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 .
Claim Objections
Claims are objected to because of the following informalities:
Claim 1 recites the limitation of “determine at least one vital sign of the living body based on a periodicity the motion” which it is believed to rather recite -- determine at least one vital sign of the living body based on a periodicity of the motion--
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims 1, 17, 19 and 20 recite “determine a motion” and “determine at least one vital sign”.
The limitation of “determine”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “processor” language, “determine” in the context of this claim encompasses the user manually determining the motion of an object. Similarly, the limitation of “determine…vital sign”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “processor” language, “determine” in the context of this claim encompasses the user thinking or making mental determination of the vital signs. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform the limitation of “determine”. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of “determine” such that it amounts 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. The claim is directed to an abstract idea.
The claims 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 abstract idea into a practical application, the additional element of using a processor to perform the “determining” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
The depending claims also recite similar abstract ideas (e.g., determine peaks in the event signal, disregard non-periodic motion, determine the periodic motion, etc.) without additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application.
Therefore, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claim 1, 4-7, 12-14 and 17-20 is rejected under 35 U.S.C. 103 as being unpatentable over Dedonato (US 20220269333 A1) in view of Yoon et al (US 20160106327 A1), Gallego et al (Event-Based Vision: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: 44, Issue: 1, 01 January 2022), Date of [first publish online] Publication: 10 July 2020) and Molinaro et al (Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview, Front. Physiol., 17 February 2022).
Regarding claims 1, 17, 19 and 20, Dedonato teaches an apparatus (see various apparatuses as shown in figs. 1, 2, 4, 5, 7, 9, 11, 13 and the associated pars.) comprising:
an interface for communicating with an event-based vision sensor (e.g., see interface as outlined in an exemplary [0031] in relation with the figures and associated pars of the figs.); and
processing circuitry (“computer systems in communication with a display … devices … a personal electronic device (e.g., a wearable electronic device, such as a watch, or a head-mounted device… eye-tracking components… a graphical user interface (GUI), one or more processors … programs or sets of instructions stored in the memory for performing multiple functions… Executable instructions for performing these functions are, optionally, included in a non-transitory computer readable storage medium or other computer program product configured for execution by one or more processors.” [0006]) configured to:
obtain an event signal from the event-based vision sensor (“obtain image data … of the face of the user that includes the eyes of the user (… an eye-tracking camera). … obtain image data that corresponds to at least a portion of the user's hand(s) and optionally arm(s) of the user (and may be referred to as a hand-tracking camera). …obtain image data that corresponds to the scene as would be viewed by the user if the display generation component 120…one or more event-based cameras,” [0062]), the event signal representing a detected change in luminance detected by the event-based vision sensor (“pixels 412 corresponding to the hand 406 have been segmented out from the background and the wrist in this map. The brightness of each pixel within the depth map 410 corresponds inversely to its depth value, i.e., the measured z distance from the image sensors 404, with the shade of gray growing darker with increasing depth.” [0078]),
and determine at least one vital sign of the living body based on a periodicity the motion (“data or records relating to a user's health or level of fitness (e.g., vital signs measurements, medication information, exercise information)” [0326]; “physiological sensors (e.g., blood pressure monitor, heart rate monitor” [0060]; “hand tracking device 140 (FIG. 1) is controlled by hand tracking unit 243 (FIG. 2) to track the position/location of one or more portions of the user's hands, and/or motions of one or more portions of the user's hands with respect to the scene 105 of FIG. 1” [0071]; “FIG. 7C, personalized user interface 714… also has heart rate affordance 716c, which was selected for inclusion in personalized user interface 714” [0110]).
As seen above, Dedonato teaches all the claimed limitations including periodicity of the motion (i.e., hand tracking device 140 (FIG. 1) is controlled by hand tracking unit 243 (FIG. 2) to track the position/location of one or more portions of the user's hands, and/or motions of one or more portions of the user's hands with respect to the scene 105 of FIG. 1 [0071]); however, if one argues in an interpretation that Dedonato does not teach determine the at least one vital sign of the living body based on a periodicity of the motion, Yoon specifically teaches this as shown in detail below.
However, in the same field of endeavor, Yoon teaches bio-information acquisition apparatus 100 may include a dynamic vision sensor (DVS) for sensing a change in the intensity (amount) of light, that is, a change in the intensity of an optical signal [0053]. The DVS may detect a change in the intensity of light, instead of the intensity of light, as digital information. For example, when the intensity of light increases, it may be presented to be +1, when there is no change in the intensity of light, it may be presented to be 0, and when the intensity of light decreases, it may be presented to be −1 [0054]. Bio-information acquisition apparatus 100 may radiate a laser beam onto the chest part 52 where the lungs are located and then the DVS may sense the laser speckle change due to the motions of lungs. The bio-information acquisition apparatus 100 may obtain information about the motions of lungs and information about respiration of the object, based on the sensed laser speckle change [0057].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with determine the at least one vital sign of the living body based on a periodicity of the motion as taught by Yoon because it helps with measuring in real time a change in the blood pressure of an individual ([0007] of Yoon).
Further, the above noted combination teaches all the claimed limitations except for determine peaks in the event signal and to determine the periodicity of the motion based on the peaks in the event signal.
However, in the same field of endeavor, Gallego teaches event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes (abst). Event cameras, such as the Dynamic Vision Sensor (DVS) respond to brightness changes in the scene asynchronously and independently for every pixel (Fig. 1b). Thus, the output of an event camera is a variable data-rate sequence of digital “events” or “spikes”, with each event representing a change of brightness (log intensity) of predefined magnitude at a pixel at a particular time. This encoding is inspired by the spiking nature of biological visual pathways. Event cameras are data-driven sensors: their output depends on the amount of motion or brightness change in the scene. The faster the motion, the more events per second are generated, since each pixel adapts its delta modulator sampling rate to the rate of change of the log intensity signal that it monitors. Events are timestamped with microsecond resolution and are transmitted with sub-millisecond latency, which make these sensors react quickly to visual stimuli. (see SECTION 2 Principle of Operation of Event Cameras).
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It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with determine peaks in the event signal and to determine the periodicity of the motion based on the peaks in the event signal as taught by Gallego because it helps to fulfill the novel methods that are required to process the unconventional output of these sensors in order to unlock their potential. (of Gallego).
Furthermore, the above noted combination teaches all the claimed limitations except for a pre-determined range of supported periodicity characteristic of the vital sign being determined.
However, in the same field of endeavor, Molinaro teaches digital cameras to estimate the main vital signs (i.e., fR, HR, SpO2, BP) in different scenarios. During the inspiration and expiration, the chest wall moves to allow the expansion and contraction of the lungs. The chest wall is considered as a structure comprising two compartments, the rib cage, and the abdomen, that experience different displacements during respiratory activity. During the inspiration, the rib cage mainly moves in the ventral and cranial directions with a displacement in the range of 3–5 mm. Lateral movements outward are very small, between 1 and 2 mm, that are useful for the elevation of the ribs. The abdomen mainly moves in the ventral direction so that it becomes circle during inspiration. At the end of the expiration, the ratio between the dorsoventral and transverse diameters is smaller for the rib cage than for the abdomen. Indeed, the abdominal cross section is near a circle than the rib cage cross section. By recording these cyclical movements through a digital camera, a respiratory pattern can be obtained by analyzing the changes in the intensity level of the pixels of each frame of the video. With digital cameras, the most frequent estimated parameter is fR (usually measured in breaths per minute—breaths/min) with analysis in the frequency domain to determine the average fR values or in the time domain for a more in-depth breath-by-breath analysis. Normal values of fR in adults’ range between 12 breaths/min and 18 breaths/min, while for newborns and infants, it ranges between 20 breaths/min and 30 breaths/min. Abnormal values of fR, such as tachypnea (fR is greater than 20 breaths/min) and bradypnea (fR is less than eight breaths/min) could indicate respiratory problems (Respiratory Frequency and Other Respiratory Parameters section).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with pre-determined range of supported periodicity characteristic of the vital sign being determined as taught by Molinaro because evidence supports the premise of digital cameras as an unobtrusive and easy-to-use technology for physiological signs monitoring (Abst of Molinaro).
Regarding claim 4, the above noted combination teaches all the claimed limitations except for wherein the processing circuitry is configured to disregard non-periodic motion represented in the event signal.
However, in the same field of endeavor, Gallego teaches event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes (abst). Event cameras, such as the Dynamic Vision Sensor (DVS) respond to brightness changes in the scene asynchronously and independently for every pixel (Fig. 1b). Thus, the output of an event camera is a variable data-rate sequence of digital “events” or “spikes”, with each event representing a change of brightness (log intensity) of predefined magnitude at a pixel at a particular time. This encoding is inspired by the spiking nature of biological visual pathways. Event cameras are data-driven sensors: their output depends on the amount of motion or brightness change in the scene. Each pixel memorizes the log intensity each time it sends an event, and continuously monitors for a change of sufficient magnitude from this memorized value (Fig. 1a). When the change exceeds a threshold, the camera sends an event, which is transmitted from the chip with the x,y location, the time t, and the 1-bit polarity p of the change (i.e., brightness increase (“ON”) or decrease (“OFF”)). This event output is illustrated in Figs. 1b, 1e and 1f. (see SECTION 2 Principle of Operation of Event Cameras).
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It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with disregard non-periodic motion represented in the event signal as taught by Gallego because it helps to fulfill the novel methods that are required to process the unconventional output of these sensors in order to unlock their potential. (of Gallego).
Regarding claim 5, the above noted combination teaches all the claimed limitations except for wherein the processing circuitry is configured to determine the periodic motion based on the peaks in the event signal and based on a pre-determined range of supported periodicity.
However, in the same field of endeavor, Gallego teaches event cameras, such as the Dynamic Vision Sensor (DVS) respond to brightness changes in the scene asynchronously and independently for every pixel (Fig. 1b). Thus, the output of an event camera is a variable data-rate sequence of digital “events” or “spikes”, with each event representing a change of brightness (log intensity) of predefined magnitude at a pixel at a particular time. Their output depends on the amount of motion or brightness change in the scene. Each pixel memorizes the log intensity each time it sends an event, and continuously monitors for a change of sufficient magnitude from this memorized value (Fig. 1a). When the change exceeds a threshold, the camera sends an event, which is transmitted from the chip with the x,y location, the time t, and the 1-bit polarity p of the change (i.e., brightness increase (“ON”) or decrease (“OFF”)). This event output is illustrated in Figs. 1b, 1e and 1f. (see SECTION 2 Principle of Operation of Event Cameras). Future even more realistic models may include the refractory period (i.e., the duration in time that the pixel ignores log brightness changes after it has generated an event; the larger the refractory period the fewer events are produced by fast moving objects), and bus congestion (see 2.4 Event Generation Model).
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It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with determine the periodic motion based on the peaks in the event signal and based on a pre-determined range of supported periodicity as taught by Gallego because it helps to fulfill the novel methods that are required to process the unconventional output of these sensors in order to unlock their potential. (of Gallego).
Regarding claim 6, the above noted combination teaches all the claimed limitations except for wherein the pre-determined range of supported periodicity imposes a lower limit and an upper limit of periodicities.
However, in the same field of endeavor, Gallego teaches event cameras, such as the Dynamic Vision Sensor (DVS) respond to brightness changes in the scene asynchronously and independently for every pixel (Fig. 1b). Thus, the output of an event camera is a variable data-rate sequence of digital “events” or “spikes”, with each event representing a change of brightness (log intensity) of predefined magnitude at a pixel at a particular time. Their output depends on the amount of motion or brightness change in the scene. Each pixel memorizes the log intensity each time it sends an event, and continuously monitors for a change of sufficient magnitude from this memorized value (Fig. 1a). When the change exceeds a threshold, the camera sends an event, which is transmitted from the chip with the x,y location, the time t, and the 1-bit polarity p of the change (i.e., brightness increase (“ON”) or decrease (“OFF”)). This event output is illustrated in Figs. 1b, 1e and 1f. (see SECTION 2 Principle of Operation of Event Cameras). set the speed and threshold voltages of the change detector in Fig. 1 and are generated by an on-chip digitally-programmed bias generator. The sensitivity C can be estimated knowing these currents. In practice, positive (“ON”) and negative (“OFF”) events may be triggered according to different thresholds, C+,C−. Typical DVS’s can set thresholds between 10 to 50 percent illumination change. The lower limit on C is determined by noise and pixel-to-pixel mismatch (variability); setting C too low results in a storm of noise events, starting from pixels with low values of C. Experimental DVS’s with higher photoreceptor gain are capable of lower thresholds, e.g., 1 percent; however these values are only obtained under very bright illumination and ideal conditions. Future even more realistic models may include the refractory period (i.e., the duration in time that the pixel ignores log brightness changes after it has generated an event; the larger the refractory period the fewer events are produced by fast moving objects), and bus congestion (see 2.4 Event Generation Model).
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It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with wherein the pre-determined range of supported periodicity imposes a lower limit and an upper limit of periodicities as taught by Gallego because it helps to fulfill the novel methods that are required to process the unconventional output of these sensors in order to unlock their potential. (of Gallego).
Regarding claim 7, Dedonato teaches wherein the processing circuitry is configured to determine at least one of a heart rate of the living body and a breathing rate of the living body based on the motion (“data or records relating to a user's health or level of fitness (e.g., vital signs measurements, medication information, exercise information)” [0326]; “physiological sensors (e.g., blood pressure monitor, heart rate monitor” [0060]; “FIG. 7C, personalized user interface 714… also has heart rate affordance 716c, which was selected for inclusion in personalized user interface 714” [0110]).
Regarding claim 12, the above noted combination teaches all the claimed limitations except for determine peaks in the event signal, and to adjust a contrast threshold of the event- based vision sensor based on a ratio between the peaks in the event signal and other portions of the event signal.
However, in the same field of endeavor, Gallego teaches event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes (abst). Event cameras, such as the Dynamic Vision Sensor (DVS) respond to brightness changes in the scene asynchronously and independently for every pixel (Fig. 1b). Thus, the output of an event camera is a variable data-rate sequence of digital “events” or “spikes”, with each event representing a change of brightness (log intensity) of predefined magnitude at a pixel at a particular time. This encoding is inspired by the spiking nature of biological visual pathways. Event cameras are data-driven sensors: their output depends on the amount of motion or brightness change in the scene. The faster the motion, the more events per second are generated, since each pixel adapts its delta modulator sampling rate to the rate of change of the log intensity signal that it monitors. Events are timestamped with microsecond resolution and are transmitted with sub-millisecond latency, which make these sensors react quickly to visual stimuli. (see SECTION 2 Principle of Operation of Event Cameras). Reducing the contrast threshold and/or increasing the resolution produces more events, which will be processed by an algorithm and platform with finite capacity (SECTION 6 Discussion). The contrast sensitivity C is determined by the pixel bias currents which set the speed and threshold voltages of the change detector in Fig. 1 and are generated by an on-chip digitally-programmed bias generator. The sensitivity C can be estimated knowing these currents (2.4 Event Generation Model).
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It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with wherein the pre-determined range of supported periodicity imposes a lower limit and an upper limit of periodicities as taught by Gallego because it helps to fulfill the novel methods that are required to process the unconventional output of these sensors in order to unlock their potential. (of Gallego).
Regarding claim 13, the above noted combination teaches all the claimed limitations except for processing circuitry is configured to sweep the contrast threshold, determine peaks in the event signal that are based on the sweep of the contrast threshold, and set a contrast threshold that yields a desired ratio between the peaks in the event signal and other portions of the event signal.
However, in the same field of endeavor, Gallego teaches an event camera [2] has independent pixels that respond to changes in their log photocurrent L≐log(I) (“brightness”). Specifically, in a noise-free scenario, an event ek≐(xk,tk,pk) is triggered at pixel xk≐(xk,yk)⊤ and at time tk as soon as the brightness increment since the last event at the pixel, i.e., reaches a temporal contrast threshold ±C (Fig. 1b), i.e., The contrast sensitivity C is determined by the pixel bias currents which set the speed and threshold voltages of the change detector in Fig. 1 and are generated by an on-chip digitally-programmed bias generator. The sensitivity C can be estimated knowing these currents. A normal distribution centered at the contrast threshold C. The 1σ width of the distribution is typically 2-4 percent temporal contrast. (2.4 Event Generation Model). Spikes may be produced by pixels of the event camera or by neurons of the SNN. Information travels along the hierarchy, from the event camera pixels to the first layers of the SNN and then through to higher (deeper) layers. Most first layer receptive fields are based on Difference of Gaussians (selective to center-surround contrast), Gabor filters (selective to oriented edges), and their combinations (3.3 Biologically Inspired Visual Processing). Reducing the contrast threshold and/or increasing the resolution produces more events, which will be processed by an algorithm and platform with finite capacity (SECTION 6 Discussion).
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It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with to sweep the contrast threshold, determine peaks in the event signal that are based on the sweep of the contrast threshold, and set a contrast threshold that yields a desired ratio between the peaks in the event signal and other portions of the event signal as taught by Gallego because it helps to fulfill the novel methods that are required to process the unconventional output of these sensors in order to unlock their potential. (of Gallego).
Regarding claim 14, the above noted combination teaches all the claimed limitations except for generate an output signal based on the determined at least one vital sign if the deter- mined at least one vital sign exceeds an upper threshold or falls below a lower threshold.
However, in the same field of endeavor, Gallego teaches event cameras, such as the Dynamic Vision Sensor (DVS) respond to brightness changes in the scene asynchronously and independently for every pixel (Fig. 1b). Thus, the output of an event camera is a variable data-rate sequence of digital “events” or “spikes”, with each event representing a change of brightness (log intensity) of predefined magnitude at a pixel at a particular time. Their output depends on the amount of motion or brightness change in the scene. Each pixel memorizes the log intensity each time it sends an event, and continuously monitors for a change of sufficient magnitude from this memorized value (Fig. 1a). When the change exceeds a threshold, the camera sends an event, which is transmitted from the chip with the x,y location, the time t, and the 1-bit polarity p of the change (i.e., brightness increase (“ON”) or decrease (“OFF”)). This event output is illustrated in Figs. 1b, 1e and 1f. (see SECTION 2 Principle of Operation of Event Cameras). set the speed and threshold voltages of the change detector in Fig. 1 and are generated by an on-chip digitally-programmed bias generator. The sensitivity C can be estimated knowing these currents. In practice, positive (“ON”) and negative (“OFF”) events may be triggered according to different thresholds, C+,C−. Typical DVS’s can set thresholds between 10 to 50 percent illumination change. The lower limit on C is determined by noise and pixel-to-pixel mismatch (variability); setting C too low results in a storm of noise events, starting from pixels with low values of C. Experimental DVS’s with higher photoreceptor gain are capable of lower thresholds, e.g., 1 percent; however these values are only obtained under very bright illumination and ideal conditions. Future even more realistic models may include the refractory period (i.e., the duration in time that the pixel ignores log brightness changes after it has generated an event; the larger the refractory period the fewer events are produced by fast moving objects), and bus congestion (see 2.4 Event Generation Model).
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It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with wherein the pre-determined range of supported periodicity imposes a lower limit and an upper limit of periodicities as taught by Gallego because it helps to fulfill the novel methods that are required to process the unconventional output of these sensors in order to unlock their potential. (of Gallego).
Regarding claim 18, Dedonato teaches wherein the camera is one of a wall-mounted cam- era, a ceiling-mounted camera, and a wearable camera (in figs. 1, 2, 4, 5, 7, 9, 11, 13 and the associated pars.).
Regarding claims 19 and 20, Dedonato teaches a computer-implemented method and computer program with a program code for performing the method (see various apparatuses as shown in figs. 1, 2, 4, 5, 7, 9, 11, 13 and the associated pars.) comprising:
obtaining an event signal from the event-based vision sensor (“obtain image data … of the face of the user that includes the eyes of the user (… an eye-tracking camera). … obtain image data that corresponds to at least a portion of the user's hand(s) and optionally arm(s) of the user (and may be referred to as a hand-tracking camera). …obtain image data that corresponds to the scene as would be viewed by the user if the display generation component 120…one or more event-based cameras,” [0062]), the event signal representing a detected change in luminance detected by the event-based vision sensor (“pixels 412 corresponding to the hand 406 have been segmented out from the background and the wrist in this map. The brightness of each pixel within the depth map 410 corresponds inversely to its depth value, i.e., the measured z distance from the image sensors 404, with the shade of gray growing darker with increasing depth.” [0078]),
determine a motion of at least a body part of a living body based on the event signal (“The controller 110 processes these depth values in order to identify and segment a component of the image (i.e., a group of neighboring pixels) having characteristics of a human hand. These characteristics, may include, for example, overall size, shape and motion from frame to frame of the sequence of depth maps.” [0078]),
and determine at least one vital sign of the living body based on the motion (“data or records relating to a user's health or level of fitness (e.g., vital signs measurements, medication information, exercise information)” [0326]; “physiological sensors (e.g., blood pressure monitor, heart rate monitor” [0060]; “FIG. 7C, personalized user interface 714… also has heart rate affordance 716c, which was selected for inclusion in personalized user interface 714” [0110]).
Claims 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Dedonato in view of Yoon et al (US 20160106327 A1) Gallego and Molinaro, and further in view of Sai et al (US20220157083A1, equivalent of WO2021050317A1).
Regarding claim 8, the above noted combination teaches all the claimed limitations except for wherein the processing circuitry is configured to aggregate the event signal over a pre-defined time interval, to determine an outline of the living body based on the aggregated event signal.
However, in the same field of endeavor, Sai teaches a gesture (e.g., of a person in the physical environment) is identified based on the subset of event camera data. In some implementations, a path (e.g., of a hand) by tracking a grouping of blocks of event camera events in the subset of event camera data (abst). As shown in FIG. 7A, a plurality of events 730 within the bounding box are detected by the event camera 422 b. In some implementations, the events 730 detected by the event camera 422 b include positive events (e.g., generated by the leading edge of the hand or fingers) and negative events (e.g., generated by the trailing edge of the hand of fingers). For example, the plurality of events 730 are detected by the event camera 422 b within the bounding box 510 [0059]. As shown in FIG. 7B, a subset of the blocks 732 of events 730 are grouped to identify an entity 700 in some implementations. The entity 700 can be tracked at multiple times to determine a path. A portion of a path 710 of the entity 700 is shown at a given time Tx in FIG. 7B. In some implementations, the path is used to determine a gesture, for example, using machine learning (ML). In some implementations, a first neural network is trained to identify gestures when a path is input [0060]. An event camera generally includes a plurality of pixel sensors like pixel sensor 915 that each output a pixel event in response to detecting changes in light intensity that exceed a comparative threshold. When aggregated, the pixel events output by the plurality of pixel sensor form a stream of pixel events that are output by the event camera [0074].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with aggregate the event signal over a pre-defined time interval, to determine an outline of the living body based on the aggregated event signal as taught by Sai because event data from a physical environment may significantly reduce the ability of such event camera-based gesture recognition systems to quickly, efficiently, and accurately identify gestures ([0002] of Sai).
Regarding claim 9, the above noted combination teaches all the claimed limitations except for wherein the processing circuitry is configured to disregard a portion of the event signal based on the outline of the living body.
However, in the same field of endeavor, Sai teaches a plurality of events 730 within the bounding box are detected [hence, disregard a portion of the event signal based on the outline of the living body] by the event camera 422 b. In some implementations, the events 730 detected by the event camera 422 b include positive events (e.g., generated by the leading edge of the hand or fingers) and negative events (e.g., generated by the trailing edge of the hand of fingers). For example, the plurality of events 730 are detected by the event camera 422 b within the bounding box 510 [0059]. As shown in FIG. 7B, a subset of the blocks 732 of events 730 are grouped to identify an entity 700 in some implementations. The entity 700 can be tracked at multiple times to determine a path. A portion of a path 710 of the entity 700 is shown at a given time Tx in FIG. 7B. In some implementations, the path is used to determine a gesture, for example, using machine learning (ML). In some implementations, a first neural network is trained to identify gestures when a path is input [0060]. An event camera generally includes a plurality of pixel sensors like pixel sensor 915 that each output a pixel event in response to detecting changes in light intensity that exceed a comparative threshold. When aggregated, the pixel events output by the plurality of pixel sensor form a stream of pixel events that are output by the event camera [0074].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with disregard a portion of the event signal based on the outline of the living body as taught by Sai because event data from a physical environment may significantly reduce the ability of such event camera-based gesture recognition systems to quickly, efficiently, and accurately identify gestures ([0002] of Sai).
Regarding claim 10, the above noted combination teaches all the claimed limitations except for wherein the living body is a living human body, wherein the processing circuitry is configured to process the outline of the living body using a machine-learning model to determine an identity of the living human body.
However, in the same field of endeavor, Sai teaches as shown in FIG. 7A, and FIG. 7B, a subset of the blocks 732 of events 730 are grouped to identify an entity 700 in some implementations. The entity 700 can be tracked at multiple times to determine a path. A portion of a path 710 of the entity 700 is shown at a given time Tx in FIG. 7B. In some implementations, the path is used to determine a gesture, for example, using machine learning (ML). In some implementations, a first neural network is trained to identify gestures when a path is input [0060]. An event camera generally includes a plurality of pixel sensors like pixel sensor 915 that each output a pixel event in response to detecting changes in light intensity that exceed a comparative threshold. When aggregated, the pixel events output by the plurality of pixel sensor form a stream of pixel events that are output by the event camera [0074].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with outline of the living body using a machine-learning model to determine an identity of the living human body as taught by Sai because event data from a physical environment may significantly reduce the ability of such event camera-based gesture recognition systems to quickly, efficiently, and accurately identify gestures ([0002] of Sai).
Regarding claim 11, the above noted combination teaches all the claimed limitations except for determine a pre-determined range of supported periodicity of the motion based on the identity of the living human body.
However, in the same field of endeavor, Sai teaches blocks of events that are associated with multiple times are identified based on blocking criteria. For example, each block may be a region having a predetermined number of events that occur at a given time or time period of predetermined length. In some implementations, an entity (e.g., a hand) is identified at each of the multiple times [0005]. A plurality of events 730 within the bounding box are detected [hence, disregard a portion of the event signal based on the outline of the living body] by the event camera 422 b. In some implementations, the events 730 detected by the event camera 422 b include positive events (e.g., generated by the leading edge of the hand or fingers) and negative events (e.g., generated by the trailing edge of the hand of fingers). For example, the plurality of events 730 are detected by the event camera 422 b within the bounding box 510 [0059]. As shown in FIG. 7B, a subset of the blocks 732 of events 730 are grouped to identify an entity 700 in some implementations. The entity 700 can be tracked at multiple times to determine a path. A portion of a path 710 of the entity 700 is shown at a given time Tx in FIG. 7B. In some implementations, the path is used to determine a gesture, for example, using machine learning (ML). In some implementations, a first neural network is trained to identify gestures when a path is input [0060]. An event camera generally includes a plurality of pixel sensors like pixel sensor 915 that each output a pixel event in response to detecting changes in light intensity that exceed a comparative threshold. When aggregated, the pixel events output by the plurality of pixel sensor form a stream of pixel events that are output by the event camera [0074].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with determine a pre-determined range of supported periodicity of the motion based on the identity of the living human body as taught by Sai because event data from a physical environment may significantly reduce the ability of such event camera-based gesture recognition systems to quickly, efficiently, and accurately identify gestures ([0002] of Sai).
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Dedonato in view of Yoon, Gallego, Molinaro and further in view of Jordan (US20210343017A1).
Regarding claim 15, the above noted combination teaches all the claimed limitations except for wherein the processing circuitry is configured to control a medical intervention device using the output signal.
However, in the same field of endeavor, Jordan teaches remote monitoring of a patient and corresponding medical device for identifying a first set of images that represent a particular location of a body of a patient in which at least one component of an implantable medical device (IMD) coincides (abst). Computing device may perform image processing using camera(s) of, or otherwise communicatively coupled to, the computing device to detect abnormalities, such as in wound healing and/or by determining potential infections at an implantation site [0050]. medical device(s) 6 may include diagnostic medical devices. In an example, medical device(s) 6 may include a device that predicts heart failure events or that detects worsening heart failure of patient 4. In a non-limiting and illustrative example, system 100 may be configured to measure impedance fluctuations of patient 4 and process impedance data to accumulate evidence of worsening heart failure. In any case, medical device(s) 6 may be configured to determine a health status relating to patient 4. Medical device(s) 6 may transmit the diagnostic data or health status to computing device(s) 2 as interrogation data, such that computing device(s) 2 may correlate the interrogation data with image data to determine whether an abnormality present with a particular one of medical device(s) 6 (e.g., an IMD) or patient 4 (e.g., infection at an implantation site) [0081].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with control a medical intervention device using the output signal as taught by Jordan because it would help with the early detection of infections associated with IMDs may allow for earlier intervention, resulting in fewer device explants ([0005] of Jordan).
Regarding claim 16, the above noted combination teaches all the claimed limitations except for intervention device is a defibrillation device or a device for controlling a release of medication to the living body.
However, in the same field of endeavor, Jordan teaches remote monitoring of a patient and corresponding medical device for identifying a first set of images that represent a particular location of a body of a patient in which at least one component of an implantable medical device (IMD) coincides (abst). IMD may include, be, or be part of a variety of devices or integrated systems, such as, but not limited to, implantable cardiac monitors (ICMs), implantable pacemakers, including those that deliver cardiac resynchronization therapy (CRT), implantable cardioverter-defibrillators (ICDs), diagnostic devices, cardiac devices, etc. [0079].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with intervention device is a defibrillation device as taught by Jordan because it would help with the early detection of infections associated with IMDs may allow for earlier intervention, resulting in fewer device explants ([0005] of Jordan).
Claims 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Dedonato in view of Yoon, Gallego, Molinaro and further in view of Addison et al (US20190209046A1).
Regarding claims 21-22, the above noted combination teaches all the claimed limitations except for disregarding non-periodic motion represented in the event signal.
However, in the same field of endeavor, Addison teaches non-contact video monitoring to measure tidal volume of a patient for determining a region of interest of a patient and monitoring that region of interest to determine tidal volume of the patient (abst). Filtering out non-physiological signals as disclosed herein. For example, an expected spectral bandwidth of breathing may be known and used to filter out non-respiratory signals from a volume signal. For example, a raw volume signal may be band-pass filtered between 0.10 and 0.66 Hz (corresponding to 10 second and 1.5 second breaths or 6 and 40 breaths per minute). Where movement falls outside of the frequency range, it may be excluded because it is unlikely to be movement associated with respiratory movement [0152].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with disregarding non-periodic motion represented in the event signal as taught by Addison because this helps to improve the device by eliminating the requirement of attachment of sensors to a patient to detect physiologic signals from the patient ([0002] of Addison).
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding the rejection of claims under 35 USC 101, the applicant argues the following;
Regarding the assertion that the originally claimed steps of "determine a motion" and "determine at least one vital sign" are mental processes, amended claim I now specifies that the processing circuitry is configured to "determine the periodic motion based on peaks in the event signal and based on a pre-determined range of supported periodicity." This is not a process that can be practically performed in the human mind. An event-based vision sensor outputs a high- volume, high-speed stream of asynchronous data, where each event represents a change in luminance at a single pixel at a specific microsecond in time. The claimed process requires a processor to computationally analyze this raw data stream, identify amplitude "peaks," and then filter or match these peaks against a "pre-determined range of supported periodicity" for example, a range of 40 to 150 events per minute for a heartbeat as disclosed in the specification. A human is not equipped to perform this level of specific, quantitative signal processing on such data in real-time.
Furthermore, these amended limitations demonstrate an improvement to the technical field of patient monitoring. By using a specific algorithm to process the unique data from an EVS, the claimed invention provides a more power-efficient and less invasive monitoring system, solving a technical problem with a technical solution. See also, the August 4,2025, USPTO Memorandum noting that a claim is eligible if it "reflects an improvement to the functioning of a computer or to another technology or technical field." This is not a mere instruction to "apply" an abstract idea using a generic computer; it is a specific implementation that improves the functioning of the monitoring technology itself.
Thus, significantly more than an abstract idea is now claimed. Moreover, each of the claims, as presently amended, to the extent the claims include an abstract idea, integrate that abstract idea into a practical application. It is requested that this rejection be reconsidered and withdrawn.
Contrary to the applicant’s assertion, claims still recite abstract idea as "determining" which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. Other than the recitation of generic computer components (“a processing circuitry”) nothing in the claim element precludes the step from practically being performed in the mind.
Further, the applicant also argues that the structural components (i.e., processing circuitry is configured to "determine” etc,) which are all generic component that is used for mere data gathering which are examples of activities that courts have found to be insignificant extra-solution activity. These components are widely practiced and commonly known with no specificity which courts have found to be insignificant extra-solution activity.
Therefore, under its broadest reasonable interpretation, claims cover performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Judicial exception is not integrated into a practical application since the claim only recites additional element generic computer components (“a processing circuitry”).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims are not patent eligible.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SERKAN AKAR whose telephone number is (571)270-5338. The examiner can normally be reached 9am-5pm M-F.
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/SERKAN AKAR/ Primary Examiner, Art Unit 3797