DETAILED ACTION
Applicant’s arguments, filed on 10/02/2025, 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.
Applicants have amended their claims, filed on 10/02/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-4, 7-14, and 17-20 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 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 1-4, 7-14, 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.
Regarding claim 1, the claim recites the limitation “the radar echo” in line 7. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear if this limitation is referring to the echo signal introduced earlier in the claim, or is an entirely new limitation. If it is referring to the echo signal from earlier in the claim, it should refer back to it. If it is referring to a different radar echo, it should be distinguished from the echo signal and the antecedent basis issue should be corrected. For purposes of examination, it is being interpreted as referring to the echo signal introduced earlier in the claim. Claims 2-4 and 7-10 are also rejected due to their dependency on claim 1.
Regarding claim 11, the claim recites the limitation “the radar echo” in line 9. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear if this limitation is referring to the echo signal introduced earlier in the claim, or is an entirely new limitation. If it is referring to the echo signal from earlier in the claim, it should refer back to it. If it is referring to a different radar echo, it should be distinguished from the echo signal and the antecedent basis issue should be corrected. For purposes of examination, it is being interpreted as referring to the echo signal introduced earlier in the claim. Claims 12-14 and 17-20 are also rejected due to their dependency on claim 11.
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.
Claims 1-2, 4, 7-8, 11-12, 14, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (CN 111374641) in further view of Zhang (CN 114176535), Shouldice (CN 111629658), Zhou (CN 111657855), and Hoon Yoo (“Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis from Chest X-ray Imaging”). Citations to CN 11374641, CN 114176535, CN 111629658, and CN 111657855 will refer to the English Machine Translations that accompany this Office Action.
Regarding independent claim 1, Yang teaches an evaluation method of sleep quality (Abstract: “Embodiments of the present invention provide a method, apparatus, computer equipment and storage medium for identifying sleep characteristic events.”), comprising:
obtaining an echo signal reflected by a transmitted signal of a radar by a human body, wherein the transmitted signal of the radar has a frequency that reflects the human body ([0116]: “the echo signal acquisition module 310 is specifically configured to transmit a preset waveform to the target user through the radar; and receive, through the radar, an echo signal returned by the target user based on the preset waveform.”);
generating sensing data based on the echo signal ([0116]: “the echo signal acquisition module 310 is specifically configured to transmit a preset waveform to the target user through the radar; and receive, through the radar, an echo signal returned by the target user based on the preset waveform.”);
transforming the sensing data into feature data ([0117]: “the judgment module 320 is specifically used to filter the echo signal; perform time-frequency transformation on the filtered echo signal to obtain an energy burst curve; detect the sleep state of the target user according to the energy burst curve, wherein the sleep state includes a first event and a second event”).
However, Yang does not teach wherein the feature data comprises a statistic of a plurality of feature points of the radar echo on a waveform.
Zhang discloses a monitoring device to monitor health status of a user. Specifically, Zhang teaches wherein the feature data comprises a statistic of a plurality of feature points of the radar echo on a waveform ([0008]: “receive the echo signal and process and extract the signal waveform”; [0027]: “The signal wave is subjected to median filtering, smoothing filtering, DC removal and low-pass filtering, and the respiration value and heart rate value are calculated according to the signal waveform characteristics, the peak-to-peak interval, the valley-to-valley interval, the change of the signal zero-crossing rate, the number of valid waveforms and the signal frequency domain value under waveform stability”), wherein the feature data further comprises a trend of the waveform ([0035]: “The processor comprehensively determines whether a human body exists and whether the human body is in a calm and stable breathing state based on … the changing trend of the signal amplitude within the set time”). Yang and Zhang are analogous arts as they are both related to monitoring sleep state of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the statistic from Zhang into the method from Yang as it allows for more analysis of the signals received, which can allow for a more accurate result and analysis from the method.
However, the Yang/Zhang combination does not teach where the trend is an intensity variation of the waveform without a pattern characteristic. Shouldice discloses an apparatus and system for motion sensing. Specifically, Shouldice teaches where the trend is an intensity variation of the waveform without a pattern characteristic ([0186]: “the detected amplitude variation may be due to changes in the intensity of the extracted received respiratory signal”). Yang, Zhang, and Shouldice are analogous arts as they are all related to monitor the state of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the intensity variation from Shouldice into the Yang/Zhang combination as it allows the method to analyze the intensity, which can provide important information about the waveform and state of the user.
The Yang/Zhang/Shouldice combination teaches determining sleep quality information according to the feature data (Yang, [0008]-[0012]: “an embodiment of the present invention provides a method for identifying a sleep characteristic event, comprising: Acquire the echo signal of the target user to be identified based on radar; Determining whether a sleep characteristic event occurs to the target user according to the echo signal; When a sleep characteristic event occurs to the target user, obtaining difference characteristics between before and after the sleep characteristic event occurs to the target user; A target characteristic event is determined according to the difference characteristic, where the target characteristic event is one of a plurality of sleep characteristic events.).
However, the Yang/Zhang/Shouldice combination does not teach wherein the sleep quality information is related to a degree of sleep quality.
Zhou discloses a sleep evaluation method and device. Specifically, Zhou teaches wherein the sleep quality information is related to a degree of sleep quality ([0049]: “The acquired sleep quality scores corresponding to the sleep time information may be one or both of: a sleep quality score corresponding to the sleep time information input by the user, and a sleep quality score determined according to a functional relationship between the sleep quality score and the sleep duration.”). Yang, Zhang, and Zhou are analogous arts as they are all related to monitoring sleep state of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the degree of sleep quality from Zhou into the Yang/Zhang/Shouldice combination as the combination analyzes sleep, but does not provide a degree of sleep quality, so including the degree of sleep quality can provide more information to the user and create a more detailed analysis of the user’s sleep and health status.
However, the Yang/Zhang/Shouldice/Zhou combination does not teach using a respiratory event in the calculation of the sleep quality.
Zhang discloses evaluating a respiratory event in the sleep evaluation ([0096]: “The method for identifying apnea is a combination of respiratory waveform characteristics and signal energy threshold changes”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the respiratory event in the sleep evaluation from Zhang into the Yang/Zhang/Shouldice/Zhou combination as it allows for the method to factor in more parameters that can impact the sleep state of the user, ensuring a more comprehensive analysis.
The Yang/Zhang/Shouldice/Zhou combination teaches predicting an event by a machine learning model ([0029]: “The target feature event is determined based on the output result of the neural network model”).
However, the Yang/Zhang/Shouldice/Zhou combination does not teach the specific type of machine learning model being based on a deep neural decision tree.
Hoon Yoo discloses a deep learning decision-tree classifier for COVID-19 diagnosis. Specifically, Hoon Yoo teaches predicting the respiratory event by a machine learning model, wherein the machine learning model is based on a deep neural decision tree (DNDT) and the deep neural decision tree (DNDT) is trained using the feature data as training data (Page 1: “This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available”). Yang, Zhang, Shouldice, Zhou, and Hoon Yoo are analogous art as they are all related to monitoring health parameters of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the machine learning model from Hoon Yoo into the Yang/Zhang/Shouldice/Zhou combination as the combination is silent on the type of neural network used, and Hoon Yoo discloses a suitable machine learning model in an analogous art.
Regarding claim 2, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the evaluation method of sleep quality of claim 1, wherein the feature points comprise at least one of a peak value and a valley value, and the statistic comprises at least one of an interval between two of the feature points, a variation of the interval, and a total number of the feature points (Zhang, [0008]: “receive the echo signal and process and extract the signal waveform”; [0027]: “The signal wave is subjected to median filtering, smoothing filtering, DC removal and low-pass filtering, and the respiration value and heart rate value are calculated according to the signal waveform characteristics, the peak-to-peak interval, the valley-to-valley interval, the change of the signal zero-crossing rate, the number of valid waveforms and the signal frequency domain value under waveform stability”).
Regarding claim 4, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the evaluation method of sleep quality of claim 1, wherein the feature data further comprises an entropy of the sensing data (Yang, [0030]: “the difference feature includes one or more of an energy difference feature, an amplitude difference feature, an entropy difference feature”).
Regarding claim 7, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the evaluation method of sleep quality of claim 6, wherein the step of predicting the respiratory event according to the feature data comprises: wherein the machine learning model is trained to understand a correlation between the feature data and the respiratory event (Yang, [0093]: “the neural network model is a complex network system formed by a large number of simple processing units (neurons) that are widely interconnected. It reflects many basic characteristics of human brain functions and is a highly complex nonlinear dynamic learning system. Specifically, the neural network model in this embodiment is trained in a supervised manner. Taking multiple sleep characteristic events such as sitting up, lying down, leaving the bed, going to bed and turning over as an example, the training data are labeled during training, and the training data are respectively labeled as sitting up, lying down, leaving the bed, going to bed or turning over for training.”; Zhang, [0105]: “In the process of respiratory search, the signal energy value within 5s is used, and the apnea energy threshold is updated in real time. Under normal human breathing conditions, if the respiratory signal energy is lower than the apnea energy threshold, apnea is determined. According to the different characteristics of the apnea signal, the apnea type is classified, which can be mainly divided into four types: obstructive apnea, central apnea, mixed apnea and hypopnea”).
Regarding claim 8, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the evaluation method of sleep quality of claim 7, wherein the machine learning model is further based on one of a deep learning neural network, and a decision tree (Hoon Yoo, Page 1: “This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available”), and the respiratory event is a normal breathing, hypopnea, a flow limitation, an obstructed breathing, an awake, or an apnea event (Zhang, [0108]: “When entering the sleep state, deep sleep is judged based on the measurement range of the signal body movement, breathing value”; [0096]: “According to different apnea waveform characteristics, apnea conditions can be divided into hypopnea state, obstructive apnea state, central apnea state, and mixed apnea state.”).
Regarding independent claim 11, Yang teaches a computing apparatus related to sleep quality ([0002]: “Embodiments of the present invention relate to the technical field of sleep recognition, and in particular, to a method, apparatus, computer device, and storage medium for recognizing sleep characteristic events.”), comprising:
a memory storing a program code ([0129]: “The storage device 628 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 630 and/or cache memory 632 .”; [0145]: “The computer readable signal medium may include a propagated data signal in baseband or as part of a carrier wave with the computer readable program code embodied therein”); and
a processor coupled to the memory and loading the program code to execute ([0040]: “When the one or more programs are executed by the one or more processors, the one or more processors implement the method for identifying sleep characteristic events as described in any embodiment of the present invention.”):
obtaining an echo signal reflected by a transmitted signal of a radar by a human body, wherein the transmitted signal of the radar has a frequency that reflects the human body ([0116]: “the echo signal acquisition module 310 is specifically configured to transmit a preset waveform to the target user through the radar; and receive, through the radar, an echo signal returned by the target user based on the preset waveform.”);
generating sensing data based on the echo signal ([0116]: “the echo signal acquisition module 310 is specifically configured to transmit a preset waveform to the target user through the radar; and receive, through the radar, an echo signal returned by the target user based on the preset waveform.”);
transforming the sensing data into feature data ([0117]: “the judgment module 320 is specifically used to filter the echo signal; perform time-frequency transformation on the filtered echo signal to obtain an energy burst curve; detect the sleep state of the target user according to the energy burst curve, wherein the sleep state includes a first event and a second event”).
However, Yang does not teach wherein the feature data comprises a statistic of a plurality of feature points of the radar echo on a waveform.
Zhang discloses a monitoring device to monitor health status of a user. Specifically, Zhang teaches wherein the feature data comprises a statistic of a plurality of feature points of the radar echo on a waveform ([0008]: “receive the echo signal and process and extract the signal waveform”; [0027]: “The signal wave is subjected to median filtering, smoothing filtering, DC removal and low-pass filtering, and the respiration value and heart rate value are calculated according to the signal waveform characteristics, the peak-to-peak interval, the valley-to-valley interval, the change of the signal zero-crossing rate, the number of valid waveforms and the signal frequency domain value under waveform stability”), wherein the feature data further comprises a trend of the waveform ([0035]: “The processor comprehensively determines whether a human body exists and whether the human body is in a calm and stable breathing state based on … the changing trend of the signal amplitude within the set time”). Yang and Zhang are analogous arts as they are both related to monitoring sleep state of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the statistic from Zhang into the method from Yang as it allows for more analysis of the signals received, which can allow for a more accurate result and analysis from the method.
However, the Yang/Zhang combination does not teach where the trend is an intensity variation of the waveform without a pattern characteristic. Shouldice discloses an apparatus and system for motion sensing. Specifically, Shouldice teaches where the trend is an intensity variation of the waveform without a pattern characteristic ([0186]: “the detected amplitude variation may be due to changes
in the intensity of the extracted received respiratory signal”). Yang, Zhang, and Shouldice are analogous arts as they are all related to monitor the state of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the intensity variation from Shouldice into the Yang/Zhang combination as it allows the method to analyze the intensity, which can provide important information about the waveform and state of the user.
The Yang/Zhang/Shouldice combination teaches determining sleep quality information according to the feature data (Yang, [0008]-[0012]: “an embodiment of the present invention provides a method for identifying a sleep characteristic event, comprising: Acquire the echo signal of the target user to be identified based on radar; Determining whether a sleep characteristic event occurs to the target user according to the echo signal; When a sleep characteristic event occurs to the target user, obtaining difference characteristics between before and after the sleep characteristic event occurs to the target user; A target characteristic event is determined according to the difference characteristic, where the target characteristic event is one of a plurality of sleep characteristic events.).
However, the Yang/Zhang/Shouldice combination does not teach wherein the sleep quality information is related to a degree of sleep quality.
Zhou discloses a sleep evaluation method and device. Specifically, Zhou teaches wherein the sleep quality information is related to a degree of sleep quality ([0049]: “The acquired sleep quality scores corresponding to the sleep time information may be one or both of: a sleep quality score corresponding to the sleep time information input by the user, and a sleep quality score determined according to a functional relationship between the sleep quality score and the sleep duration.”). Yang, Zhang, and Zhou are analogous arts as they are all related to monitoring sleep state of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the degree of sleep quality from Zhou into the Yang/Zhang/Shouldice combination as the combination analyzes sleep, but does not provide a degree of sleep quality, so including the degree of sleep quality can provide more information to the user and create a more detailed analysis of the user’s sleep and health status.
However, the Yang/Zhang/Shouldice/Zhou combination does not teach using a respiratory event in the calculation of the sleep quality.
Zhang discloses evaluating a respiratory event in the sleep evaluation ([0096]: “The method for identifying apnea is a combination of respiratory waveform characteristics and signal energy threshold changes”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the respiratory event in the sleep evaluation from Zhang into the Yang/Zhang/Shouldice/Zhou combination as it allows for the method to factor in more parameters that can impact the sleep state of the user, ensuring a more comprehensive analysis.
The Yang/Zhang/Shouldice/Zhou combination teaches predicting an event by a machine learning model ([0029]: “The target feature event is determined based on the output result of the neural network model”).
However, the Yang/Zhang/Shouldice/Zhou combination does not teach the specific type of machine learning model being based on a deep neural decision tree.
Hoon Yoo discloses a deep learning decision-tree classifier for COVID-19 diagnosis. Specifically, Hoon Yoo teaches predicting the respiratory event by a machine learning model, wherein the machine learning model is based on a deep neural decision tree (DNDT) and the deep neural decision tree (DNDT) is trained using the feature data as training data (Page 1: “This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available”). Yang, Zhang, Shouldice, Zhou, and Hoon Yoo are analogous art as they are all related to monitoring health parameters of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the machine learning model from Hoon Yoo into the Yang/Zhang/Shouldice/Zhou combination as the combination is silent on the type of neural network used, and Hoon Yoo discloses a suitable machine learning model in an analogous art.
Regarding claim 12, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the computing apparatus related to sleep quality of claim 11, wherein the feature points comprise at least one of a peak value and a valley value, and the statistic comprises at least one of an interval between two of the feature points, a variation of the interval, and a total number of the feature points (Zhang, [0008]: “receive the echo signal and process and extract the signal waveform”; [0027]: “The signal wave is subjected to median filtering, smoothing filtering, DC removal and low-pass filtering, and the respiration value and heart rate value are calculated according to the signal waveform characteristics, the peak-to-peak interval, the valley-to-valley interval, the change of the signal zero-crossing rate, the number of valid waveforms and the signal frequency domain value under waveform stability”).
Regarding claim 14, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the computing apparatus related to sleep quality of claim 11, wherein the feature data further comprises an entropy of the sensing data (Yang, [0030]: “the difference feature includes one or more of an energy difference feature, an amplitude difference feature, an entropy difference feature”).
Regarding claim 17, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the computing apparatus related to sleep quality of claim 16, wherein the machine learning model is trained to understand a correlation between the feature data and the respiratory event (Yang, [0093]: “the neural network model is a complex network system formed by a large number of simple processing units (neurons) that are widely interconnected. It reflects many basic characteristics of human brain functions and is a highly complex nonlinear dynamic learning system. Specifically, the neural network model in this embodiment is trained in a supervised manner. Taking multiple sleep characteristic events such as sitting up, lying down, leaving the bed, going to bed and turning over as an example, the training data are labeled during training, and the training data are respectively labeled as sitting up, lying down, leaving the bed, going to bed or turning over for training.”; Zhang, [0105]: “In the process of respiratory search, the signal energy value within 5s is used, and the apnea energy threshold is updated in real time. Under normal human breathing conditions, if the respiratory signal energy is lower than the apnea energy threshold, apnea is determined. According to the different characteristics of the apnea signal, the apnea type is classified, which can be mainly divided into four types: obstructive apnea, central apnea, mixed apnea and hypopnea”).
Regarding claim 18, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the computing apparatus related to sleep quality of claim 17, wherein the machine learning model is further based on one of a deep learning neural network, and a decision tree (Hoon Yoo, Page 1: “This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available”), and the respiratory event is a normal breathing, hypopnea, a flow limitation, an obstructed breathing, an awake, or an apnea event (Zhang, [0108]: “When entering the sleep state, deep sleep is judged based on the measurement range of the signal body movement, breathing value”; [0096]: “According to different apnea waveform characteristics, apnea conditions can be divided into hypopnea state, obstructive apnea state, central apnea state, and mixed apnea state.”).
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination as applied to claims 1 and 11 above, and further in view of Wikipedia (“In-phase and quadrature components”).
Regarding claim 3, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the evaluation method of sleep quality of claim 1, wherein the feature data further comprises a variance between two channels or within a single channel in the sensing data (Yang, [0065]: “Adaptively estimate the mean and variance of the energy burst curve”).
However, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination is silent on the channels being analyzed.
Wikipedia discloses components a signal can be divided into. Specifically, Wikipedia discloses wherein the two channels comprise an in-phase signal and a quadrature signal (Page 1: “The amplitude modulated sinusoids are known as in-phase and quadrature components”). Yang, Zhang, Shouldice, Zhou, Hoon Yoo, and Wikipedia are analogous arts as they are all related to signal processing.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the channel types from Wikipedia into the combination as the combination is silent on the channel types and Wikipedia discloses suitable channels in an analogous art.
Regarding claim 13, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the computing apparatus related to sleep quality of claim 11, wherein the feature data further comprises a variance between two channels or within a single channel in the sensing data (Yang, [0065]: “Adaptively estimate the mean and variance of the energy burst curve”).
However, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination is silent on the channels being analyzed.
Wikipedia discloses components a signal can be divided into. Specifically, Wikipedia discloses wherein the two channels comprise an in-phase signal and a quadrature signal (Page 1: “The amplitude modulated sinusoids are known as in-phase and quadrature components”). Yang, Zhang, Shouldice, Zhou, Hoon Yoo, and Wikipedia are analogous arts as they are all related to signal processing.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the channel types from Wikipedia into the combination as the combination is silent on the channel types and Wikipedia discloses suitable channels in an analogous art.
Claims 9-10 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination as applied to claims 1 and 11 above, and further in view of Shouldice ‘125 (CN 111655125). Citations to CN 111655125 will refer to the English Machine Translation that accompanies this Office Action.
Regarding claim 9, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the evaluation method of sleep quality of claim 1.
However, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination does not teach wherein the sleep quality information further comprises a sleep statistical indicator, the sleep statistical indicator is a respiratory disturbance index or an apnea-hypopnea index, and the step of predicting the respiratory event according to the feature data comprises: determining the sleep statistical indicator according to the predicted respiratory event.
Shouldice ‘125 teaches wherein the sleep quality information further comprises a sleep statistical indicator, the sleep statistical indicator is a respiratory disturbance index or an apnea-hypopnea index, and the step of predicting the respiratory event according to the feature data comprises: determining the sleep statistical indicator according to the predicted respiratory event ([0275]: “By detecting and counting apnea and hypopnea events, the AHI (Apnea-Hypopnea Index) can be assessed.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the statistical indicator from Shouldice ‘125 into the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination as it allows the combination to have another type of analysis of the data, which can provide more information to the system to provide a more accurate analysis.
Regarding claim 10, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the evaluation method of sleep quality of claim 1.
However, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination does not teach wherein the sleep quality information further comprises a sleep statistical indicator, the sleep statistical indicator is a respiratory disturbance index or an apnea-hypopnea index, and the step of predicting the respiratory event according to the feature data comprises: determining the sleep statistical indicator according to the predicted respiratory event.
Shouldice ‘125 teaches wherein the sleep quality information comprises a sleep statistical indicator, the sleep statistical indicator is a respiratory disturbance index or an apnea-hypopnea index, and the step of determining the sleep quality information according to the feature data comprises: predicting the sleep statistical indicator according to the feature data ([0275]: “By detecting and counting apnea and hypopnea events, the AHI (Apnea-Hypopnea Index) can be assessed.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the statistical indicator from Shouldice ‘125 into the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination as it allows the combination to have another type of analysis of the data, which can provide more information to the system to provide a more accurate analysis.
Regarding claim 19, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the computing apparatus related to sleep quality of claim 11.
However, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination does not teach wherein the sleep quality information further comprises a sleep statistical indicator, the sleep statistical indicator is a respiratory disturbance index or an apnea-hypopnea index, and the step of predicting the respiratory event according to the feature data comprises: determining the sleep statistical indicator according to the predicted respiratory event.
Shouldice ‘125 teaches wherein the sleep quality information further comprises a sleep statistical indicator, the sleep statistical indicator is a respiratory disturbance index or an apnea-hypopnea index, and the processor further executes: determining the sleep statistical indicator according to the predicted respiratory event ([0275]: “By detecting and counting apnea and hypopnea events, the AHI (Apnea-Hypopnea Index) can be assessed.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the statistical indicator from Shouldice ‘125 into the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination as it allows the combination to have another type of analysis of the data, which can provide more information to the system to provide a more accurate analysis.
Regarding claim 20, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination teaches the computing apparatus related to sleep quality of claim 11.
However, the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination does not teach wherein the sleep quality information further comprises a sleep statistical indicator, the sleep statistical indicator is a respiratory disturbance index or an apnea-hypopnea index, and the step of predicting the respiratory event according to the feature data comprises: determining the sleep statistical indicator according to the predicted respiratory event.
Shouldice ‘125 teaches wherein the sleep quality information comprises a sleep statistical indicator, the sleep statistical indicator is a respiratory disturbance index or an apnea-hypopnea index, and the processor further executes: predicting the sleep statistical indicator according to the feature data ([0275]: “By detecting and counting apnea and hypopnea events, the AHI (Apnea-Hypopnea Index) can be assessed.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the statistical indicator from Shouldice ‘125 into the Yang/Zhang/Shouldice/Zhou/Hoon Yoo combination as it allows the combination to have another type of analysis of the data, which can provide more information to the system to provide a more accurate analysis.
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant’s arguments with respect to claims 1-4, 7-14, and 17-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791