This Office Action replaces the Office Action mailed on 01/12/2026.
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 .
Response to Preliminary Amendment
This Office Action is responsive to the amendment filed on 09 Aug 2023 under PCT Article 34. Thus, claims 1-14 are presently pending in this application.
Claim Objections
Claims 2, 3, 9, and 14 are objected to because of the following informalities:
Claim 2:
“assigning the heartbeat frequency encoding an identification label” on page 23, lines 8-9 should read “assigning the heartbeat frequency encoding to an identification label”
The comma after “dataset” on page 23, line 13 should be omitted
Claim 3: “a linear mapping” should read “the linear mapping”
Claim 9:
“assigning the heartbeat frequency encoding an identification label” on page 25, lines 21-22 should read “assigning the heartbeat frequency encoding to an identification label”
The comma after “dataset” on page 26, line 5 should be omitted
Claim 14 should read “The system of claim 8, wherein the system is configured to simultaneously monitor or identify multiple subjects in a location.”
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Determination as to whether a claim satisfies the criteria for subject matter eligibility is a stepwise process (MPEP 2016).
Step 1: Does the claim fall within a statutory category of invention?
Claim 1 recites a process (method), and claim 8 recites a machine (system), which are within the four statutory categories. Therefore, claims 1 and 8 are directed to a statutory category of invention.
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1 and 8 are directed to an abstract idea.
Claim 1 is directed to receiving a reflection THz radar signal reflected from a body tissue of a monitored subject using contactless detection; deriving a cardiac ballistocardiogram (BCG) signal from the reflection THz radar signal; segmenting the derived BCG signal into a plurality of discrete temporal segments, each temporal segment being of a selected time duration; applying at least one linear mapping to each of the temporal segments to produce a heartbeat frequency encoding; and
applying at least one machine learning model for subject classification on the heartbeat frequency encoding during an identification stage to classify the heartbeat frequency encoding as belonging to a reference subject if a matching classification is obtained, or to determine that the heartbeat frequency encoding belongs to a non-reference subject if no matching classification is obtained.
Claim 8 recites a system that carries out the same method disclosed in claim 1.
The limitations of segmenting the derived BCG signal, applying at least one linear mapping, and applying at least one machine learning model, as drafted, under their broadest reasonable interpretations, are merely mental processes, because these steps are akin to having a doctor or other human actor performing these operations with pen and paper. For example, “segmenting the derived BCG signal” encompasses nothing more than a human actor mentally evaluating the collected data and deciding how to segment it. The limitation of “applying at least one machine learning model…to classify the heart beat frequency encoding” encompasses nothing more than a human actor mentally evaluating the heartbeat frequency encoding by comparing it to a reference to reach a conclusion about the encoding.
Therefore, claims 1 and 10 recite an abstract idea.
Claims 2-7 depend on claim 1, and claims 9-14 depend on claim 8. These dependent claims only recite additional features of the analysis described in claims 1 and 8, which may also be performed by a human actor mentally and using a pen and paper. For example, claims 3 and 10 recite filtering with a plurality of bandpass filters, which encompasses nothing more than a human actor performing mathematical operations with pen and paper.
Therefore, claims 1-18 recite an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
This judicial exception is not integrated into a practical application.
Claim 8 only recites the additional limitations “a cardiac signal processor” and “a machine learning processor”. These additional elements are recited at a high level of generality (i.e. most generic computers would be known to have these components). Pages 12-13 of the specification describe the processors at a high level of generality. These generic processor limitations are no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore claim 10 does not integrate the judicial exception into a practical application.
Claim 8 recites the additional limitation “a cardiac signal detector”, which amounts to no more than mere pre-solution activity of data gathering. Therefore the claimed generic detector element does not integrate the judicial exception into a practical application.
Claims 5 and 12 recite the additional limitation of “a remote non-invasive radar detector”, which amounts to no more than pre-solution activity of data gathering. Therefore the claimed generic detector does not integrate the judicial exception into a practical application.
Thus, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea. As described above, dependent claims 2-4, 6-7, 9-11, and 13-14 only recite other limitations of processing and analyzing the cardiac signal, which may be done mentally by a human actor and/or with a pen and paper.
Step 2B: Does the claim include additional elements that are sufficient to amount to significantly more than the judicial exception?
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above with respect to the integration of the judicial exception into a practical application (Step 2A, Prong 2), the additional elements of using computer components to perform the process steps amounts to no more than mere instructions to apply the judicial exception using generic computer elements. The structural elements recited in claim 8 are “a cardiac signal processor”, and “a machine learning processor”. These additional elements are recited at a high level of generality (i.e. most generic computers would be known to have these components). Pages 12-13 of the specification describe the processors at a high level of generality, and only provides conventional, well-known computing functions that do not add meaningful limits to practicing the abstract idea.
Claims 5 and 12 recite the additional limitation “a remote non-invasive radar detector”. As discussed above with respect to integration of the abstract idea into a practical application (Step 2A, Prong 2), the additional element of a detector to collect data amounts to no more than mere pre-solution activity of data gathering. This pre-solution activity of data gathering using a remote non-invasive radar detector is well-understood, routine, and conventional in the field of radar-based identity authentication technology. For example, see Islam et al. (“Radar-Based Non-Contact Continuous Identity Authentication”, 2020, cited in IDS filed 02 Jan 2026), which describes known methods of remotely measuring vital signs using radar (Section 3.2. Radar-Based Identity Authentication through Heart-Based Features). Therefore, the claimed generic radar detector and computer processing elements are all well-understood, routine, and conventional in the field of radar-based identity authentication technology.
Therefore, claims 1-18 are not patent-eligible under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-14 are rejected under 35 U.S.C. 103 as being obvious over Steinberg et al. (US 20220079464 A1), hereinafter Steinberg, in view of Liu et al. (US 20170188971 A1), hereinafter Liu.
The applied Steinberg reference has a common applicant with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2).
This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
Regarding claim 1, Steinberg discloses a method for biometric identification (paragraphs [0100], [0108]), the method comprising the procedures of:
receiving a reflection THz radar signal reflected from a body tissue of a monitored subject using contactless detection (paragraph [0080], "a remote portable non-contact detection system ... one or more reception means for receiving the sub-THz and THz signal of the subject. The received sub-THz and THz signals being a reflection of the sub-THz and THz signal from subject thereby");
deriving a cardiac ballistocardiogram (BCG) signal from the reflection THz radar signal (paragraph [0090], "The microprocessor is further configured to perform analysis, calculation, data processing, automated reasoning, storing and/or processing the received sub-THz and THz signals and detect at least one physiological parameter"; paragraph [0088], "The term ‘Physiological Parameters’ herein refers to any physiological indicator...such as...ballistocardiogram(BCG), BCG amplitude variability"); and
segmenting the derived BCG signal into a plurality of discrete temporal segments, each temporal segment being of a selected time duration (paragraph [0133], "folding or mirroring the signals and decimating selected portions of the folded signals and removing folded segments");
applying at least one linear mapping to each of the temporal segments to produce a heartbeat frequency encoding (paragraph [0133], bandpass filtering; page 19, line 15 of the instant specification discloses that linear mapping can include band pass filtering); and
identifying a subject based on the BCG signal according to stored data (paragraphs [0100], [0108], [0113]).
Steinberg does not explicitly disclose applying at least one linear mapping to each of the temporal segments to produce a heartbeat frequency encoding; and applying at least one machine learning model for subject classification on the heartbeat frequency encoding during an identification stage to classify the heartbeat frequency encoding as belonging to a reference subject if a matching classification is obtained, or to determine that the heartbeat frequency encoding belongs to a non-reference subject if no matching classification is obtained.
However, Liu teaches a method for ECG authentication (Abstract) comprising:
segmenting the signal into a plurality of discrete temporal segments, each temporal segment being of a selected time duration (Fig. 2, paragraph [0055], step 220, "The preprocessing includes detection of a fiducial point and acquirement of a data segment"; paragraph [0067], acquirement of data segments);
applying at least one linear mapping to each of the temporal segments to produce a heartbeat frequency encoding (Fig. 2, paragraph [0055], step 220, "the ECG authentication apparatus filters the ECG signal using a band pass filter configured to pass a predefined frequency band"; paragraphs [0017]-[0018], [0065], [0095]-[0097]; page 19, line 15 of the instant specification discloses that linear mapping can include band pass filtering); and
applying at least one machine learning model for subject classification on the heartbeat frequency encoding during an identification stage to classify the heartbeat frequency encoding as belonging to a reference subject if a matching classification is obtained, or to determine that the heartbeat frequency encoding belongs to a non-reference subject if no matching classification is obtained (Fig. 2, paragraph [0061], step 240, "The ECG authentication apparatus calculates a similarity between the semantic feature and a predefined registered feature or a reference feature corresponding to a target to be compared with the semantic feature. The authentication apparatus determines an authentication result to be a success in authentication or a fail in authentication based on a comparison result of the calculated similarity and a threshold").
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 modify Steinberg with the teachings of Liu so that the method comprises applying at least one linear mapping to each of the temporal segments to produce a heartbeat frequency encoding; and applying at least one machine learning model for subject classification on the heartbeat frequency encoding during an identification stage to classify the heartbeat frequency encoding as belonging to a reference subject if a matching classification is obtained, or to determine that the heartbeat frequency encoding belongs to a non-reference subject if no matching classification is obtained, because doing so provides a method of authentication using markers that are not easily stolen, lost, or forged (Liu, paragraph [0005]).
Regarding claim 2, the method of claim 1 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses:
for each of a plurality of reference subjects (paragraphs [0109], [0115]),
receiving a reflection THz radar signal reflected from a body tissue of a monitored subject using contactless detection (paragraph [0080], "a remote portable non-contact detection system ... one or more reception means for receiving the sub-THz and THz signal of the subject. The received sub-THz and THz signals being a reflection of the sub-THz and THz signal from subject thereby");
deriving a cardiac ballistocardiogram (BCG) signal from the reflection THz radar signal (paragraph [0090], "The microprocessor is further configured to perform analysis, calculation, data processing, automated reasoning, storing and/or processing the received sub-THz and THz signals and detect at least one physiological parameter"; paragraph [0088], "The term ‘Physiological Parameters’ herein refers to any physiological indicator...such as...ballistocardiogram(BCG), BCG amplitude variability"); and
segmenting the derived BCG signal into a plurality of discrete temporal segments, each temporal segment being of a selected time duration (paragraph [0133], "folding or mirroring the signals and decimating selected portions of the folded signals and removing folded segments");
applying at least one linear mapping to each of the temporal segments to produce a heartbeat frequency encoding (paragraph [0133], bandpass filtering; page 19, line 15 of the instant specification discloses that linear mapping can include band pass filtering); and
identifying a subject based on the BCG signal according to stored data (paragraphs [0100], [0108], [0113]).
Steinberg does not explicitly disclose forming a training dataset comprising a plurality of heartbeat frequency encodings obtained from the plurality of reference subjects; and applying at least one machine learning process to the training dataset, to identify classification profiles and patterns of the reference subjects for generating at least one predictive model for predicting a subject classification.
However, Liu further teaches:
forming a training dataset comprising a plurality of heartbeat frequency encodings obtained from the plurality of reference subjects (paragraph [0055], "A characteristic of a passing frequency band of the band pass filter is determined in a process of training a neural network model used for extracting a semantic feature of the ECG"; paragraph [0065], "the training device acquires ECG training data having various frequency bands using a plurality of band pass filters corresponding to different passbands"; paragraph [0095]); and
applying at least one machine learning process to the training dataset to identify classification profiles and patterns of the reference subjects for generating at least one predictive model for predicting a subject classification (paragraph [0059], "the ECG authentication apparatus extracts the semantic feature of the ECG signal using the neural network model. The neural network model is a feature extracting model previously trained based on training data"; paragraph [0073], "The deep training method indicates a machine learning algorithm").
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 modify Steinberg with the teachings of Liu so that the method comprises forming a training dataset comprising a plurality of heartbeat frequency encodings obtained from the plurality of reference subjects; and applying at least one machine learning process to the training dataset, to identify classification profiles and patterns of the reference subjects for generating at least one predictive model for predicting a subject classification, because doing so improves the performance of the model (Liu, paragraphs [0071]-[0072]).
Regarding claim 3, the method of claim 1 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses that the procedure of applying a linear mapping comprises filtering with a plurality of bandpass filters (paragraph [0133]). Liu also further teaches filtering with a plurality of bandpass filters (paragraph [0065], "the training device acquires ECG training data having various frequency bands using a plurality of band pass filters corresponding to different passbands").
Regarding claim 4, the method of claim 1 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses applying at least one nonlinear mapping operation to the temporal segment, wherein the nonlinear mapping operation is gain control (Fig. 2, paragraph [0098], gain control).
Regarding claim 5, the method of claim 1 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses that the radar signal is obtained using a remote non-invasive radar detector (Fig. 1, paragraph [0080], "a remote portable non-contact detection system") comprising:
at least one radar transmitter, configured to transmit a THz radar signal to a predefined body tissue of the subject (Fig. 1, paragraph [0081], transmitter 110; paragraph [0080], "one or more transmission means for transmitting sub-THz and THz signals to a subject body or tissue");
at least one radar receiver, configured to receive a reflection of the transmitted radar signal reflected from the body tissue of the subject (Fig. 1, paragraph [0081], receiver 120; paragraph [0080], "one or more reception means for receiving the sub-THz and THz signal of the subject. The received sub-THz and THz signals being a reflection of the sub-THz and THz signal from subject thereby").
Regarding claim 6, the method of claim 1 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses that the subject classification comprises at least one characteristic selected from the group consisting of: age; gender; race; a physiological condition; a mental condition; a health condition; and any combination thereof (paragraph [0048]).
Regarding claim 7, the method of claim 1 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses simultaneously monitoring or identifying multiple subjects in a location (paragraph [0115]).
Regarding claim 8, Steinberg discloses a system for biometric identification (Fig. 1, paragraphs [0100], [0108]), the system comprising:
a cardiac signal detector, configured to receive a reflection THz radar signal reflected from a body tissue of a monitored subject using contactless detection, and to derive a cardiac ballistocardiogram (BCG) signal from the reflection THz radar signal (paragraph [0080], "a remote portable non-contact detection system ... one or more reception means for receiving the sub-THz and THz signal of the subject. The received sub-THz and THz signals being a reflection of the sub-THz and THz signal from subject thereby");
a cardiac signal processor (Fig. 1, paragraph [0082], data processing means 150; paragraph [0090]), configured to:
segmenting the derived BCG signal into a plurality of discrete temporal segments, each temporal segment being of a selected time duration (paragraph [0133], "folding or mirroring the signals and decimating selected portions of the folded signals and removing folded segments");
applying at least one linear mapping to each of the temporal segments to produce a heartbeat frequency encoding (paragraph [0133], bandpass filtering; page 19, line 15 of the instant specification discloses that linear mapping can include band pass filtering); and
identifying a subject based on the BCG signal according to stored data (paragraphs [0100], [0108], [0113]).
Steinberg does not explicitly disclose applying at least one linear mapping to each of the temporal segments to produce a heartbeat frequency encoding; and a machine learning processor, configured to: apply at least one machine learning model for subject classification on the heartbeat frequency encoding during an identification stage to classify the heartbeat frequency encoding as belonging to a reference subject if a matching classification is obtained, or to determine that the heartbeat frequency encoding belongs to a non-reference subject if no matching classification is obtained.
However, Liu teaches an apparatus for ECG authentication (Abstract) comprising a processor (Fig. 10, paragraph [0122], processor 1010) configured to:
segment the signal into a plurality of discrete temporal segments, each temporal segment being of a selected time duration (Fig. 2, paragraph [0055], step 220, "The preprocessing includes detection of a fiducial point and acquirement of a data segment"; paragraph [0067], acquirement of data segments);
apply at least one linear mapping to each of the temporal segments to produce a heartbeat frequency encoding (Fig. 2, paragraph [0055], step 220, "the ECG authentication apparatus filters the ECG signal using a band pass filter configured to pass a predefined frequency band"; paragraphs [0017]-[0018], [0065], [0095]-[0097]; page 19, line 15 of the instant specification discloses that linear mapping can include band pass filtering); and
applying at least one machine learning model for subject classification on the heartbeat frequency encoding during an identification stage to classify the heartbeat frequency encoding as belonging to a reference subject if a matching classification is obtained, or to determine that the heartbeat frequency encoding belongs to a non-reference subject if no matching classification is obtained (Fig. 2, paragraph [0061], step 240, "The ECG authentication apparatus calculates a similarity between the semantic feature and a predefined registered feature or a reference feature corresponding to a target to be compared with the semantic feature. The authentication apparatus determines an authentication result to be a success in authentication or a fail in authentication based on a comparison result of the calculated similarity and a threshold").
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 modify Steinberg with the teachings of Liu so that the method comprises applying at least one linear mapping to each of the temporal segments to produce a heartbeat frequency encoding; and applying at least one machine learning model for subject classification on the heartbeat frequency encoding during an identification stage to classify the heartbeat frequency encoding as belonging to a reference subject if a matching classification is obtained, or to determine that the heartbeat frequency encoding belongs to a non-reference subject if no matching classification is obtained, because doing so provides a method of authentication using markers that are not easily stolen, lost, or forged (Liu, paragraph [0005]).
Regarding claim 9, the system of claim 8 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses:
for each of a plurality of reference subjects (paragraphs [0109], [0115]),
the cardiac signal detector is configured to:
receive a reflection THz radar signal reflected from a body tissue of a monitored subject using contactless detection (paragraph [0080], "a remote portable non-contact detection system ... one or more reception means for receiving the sub-THz and THz signal of the subject. The received sub-THz and THz signals being a reflection of the sub-THz and THz signal from subject thereby"); and
derive a cardiac ballistocardiogram (BCG) signal from the reflection THz radar signal (paragraph [0090], "The microprocessor is further configured to perform analysis, calculation, data processing, automated reasoning, storing and/or processing the received sub-THz and THz signals and detect at least one physiological parameter"; paragraph [0088], "The term ‘Physiological Parameters’ herein refers to any physiological indicator...such as...ballistocardiogram(BCG), BCG amplitude variability"); and
the cardiac signal processor is configured to:
segment the derived BCG signal into a plurality of discrete temporal segments, each temporal segment being of a selected time duration (paragraph [0133], "folding or mirroring the signals and decimating selected portions of the folded signals and removing folded segments");
apply at least one linear mapping to each of the temporal segments to produce a heartbeat frequency encoding (paragraph [0133], bandpass filtering; page 19, line 15 of the instant specification discloses that linear mapping can include band pass filtering); and
identifying a subject based on the BCG signal according to stored data (paragraphs [0100], [0108], [0113]).
Steinberg does not explicitly disclose forming a training dataset comprising a plurality of heartbeat frequency encodings obtained from the plurality of reference subjects; and applying at least one machine learning process to the training dataset, to identify classification profiles and patterns of the reference subjects for generating at least one predictive model for predicting a subject classification.
However, Liu further teaches:
forming a training dataset comprising a plurality of heartbeat frequency encodings obtained from the plurality of reference subjects (paragraph [0055], "A characteristic of a passing frequency band of the band pass filter is determined in a process of training a neural network model used for extracting a semantic feature of the ECG"; paragraph [0065], "the training device acquires ECG training data having various frequency bands using a plurality of band pass filters corresponding to different passbands"; paragraph [0095]); and
applying at least one machine learning process to the training dataset to identify classification profiles and patterns of the reference subjects for generating at least one predictive model for predicting a subject classification (paragraph [0059], "the ECG authentication apparatus extracts the semantic feature of the ECG signal using the neural network model. The neural network model is a feature extracting model previously trained based on training data"; paragraph [0073], "The deep training method indicates a machine learning algorithm").
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 modify Steinberg with the teachings of Liu so that the method comprises forming a training dataset comprising a plurality of heartbeat frequency encodings obtained from the plurality of reference subjects; and applying at least one machine learning process to the training dataset, to identify classification profiles and patterns of the reference subjects for generating at least one predictive model for predicting a subject classification, because doing so improves the performance of the model (Liu, paragraphs [0071]-[0072]).
Regarding claim 10, the system of claim 8 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses that the procedure of applying a linear mapping comprises filtering with a plurality of bandpass filters (paragraph [0133]). Liu also further teaches filtering with a plurality of bandpass filters (paragraph [0065], "the training device acquires ECG training data having various frequency bands using a plurality of band pass filters corresponding to different passbands").
Regarding claim 11, the system of claim 8 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses applying at least one nonlinear mapping operation to the temporal segment, wherein the nonlinear mapping operation is gain control (Fig. 2, paragraph [0098], gain control).
Regarding claim 12, the system of claim 8 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses that the radar signal is obtained using a remote non-invasive radar detector (Fig. 1, paragraph [0080], "a remote portable non-contact detection system") comprising:
at least one radar transmitter, configured to transmit a THz radar signal to a predefined body tissue of the subject (Fig. 1, paragraph [0081], transmitter 110; paragraph [0080], "one or more transmission means for transmitting sub-THz and THz signals to a subject body or tissue");
at least one radar receiver, configured to receive a reflection of the transmitted radar signal reflected from the body tissue of the subject (Fig. 1, paragraph [0081], receiver 120; paragraph [0080], "one or more reception means for receiving the sub-THz and THz signal of the subject. The received sub-THz and THz signals being a reflection of the sub-THz and THz signal from subject thereby").
Regarding claim 13, the system of claim 8 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses that the subject classification comprises at least one characteristic selected from the group consisting of: age; gender; race; a physiological condition; a mental condition; a health condition; and any combination thereof (paragraph [0048]).
Regarding claim 14, the system of claim 8 is obvious over Steinberg and Liu, as explained above. Steinberg further discloses simultaneously monitoring or identifying multiple subjects in a location (paragraph [0115]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kiaei (US 20170105659 A1) discloses a method for determining a rate of repetitive bodily motion of an individual with negligible contact (Abstract) by detecting reflected radiofrequency signals (paragraph [0029])
Yang et al. (US 12114983 B2) discloses obtaining a signal collected by a radar sensor that can be measured by a non-contact radar sensor (column 5, lines 9-10) and analyzing the signal using a generative adversarial network (column 5, lines 32-33).
Port (EP 3375362 A1) discloses a method of identifying an individual based on reflected wireless signals and the heartbeat features extracted from the reflected signals (paragraph [0025]).
Nakajima et al. (US 20210085217 A1) discloses a bioinformation acquiring apparatus that divides ballistocardiogram signals (paragraph [0014]) into segments (paragraphs [0045]-[0048]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTINE SISON whose telephone number is (703)756-4661. The examiner can normally be reached 8 am - 5 pm PT, Mon - Fri.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer McDonald can be reached at (571) 270-3061. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTINE SISON/Examiner, Art Unit 3796
/Jennifer Pitrak McDonald/Supervisory Patent Examiner, Art Unit 3796