DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-3, 9, and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, i.e. abstract idea, without significantly more.
Step 1 of the Patent Subject Matter Eligibility Guidance (see MPEP 2106.03):
Claims 1-3, 9, and 10 are directed to a “system”, which describes one of the four statutory categories of patentable subject matter, i.e. a machine.
Step 2A of the Revised Patent Subject Matter Eligibility Guidance (see MPEP 2106.04):
Claim(s) 1-3, 9, and 10 recite the following mental process:
… extract PPG features from a PPG signal by using a first deep learning model;
… extract user features, by using a second deep learning model, from a first test PPG signal measured using the PPG signal detection ring, systolic and diastolic test blood pressures measured using a conventional blood pressure monitor simultaneously with the first test PPG signal, and user information; and
… estimate systolic and diastolic blood pressures from the PPG features and the user features by using a third deep learning model,
Based on broadest reasonable interpretation, these limitations are directed to receiving data and performing a mathematical operation, which can be done mentally or using pen and paper.
This judicial exception is not integrated into a practical application because the additional limitations of “using a photoplethysmography (PPG) signal detection ring” and “wherein the PPG signal is measured using the PPG signal detection ring” in claim 1 add insignificant pre-solution activity, i.e. data gathering, to the abstract idea that merely collects data to be used by the mental process. Furthermore, the additional limitations of “A deep-learning-based blood pressure estimation system …, the system comprising a server, wherein the server comprises”, “a signal feature extraction component configured to”, “a user feature extraction component configured to”, “a blood pressure estimation component configured to”, “and the server receives the PPG signal from the PPG signal detection ring through a terminal” in claim 1, “wherein the server further comprises a signal quality classification component configured to” in claim 9, and “wherein the server further comprises a blood pressure index calculation component configured to” in claim 10, are merely parts of a computer to be used as a tool to perform the mental process.
Step 2B of the Patent Subject Matter Eligibility Guidance (see MPEP 2106.05):
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered separately and in combination.
Analyzing the additional claim limitations individually, the additional limitations that are not directed to the mental process are “using a photoplethysmography (PPG) signal detection ring” and “wherein the PPG signal is measured using the PPG signal detection ring” in claim 1. Such limitations are conventional and routine in the art (see Clifton et al., WO 2013/027027 A2, which is discussed below in the rejection under 35 U.S.C. 103), and add insignificant pre-solution activity to the abstract idea that merely collects data to be used by the abstract idea.
The additional limitations “using a photoplethysmography (PPG) signal detection ring” and “wherein the PPG signal is measured using the PPG signal detection ring” in claim 1 add insignificant pre-solution activity, i.e. data gathering, to the abstract idea that merely collects data to be used by the mental process. Furthermore, the additional limitations of “A deep-learning-based blood pressure estimation system …, the system comprising a server, wherein the server comprises”, “a signal feature extraction component configured to”, “a user feature extraction component configured to”, “a blood pressure estimation component configured to”, “and the server receives the PPG signal from the PPG signal detection ring through a terminal” in claim 1, “wherein the server further comprises a signal quality classification component configured to” in claim 9, and “wherein the server further comprises a blood pressure index calculation component configured to” in claim 10, are merely parts of a computer to be used as a tool to perform the mental process.
The additional limitations of dependent claims 2, 3, 9 and 10 are merely directed to and further narrow the scope of the mental process.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide computer implementation of the abstract idea using collected data without: improvement to the functioning of a computer or to any other technology or technical field; applying the mental process with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; applying or using the mental process in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment; or adding a specific limitation other than what is well-understood, routine, conventional activity in the field.
Claim Interpretation
4. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
5. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
6. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a signal feature extraction component” in claim 1, which corresponds to the structure of a component of a “server 300” in the specification;
“a user feature extraction component” in claim 1, which corresponds to the structure of a component of a “server 300” in the specification;
“a blood pressure estimation component” in claim 1, which corresponds to the structure of a component of a “server 300” in the specification;
“a photoelectric conversion device” in claim 4, which corresponds to the structure of “photo diode” in the specification;
“a light source control component” in claim 5, which corresponds to the structure of a component of a “first terminal 200” in the specification;
“a sensor selection component” in claim 6, which corresponds to the structure of a component of a “first terminal 200” in the specification;
“a signal quality classification component” in claim 9, which corresponds to the structure of a component of a “server 300” in the specification; and
“a blood pressure index calculation component” in claim 10, which corresponds to the structure of a component of a “server 300” in the specification.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
7. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
8. Claims 1-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites the limitation “a user feature extraction component configured to extract user features, …”. However, the original disclosure, filed 14 March 2024, lacks any explanation of what “user features” are. Applicant’s specification merely states (see page 9):
As illustrated in FIG. 6, the signal feature extraction component 330 may extract PPG features from a PPG signal by using a first deep learning model. The user feature extraction component 340 may extract user features from a test PPG signal, systolic and diastolic test blood pressures, and user information by using a second deep learning model. Systolic and diastolic test blood pressures may be measured simultaneously with the test PPG signal by using a conventional blood pressure monitor. User information may include at least one of the age, weight, height, and gender of a user. The blood pressure estimation component 350 may estimate systolic and diastolic blood pressures from PPG features and user features by using a third deep learning model. Test PPG signals, systolic and diastolic test blood pressure, and user information may be updated periodically.
However, this passage does not describe what the “user features” are, and only describes that they are extracted from “test PPG signal, systolic and diastolic test blood pressures, and user information by using a second deep learning model”.
Claims 2-10 are rejected due to their dependencies, either directly or indirectly to base claim 1.
Claim Rejections - 35 USC § 102
9. 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.
10. 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.
11. Claims 1-3, 9, and 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zou et al., C.N. Patent No. 113647905 A (“Zou”).
As to Claim 1, Zou teaches the following:
A deep-learning-based blood pressure estimation system (see “The invention relates to the technical field of artificial intelligence, especially a training method for predicting blood pressure of deep neural network, a computer device and a storage medium.” in para. [0001]) using a photoplethysmography (PPG) signal detection ring (matter of intended use of the claimed system in which Zou merely teaches “obtaining pulse wave signal” in para. [0005], but would suggest using a photoplethysmography (PPG) signal detection ring), the system comprising a server (“main frame,… network or distributed computing environment, independent or integrated computer platform;…”, not labeled) (see “Further, the method can be operatively connected to any type of computing platform suitable for implementation, including but not limited to personal computer, mini computer, main frame, working station, network or distributed computing environment, independent or integrated computer platform; or communicating with a charged particle tool or other imaging device, and so on. each aspect of the invention can be stored on the non-temporary storage medium or device on the machine readable code to realize, whether it is movable or integrated to the computing platform, such as hard disk, optical reading and/or writing storage medium, RAM; ROM and so on, so that it can be read by the programmable computer, when the storage medium or device is read by the computer can be used for configuring and operating the computer to perform the process described herein. … When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention of the present embodiment includes these and other different types of non-transitory computer-readable storage media.” in para. [0062]), wherein the server comprises:
a signal feature extraction component configured to extract PPG features from a PPG signal by using a first deep learning model (see “in the step S4, the pulse wave signal and the electrocardiogram signal input to the depth neural network model, extracting different scale features by deep neural network model for multi-task regression prediction. Referring to FIG. 3, deep neural network model firstly performing a convolution processing and maximum pool processing of the pulse wave signal and a electrocardiogram signal, then each channel in the deep neural network model different receptive field the pulse wave signal and electrocardiogram signal different scale feature; …” in para. [0050]);
a user feature extraction component configured to extract user features, by using a second deep learning model, from a first test PPG signal measured using the PPG signal detection ring, systolic and diastolic test blood pressures measured using a conventional blood pressure monitor simultaneously with the first test PPG signal, and user information (see “… different scale feature extracted by each channel passes through BN layer and Relu layer in the channel; the cell layer is used for averaging the characteristic extracted by each channel; the full connection layer performs regression analysis to the characteristic after the average pool, to obtain the actual output of the deep neural network model. wherein each channel through a plurality of convolution layer extraction feature, at the same time through the BN layer, can accelerate the convergence speed; the characteristic extracted by each channel is processed by the Relu layer; it can prevent the gradient from disappearing; it is good for deep training; at the same time, it can relieve the over-fitting problem; the 3 channel respectively carried out the average pool, can reduce the characteristic redundancy.” in para. [0050]); and
a blood pressure estimation component configured to estimate systolic and diastolic blood pressures from the PPG features and the user features by using a third deep learning model (see “the deep neural network model trained by step S1-S6 has the ability of predicting blood pressure according to the pulse wave signal and the electrocardiogram signal high precision, namely the pulse wave signal measured from the human body and the electrocardiogram signal input to the deep neural network model for learning; deep neural network model can extract the pulse wave signal and blood pressure information contained in the, electrocardiogram signal as to output systolic pressure, diastolic pressure and average pulse pressure value.” in para. [0051]),
wherein the PPG signal is measured using the PPG signal detection ring (matter of intended use of the claimed system in which Zou merely teaches “obtaining pulse wave signal” in para. [0005], but would suggest using a photoplethysmography (PPG) signal detection ring), and the server receives the PPG signal from the PPG signal detection ring through a terminal (“working station”, not labeled) (see “Further, the method can be operatively connected to any type of computing platform suitable for implementation, including but not limited to personal computer, mini computer, main frame, working station, network or distributed computing environment, independent or integrated computer platform; or communicating with a charged particle tool or other imaging device, and so on.” in para. [0062]).
As to Claim 2, Zou teaches the following:
wherein the user information comprises at least one of age, weight, height, and gender of a user (see para. [0054]).
As to Claim 3, Zou teaches the following:
wherein the test PPG signal, the systolic and diastolic test blood pressures, and the user information are periodically updated (see “in the step S1, obtaining the pulse wave signal PPG, electrocardiogram signal and arterial blood pressure signal ABP, to form a training set and a test set. wherein the multi-section pulse wave signal can be directly measured from the human body; multi-section electrocardiogram signal and multi-section arterial blood pressure signal. then processing the pulse wave signal, the electrocardiogram signal-processing process of the arterial blood pressure signal. Specifically, the measured pulse wave signal, electrocardiogram signal and arterial blood pressure signal for filter, the filter standard comprises time limit, amplitude peak size limit, peak time interval limit, such as set time limit is greater than 8 minutes, then can filter than 8 minutes of pulse wave signal; a electrocardiogram signal and arterial blood pressure signal; setting the peak time interval to be more than 0.6s, then it can filter the arterial blood pressure signal with the peak time interval greater than 0.6s; and according to the amplitude peak size limit, it can remove the abnormal value and false peak signal interference, so that the pulse wave signal of the filter electrocardiogram signal and arterial blood pressure signal are effective signal.” in para. [0046]).
As to Claim 9, Zou teaches the following:
wherein the server further comprises a signal quality classification component configured to classify signal quality of the PPG signal as one of good and bad (see “The depth neural network model obtained by training in the embodiment of the training method can realize systolic pressure; the average error respectively diastolic pressure and average arterial pressure is 0.007; 0.022; 0.009mmHg, the average absolute error respectively 4.04, 2.29; 2.46mmHg, the standard deviation respectively 5.81, 3.55, 3.58mmHg Pearson correlation coefficient respectively 0.96, 0.92, 0.94, the AAMI standard, the BHS index in the evaluation of A; The method of the invention has good feasibility and effectiveness. The existing ECG and PPG signal and predicting blood pressure by machine learning method, obtaining the shrinkage pressure; the average absolute error of diastolic pressure prediction precision 11.17mmHg 11.17mmHg and 5.35mmHg, the method of the invention predicts the blood pressure precision is greatly improved. Compared with the existing method of using ECG signal combined deep learning the method for predicting blood pressure, the average absolute error respectively the prediction precision of the systolic pressure and diastolic pressure reaches 7.10; the 4.61mmHg of the invention predicts the blood pressure precision is also obviously improved. Generally speaking, the method of the invention realizes the high precision pressure prediction without calibration process, providing a feasible method for realizing continuous blood pressure measurement in the wearable device.” in para. [0054]).
As to Claim 10, Zou teaches the following:
wherein the server further comprises a blood pressure index calculation component configured to calculate a blood pressure index, and the blood pressure index is defined by a ratio between a time during which the quality of a PPG signal is classified as good by the signal quality classification component and the systolic blood pressure deviates from the first normal range or the diastolic blood pressure deviates from the second normal range and a time during which the quality of a PPG signal is classified as good by the signal quality classification component (see “The depth neural network model obtained by training in the embodiment of the training method can realize systolic pressure; the average error respectively diastolic pressure and average arterial pressure is 0.007; 0.022; 0.009mmHg, the average absolute error respectively 4.04, 2.29; 2.46mmHg, the standard deviation respectively 5.81, 3.55, 3.58mmHg Pearson correlation coefficient respectively 0.96, 0.92, 0.94, the AAMI standard, the BHS index in the evaluation of A; The method of the invention has good feasibility and effectiveness. The existing ECG and PPG signal and predicting blood pressure by machine learning method, obtaining the shrinkage pressure; the average absolute error of diastolic pressure prediction precision 11.17mmHg 11.17mmHg and 5.35mmHg, the method of the invention predicts the blood pressure precision is greatly improved. Compared with the existing method of using ECG signal combined deep learning the method for predicting blood pressure, the average absolute error respectively the prediction precision of the systolic pressure and diastolic pressure reaches 7.10; the 4.61mmHg of the invention predicts the blood pressure precision is also obviously improved. Generally speaking, the method of the invention realizes the high precision pressure prediction without calibration process, providing a feasible method for realizing continuous blood pressure measurement in the wearable device.” in para. [0054]).
Claim Rejections - 35 USC § 103
12. 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.
13. 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.
14. Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Zou, as applied to claim 1 above, and further in view of Tchertkov et al., U.S. Patent Application Publication No. 2017/0079535 A1 (“Tchertkov”).
As to Claims 4 and 5, Zou teaches the subject matter of claim 1 above. Zou does not teach the following:
wherein the PPG signal detection ring includes a plurality of sensors configured to simultaneously measure a plurality of PPG signals at different locations, and each of the plurality of sensors includes a light source and a photoelectric conversion device; and
wherein the terminal comprises a light source control component configured to control the light source of each of the plurality of sensors such that a DC component of each second test PPG signal measured using the plurality of sensors is within a predetermined range.
However, Tchertkov teaches the following:
a PPG signal detection ring (“wrist ring”, not labeled) (see fig. 7) includes a plurality of sensors (“plurality of discrete PPG sensors”) 710 configured to simultaneously measure a plurality of PPG signals at different locations, and each of the plurality of sensors 710 includes a light source (“light emitting component”, not labeled) and a photoelectric conversion device (“light collecting component(s)”, not labeled) (see “The PPG sensor shown here may be composed of a plurality of discrete PPG sensors 710. Each discrete PPG sensor 710 may be spaced uniformly around the inner circumference of the wrist ring (e.g., 6 millimeters apart). The discrete PPG sensors 710 may be embedded into the inner surface of the wrist ring and spaced closely enough such that they are effectively a uniform light source. Each individual discrete PPG sensor 710 may include its own light emitting component and light collecting component(s) for detecting reflected light emitted by the light emitting component. While the PPG sensor is composed of discrete components, the implementation may obtain similar PPG measurements as obtained by the implementation in FIG. 6 which comprises a continuous light source.” in para. [0083]); and
a terminal (“PPG measurement module”) 192 comprises a light source control component (“processor”) 110 configured to control the light source of each of the plurality of sensors such that a DC component of each second test PPG signal measured using the plurality of sensors is within a predetermined range (“determine a PPG measurement that is indicative of the user's blood volume”) (see “PPG measurement module 192 is configured to, when executed by processor 110, obtain a photoplethysmography (PPG) measurement. The PPG measurement may be a measurement of blood volume of a user operating the mobile device 100. The PPG measurement may be obtained by the PPG measurement module 192 in response to a user action. The PPG measurement module 192 may interface with the light source 185 and light sensors 182 in order to obtain the PPG measurement. Upon indication by the user of a need for a PPG measurement, the PPG measurement module 192 may direct the light source 185, or multiple light sources, to emit light through the user's body. As described above, the emitted light may reflect off or transmitted through blood vessels within the user's body and may be detected by one or more light sensors 182 within the mobile device 100. The PPG measurement module 192 may measure, by interfacing with the one or more light sensors, the amount of reflected or transmitted light detected by the one or more light sensors 182. The PPG measurement module 192 may then determine a PPG measurement that is indicative of the user's blood volume based on the measurement of the reflected light.” in para. [0056]).
Thus, it would have been obvious for one of ordinary skill in the art at the time the present application was effectively filed to modify Zou’s teaching of “obtaining pulse wave signal” to obtaining the pulse wave signal from, as taught by Tchertkov, a PPG signal detection ring (“wrist ring”, not labeled) (see fig. 7) that includes a plurality of sensors (“plurality of discrete PPG sensors”) 710 configured to simultaneously measure a plurality of PPG signals at different locations, and each of the plurality of sensors 710 includes a light source (“light emitting component”, not labeled) and a photoelectric conversion device (“light collecting component(s)”, not labeled), and a terminal (“PPG measurement module”) 192 comprises a light source control component (“processor”) 110 configured to control the light source of each of the plurality of sensors such that a DC component of each second test PPG signal measured using the plurality of sensors is within a predetermined range, because Zou suggests the combination “[t]he purpose of the invention is aiming at the defect of the existing technology, claims a photoplethysmography signal quality detecting method and device, …” (see Zou, para. [003]).
Allowable Subject Matter
15. Claims 6-8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
16. Claims 6-8 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112, set forth in this Office Action.
17. The following is a statement of reasons for the indication of allowable subject matter:
As to Claims 6-8, neither Zou, , nor the prior art of record teaches the system of base claim 1, including the following, in combination with all other limitations of the base claim:
wherein the terminal further comprises a sensor selection component configured to select, from among the plurality of sensors, as a sensor for measuring the PPG signal, a sensor that has measured a second test PPG signal having a highest signal quality among the plurality of second test PPG signals.
Conclusion
18. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVIN NATNITHITHADHA whose telephone number is (571)272-4732. The examiner can normally be reached Monday - Friday 8:00 am - 8:00 am - 4:00 pm.
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/NAVIN NATNITHITHADHA/Primary Examiner, Art Unit 3791 03/20/2026
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