DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Applicant's election with traverse of the invention of Group I (claims 1-6, 8) including Group III (claims 23-25, 28, 30) in the reply filed on 02/19/2026 is acknowledged.
Applicant’s arguments, see pgph. 3-4, filed 02/19/2026, with respect to excluding Ha et al. (Unsoo Ha, Sohrab Madani, and Fadel Adi, 2021, “WiStress: Contactless Stress Monitoring Using Wireless Signals”) as prior art have been fully considered and are persuasive. Therefore, the restriction using Ha et al. as prior art has been withdrawn.
Applicant further traversed the restriction on the ground(s) that Groups I/III relate to a single general inventive concept because both have the same or corresponding special technical feature. This is not found persuasive because of the following reasons:
Group I teaches measuring wireless signal reflections to generate a signal representative of a subject’s distance from a sensor. Group III teaches receiving time-domain signals and extracting features that represent a subject’s vital signs and body movements. While both groups teach a method for measuring stress by providing feature data to a stress classification network, the processes of sensing and obtaining the feature data are separate inventive concepts (see below for additional details).
Restriction between Groups I/II/III is required using Yuen et al. (US Pre-Grant Publication 2010/0130873) as prior art.
Group I: claims 1-6, 8, 11, drawn to a method for measuring stress of a subject by transmitting and measuring wireless signal reflections.
Group II: claims 12-14, 16, 19-20, 22, drawn to a method for extracting heartbeats intervals.
Group III: claims 23-25, 28, 30, drawn to a method for measuring stress of a subject by receiving time-domain signals.
The groups of inventions listed above do not relate to a single general inventive concept under PCT Rule 13.1 because, under PCT Rule 13.2, they lack the same or corresponding special technical features for the following reasons:
Groups I, II lack unity of invention because even though the inventions of these groups require the technical features of: processing a physiological signal, extracting components from a time-domain signal, these technical features are not special technical features as they do not make a contribution over the prior art in view of Yuen et al. (US Pre-Grant Publication 2010/0130873). Yuen teaches a rate estimation algorithm to process samples and estimate a time domain respiration rate [0028].
Groups I, III lack unity of invention because even though the inventions of these groups require the technical features of: extracting feature data and providing the feature data as input to a stress classification network to determine stress level, these technical features are not special technical features as they do not make a contribution over the prior art in view of Yuen et al. (US Pre-Grant Publication 2010/0130873). Yuen teaches a method of measuring paradoxical breathing using an algorithm that processes a motion signal to detect events [0038].
Groups II, III lack unity of invention because even though the inventions of these groups require the technical features of: extracting components from a time-domain signal, these technical features are not special technical features as they do not make a contribution over the prior art in view of Yuen et al. (US Pre-Grant Publication 2010/0130873). Yuen teaches a method of estimating a time domain respiration rate [0028].
Claims 12-14, 16, 19-20, 22-25, 28, 30 (Groups II, III) are withdrawn from further consideration as being drawn to nonelected methods for extracting heartbeats intervals and measuring stress of a subject by receiving time-domain signals, respectively. Claims 1-6, 8, 11 (Group I) are being examined.
The requirement is still deemed proper and is therefore made FINAL.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8, 11 are rejected under 35 U.S.C 101 because the claimed invention is directed to non-statutory subject matter of abstract ideas under the mental processes grouping, without significantly more.
The framework for establishing a prima facie case of lack of subject matter eligibility requires that the Examiner determine: (1) Does the claim fall within the four categories of patent eligible subject matter; (2a) Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon and (2a) Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application; and (2b) Does the claim recite additional elements that amount of significantly more than the judicial exception.
Step (1)
The claimed invention in claims 1-6, 8, 11 are directed to a method, and thus, the claims all fall under one of the four patent eligible categories.
Step (2a) Prong 1 (Judicial Exception)
Regarding claims 1-6, 8, 11, the recited steps are directed towards mental processes of performing concepts in a human mind or by a human using a pen and paper (See MPEP 2106.05(a)(2) subsection (I)).
Independent claim 1 recites:
processing the physiological signal to extract feature data of the subject;
providing the feature data as input to a stress classification network to determine a stress level of the subject.
Under the broadest reasonable interpretation, these limitations require processing a signal to extract components, and providing the components to a stress classification network to determine a stress level. These limitations are processes that, as drafted, cover that which can be wholly performed in a person’s mind via a series of mental observations and judgements. In particular, a person can extract relevant components from a signal and use them to calculate a user’s stress level. These are data gathering and processing steps (process, extract, provide, determine) that reflect mental processes.
Accordingly, claim 1 is directed to a judicial exception including one or more abstract ideas, specifically mental processes.
The dependent claims recite additional limitations for the signal processing, including types of feature data/components, additional data processing steps, and types of wireless signals. These limitations also fall within the judicial exception of mental processes.
Step (2a) Prong 2 (Integration into a Practical Application)
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. MPEP 2106.04(d).
For claims 1-6, 8, 11, the judicial exception is not integrated into a practical application.
Regarding claim 1, the additional element of a sensor that transmits a wireless signal amounts to recitation of a generic sensor. Under the broadest reasonable interpretation, this element is nothing more than the pre-solution activity of mere data gathering using generic components.
Regarding claim 1, the additional element of sending a signal and measuring reflections of a wireless signal to generate a physiological signal is also nothing more than the pre-solution activity of mere data gathering.
Regarding claim 1, the additional element of a stress classification network amounts to recitation of a generic algorithm/computer. This additional element merely defines the field of user of the current claim. This additional element does not practically integrate the judicial exception because this element does not provide improvements to the functioning of a computer or to any the technical field under MPEP 2106.05(a). Furthermore, when the claims, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it is still in the mental processes grouping unless the claim limitation cannot practically be performed in the mind. Likewise, performance of a claim limitation using generic computer components does not preclude the claim limitation from being in the mental processes grouping.
Step (2b) (Inventive Concept)
The claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements of a sensor and classification algorithm in the field of patient vital sign monitoring are well-understood, routine and conventional activities previously known in the industry as indicated in the following references:
Barsimantov et al. (US Pre-Grant Publication 2016/0361041) teaches a transducer that senses chest wall movements [0092] and a signal analysis algorithm [0048].
Yuen et al. (US Pre-Grant Publication 2010/0130873) teaches a physiological motion sensor system (100, Fig. 1A) with a radar (101, Fig. 1A) [0093] and a rate estimation algorithm [0209].
Dependent claim 11 recites an antenna array, which is also recited at a high level of generality and is considered to be well-known, routine and conventional in the art as indicated in the following references:
Heneghan et al. (US Pre-Grant Publication 2014/0163343) teaches an antenna array [0061].
Yuen et al. (US Pre-Grant Publication 2010/0130873) teaches an antenna array [0336].
Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-6, 8, 11 are thus rejected under 35 USC 101 for reciting patent-ineligible subject matter- abstract ideas.
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.
Claim(s) 1-4, 6, 8, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yuen et al. (US Pre-Grant Publication 2010/0130873), hereinafter ‘Yuen’, in view of Della Torre et al. (US Pre-Grant Publication 2015/0245777), hereinafter ‘Della Torre’.
Regarding claim 1, Yuen teaches a method for measuring stress of a subject ([0032], psycho-physiological state monitor, Fig. 7), the method comprising:
transmitting, by a sensor ([0160], sensor 700 includes transmitter 701, Fig. 7), a wireless signal within an environment comprising the subject ([0166], signal transmitted by transmitters and scattered by subject);
measuring reflections of the wireless signal to generate a physiological signal responsive to changes in distance between the subject and the sensor over time ([0007], extracting Doppler shifted signal from scattered radiation, transforming to digitized motion signal that corresponds to motion of the subject, [0231], chest and abdomen expansion/contraction impacts received reflecting signals, Fig. 7); and
processing the physiological signal to extract feature data of the subject ([0007], demodulating frames of the digitized motion signal and processing to obtain information corresponding to physiological movement of the subject).
While Yuen does teach that the processing of the measured signals may be useful as biofeedback for stress (e.g. [0272]), Yuen does not specifically teach inputting the data to a stress classification network to determine the subject’s stress level.
In a similar field of endeavor, Della Torre teaches a system and method for identifying a stress state of a user based on patient signals (abstract, [0057]), the method further comprising:
providing the feature data ([0047], data from a user decomposed into features) as input to a stress classification network to determine a stress level of the subject ([0047], classification of the stress state of the user, [0075], types of classifiers).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yuen to incorporate the teachings of Della Torre to include providing data to a stress classification network. Doing so would allow for a trainable classifier to more accurately evaluate the emotional state of the subject, as recognized by Della Torre [0076].
Regarding claim 2, Yuen and Della Torre teach the method according to claim 1. Yuen teaches the method further comprising:
wherein the feature data comprises data representing respiration of the subject ([0009], motion due to respiratory activity of the subject).
Regarding claim 3, Yuen and Della Torre teach the method according to claim 2. Yuen teaches the method further comprising:
wherein the processing of the physiological signal comprises:
filtering the physiological signal using a band-pass filter to generate a respiration signal responsive to respiration of the subject ([0044], filtering digitized motion signal using low pass filter to identify cardiopulmonary motion); and
identifying local maxima and minima of the respiration signal to extract the data representing respiration of the subject ([0181], peak detection for breath-to-breath interval determination involves finding local maxima and minima).
Regarding claim 4, Yuen and Della Torre teach the method according to claim 1. Yuen teaches the method further comprising:
wherein the feature data comprises data representing heartbeats of the subject ([0009], motion due to cardiac activity of the subject).
Regarding claim 6, Yuen and Della Torre teach the method according to claim 1. Yuen teaches the method further comprising:
wherein the feature data comprises data representing body movements of the subject, said movements being associated with respiration and/or heartbeat of the subject ([0009], motion due to cardiac activity/respiratory activity of the subject).
Regarding claim 8, Yuen and Della Torre teach the method according to claim 1. Yuen teaches the method further comprising:
wherein the transmitting of the wireless signal comprises transmitting at least one of a millimeter wave signal ([0161], transceiver can operate at any frequency between 100MHz – 100GHz) and a Frequency- Modulated Continuous Wave (FMCW) wireless signal ([0161], continuous wave implementation, [0163], modulating frequency).
Regarding claim 11, Yuen and Della Torre teach the method according to claim 1. Yuen teaches the method further comprising:
wherein the transmitting of the wireless signal comprises transmitting the wireless signal via an antenna array of the sensor ([0343], antenna array, Fig. 31) and the environment comprises multiple subjects ([0113], monitor/measure other subjects nearby, suppressing motion sources), the method further comprising beamforming the wireless signal in a direction of the subject ([0344], antenna radiation beams focused on target).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yuen et al. (US Pre-Grant Publication 2010/0130873) in view of Della Torre et al. (US Pre-Grant Publication 2015/0245777), further in view of Giancardo et al. (US Pre-Grant Publication 2015/0272504), hereinafter ‘Giancardo’.
Regarding claim 5, Yuen and Della Torre teach the method according to claim 4. Yuen teaches the method further comprising:
wherein the processing of the physiological signal comprises:
dividing the physiological signal into a plurality of time-domain segments ([0007], one or more frames in the digitized motion signal);
extracting a plurality of time-domain ([0330], time domain rate estimation algorithm for cardiac activity) features from the physiological signal by processing individual ones of the plurality of time-domain segments using a feature extraction network ([0007], demodulating frames of the digitized motion signal and processing to obtain information corresponding to physiological movement of the subject);
generating a matrix by cross-correlating the plurality of time- domain features ([0023], covariance matrices between frames); and
using the matrix to extract the data representing heartbeats of the subject ([0180], algorithms used to isolate physiological motion signals, one embodiment being isolating heart signals).
Yuen and Della Torre do not teach that the matrix is a self-similarity matrix (SSM).
Giancardo teaches a system and method for monitoring a person’s motor function to indicate a condition of a user [0044], further comprising calculating a self-similarity matrix for a plurality of distributions [0086].
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yuen and Della Torre to incorporate the teachings of Giancardo to include a self-similarity matrix. Doing so would allow for an indication of the degree of variation amongst data pieces/distributions, as recognized by Giancardo [0088].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al. (US Pre-Grant Publication 2020/0138306) teaches feature selection for cardiac arrhythmia detection.
Barsimantov et al. (US Pre-Grant Publication 2016/0361041) teaches a system for cardiac output assessment.
Houlton et al. (US Pre-Grant Publication 2015/0038856) teaches a system for assessment of cardiac contractility.
Lee et al. (Lee, H., & Whang, M. (2018). Heart Rate Estimated from Body Movements at Six Degrees of Freedom by Convolutional Neural Networks. Sensors (Basel, Switzerland), 18(5), 1392. https://doi.org/10.3390/s18051392) teaches heart rate estimation using neural networks.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH L OKONAK whose telephone number is (571)272-1594. The examiner can normally be reached Monday-Friday 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at (571) 270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.L.O./
Examiner, Art Unit 3792
/MALLIKA D FAIRCHILD/Primary Examiner, Art Unit 3792
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