DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on September 22, 2025 was considered by the examiner. Claims 1-3, 5-7, 9-15, and 17-20 are pending in the application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6-7, 9, and 17-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites “a normalized heart rate variability power spectral density” in line 7, but it is not clear if this recitation is the same as, related to, or different from the recitation “a normalized heart rate variability power spectral density” in line 4. The similar phraseology suggests that they are the same, but the indefinite article “a” suggests that they are different. If the recitations are the same, the present recitation should be “the normalized heart rate variability power spectral density”. If the recitations are different, the relationship between these recitations should be made clear and they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements). The recitations are being interpreted as the same for the purposes of examination. Appropriate correction is required.
Claim 6 recites the limitation “the heart rate variability power spectral density” in lines 11-12. There is insufficient antecedent basis for this limitation in the claim. Amending the recitation to recite “the normalized heart rate variability power spectral density” would overcome this rejection. The claim is being read as such for the purposes of examination. Appropriate correction is required.
Claims 7, 9, and 17-20 are rejected by virtue of their dependence from claim 6.
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, 5-7, 9-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards abstract ideas without significantly more.
Claim 1 interpretation: Under the broadest reasonable interpretation (BRI), the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111. Based on the specification, the recitation “generating a vital sign feature value from the PPG signal” (see specification pg. 26 ¶2 – pg. 27 ¶3) is being interpreted as mathematical calculations/evaluations. The recitation “deriving a heart rate variability power spectral density from the PPG signal; convolving a high frequency portion of the normalized heart rate variability power spectral density with a convolution filter to generate a plurality of convolution values representing respective patterns of high and low powers in the high frequency portion of the heart rate variability power spectral density” (see specification pg. 9 ¶3, pg. 11 ¶2, pg. 27 ¶2, and pg. 28 ¶3 – pg. 32 ¶1) is being interpreted as mathematical calculations/evaluations. The recitation “normalizing the heart rate variability power spectral density by dividing a power at each frequency by a total power across low and high frequency portions of the PPG signal to obtain a normalized heart rate variability power spectral density” (see specification pg. 27 ¶1) is being interpreted as mathematical calculations/evaluations. The recitation “performing an activation function operation on the plurality of convolution values” (see specification pg. 10 ¶2 – pg. 11 ¶1, pg. 26 ¶3, pg. 27 ¶3, and pg. 29 ¶1 – pg. 32 ¶1) is being interpreted as mathematical calculations/evaluations. The recitation “selecting one of the convolution values based on a result of the activation function operation” (see specification pg. 10 ¶2 – pg. 11 ¶1, pg. 26 ¶3, pg. 27 ¶3, and pg. 29 ¶1 – pg. 32 ¶1) is being interpreted as mathematical calculations/evaluations and judgements. The recitation “generating the vital sign feature value as the selected convolution value” (see specification pg. 26 ¶2 – pg. 27 ¶3) is being interpreted as mathematical calculations/evaluations. The recitation “detecting the sleep stage based on the generated vital sign feature value” (see specification pg. 19 ¶3 – pg. 22 ¶1) is being interpreted as mathematical calculations/evaluations and judgements. The recitations are computer-implemented, as indicated in the specification (see pg. 16 ¶1, pg. 28 ¶1, and pg. 41; Figs. 12A-13).
Claim 6 interpretation: Under the broadest reasonable interpretation (BRI), the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111. Based on the specification, the recitation “generating a vital sign feature value from a PPG signal” (pg. 26 ¶2 – pg. 27 ¶3) is being interpreted as mathematical calculations/evaluations. The recitation “wherein the normalized heart rate variability power spectral density is normalized by dividing a power at each frequency by a total power across low and high frequency portions of the PPG signal to obtain a normalized heart rate variability power spectral density” (see specification pg. 27 ¶1) is being interpreted as mathematical calculations/evaluations. The recitation “creating a model including a convolution filter using machine learning based on the vital sign feature with reference to the reference sleep stage information” (see specification pg. 9 ¶3, pg. 11 ¶2, pg. 20 ¶2 – pg. 25 ¶2, pg. 27 ¶2-3, and pg. 29 ¶1 – pg. 32 ¶1; Fig. 3A) is being interpreted as mathematical calculations/evaluations. The recitation “generate a plurality of convolution values representing respective patterns of high and low powers in a high frequency portion of the heart rate variability power spectral density” (see specification pg. 9 ¶3, pg. 11 ¶2, pg. 27 ¶2, and pg. 28 ¶3 – pg. 32 ¶1) is being interpreted as mathematical calculations/evaluations. The recitations are computer-implemented, as indicated in the specification (see pg. 16 ¶1, pg. 28 ¶1, and pg. 41; Figs. 12A-13).
Step 1: This part of eligibility analysis evaluates whether the claim falls within any statutory category. MPEP 2106.03. Claims 1 and 6 recite a method, which are directed towards a process (a statutory category of invention). Step 1: YES.
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04(a)(2)(I). The courts consider mathematical calculations, when the claim is given its BRI in light of the specification, as falling within the “mathematical concept” grouping of abstract ideas. A claim does not have to recite “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using a mathematical method, or “performing” a mathematical operation, may also be considered a mathematical calculation when the BRI of the claim in light of the specification encompasses a mathematical calculation. As discussed in the claim interpretation section, the limitations include, under the BRI, mathematical calculations/evaluations. Accordingly, the limitations as seen in claims 1 and 6 recite judicial exceptions (abstract ideas that fall within the mathematical calculations grouping of mathematical concepts).
Alternatively or additionally, these steps describe the concept of using implicit mathematical formulas (i.e., calculations to detect peaks, determine features/parameters from the EEG signal, and classify the EEG signal) to derive a conclusion based on input of data, which corresponds to concepts identified as abstract ideas by the courts (Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)). The concept of the recited limitations identified as mathematical concepts above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
Furthermore, as explained in MPEP 2106.04(a)(2)(III). The courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). The “mental processes” abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions. As discussed in the claim interpretation section, the limitations include, under the BRI, multiple evaluations; and for claim 1, judgements to select one of the convolution values and to detect a sleep stage based on the generated vital sign feature value. Accordingly, the limitations as seen in claims 1 and 6 recite judicial exceptions (abstract ideas that fall within the mental process grouping).
In particular, claim 1 recites the following elements, which are part of the abstract idea (i.e., the algorithm):
a method of detecting a sleep stage, comprising:
generating a vital sign feature value from the PPG signal by:
deriving a heart rate variability power spectral density from the PPG signal;
normalizing the heart rate variability power spectral density by dividing a power at each frequency by a total power across low and high frequency portions of the PPG signal to obtain a normalized heart rate variability power spectral density;
convolving a high frequency portion of the normalized heart rate variability power spectral density with a convolution filter to generate a plurality of convolution values representing respective patterns of high and low powers in the high frequency portion of the heart rate variability power spectral density, the convolution filter relating to a convolutional neural network model;
performing an activation function operation on the plurality of convolution values;
selecting one of the convolution values based on a result of the activation function operation; and
generating the vital sign feature value as the selected convolution value; and
detecting the sleep stage based on the generated vital sign feature value.
Furthermore, claim 6 recites the following elements, which are part of the abstract idea (i.e., the algorithm):
a method of creating a model for generating a vital sign feature value, comprising:
receiving, in association with reference sleep stage information, a vital sign feature representing a normalized heart rate variability power spectral density derived from the PPG signal, wherein the normalized heart rate variability power spectral density is normalized by dividing a power at each frequency by a total power across low and high frequency portions of the PPG signal to obtain a normalized heart rate variability power spectral density; and
creating a model including a convolution filter using machine learning based on the vital sign feature with reference to the reference sleep stage information,
wherein the convolution filter is configured to generate a plurality of convolution values representing respective patterns of high and low powers in a high frequency portion of the heart rate variability power spectral density.
Step 2A Prong One: YES.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim as a
whole integrates the judicial exceptions into a practical application of the exception. This
evaluation is performed by (a) identifying whether there are any additional elements recited in
the claim beyond the judicial exceptions, and (b) evaluating those additional elements
individually and in combination to determine whether the claim as a whole integrates the
exceptions into a practical application. Claim 6 recites no other element, such that claim 6 recites no element that integrates the judicial exceptions into a practical application. The method is merely instructions to implement an abstract idea on a generic computer or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
Claim 1 further recites an additional element of a sensor. The sensor does not qualify as integration into a practical application because this limitation is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a high level of generality – see MPEP 2106.04(d) and MPEP 2106.05(g) using a generic component (i.e., the sensor is generic).
Step 2A Prong Two: NO.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05. As explained with Step 2A Prong Two, the claim 6 recites no other element, such that claim 6 recites no element that adds an inventive concept to the claim and/or amounts to significantly more than the recited exception. The methods (claims 1 and 6) utilizing a generic computer do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Claim 1 further recites an additional element of a sensor. The sensor does not qualify as significantly more because (1) this is simply appending well-understood, routine, conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the industry and/or (2) this limitation is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a high level of generality – see MPEP 2106.04(d) and MPEP 2106305(g) using generic components (i.e., the sensor is generic). In this case, the sensor is claimed generically, and thus may be considered a generic computer component.
Alternatively and/or additionally, Klee et al. (US Patent Application Publication 2018/0199882 – cited in prior action) teaches a device/method for monitoring a patient via vital signs (see abstract) in which PPG sensors, which are known in the art, are used to measure cardiac activity (see ¶[0095]-[0096]). Therefore, the PPG sensor does not integrate the abstract ideas into a practical application or amount to significantly more.
Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no
indication that the combination of elements improves the functioning of a computer, for
example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is
no indication that the combination of elements includes a particular solution to a computer-based
problem or a particular way to achieve a desired computer-based outcome. Rather, the collective
functions of the claimed invention merely provide conventional computer implementation, i.e.,
the computer is simply a tool to perform the process. Step 2B: NO.
Claims 1 and 6 are not eligible.
Claims 2-3, 5, and 10-15; and 7, 9, and 17-20 depend from claims 1 and 6, respectively, merely further define the abstract ideas of claims 1 and 6. Claims 2-3, 5, 10-15, and 17-20 recite additional elements directed towards a generic computer and/or computer-readable medium. The claims recite no element that integrates the judicial exceptions into a practical application. The method/devices are merely instructions to implement an abstract idea on a generic computer or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). The claims recite no element that adds an inventive concept to the claim and/or amounts to significantly more than the recited exception. The method/devices utilizing a generic computer do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome.
Claim Rejections - 35 USC § 103
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-6, 9-10, 12, 14, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over DeVries et al. (US Patent Application Publication 2017/0249445 – cited by applicant), hereinafter DeVries, and in view of “Heart rate variability: standards of measurement, physiological interpretation, and clinical use”, (Electrophysiology, Task Force of the European Society of Cardiology the North American Society of Pacing, Circulation 93.5 (1996): 1043-1065), hereinafter HRV Standards.
Regarding Claim 1, DeVries teaches a system/method for monitoring nutritional intake of a user utilizing a biosensor (see abstract), in which the system/method may also measure sleep metrics of a user (see ¶[0192]), and the physiological data measured may be heart rate variability (see ¶[0085], ¶[0140], and ¶[0192]). DeVries teaches a method of detecting a sleep stage (¶[0054], ¶[0153] and ¶[0192] the method may monitor for the state of sleep of the user via output of the model), the method comprising:
receiving a PPG signal from a sensor (¶[0041]-[0046], ¶[0146]-[0147] the heart rate data or beat period data used in the HRV may be from a pulse profile sensor 52, which may be implemented as a PPG sensor; Fig. 1),
generating a vital sign feature value from the PPG signal (see abstract, ¶[0085], ¶[0140], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192], the heart rate variability (HRV) that may be input into the model, which may be implemented as a convolutional neural network (CNN) in which the output may be related to sleep and/or stress parameters, the output would be considered the vital sign feature, as it is based off of the input vital sign information (HRV and parameters)) by:
deriving a heart rate variability power spectral density from the PPG signal (¶[0085], ¶[0140], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192] the heart rate variability (HRV) that may be used as input into a model, parameters from the HRV may also be input into the model, such as LF/HF, which is the ratio of the power of the low frequency portion and the power of the high frequency portion, so the high frequency portion is utilized in the models as required by the claim, the high/low frequency portions are referring to the power spectral density of the HRV, ¶[0041]-[0046], ¶[0146]-[0147] the heart rate data or beat period data used in the HRV may be from a pulse profile sensor 52, which may be implemented as a PPG sensor; Fig. 1);
normalizing the heart rate variability power spectral density (¶[0085], ¶[0140], ¶[0147], and ¶[0192] the LF/HF ratio of the low frequency portion and the high frequency portion would be the normalized power spectral density, ¶[0065], ¶[0076], ¶[0082], and ¶[0139] the signal may be normalized, such as to remove the DC component);
convolving a high frequency portion of the normalized heart rate variability power spectral density (¶[0085], ¶[0140], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192] the heart rate variability (HRV) that may be used as input into a model, parameters from the HRV may also be input into the model, such as LF/HF, which is the ratio of the power of the low frequency portion and the power of the high frequency portion, so the high frequency portion is utilized in the models as required by the claim, the high/low frequency portions are referring to the power spectral density of the HRV, ¶[0041]-[0046], ¶[0146]-[0147] the heart rate data or beat period data used in the HRV may be from a pulse profile sensor 52, which may be implemented as a PPG sensor; Fig. 1) with a convolution filter to generate a plurality of convolution values representing respective patterns of high and low powers in the high frequency portion of the heart rate variability power spectral density (¶[0085], ¶[0140], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192] the heart rate variability (HRV) that may be used as input into a model, parameters from the HRV may also be input into the model, ¶[0085], ¶[0140], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192] the heart rate variability (HRV) that may be used as input into a model, parameters from the HRV may also be input into the model, such as LF/HF, which is the ratio of the power of the low frequency portion and the power of the high frequency portion, so the high frequency portion is utilized in the models as required by the claim, the high/low frequency portions are referring to the power spectral density of the HRV, ¶[0092], ¶[0096]-[0097], ¶[0101], and ¶[0171]-[0178] as the input (HRV and parameters) has been through the CNN, they would at this point be convolution values),
the convolution filter relating to a convolutional neural network model (¶[0101], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192] a convolutional neural network (CNN) may be used as the model, in which convolution kernels may be used, which is a convolution filter);
performing an activation function operation on the plurality of convolution values (¶[0092], ¶[0096]-[0097], ¶[0101], and ¶[0171]-[0178] the CNN may utilize an activation function, such as a rectified linear unit, as the output neuron, as the input (HRV and parameters) has been through the CNN, they would at this point be convolution values, the activation function takes the inputs (and their weights) from the other nodes in the CNN (the convolution values) and outputs one output value, which would be considered the selected convolution value);
selecting one of the convolution values based on a result of the activation function operation (¶[0092], ¶[0096]-[0097], ¶[0101], and ¶[0171]-[0178] the CNN may utilize an activation function, such as a rectified linear unit, as the output neuron, as the input (HRV and parameters) has been through the CNN, they would at this point be convolution values, the activation function takes the inputs (and their weights) from the other nodes in the CNN (the convolution values) and outputs one output value, which would be the selected convolution value); and
generating the vital sign feature value as the selected convolution value (abstract, ¶[0085], ¶[0140], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192] the output of the model (CNN) may be sleep and/or stress related metrics, which would be considered the vital sign feature, as it is based off of the input vital sign information (HRV and parameters)); and
detecting the sleep stage based on the generated vital sign feature value (¶[0054], ¶[0153] ¶[0171]-[0178], and ¶[0192] the output of the model may include the state of sleep of the user, which is the vital sign feature value that has been convolved, as it has been through the CNN).
DeVries does not specifically teach that the HRV is normalized by dividing a power at each frequency by a total power across low and high frequency portions of the PPG signal to obtain a normalized heart rate variability power spectral density.
HRV Standards details the standards of HRV measurement and use as set forth by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (see abstract), in which in spectral components, the high and low frequency portions (LF and HF) are may typically be normalized to represent the relative value of power of each power component in proportion to the total power minus the VLF component (see pg. 5-6, § Spectral Components, §§ Short-term recordings and §§ Long-term recordings).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the HRV power spectral density normalization of HRV Standards with the HRV power spectral density of DeVries because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results; and/or (2) DeVries requires a HRV power spectral density determination modality and HRV Standards teaches one such modality; and/or (3) removing the VLF frequency portions during the normalization process would help to improve the accuracy/usefulness of the HRV power spectral density, as the VLF component is a dubious measure and should be avoided (see pg. 5-6, § Spectral Components, §§ Short-term recordings).
Regarding Claim 2, DeVries in view of HRV Standards teaches the method of claim 1 as stated above. DeVries further teaches the selected convolution value corresponds to a largest value resulting from the performed activation function (¶[0174] the CNN may have multiple layers of learned features, including the max operation and the activation function).
Regarding Claim 3, DeVries in view of HRV Standards teaches the method of claim 1 as stated above. DeVries further teaches the high frequency portion ranges from 0.15 Hz to 0.4 Hz (¶[0147] the high frequency range is about 0.15 Hz to about 0.40 Hz).
Regarding Claim 5, DeVries in view of HRV Standards teaches the method of claim 1 as stated above. DeVries further teaches the activation function operation relates to one of a rectified linear unit, a soft relu and a sigmoid function (¶[0092], ¶[0096]-[0097], ¶[0101], and ¶[0171]-[0178] the CNN may utilize an activation function, such as a rectified linear unit).
Regarding Claim 6, DeVries teaches a system/method for monitoring nutritional intake of a user utilizing a biosensor (see abstract), in which the system/method may also measure sleep metrics of a user (see ¶[0192]), and the physiological data measured may be heart rate variability (see ¶[0085], ¶[0140], and ¶[0192]). DeVries teaches a method of creating a model for generating a vital sign feature value from a PPG signal (see abstract, ¶[0085], ¶[0140], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192], the heart rate variability (HRV) that may be input into the model, which may be implemented as a convolutional neural network (CNN) in which the output may be related to sleep and/or stress parameters, the output would be considered the vital sign feature, as it is based off of the input vital sign information (HRV and parameters)), comprising:
receiving, in association with reference sleep stage information (¶[0054], ¶[0153] and ¶[0192] the method may monitor for the state of sleep of the user via output of the model), a vital sign feature representing a normalized heart rate variability power spectral density derived from the PPG signal (¶[0085], ¶[0140], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192] the heart rate variability (HRV) that may be used as input into a model, parameters from the HRV may also be input into the model, such as LF/HF, which is the ratio of the power of the low frequency portion and the power of the high frequency portion, so the high frequency portion is utilized in the models as required by the claim, the high/low frequency portions are referring to the power spectral density of the HRV, ¶[0041]-[0046], ¶[0146]-[0147] the heart rate data or beat period data used in the HRV may be from a pulse profile sensor 52, which may be implemented as a PPG sensor, ¶[0085], ¶[0140], ¶[0147], and ¶[0192] the LF/HF ratio of the low frequency portion and the high frequency portion would be the normalized power spectral density, ¶[0065], ¶[0076], ¶[0082], and ¶[0139] the signal may be normalized, such as to remove the DC component; Fig. 1); and
creating a model including a convolution filter (¶[0101], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192] a convolutional neural network (CNN) may be used as the model, in which convolution kernels may be used, which is a convolution filter) using machine learning based on the vital sign feature with reference to the reference sleep stage information (¶[0171]-[0178] the training of the CNN is the creating of the model, the supervised training dataset would be the correlated values of what the CNN is designed to output, as DeVries teaches utilizing heart rate variability power spectral density for the output of sleep states, the supervised training dataset would comprise correlated values of those two parameters; Fig. 15),
wherein the convolution filter is configured to generate a plurality of convolution values representing respective patterns of high and low powers in the high frequency portion of the heart rate variability power spectral density (¶[0085], ¶[0140], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192] the heart rate variability (HRV) that may be used as input into a model, parameters from the HRV may also be input into the model, ¶[0085], ¶[0140], ¶[0146]-[0147], ¶[0171]-[0178], and ¶[0192] the heart rate variability (HRV) that may be used as input into a model, parameters from the HRV may also be input into the model, such as LF/HF, which is the ratio of the power of the low frequency portion and the power of the high frequency portion, so the high frequency portion is utilized in the models as required by the claim, the high/low frequency portions are referring to the power spectral density of the HRV, ¶[0092], ¶[0096]-[0097], ¶[0101], and ¶[0171]-[0178] as the input (HRV and parameters) has been through the CNN, they would at this point be convolution values).
DeVries does not specifically teach that the HRV is normalized by dividing a power at each frequency by a total power across low and high frequency portions of the PPG signal to obtain a normalized heart rate variability power spectral density.
HRV Standards details the standards of HRV measurement and use as set forth by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (see abstract), in which in spectral components, the high and low frequency portions (LF and HF) are may typically be normalized to represent the relative value of power of each power component in proportion to the total power minus the VLF component (see pg. 5-6, § Spectral Components, §§ Short-term recordings and §§ Long-term recordings).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the HRV power spectral density normalization of HRV Standards with the HRV power spectral density of DeVries because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results; and/or (2) DeVries requires a HRV power spectral density determination modality and HRV Standards teaches one such modality; and/or (3) removing the VLF frequency portions during the normalization process would help to improve the accuracy/usefulness of the HRV power spectral density, as the VLF component is a dubious measure and should be avoided (see pg. 5-6, § Spectral Components, §§ Short-term recordings).
Regarding Claim 9, DeVries in view of HRV Standards teaches the method of claim 6 as stated above. DeVries further teaches the machine learning includes a convolutional neural network (¶[0101], ¶[0171]-[0178], and ¶[0192] a convolutional neural network (CNN) may be used as the model).
Regarding Claim 10, DeVries in view of HRV Standards teaches the method of claim 1 as stated above. DeVries further teaches a non-transitory computer-readable medium comprising instructions for causing a processor (¶[0047]-[0048] the processing circuitry 56 to implement the method via the computer-readable instructions) to perform the method of claim 1 (see above claim 1).
Regarding Claim 12, DeVries in view of HRV Standards teaches the method of claim 1 as stated above. DeVries further teaches a computing device comprising: a processor; and a storage device comprising instructions (¶[0047]-[0048] the processing circuitry 56 to implement the method via the computer-readable instructions, ¶[0056] and ¶[0205] the memory) for causing the processor to perform the method of claim 1 (see above claim 1).
Regarding Claim 14, DeVries in view of HRV Standards teaches the method of claim 1 as stated above. DeVries further teaches a wearable device (¶[0039] the device 50 and method may be implemented on a wearable device, such as a bracelet or a watch) comprising: a storage device comprising instructions for causing a processor (¶[0047]-[0048] the processing circuitry 56 to implement the method via the computer-readable instructions, ¶[0056] and ¶[0205] the memory) to perform the method of claim 1 (see above claim 1).
Regarding Claim 17, DeVries in view of HRV Standards teaches the method of claim 6 as stated above. DeVries further teaches a non-transitory computer-readable medium comprising instructions for causing a processor (¶[0047]-[0048] the processing circuitry 56 to implement the method via the computer-readable instructions) to perform the method of claim 6 (see above claim 6).
Regarding Claim 19, DeVries in view of HRV Standards teaches the method of claim 6 as stated above. DeVries further teaches a computing device comprising :a processor; and a storage device comprising instructions (¶[0047]-[0048] the processing circuitry 56 to implement the method via the computer-readable instructions, ¶[0056] and ¶[0205] the memory) for causing the processor to perform the method of claim 1 (see above claim 6).
Regarding Claim 20, DeVries in view of HRV Standards teaches the method of claim 6 as stated above. DeVries further teaches a wearable device (¶[0039] the device 50 and method may be implemented on a wearable device, such as a bracelet or a watch) comprising: a storage device comprising instructions for causing a processor (¶[0047]-[0048] the processing circuitry 56 to implement the method via the computer-readable instructions, ¶[0056] and ¶[0205] the memory) to perform the method of claim 6 (see above claim 6).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over DeVries in view of HRV Standards as applied to claim 6 above, and in view of Pantelopoulos et al. (US Patent Application Publication 2017/0209053 – cited in prior action), hereinafter Pantelopoulos.
Regarding Claim 7, DeVries in view of HRV Standards teaches the method of claim 6 as stated above. DeVries further teaches the collection of data over various intervals (see ¶[0062], ¶[0081], ¶[0092], ¶[0192]), but does not specifically teach the vital sign feature is derived from a physiological signal for an interval of three minutes within a respective interval of five minutes.
Pantelopoulos teaches devices/methods for estimating blood pressure (see abstract), but may also be utilized for sleep stage monitoring via heart rate variability (see ¶[0190]-[0191]). Pantelopoulos teaches that the data recording may only be sampled once every 10 minutes or 10 seconds out of every minute so as to control power consumption (see ¶[0183]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the controlled power consumption of Pantelopoulos with the method of DeVries because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) the non-continuous data sampling would help to conserve power (see Pantelopoulos ¶[0183]).
The modified DeVries contemplates different degrees of non-continuous data sampling, which suggests that the degree of data sampling (the amount of time the data is sampled related to the amount of time the data is not sampled) is subject to optimization based on the desired performance (the amount of energy that is needed to be conserved and the amount of data needed for a desired level of accurate result). As such, the degree of data sampling would have been optimized through routine experimentation based on the desired performance. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to select a degree of data sampling, using the basis of Pantelopoulos as a starting point, so as to obtain the desired performance. Thus, a degree of sampling of three minutes within a respective interval of five minutes would have been obvious.
Claims 11, 13, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over DeVries in view of HRV Standards as applied to claims 10, 12, 14, and 17 above, respectively, and in view of Arnold et al. (US Patent Application Publication 2018/0061204 – cited in prior action), hereinafter Arnold.
Regarding Claim 11, DeVries in view of HRV Standards teaches the non-transitory computer-readable medium of claim 10 as stated above. DeVries is silent regarding the instructions are adapted to be implemented in firmware.
Arnold teaches systems/methods for determining a sedentary state of a user (see abstract), involving the use of a neural network (see ¶[0024] and ¶[0098]) and physiological data from the user (see ¶[0045]-[0047]), and may be implemented as a watch (see ¶[0119]; Fig. 8). Arnold teaches that the systems/methods may be implemented via processors and memory, and combinations of software, firmware, and/or hardware for the specific application (see ¶[0042]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the instructions of DeVries via the firmware of Arnold because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) firmware is useful for initiating the startup of hardware and basic input/output of data and/or (3) firmware is a known implementation to implement the claimed recitation.
Regarding Claim 13, DeVries in view of HRV Standards teaches the computing device of claim 12 as stated above. DeVries is silent regarding the storage device is a firmware chip.
Arnold teaches systems/methods for determining a sedentary state of a user (see abstract), involving the use of a neural network (see ¶[0024] and ¶[0098]) and physiological data from the user (see ¶[0045]-[0047]), and may be implemented as a watch (see ¶[0119]; Fig. 8). Arnold teaches that the systems/methods may be implemented via processors and memory, and combinations of software, firmware, and/or hardware for the specific application (see ¶[0042], the memory containing the firmware is the firmware chip).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the instructions of DeVries via the firmware/memory (firmware chip) of Arnold because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) firmware is useful for initiating the startup of hardware and basic input/output of data and/or (3) firmware is a known implementation to implement the claimed recitation.
Regarding Claim 15, DeVries in view of HRV Standards teaches the wearable device of claim 14 as stated above. DeVries is silent regarding the storage device is a firmware chip.
Arnold teaches systems/methods for determining a sedentary state of a user (see abstract), involving the use of a neural network (see ¶[0024] and ¶[0098]) and physiological data from the user (see ¶[0045]-[0047]), and may be implemented as a watch (see ¶[0119]; Fig. 8). Arnold teaches that the systems/methods may be implemented via processors and memory, and combinations of software, firmware, and/or hardware for the specific application (see ¶[0042], the memory containing the firmware is the firmware chip).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the instructions of DeVries via the firmware/memory (firmware chip) of Arnold because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) firmware is useful for initiating the startup of hardware and basic input/output of data and/or (3) firmware is a known implementation to implement the claimed recitation.
Regarding Claim 18, DeVries in view of HRV Standards teaches the non-transitory computer-readable medium of claim 17 as stated above. DeVries is silent regarding the instructions are adapted to be implemented in firmware.
Arnold teaches systems/methods for determining a sedentary state of a user (see abstract), involving the use of a neural network (see ¶[0024] and ¶[0098]) and physiological data from the user (see ¶[0045]-[0047]), and may be implemented as a watch (see ¶[0119]; Fig. 8). Arnold teaches that the systems/methods may be implemented via processors and memory, and combinations of software, firmware, and/or hardware for the specific application (see ¶[0042]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the instructions of DeVries via the firmware of Arnold because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) firmware is useful for initiating the startup of hardware and basic input/output of data and/or (3) firmware is a known implementation to implement the claimed recitation.
Response to Arguments
Applicant’s arguments, 35 U.S.C. § 112(b)
Applicant’s arguments, see pg. 5, filed September 22, 2025, with respect to the rejections of claims 1-5 and 10-15 under 35 U.S.C. § 112(b) have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new grounds of rejection are made that were necessitated by Applicant’s amendment filed on September 22, 2025.
Applicant’s arguments, 35 U.S.C. § 101
Applicant’s arguments, see pg. 6-7, filed September 22, 2025, with respect to the rejections of claims 1-15 and 17-20 under 35 U.S.C. § 101 have been fully considered and are NOT persuasive. The Applicant argues that the claims provide improvement to technology beyond improving the accuracy of the prediction. The examiner respectfully disagrees. The Applicant details that the improvements are directed towards being implemented on firmware; and, “improve the functioning of wearable devices by enabling computationally efficient, accurate REM detection using normalized HF PSF convolution, reducing power consumption in battery-limited environments compared to conventional polysomnography”. The claims do not recite “accurate REM detection”. Furthermore, the firmware is not recited in the independent claims. Nevertheless, even if such recitations were recited in the independent claims, Applicant’s arguments would still not be persuasive. The improvements are not directed towards firmware or functioning of a wearable device, all recitations of which in the claims are claimed generically. The improvements of Applicant’s invention are only directed towards the computer-implemented processes, i.e., the abstract ideas themselves (i.e., the calculations/evaluations as indicated above). An improved abstract idea is still an abstract idea even if such an abstract idea results in more accurate results.1,2 Also, having the claims focus on the computer-implemented processes is not itself limiting the claims to improving the technology because cases that involve practical, technological improvements extend beyond simply improving the accuracy of a prediction.3 See, e.g., McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, 1315 (Fed. Cir. 2016) (“The claimed process uses a combined order of specific rules that renders information into a specific format that is then used and applied to create desired results: a sequence of synchronized, animated characters.”); Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1304 (Fed. Cir. 2018) (finding patent eligible a claim drawn to a behavior-based virus scan that protects against viruses that have been “cosmetically modified to avoid detection by code-matching virus scans”); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1330, 1333 (Fed. Cir. 2016) (discussing patent eligible claims directed to “an innovative logical model for a computer database” that included a self-referential table allowing for greater flexibility in configuring databases, faster searching, and more effective storage); CardioNet, LLC v. InfoBionic, Inc., 955 F.3d 1358, 1368 (Fed.