DETAILED ACTION
Applicant’s arguments, filed 01/13/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application. Applicant has canceled claims 4-5, 14-15, and 20.
Claims 1-3, 6-13, and 16-19 are the current claims hereby under examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
Applicant has amended the terms “pressure sensing module” and “processing module” to recite “pressure sensor” and “processor”, respectively. Thus, these terms are no longer being interpreted under 35 U.S.C. 112(f). Applicant has amended the term “input module” to recite “input device”; however, “input device” is still a generic placeholder coupled with functional language lacking sufficient structure to perform the recited function. Thus, this term is still interpreted under 35 U.S.C. 112(f) as laid out in the Office Action filed 11/18/2025.
Claim Rejections - 35 USC § 112
Claims 1-3, 6-13, and 16-19 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.
Regarding claims 1 and 10, it is unclear how the processor “generates” the to-be-predicted data of the first arterial waveform. The claims recite that the processor generates pulse wave intensity and phase difference based on the pulse wave data of a plurality of meridians; however, the claims do not recite how this calculation is made from the arterial waveform. What data is used from the first arterial waveform to calculate these pieces of first pulse wave data? For examination purposes, the “generates” step will be interpreted to mean “calculates” the to-be-predicted data from arterial waveform measurements. For similar reasons, claims 2 and 11 are rejected. Claims 2-3, 6-9, 11-13, and 16-19 are also rejected due to their dependence on claims 1 and 10.
Regarding claims 1 and 10, it is unclear how the pulse condition prediction model “generates” the predicated probability values of the pulse conditions. What algorithm or calculation does the model use to predict a probability of the pulse conditions based on pulse wave intensity, phase difference, or their respective standard deviations? For examination purposes, the “generates” step will be interpreted to mean “calculates” the probability values based on measured data. For similar reasons, claims 2 and 11 are rejected. Claims 2-3, 6-9, 11-13, and 16-19 are also rejected due to their dependence on claims 1 and 10.
Claim 11 recites the limitation "the pulse condition prediction" in line 13. There is insufficient antecedent basis for this limitation in the claim. It is unclear if this limitation refers to the pulse condition prediction model, or the plurality of pulse conditions as recited in claim 1. For examination purposes, it will be interpreted to mean the pulse condition prediction model.
Regarding claims 2 and 11, it is unclear if the initial neural network model “generates” a new pulse condition prediction model or if the training of an initial neural network model is trained to become a pulse condition prediction model to perform the steps of claim 1. That is, are these two separate, distinct models, or is only one model trained? For examination purposes, the two models will be treated as the same model. Claims 6-9, 12, and 16-19 are also rejected due to their dependence on claims 2 and 11.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6-13, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over:
Furness (US 20170258336 – previously cited),
Sadriev (US 20190038235),
Hatib (US 20100204590 – previously cited), and
Lan (TW 202112304 – previously cited).
Regarding claims 1, Furness discloses sensing, by a pressure sensor, a first arterial waveform of an artery of a first subject (Paragraph 0012; Fig. 1, sensor/transducer 108 measuring pulse of subjects 106; Figs. 2A-C),
generating, by a processor, to-be-predicted data based on the first arterial waveform, wherein the to-be-predicted data comprises a plurality of pieces of first pulse wave data of a plurality of meridians (Paragraph 0105); wherein each of the plurality of pieces of first pulse wave data comprises a pulse wave intensity tag (Paragraph 0210, pulse width/strength); and
inputting, by the processor, the to-be-predicted data into a pulse condition prediction model (Paragraph 0210), and a plurality of predicted pulse conditions being generated by the pulse condition prediction model having the to-be-predicted data (Paragraph 0046; Paragraph 0186).
Furness fails to explicitly disclose measuring phase difference between a phase of a waveform and a default phase, determining a standard deviation of the pressure wave intensity and phase difference values, and determining a probability value. Lastly, Furness fails to disclose wherein the plurality of labelled probability values is associated with an abnormal condition and the abnormal condition indicates a corresponding one of the first standard deviations
However, Sadriev teaches a method for detecting blood pressure changes (Abstract) wherein a system computes phase shift values based on a difference between a historical mode and a more recent mode (Paragraph 0049). Sadriev discusses that phase shift values may indicate changes in the blood pressure (Paragraph 0034). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Furness to incorporate the phase shift calculation of Sadriev to indicate changes in the blood pressure.
Hatib teaches an analogous pressure sensor that uses a statistical model for detection of vascular conditions from arterial pressure waveform data, wherein the standard deviation of parameters from the arterial pressure waveform is measured (Paragraphs 0020 – 0021). One of ordinary skill in the art would have been motivated in applying this known method of Hatib to the pulse condition method of Furness and Sadriev and the results of calculating a standard deviation would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method Furness and Sadriev to incorporate the teachings of calculating a standard deviation of Hatib and the results would have been predictable to one of ordinary skill in the art.
Lastly, Lan teaches a pulse condition analysis method wherein a prediction model generates probability values of pulse conditions (Page 4, paragraph 2, “Among them, in the case of pulse classification in this embodiment, there may be several possibilities for the interpretation of the pulse. For example, the probability of Ping mai is 85%, and the probability of Xian mai is 85%. The probability of pulse is 10%, the probability of thin pulse is 3%, and the probability of Shen pulse is 1%. Please follow the picture shown in Figure 6. Reply to the content displayed on the smartphone 40, which can be set to display only the highest probability The pulse condition of (for example, only the prediction result is Ping mai), or in addition to the prediction result, the possibility of various pulse conditions can also be displayed together, and the probability can be displayed together for the reference of subject 1 or medical staff”). Lan discusses these steps are useful to determine the patient’s health status/state and disease risk. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Furness, Sadriev, and Hatib to incorporate the teachings of generating a probability of pulse conditions using a trained neural network of Lan for assessing a patient’s health and disease risk.
Regarding claims 2 and 6-9, Furness as modified further discloses sensing a plurality of arteries of a plurality of second subjects, and a plurality of second arterial waveforms being obtained by the plurality of arteries of the plurality of second subjects (Paragraphs 0069; Fig. 1, input layer 184a), generating a plurality of pieces of training data based on the plurality of second arterial waveforms (Paragraphs 0080-0081), wherein the training data comprises a plurality of meridians and parameters (Paragraphs 0069 and 0078) comprising pulse wave intensity (Paragraph 0210, pulse width and strength; Paragraph 0213, wherein the width is the intensity of the arterial pulse).
Furness as modified fails to disclose wherein the second pulse wave data comprises a phase difference, calculating standard deviations of the second pulse wave data, the training data has labelled probability values corresponding to the pulse conditions and an abnormal condition, and inputting the training data into an initial neural network model to generate the pulse condition prediction model.
Furness as modified also fails to disclose wherein the input layer has neurons equal to the number of input parameters; however, it is well-known to one of ordinary skill in the art for the input layer to have neurons equal to the number of input parameters.
However, Sadriev teaches a method for detecting blood pressure changes (Abstract) wherein a system computes phase shift values based on a difference between a historical mode and a more recent mode (Paragraph 0049). Sadriev discusses that phase shift values may indicate changes in the blood pressure (Paragraph 0034). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Furness to incorporate the phase shift calculation of Sadriev to indicate changes in the blood pressure.
Hatib teaches an analogous pressure sensor that uses a statistical model for detection of vascular conditions from arterial pressure waveform data, wherein the standard deviation of parameters from the arterial pressure waveform is measured (Paragraphs 0020 – 0021) and wherein the standard deviation is used to differentiate between a subject who is experiencing a condition and a subject who is not experiencing a condition (Paragraph 0021). One of ordinary skill in the art would have been motivated in applying this known method of Hatib to the pulse condition method of Furness and Sadriev and the results of calculating a standard deviation would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method Furness and Sadriev to incorporate the teachings of calculating a standard deviation of Hatib and the results would have been predictable to one of ordinary skill in the art.
Lastly, Lan teaches a pulse condition analysis method wherein a prediction model generates probability values of pulse conditions (Page 4, paragraph 2, “Among them, in the case of pulse classification in this embodiment, there may be several possibilities for the interpretation of the pulse. For example, the probability of Ping mai is 85%, and the probability of Xian mai is 85%. The probability of pulse is 10%, the probability of thin pulse is 3%, and the probability of Shen pulse is 1%. Please follow the picture shown in Figure 6. Reply to the content displayed on the smartphone 40, which can be set to display only the highest probability The pulse condition of (for example, only the prediction result is Ping mai), or in addition to the prediction result, the possibility of various pulse conditions can also be displayed together, and the probability can be displayed together for the reference of subject 1 or medical staff”). Additionally, Lan teaches wherein the inputting to the pulse condition prediction model comprises inputting the plurality of pieces of training data into an input layer initial neural network model, and the pulse condition prediction model being generated by the initial neural network model having the plurality of pieces of training data (Page 3, paragraphs 2-3, wherein the prediction model is obtained by pre-training by the steps identified above; Page 7, paragraph 1, wherein the model is a neural network) and each of the plurality of pieces of training data has second pulse wave data having a plurality of labelled probability values corresponding to the plurality of pulse conditions and an abnormal condition (Page 3, paragraph 2, wherein at least one kind of pulse condition data is collected; Page 3, paragraph 3, wherein the pulse condition results of the training samples are known; Page 4, paragraph 3, wherein the probabilities are related to conditions of the pulse). Lan discusses these steps are useful to determine the patient’s health status/state and disease risk. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Furness, Sadriev, and Hatib to incorporate the teachings of training a neural network of Lan for assessing a patient’s health and disease risk.
Regarding claim 3, Furness as modified further discloses wherein before inputting the to-be-predicted data into the pulse condition prediction model, the method further comprises:
inputting the plurality of pieces of first pulse wave data is input into an input layer of the pulse condition prediction model (Paragraphs 0069 and 0078; Fig. 1, input layer 184a).
While Furness as modified fails to disclose the number of neurons in the input layer, it is well-known to one of ordinary skill in the art for the input layer to have neurons equal to the number of input parameters.
Regarding claim 10, Furness discloses a pulse condition prediction system, comprising:
pressure sensor configured to sense a first arterial waveform from an artery of a first subject (Paragraph 0012; Fig. 1, sensor/transducer 108 measuring pulse of subjects 106; Figs. 2A-C);
a processor connected to the pressure sensing module (Fig. 1, system 102 with pulse sensors 108 connected to processors 144), and to-be-predicted data being generated via the first arterial waveform by the processing module, the to-be-predicted data being inputted into a pulse condition prediction model (Paragraph 0210) by the processing module, and a plurality of predicted pulse conditions being generated by the pulse condition prediction model having the to-be-predicted data (Paragraph 0046; Paragraph 0186),
wherein the to-be-predicted data comprises a plurality of pieces of first pulse wave data of a plurality of meridians (Paragraph 0105); and
Furness fails to disclose determining a probability value, measuring phase difference between a phase of a waveform and a default phase, and a standard deviation of the pressure wave intensity and phase difference values.
However, Sadriev teaches a method for detecting blood pressure changes (Abstract) wherein a system computes a phase shift values based on a difference between a historical mode and a more recent mode (Paragraph 0049). Sadriev discusses that phase shift values may indicate changes in the blood pressure (Paragraph 0034). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Furness to incorporate the phase shift calculation of Sadriev to indicate changes in the blood pressure.
Hatib teaches an analogous pressure sensor that uses a statistical model for detection of vascular conditions from arterial pressure waveform data (Abstract), wherein the standard deviation of parameters from the arterial pressure waveform is measured (Paragraphs 0020 – 0021). One of ordinary skill in the art would have been motivated in applying this known method of Hatib to the pulse condition method/system of Furness and Lan and the results of calculating a standard deviation would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method Furness and Sadriev to incorporate the teachings of calculating a standard deviation of Hatib and the results would have been predictable to one of ordinary skill in the art.
However, Lan teaches a pulse condition analysis method wherein a prediction model generates probability of pulse conditions (Page 4, paragraph 2, “Among them, in the case of pulse classification in this embodiment, there may be several possibilities for the interpretation of the pulse. For example, the probability of Ping mai is 85%, and the probability of Xian mai is 85%. The probability of pulse is 10%, the probability of thin pulse is 3%, and the probability of Shen pulse is 1%. Please follow the picture shown in Figure 6. Reply to the content displayed on the smartphone 40, which can be set to display only the highest probability The pulse condition of (for example, only the prediction result is Ping mai), or in addition to the prediction result, the possibility of various pulse conditions can also be displayed together, and the probability can be displayed together for the reference of subject 1 or medical staff”), which Lan discusses is useful to determine the patient’s health status/state and disease risk.
Regarding claims 11 and 16-19, Furness as modified further discloses sensing a plurality of arteries of a plurality of second subjects, and a plurality of second arterial waveforms being obtained by the plurality of arteries of the plurality of second subjects (Paragraphs 0069; Fig. 1, input layer 184a), generating a plurality of pieces of training data based on the plurality of second arterial waveforms (Paragraphs 0080-0081), wherein the training data comprises a plurality of meridians and parameters (Paragraphs 0069 and 0078) comprising pulse wave intensity (Paragraph 0210, pulse width and strength; Paragraph 0213, wherein the width is the intensity of the arterial pulse).
Furness as modified fails to disclose wherein the second pulse wave data comprises a phase difference, calculating standard deviations of the second pulse wave data, the training data has labelled probability values corresponding to the pulse conditions and an abnormal condition, and inputting the training data into an initial neural network model to generate the pulse condition prediction model.
Furness as modified also fails to disclose wherein the input layer has neurons equal to the number of input parameters; however, it is well-known to one of ordinary skill in the art for the input layer to have neurons equal to the number of input parameters.
However, Sadriev teaches a method for detecting blood pressure changes (Abstract) wherein a system computes phase shift values based on a difference between a historical mode and a more recent mode (Paragraph 0049). Sadriev discusses that phase shift values may indicate changes in the blood pressure (Paragraph 0034). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Furness to incorporate the phase shift calculation of Sadriev to indicate changes in the blood pressure.
Hatib teaches an analogous pressure sensor that uses a statistical model for detection of vascular conditions from arterial pressure waveform data, wherein the standard deviation of parameters from the arterial pressure waveform is measured (Paragraphs 0020 – 0021) and wherein the standard deviation is used to differentiate between a subject who is experiencing a condition and a subject who is not experiencing a condition (Paragraph 0021). One of ordinary skill in the art would have been motivated in applying this known method of Hatib to the pulse condition method of Furness and Sadriev and the results of calculating a standard deviation would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method Furness and Sadriev to incorporate the teachings of calculating a standard deviation of Hatib and the results would have been predictable to one of ordinary skill in the art.
Lastly, Lan teaches a pulse condition analysis method wherein a prediction model generates probability values of pulse conditions (Page 4, paragraph 2, “Among them, in the case of pulse classification in this embodiment, there may be several possibilities for the interpretation of the pulse. For example, the probability of Ping mai is 85%, and the probability of Xian mai is 85%. The probability of pulse is 10%, the probability of thin pulse is 3%, and the probability of Shen pulse is 1%. Please follow the picture shown in Figure 6. Reply to the content displayed on the smartphone 40, which can be set to display only the highest probability The pulse condition of (for example, only the prediction result is Ping mai), or in addition to the prediction result, the possibility of various pulse conditions can also be displayed together, and the probability can be displayed together for the reference of subject 1 or medical staff”). Additionally, Lan teaches wherein the inputting to the pulse condition prediction model comprises inputting the plurality of pieces of training data into an input layer initial neural network model, and the pulse condition prediction model being generated by the initial neural network model having the plurality of pieces of training data (Page 3, paragraphs 2-3, wherein the prediction model is obtained by pre-training by the steps identified above; Page 7, paragraph 1, wherein the model is a neural network) and each of the plurality of pieces of training data has second pulse wave data having a plurality of labelled probability values corresponding to the plurality of pulse conditions and an abnormal condition (Page 3, paragraph 2, wherein at least one kind of pulse condition data is collected; Page 3, paragraph 3, wherein the pulse condition results of the training samples are known; Page 4, paragraph 3, wherein the probabilities are related to conditions of the pulse). Lan discusses these steps are useful to determine the patient’s health status/state and disease risk. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Furness, Sadriev, and Hatib to incorporate the teachings of training a neural network of Lan for assessing a patient’s health and disease risk.
Regarding claim 12, while Furness as modified fails to explicitly disclose using an input device to label the probability values, Furness discloses an input/output device (Fig. 1, device 162; Paragraph 0066; Paragraph 0071, the symptom information can take the form of a description of one or more primary and/or secondary diagnoses by the practitioner 116 for the specific subject 106. The primary and/or secondary diagnosis may be entered in freeform text, for instance via a freeform text field of a user interface … The primary and/or secondary diagnosis may only be entered when collecting data or information for initially populating or training the system 100). One of ordinary skill would understand that labeling data would require input from an input device, thus, Furness as modified reads on the claim.
Regarding claim 13, Furness further discloses wherein the data is input into an input layer of the pulse condition prediction model (Paragraph 0078; Fig. 1, input layer 184a).
While Furness as modified fails to disclose the number of neurons in the input layer, it is well-known to one of ordinary skill in the art for the input layer to have neurons equal to the number of input parameters.
Response to Arguments
Applicant’s arguments, see page 13, filed 01/13/2026, with respect to the drawings objection have been fully considered and are persuasive. Applicant has amended the drawings per suggestion of the Examiner. The objection of the drawings has been withdrawn.
Applicant’s arguments, see page 13, filed 01/13/2026, with respect to the specification objection have been fully considered and are persuasive. Applicant has amended the specification per suggestion of the Examiner. The objection of the specification has been withdrawn.
Applicant’s arguments, see page 14, filed 01/13/2026, with respect to the claim objections have been fully considered and are persuasive. Applicant has amended the claims per suggestion of the Examiner. The objections to the claims have been withdrawn.
Applicant’s arguments, see page 15, filed 01/13/2026, with respect to the 35 U.S.C. §112(b) rejections have been fully considered and are persuasive. Applicant has amended the drawings per suggestion of the Examiner. The rejections of the claims have been withdrawn.
Applicant’s arguments, see page 16, filed 01/13/2026, with respect to the 35 U.S.C. §101 rejection have been fully considered and are persuasive. Applicant has amended the claim to remove positive recitation of “an artery”. The rejection to the claims has been withdrawn.
Applicant’s arguments, see page 17, filed 01/13/2026, with respect to the 35 U.S.C. §103 rejections of claims 1-20 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in further view of Sadriev above.
Applicant asserts that Hatib fails to disclose or suggest the specific combination of calculating standard deviations of pulse wave intensity or phase difference. Examiner notes that calculating standard deviations is well known in the art and calculating a standard deviation of a dataset such as pulse wave intensity or phase difference is within the skill of one of ordinary skill in the art.
Applicant further asserts that the standard deviations provide high-precision data that allow the model to output a suitable probability for diagnosis. Examiner points out that these limitations are not present in the claims. While the claims are interpreted in light of the specification, limitations from the specification are not read into the claims (See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). However, Examiner notes that there is also no disclosure within the specification that the standard deviations provide “high-precision” data to output a probability suitable for diagnosis. Applicant provides plantar fasciitis as an example condition; however, there is no disclosure of determining and/or confirming plantar fasciitis.
Conclusion
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/NOAH M HEALY/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791