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 .
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of FI20225646, filed in Republic of Finland on 7/8/2022.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 1/8/2025 and 7/28/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached by the examiner.
Preliminary Amendment
Applicant submitted a preliminary amendment on 1/8/2025 and 7/29/2026. The Examiner acknowledges the amendment and has reviewed the claims accordingly. Claims 2, 13, 18, and 23-25 are cancelled.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-8, 10-12, 19-21, 22, and 26 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kovacs (WO2018145603A1).
Regarding claim 1, Kovacs teaches a method for biometric verification (¶2 This disclosure relates generally to biometric systems, and more specifically to techniques and systems that verify at least one of identity or liveness for detecting spoofing or presentation attacks on biometric systems,) the method comprising:
Obtaining a first biosignal indicative of a heart rate of a subject, wherein the first biosignal has been measured by a first sensor (¶68 In the example of FIG. 4, ECG signal 401 is shown as a solid line and is representative of electrical activity sensed by sensor 102N of a heart of subject 103. First physiological signal 402 is shown as a solid line and is representative of blood flow sensed by sensor 102A from an index finger of a left hand of subject 103 and indicative of a pulse arrival time (PAT);)
Obtaining a second biosignal indicative of the heart rate of the subject, wherein the second biosignal has been measured by a second sensor that is different from the first sensor, and wherein the first and second biosignals have been measured at the same time (¶68 second physiological signal 404 is shown as a dashed line and is representative of blood flow sensed by sensor 102B from a big toe of a right foot of subject 103 and indicative of a PAT with respect to that big toe. As depicted in FIG. 4, electrical activity of the heart of subject 103 reaches a peak at time TO (e.g., an R-wave of ECG 401), while first physiological signal 402 exhibits a peak blood flow at time Tl with respect to the same heartbeat, and second physiological signal 404 exhibits a peak blood flow at time T2 with respect to the same heartbeat; ¶69 states that the same heartbeats are taken for the sensors.)
determining a delay between the first and second biosignals; and (¶69 The delay between the R-wave of the heart and pulsatile flow peak at the index finger of the left hand of subject 103 is represented in FIG. 4 as phase delay 406 between ECG 401 and first physiological signal 402. The delay between the R-wave of the heart and pulsatile flow peak at big toe of the right foot of subject 103 is represented in FIG. 4 as phase delay 407 between ECG 401 and second physiological signal 403.)
verifying liveness of the subject based on the determined delay (¶70 phase delays 406 and 407 are repeatable for normal humans, with minor statistical deviations. For example, either phase delay 406, phase delay 407, or the difference 408 between phase delay 406 and 407 may be used by liveness measurement unit to determine the spoofing attack detection status. Although phase delays 406 and 407 are repeatable for each cardiac cycle of subject 103, the exact time of phase delay 406 or 407 likely varies slightly from beat to beat. This expected variation of phase delay 406, phase delay 407, or the difference 408 between phase delay 406 and 407 may additionally or alternatively be used by liveness measurement unit 104 to determine the spoofing attack detection status.)
Regarding claim 3, Kovacs teaches the method of claim 1, wherein verifying the liveness of the subject comprises comparing the determined delay to an expected delay, and, if the determined delay matches the expected delay, passing liveness verification of the subject, and otherwise failing the liveness verification of the subject (Kovacs, ¶72 Attack detection unit 106 may detect a spoofing attack by applying a set of one or more rules to the phase delay 406 determined by liveness measurement unit 104. For example, upon determining that phase delay 406 is within a range of about 100 milliseconds to about 300 milliseconds, which is typical for normal humans, attack detection unit 106 determines that first physiological signal 402 and second physiological signal 404 are from living biological tissue from the same human subject. In contrast, upon attack detection unit 106 determining that phase delay 406 is outside of this range expected physiological range, which is abnormal behavior for a human, attack detection unit 106 determines that first physiological signal 402 and second physiological signal 404 are representative of a spoofing attack on system 100.)
Regarding claim 4, Kovacs teaches the method of claim 3, wherein the method comprises determining a delay signal comprising a plurality of delay values between the first and second biosignals, and wherein each value of the delay signal is compared to the expected delay (Kovacs, ¶70 phase delays 406 and 407 are repeatable for normal humans, with minor statistical deviations. For example, either phase delay 406, phase delay 407, or the difference 408 between phase delay 406 and 407 may be used by liveness measurement unit to determine the spoofing attack detection status. Although phase delays 406 and 407 are repeatable for each cardiac cycle of subject 103, the exact time of phase delay 406 or 407 likely varies slightly from beat to beat,) and, if all values of the delay signal match the expected delay, passing liveness verification of the subject, and otherwise failing the liveness verification of the subject (Kovacs, ¶70 This expected variation of phase delay 406, phase delay 407, or the difference 408 between phase delay 406 and 407 may additionally or alternatively be used by liveness measurement unit 104 to determine the spoofing attack detection status. ¶71-72 give further detail how this is done.)
Regarding claim 5, Kovacs teaches the method of claim 4, wherein each delay value of the delay signal corresponds to a delay of a single heartbeat between the first biosignal and the second biosignal (Kovacs, ¶69 he delay between the R-wave of the heart and pulsatile flow peak at big toe of the right foot of subject 103 is represented in FIG. 4 as phase delay 407 between ECG 401 and second physiological signal 403. As an example of the above, phase delay 406 is generally about 100 milliseconds to about 300 milliseconds; ¶70 Although phase delays 406 and 407 are repeatable for each cardiac cycle of subject 103, the exact time of phase delay 406 or 407 likely varies slightly from beat to beat.)
Regarding claim 6, Kovacs teaches the method of claim 1, wherein the first sensor is of a different type than the second sensor (Kovacs, ¶75 In some examples, sensors 102 comprise one or more imaging sensors, such as thermal sensors, red- blue-green (RGB) image sensors, black and white image sensors, multispectral sensors, microwave thermometry, doppler radar, or OCT volumetric imaging devices. In some examples, sensors 102 are configured to generate photoplethysmography data for subject 103. In further examples, sensors 102 comprise one or more electrodes configured to generate impedance plethysmogram data, ballistocardiography (BCG) data, electrocardiogram (ECG) data, or electroencephalography (EEG) data from subject 103.)
Regarding claim 7, Kovacs teaches the method of any preceding claim 1, wherein the first biosignal is of a different type than the second biosignal (Kovacs, Fig. 4; ¶69 The delay between the R-wave of the heart and pulsatile flow peak at the index finger of the left hand of subject 103 is represented in FIG. 4 as phase delay 406 between ECG 401 and first physiological signal 402. The delay between the R-wave of the heart and pulsatile flow peak at big toe of the right foot of subject 103 is represented in FIG. 4 as phase delay 407 between ECG 401 and second physiological signal 403 (one signal is from index finger and second is from big toe); ¶75 In some examples, sensors 102 include one or more imaging sensors, such as thermal sensors, red-blue-green (RGB) image sensors, black and white image sensors, multi spectral sensors, microwave thermometry, doppler radar, or OCT volumetric imaging devices. In some examples, sensors 102 are configured to generate photoplethysmography data for subject 103. In further examples, sensors 102 include one or more electrodes configured to generate impedance plethysmography data, ballistocardiography (BCG) data, electrocardiogram (ECG) data, or electroencephalography (EEG) data from subject 103. In some examples, sensors 102 are configured to sense one or more of a fingerprint, a face, a retina, an iris, or a voiceprint of subject 103. In some examples, sensors 102 are configured to sense blood pressure pulse transit time, blood flow, galvanic skin response, a respiratory rate, or breathing rate of subject 103. System 100 may employ one or more of these sensors to obtain data representative of the subject 103.)
Regarding claim 8, Kovacs teaches the method of claim 1, wherein the first biosignal comprises a remote photoplethysmography signal, and the second biosignal comprises a ballistocardiography signal (Kovacs, ¶75 In some examples, sensors 102 include one or more imaging sensors, such as thermal sensors, red-blue-green (RGB) image sensors, black and white image sensors, multi spectral sensors, microwave thermometry, doppler radar, or OCT volumetric imaging devices. In some examples, sensors 102 are configured to generate photoplethysmography data for subject 103. In further examples, sensors 102 include one or more electrodes configured to generate impedance plethysmography data, ballistocardiography (BCG) data, electrocardiogram (ECG) data, or electroencephalography (EEG) data from subject 103. In some examples, sensors 102 are configured to sense one or more of a fingerprint, a face, a retina, an iris, or a voiceprint of subject 103. In some examples, sensors 102 are configured to sense blood pressure pulse transit time, blood flow, galvanic skin response, a respiratory rate, or breathing rate of subject 103. System 100 may employ one or more of these sensors to obtain data representative of the subject 103.)
Regarding claim 10, Kovacs teaches the method of claim 1, wherein the first biosignal and the second biosignal are measured from different body parts of the subject (Kovacs, Fig. 4; ¶69 The delay between the R-wave of the heart and pulsatile flow peak at the index finger of the left hand of subject 103 is represented in FIG. 4 as phase delay 406 between ECG 401 and first physiological signal 402. The delay between the R-wave of the heart and pulsatile flow peak at big toe of the right foot of subject 103 is represented in FIG. 4 as phase delay 407 between ECG 401 and second physiological signal 403 (one signal is from index finger and second is from big toe).)
Regarding claim 11, Kovacs teaches the method of claim 1, wherein the first biosignal is measured from a first body part having a first distance to the heart of the subject, and the second biosignal is measured from a second body part having a second distance to the heart of the subject, wherein the first and second distances are different (Kovacs, ¶69 Further, in typical humans, there is a difference between the distance from the heart to the index finger as compared to the distance from the heart to the big toe of about 0.2 meters to about 0.5 meters. Thus, in response to the same heartbeat, blood flow and blood pressure in arteries at the index finger of the left hand of subject 103 peak slightly before blood flow and blood pressure at the big toe of the right foot of subject 103.)
Regarding claim 12, Kovacs teaches the method of claim 1, wherein the first biosignal is measured from the face of the subject, and the second biosignal is measured from the hand of the subject (Kovacs, ¶75 A first sensor, such as sensor 102A, generates a first physiological signal representative of a first characteristic sensed from a first tissue region of subject 103 (502). A second sensor, such as sensor 102B, generates a second physiological signal representative of a second characteristic sensed from at least one of a second tissue region and the same first tissue region of subject 103 (504)... the tissue regions include one or more of a hand of subject 103, a finger of subject 103, an iris of subject 103, a face of subject 103, a toe of subject 103, a foot of subject 103, or a torso of subject 103.)
Regarding claim 19, Kovacs teaches the method of claim 1, wherein the second sensor comprises a movement sensor configured to measure movement sensor data indicative of the heart rate of the subject (Kovacs, ¶55 As another example, sensor 202F includes an accelerometer configured to generate ballistocardiography data for subject 103.)
Regarding claim 20, Kovacs teaches the method of claim 19, wherein the second biosignal comprises a ballistocardiography signal based on the movement sensor data (Kovacs, ¶55 As another example, sensor 202F includes an accelerometer configured to generate ballistocardiography data for subject 103.)
Regarding claim 21, Kovacs teaches the method of claim 1, wherein the method further comprises measuring the first and second biosignals using the same device (Kovacs, ¶21 In some examples, sensors 102 include one or more wired sensors. In other examples, sensors 102 include one or more wireless sensors. In some examples, sensors 102 are incorporated into a mobile device, such as a laptop, tablet, cell phone or smart phone, personal digital assistant (PDA), "smart" watch, pager, "smart" clothing and/or wearable electronic device, and the like; ¶86 Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various operations and functions described in this disclosure. In addition, any of the described units, modules or components may be implemented together or separately as discrete but interoperable logic devices; This shows is just a simple design choice one with ordinary skill could implement.)
Regarding claim 22, claim 22 has been analyzed with regard to claim 1 and is rejected for the same reasons of anticipation as used above as well as in accordance with Kovacs further teaching on: An apparatus comprising at least one processor, at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform instructions (Kovacs, ¶59 Processing circuitry 302, in one example, is configured to implement functionality and/or process instructions for execution within computing device 300. For example, processing circuitry 302 may be configured to process instructions stored in memory 316. Examples of processing circuitry 302 may include, any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or integrated logic circuitry.)
Regarding claim 26, claim 26 has been analyzed with regard to claim 1 and is rejected for the same reasons of anticipation as used above as well as in accordance with Kovacs further teaching on: A non-transitory computer-readable medium comprising computer program code configured to, when executed by at least one processor, cause an apparatus to perform instructions (Kovacs, ¶7 his disclosure describes a non-transitory computer-readable medium including instructions that, when executed, cause one or more processors of a biometric access control system)
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 9 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kovacs (WO2018175603A1) in view of Wu (U.S. Patent Pub. No. 2021/0386307).
Regarding Claim 9, Kovacs teaches the method of claim 1, wherein the first biosignal comprises a remote photoplethysmography signal comprising color distributions of one or more skin regions of the face of the subject (¶21 Each of sensors 102 generates a physiological signal representative of a characteristic sensed from a tissue region of subject 103…the tissue regions include one or more of a hand of subject 103, a finger of subject 103, an iris of subject 103, a face of subject 103, a toe of subject 103, a foot of subject 103, or a torso of subject 103; ¶27 one or more sensors 102 may record a photoplethysmogram (PPG) from one or both hands of subject 103 (this specific example uses hands, but as cited ¶21 says it can be face as well).)
Kovacs does not explicitly disclose wherein the first biosignal comprises a remote photoplethysmography signal comprising color distributions of one or more skin regions of the face of the subject.
Wu is in the same field of art of image analysis. Further, Wu teaches wherein the first biosignal comprises a remote photoplethysmography signal comprising color distributions of one or more skin regions of the face of the subject (¶38 certain embodiments provide a remote photoplethysmography (rPPG) system that is robust for purposes of pulse rate and/or pulse rate variability extraction from fitness face video; ¶74 FIG. 7 illustrates an example rPPG system 700; ¶76 the system 700 may also include a spatial averaging block 715 configured to spatially average the three color channels in red, green, and blue inside the detected facial skin region, to produce a RGB face color signal. A pulse color mapping block 720 may be configured to map the face color temporal measurement to a color direction that generates the highest pulse SNR.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kovacs by implementing a remote photoplethysmography method that is taught by Wu; thus, one of ordinary skilled in the art would be motivated to combine the references to accurately capture heart rates using videos (Wu ¶5).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 14, Kovacs in view of Wu discloses the method of claim 1, wherein the first sensor comprises a camera configured to measure image data (Kovacs, ¶75 sensors 102 include one or more imaging sensors, such as thermal sensors, red-blue-green (RGB) image sensors, black and white image sensors, multi spectral sensors, microwave thermometry, doppler radar, or OCT volumetric imaging devices.)
Kovacs does not explicitly disclose a camera configured to measure image data indicative of the heart rate of a subject. However, Wu teaches a camera (Wu, ¶40 the system may include a camera) configured to measure image data indicative of the heart rate of the subject (Wu, ¶74 an rPPG system for robust estimation during fitness exercise of heart rate (HR) or pulse rate (PR) and interbeat interval (IBI) signal is provided, whereby the IBI signal can support the estimation of heart/pulse rate variability (HRV/PRV). FIG. 7 illustrates an example rPPG system 700… the system 700 may take one or more videos 705 as an input.)
The reasons for combining Kovacs and Wu are similar to that stated in the rejection of claim 9. In addition, this same reasoning is pertinent and applicable to the rejections of claims 15-17 below.
Regarding Claim 15, Kovacs in view of Wu discloses the method of claim 14, wherein obtaining the first biosignal comprises: acquiring a plurality of color image data frames depicting the face of the subject; detecting color content indicative of the heart rate of the subject in the plurality of color image data frames; and generating the first biosignal based on the detected color content (Wu, ¶75 in the skin detection block 710, an ellipsoid-shaped skin classifier may be learned from the face pixel colors in the first few frames of the video 705 and may be deployed in the subsequent frames to detect facial skins; ¶76 the system 700 may also include a spatial averaging block 715 configured to spatially average the three color channels in red, green, and blue inside the detected facial skin region, to produce a RGB face color signal. A pulse color mapping block 720 may be configured to map the face color temporal measurement to a color direction that generates the highest pulse SNR.)
Regarding Claim 16, Kovacs in view of Wu discloses the method of claim 15, wherein detecting the color content comprises: identifying one or more skin regions in each of the plurality of color image data frames; extracting a skin region data set from each of the one or more identified skin regions in each of the plurality of color image data frames; and detecting the color content of each extracted skin region data set (Wu, ¶75 in the skin detection block 710, an ellipsoid-shaped skin classifier may be learned from the face pixel colors in the first few frames of the video 705 and may be deployed in the subsequent frames to detect facial skins; ¶76 the system 700 may also include a spatial averaging block 715 configured to spatially average the three color channels in red, green, and blue inside the detected facial skin region, to produce a RGB face color signal. A pulse color mapping block 720 may be configured to map the face color temporal measurement to a color direction that generates the highest pulse SNR.)
Regarding Claim 17, Kovacs in view of Wu discloses the method of claim 16, wherein the method further comprises: computing a plurality of color distributions, each color distribution being computed on the basis of one of the plurality of skin region data sets; and detecting the color content of each extracted skin region data set based on the color distribution computed on the basis of said skin region data set (Wu, ¶75 in the skin detection block 710, an ellipsoid-shaped skin classifier may be learned from the face pixel colors in the first few frames of the video 705 and may be deployed in the subsequent frames to detect facial skins; ¶76 the system 700 may also include a spatial averaging block 715 configured to spatially average the three color channels in red, green, and blue inside the detected facial skin region, to produce a RGB face color signal. A pulse color mapping block 720 may be configured to map the face color temporal measurement to a color direction that generates the highest pulse SNR.)
Conclusion
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/DUSTIN BILODEAU/Examiner, Art Unit 2664
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