of the single breath waveform to the machine learning model, and determining by the machine learning model from the extracted features of the single breath waveform a classification of the capnogram are performed in real time,” which fails to integrate the claims into a practical application and do not recite significantly more to the abstract independent claims as they merely further limit the abstract idea.
Therefore, claims 1, 3-15, 17-27, and 29-34 are not patent eligible under 35 USC 101.
Response to Arguments
Applicant's arguments filed with respect to the 35 USC 101 rejections raised in the previous office action have been fully considered, but they are not persuasive. Applicant raises the following main arguments, which Examiner addresses each, in turn, below:
Claim 1 does not recite a mathematical concept or formula (remarks, pgs. 2-4);
Examiner respectfully disagrees. As set forth in the rejection heading above, Examiner identified limitations that correspond to mathematical concepts despite the claims not expressly reciting variations of the word “calculate”. Here, concepts such as performing “normalizing”, a known mathematical/statistical concept1, to produce a normalized waveform, as set forth in the instant claims, equates to a mathematical relationship/concept. Moreover, determining one or more transition points, identifying a hump artifact, and inputting extracted features into a machine learning model in order to perform a classification thereafter also equate to mathematical concepts. Applicant goes on to argue that “a machine learning model” does not correspond to a mathematical concept. Examiner respectfully disagrees as the plain and ordinary meaning of “machine learning” equates to a series of simple and/or complex calculations performed over time. As the claims lack specificity with respect to “machine learning model”, Examiner maintains that the claims also recite a mental process as nothing suggests that the skilled artisan would not be able to practically perform the identified abstract idea mentally, or using simple pen/paper. For at least these reasons, Examiner maintains that the claims recite mathematical concepts and/or a mental process.
Reciting a machine learning model as in claim 1 is not the same as reciting a mathematical concept (remarks, pgs. 4-5);
Examiner respectfully disagrees. Again, the plain and ordinary meaning of “machine learning” equates to a series of simple and/or complex calculations performed over time. As the claims lack specificity with respect to “machine learning model”, Examiner maintains that the claims also recite a mental process as nothing suggests that the skilled artisan would not be able to practically perform the identified abstract idea mentally, or using simple pen/paper.
Claim 1 does not recite any limitations that can be performed in the human mind (remarks, pgs. 5-7);
Examiner respectfully disagrees. Again, as the claims lack specificity with respect to “machine learning model”, Examiner maintains that the claims, including the generically recited machine learning model, also recite a mental process as nothing suggests that the skilled artisan would not be able to practically perform the identified abstract idea mentally, or using simple pen/paper. Applicant goes on to argue that the claims “involve a several-step manipulation of the capnogram data and thus cannot practically be performed in the human mind”. While it is certainly true that the claims recite several/multiple data manipulation steps, nothing suggests that the skilled artisan would not be able to practically perform multiple data manipulation steps mentally, or using simple pen/paper.
Claim 1 integrates any allegedly abstract ideas into the practical application of improving detection of cardiorespiratory diseases using capnometry (remarks, pg. 7);
Examiner respectfully disagrees. This similar argument was previously raised. Examiner maintains that the purported improvement (alleged greater accuracy) appears to lie within the judicial exception itself. However, an alleged improved mathematical calculation is still a calculation nonetheless and would not be patent eligible2. Moreover, there is no improvement to the additional/structural limitations, i.e. highly generalized capnogram. As such, any improvement that lies within the mathematical concept and/or mental process fails to integrate the claimed invention into a practical application.
Claim 1 recites elements related to hump artefacts that amount to significantly more (remarks, pgs. 7-8);
Examiner respectfully disagrees. The step of identifying hump artefacts corresponds to a limitation that was considered an abstract idea, i.e. it is not an additional limitation that amounts to significantly more. As such, the Berkheimer analysis applied only to additional (e.g. structural limitations), requiring evidence showing that an additional limitation was not well-understood, routine, and conventional, does not apply to the step of identifying hump artefacts. Therefore, this argument is moot.
The Office has not addressed each dependent claim individually (remarks, pg. 8).
Examiner respectfully disagrees. Applicant appears to take issue with Examiner’s concise and grouped approach to assessing patent eligibility with respect to the dependent claims. While the spirit of Examiner’s previous rejections is maintained (as they apply to the dependent claims), Examiner now addresses each dependent claim above for applicant’s convenience. See above for more detail.
Conclusion
No claim is allowed.
THIS ACTION IS MADE FINAL. 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.
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/PUYA AGAHI/Primary Examiner, Art Unit 3791
1 https://en.wikipedia.org/wiki/Normalization_(statistics)
2 See MPEP 2106.05(a) Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field… It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements
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