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 .
Status of Claims
Claims 7, 9-10, 12-22, 26-27, 29, and 31-32 are cancelled.
Claims 23-25, 28, and 30 are withdrawn.
Claims 33-39 are new.
Claims 1-6, 8, 11, and 33-39 are examined.
Response to Arguments
Applicant's arguments, see pg. 8-10, filed 06/30/2026, regarding the 35 USC 101 rejection of claims 1-6, 8, and 11 have been fully considered but they are not persuasive.
Applicant argues that claim 1, as amended, is not directed towards a mental process or abstract idea, and specifically references the transmitting/measuring of a signal and generation of an output (see pg. 8 of remarks). The examiner disagrees. As stated in the non-final rejection (pg. 7, filed 03/31/2026), the limitations: “transmitting, by a sensor, a wireless signal” and “measuring reflections of a wireless signal” amount to nothing more than the pre-solution activity of mere data gathering using generic components. The amendments to claim 1 to include “generating, by an output device, an output indicative of the stress level of the subject” amounts to nothing more than the post-solution activity of providing results.
Applicant further argues that the features, when taken together, amount to significantly more and practically integrate the abstract idea. The examiner disagrees. Although the claim does recite features that automatically determine and provide feedback to the subject regarding their stress level, this is not a practical application since the claim does not teach any adjustment to any parameter or treatment based on the results provided. Simply providing results, as stated above, amounts to nothing more than post-solution activity.
Applicant argues that the use of wireless sensors, wireless signal reflections, and an output device indicates the claim cannot be performed in a human mind/using a pen and paper. The examiner disagrees. As stated in the non-final office action (pg. 5), a person could analyze a reflected signal to extract relevant components related to a user’s stress level. The additional element of a sensor amounts to recitation of a generic sensor. Merely stating that the abstract idea will be for "measuring stress of a subject" is an instruction to apply the abstract idea in a particular technological environment. As in Alice Corp. v. CLS Bank, 573 U.S. 208, 223 (2014), limiting an abstract idea to a field of use or adding generic hardware does not integrate the exception into a practical application.
Applicant's arguments see pg. 10-13, regarding Della Torre disclosing stress-correlated biometrics have been fully considered but they are not persuasive. Della Torre recites in [0056] (emphasis added):
“The timing of each phase was recorded so that the biometric data from each phase could be identified and labeled with the specific emotional state of the subject.”
Della Torre further recites in the abstract (emphasis added):
“A system and a method are disclosed for identifying and characterizing a stress state of a user based on features of blood flow identified from optical signals.”
Therefore, under the broadest reasonable interpretation, Della Torre discloses stress-correlated biometrics.
Applicant’s arguments, see pg. 12-13, regarding the 35 USC 103 rejection of the claims, have been fully considered. The amendments to independent claim 1 to include the added limitations overcome the rejection detailed in the non-final filed 03/31/2026. Since independent claim 1 was amended to include new limitations, new grounds of rejection are warranted for independent claim 1 and dependent claims 2-6, 8, and 11. See prior art rejections below.
Claim Objections
Claims 1 and 37 are objected to because of the following informalities:
Regarding claim 1, “on” should be added so the claim reads: “based on one or more” (line 8).
Regarding claim 37, “predetermine” should be changed to “predetermined” (line 3).
Appropriate correction is required.
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, and 33-39 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, and 33-39 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, and 33-39, 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 (III)).
Independent claim 1 recites:
processing the physiological signal to extract feature data of the subject for one or more stress-correlated biometrics; and
providing the feature data as input to a stress classification network to determine a stress level of the subject based on one or more of the stress-correlated biometrics.
Independent claim 39 recites:
processing the physiological signal to extract feature data of the subject, and
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, claims 1 and 39 are 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/storage steps, types of wireless signals, and display options. 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, and 33-39, the judicial exception is not integrated into a practical application.
Regarding claims 1 and 39, 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 claims 1 and 39, 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 using generic components.
Regarding claims 1 and 39, the additional elements of generating an output amount to recitation of a generic display. Under the broadest reasonable interpretation, these elements are nothing more than the post-solution activity of providing results using generic components.
Regarding claims 1 and 39, 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, classification algorithm, and display 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], a signal analysis algorithm [0048], and a display [0044].
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], a rate estimation algorithm [0209], and a display (203, Fig. 2, [0101]).
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, and 33-39 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.
Claims 1-4, 6, 8, 11, 33-35, and 38-39 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 for one or more stress-correlated biometrics ([0007], demodulating frames of the digitized motion signal and processing to obtain information corresponding to physiological movement of the subject, [0117], presence/degree of distressed breathing).
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. Yuen also discloses a display (see Fig. 2, display 203, [0101]), but does not specifically teach that the stress level is displayed.
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) based on one or more of the stress-correlated biometrics ([0056], biometric data labeled with emotional state of subject); and
generating, by an output device (display 104, Fig. 1), an output indicative of the stress level of the subject ([0045], determined stress state is displayed to user).
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 and displaying the stress level of the user. Doing so would allow for a trainable classifier to more accurately evaluate the emotional state of the subject, and to alert the user if they are experiencing a high amount of stress, as recognized by Della Torre ([0076] and [0045]).
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 body 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).
Regarding claim 33, Yuen and Della Torre teach the method according to claim 6. Yuen teaches the method further comprising:
wherein the processing of the physiological signal comprises:
processing the physiological signal to extract a signal representing power of displacement (2502, Fig. 25B, chest displacement, [0141], detect human motion at a distance such as surface displacements); and
processing the signal representing power of displacement to determine an intensity ([0029], peak detection) and a duration ([0142], duration of activity) to extract the data representing body movements of the subject.
Regarding claim 34, Yuen and Della Torre teach the method according to claim 1. Yuen teaches the method further comprising:
wherein generating, by the output device, the output indicative of the stress level of the subject includes sending information about the stress level to a database for storing stress level data ([0102], forward data to remote database).
Regarding claim 35, Yuen and Della Torre teach the method according to claim 34. Della Torre teaches the method further comprising:
storing the stress level of the subject at different points in time to track the stress level of the subject over time ([0091], detection of emotional states over time).
Regarding claim 38, Yuen and Della Torre teach the method according to claim 34. Yuen teaches the method further comprising:
wherein the method further comprises sending, by the processing device, information about the stress level of the subject to the output device (Fig. 2, signal processor 202 in communication with display 203).
Regarding claim 39, 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, [0117], presence/degree of distressed breathing), wherein the feature data comprises one or more of data representing body movements of the subject, data representing heartbeats of the subject, or data representing respiration of the subject (Fig. 25B, respiratory rate, [0141], detect human motion including fidgeting and cardiopulmonary activity).
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. Yuen also discloses a display (see Fig. 2, display 203, [0101]), but does not specifically teach that the stress level is displayed.
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) by combining two or more of the data representing body movements of the subject, data representing heartbeats of the subject, or data representing respiration of the subject ([0057-0067], signals that can be used include motion, ECG, respiration rate); and
generating, by an output device (display 104, Fig. 1), an output indicative of the stress level of the subject ([0045], determined stress state is displayed to user).
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 and displaying the stress level of the user. Doing so would allow for a trainable classifier to more accurately evaluate the emotional state of the subject, and to alert the user if they are experiencing a high amount of stress, as recognized by Della Torre ([0076] and [0045]).
Claim 5 is 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].
Claims 36-37 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 Galm et al. (US Pre-Grant Publication 2019/0223773), hereinafter ‘Galm’.
Regarding claim 36, Yuen and Della Torre teach the method according to claim 1, but do not specifically teach displaying a graphical representation of the subject’s stress.
Galm teaches a device that detects/displays a mental state of a user (abstract), further comprising:
displaying, by the output device, a graphical representation showing the stress level of the subject ([0056], stress level presented on display in graphical manner, Fig 7).
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 Galm to include displaying a graphical representation of the subject’s stress. Doing so would enable the user to utilize the information in various practical ways, as recognized by Galm [0085].
Regarding claim 37, Yuen and Della Torre teach the method according to claim 1, but do not specifically teach displaying a numeric value of the stress level.
Galm teaches a device that detects/displays a mental state of a user (abstract), further comprising:
wherein generating, by the output device, the output indicative of the stress level of the subject includes encoding the stress level as a numeric value (score icon 802, Figs. 8A-8E) within a predetermine range ([0056], stress level presented on display in numeric manner) ([0100], range of 1 to 99).
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 Galm to include displaying a numerical representation of the subject’s stress. Doing so would enable the user to utilize the information in various practical ways, as recognized by Galm [0085].
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cnaan et al. (US Pre-Grant Publication 2021/0377657) teaches a system that predicts a user’s emotional state/stress level (see [0258]).
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.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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
/SHIRLEY X JIAN/Primary Examiner, Art Unit 3792
August 18, 2026
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