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 1, 3, 7, 10, and 17 are objected to because of the following informalities:
In claim 1, lines 3 and 6-7, “the sensor module” should read “the at least one sensor module”.
In claims 3 and 10, line 3, the “detects” should read “is configured to detect”.
In claims 7, lines 4 and 7, and 17, lines 3 and 6, “an object” should read “the object”.
Appropriate correction is required.
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 5, 10, 13, 15, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jung et al (US 20230148870), hereinafter Jung.
Regarding claim 1, Jung teaches a multi-object biometric information measurement device (10) (“A multi-object thermal radiation measuring device” Abstract; Figs. 3, 5, and 7) comprising:
at least one sensor module (700) (“The sensing unit 700 of the thermal radiation measuring device 10 includes a thermal sensor, an RGB sensor, and a depth sensor (or depth camera)” [0060]); and
a processor connected to the sensor module (“the components in accordance with the embodiment of the present disclosure may be each implemented in software or implemented as a hardware component such as DSP (Digital Signal Processor),” [0088]; “Since the computer program instructions can be mounted on a processor of a general-purpose computer, a special computer, or other programmable data processing equipment, the instructions executed through the processor of the computer or the programmable data processing equipment generate means for executing functions described in the flowchart blocks.” [0092]; Fig. 3),
wherein the processor recognizes at least one of sneezing information, heart rate/respiratory rate information, and core temperature information of an object located in a measurement target space based on sensing data collected through the sensor module (“When the thermal radiation measurement result values are abnormal, the multi-object thermal radiation measuring device 10 detects a region in which an abnormal object is present, precisely and continuously monitors the behaviors of the abnormal object such as cough and sneeze in the region, and thus detects the symptoms of a respiratory infectious disease at the early stage.” [0051]; “the abnormal object behavior detection module 540 detects the behavior of the abnormal object, such as cough, sneeze, or respiratory difficulty, from the thermal image or the RGB image in which the abnormal object appears.” [0068]).
Regarding claim 3, Jung teaches the multi-object biometric information measurement device of claim 1, wherein the processor includes:
an object detection unit (200) that detects an object corresponding to a preset class (“an abnormal object” [0051]; ““the face, forehead, and inner canthus” [0064]) (“When the thermal radiation measurement result values are abnormal, the multi-object thermal radiation measuring device 10 detects a region in which an abnormal object is present” [0051]; Fig. 3; “inner canthus of an object…The position of the inner canthus is marked as a circle in FIGS. 6A and 6B.” [0064]; Figs. 6A-B) in a thermal image acquired through a first image sensor (“a thermal sensor” [0060]) and a real image acquired through a second image sensor (“an RGB sensor” [0060]) (“The object detection unit 200 performs object detection or object tracking by applying a deep learning model to the thermal image or RGB image.” [0062]; “The multi-object thermal radiation measuring unit 400 detects the face, forehead, and inner canthus of an object in the thermal image and the RGB image by applying the image recognition technology to the images,” [0064]; Figs. 1, 5, and 6A-B); and
a biometric information recognition unit (540) that recognizes biometric information including at least one of sneezing information, heart rate/respiratory rate information, and core temperature information of the object detected by the object detection unit (“the abnormal object behavior detection module 540 detects the behavior of the abnormal object, such as cough, sneeze, or respiratory difficulty, from the thermal image or the RGB image in which the abnormal object appears.” [0068]).
Regarding claim 5, Jung teaches the multi-object biometric information measurement device of claim 3, wherein the biometric information recognition unit recognizes the sneezing information by applying the detected object to a behavior detection model (“behavior detection model” [0068]) to determine whether the object is coughing/sneezing, and counting the number of times the object coughs/sneezes while tracking the object that coughs or sneezes (“continuously monitors the behaviors of the abnormal object such as cough and sneeze in the region” [0051]; “the abnormal object behavior detection module 540 tracks the location of an object who coughs or sneezes or the number of times that the object coughs or sneezes, by using a cough/sneeze recognition model (behavior detection model) generated through a supervised learning technique based on a CNN (Convolutional Neural Network), in order to detect the disease symptom of the abnormal object.” [0068]).
Regarding claim 10, Jung teaches the multi-object biometric information measurement device of claim 3, wherein the processor further includes an abnormal-signs-of disease detection unit that detects abnormal signs of disease of multiple objects within the measurement target space (“FIG. 4A illustrates measurement target spaces (measurement sections) which are divided according to the rotation angle range of the sensing unit 700 of the multi-object thermal radiation measuring device 10, and FIG. 4B illustrates inventory switching times allocated to the respective measurement sections along the time axis.” [0052]) based on the biometric information recognized by the biometric information recognition unit, and predicts a disease occurrence risk (the multi-object thermal radiation measuring device 10 (or the sensing unit 700 of the multi-object thermal radiation measuring device 10) that is attached to the ceiling of the concentrated/sealed/crowded space and coupled to the motion controller 610 measures the thermal radiations of the multiple objects in the concentrated/sealed/crowded space while rotated in the top-to-bottom direction and the side-to-side direction. When the thermal radiation measurement result values are abnormal, the multi-object thermal radiation measuring device 10 detects a region in which an abnormal object is present, precisely and continuously monitors the behaviors of the abnormal object such as cough and sneeze in the region, and thus detects the symptoms of a respiratory infectious disease at the early stage.” [0051]).
Regarding claim 13, Jung teaches a method of detecting abnormal signs of disease (S08) (Fig. 8), comprising:
receiving, by a processor (“Since the computer program instructions can be mounted on a processor of a general-purpose computer, a special computer, .., the instructions executed through the processor of the computer or the programmable data processing equipment generate means for executing functions described in the flowchart blocks.” [0092]; Fig. 3), sensing data (“the thermal radiation measurement result values” [0051]) including at least one of a thermal image, a real image (“the thermal image or RGB image.” [0062]), distance measurement information, and an ambient temperature from a sensor module (700) (“The sensing unit 700 of the thermal radiation measuring device 10 includes a thermal sensor, an RGB sensor, and a depth sensor (or depth camera)” [0060]; “the abnormal object behavior detection module 540 detects the behavior of the abnormal object, such as cough, sneeze, or respiratory difficulty, from the thermal image or the RGB image in which the abnormal object appears.” [0068]; Fig. 5);
detecting, by the processor, an object corresponding to a preset class (“an abnormal object” [0051]; ““the face, forehead, and inner canthus” [0064]) (“When the thermal radiation measurement result values are abnormal, the multi-object thermal radiation measuring device 10 detects a region in which an abnormal object is present” [0051]; Fig. 3; “inner canthus of an object…The position of the inner canthus is marked as a circle in FIGS. 6A and 6B.” [0064]; Figs. 6A-B) in the thermal image and the real image (“The object detection unit 200 performs object detection or object tracking by applying a deep learning model to the thermal image or RGB image.” [0062]; “The multi-object thermal radiation measuring unit 400 detects the face, forehead, and inner canthus of an object in the thermal image and the RGB image by applying the image recognition technology to the images,” [0064]; Figs. 1, 5, and 6A-B); and
recognizing, by the processor, biometric information including at least one of sneezing information, heart rate/respiratory rate information, and core temperature information of the detected object (“the abnormal object behavior detection module 540 detects the behavior of the abnormal object, such as cough, sneeze, or respiratory difficulty, from the thermal image or the RGB image in which the abnormal object appears.” [0068]).
Regarding claim 15, Jung teaches the method of claim 13, wherein, in the recognizing of the biometric information, the processor recognizes the sneezing information by applying the detected object to a behavior detection model (“behavior detection model” [0068]) to determine whether the object is coughing/sneezing, and counting the number of times the object coughs/sneezes while tracking the object that coughs or sneezes (“continuously monitors the behaviors of the abnormal object such as cough and sneeze in the region” [0051]; “the abnormal object behavior detection module 540 tracks the location of an object who coughs or sneezes or the number of times that the object coughs or sneezes, by using a cough/sneeze recognition model (behavior detection model) generated through a supervised learning technique based on a CNN (Convolutional Neural Network), in order to detect the disease symptom of the abnormal object.” [0068]).
Regarding claim 20, Jung teaches the method of claim 13, further comprising, after the recognizing of the biometric information, detecting, by the processor, abnormal signs of disease of multiple objects within a measurement target space (“FIG. 4A illustrates measurement target spaces (measurement sections) which are divided according to the rotation angle range of the sensing unit 700 of the multi-object thermal radiation measuring device 10, and FIG. 4B illustrates inventory switching times allocated to the respective measurement sections along the time axis.” [0052]) based on the recognized biometric information and predicting a disease occurrence risk (the multi-object thermal radiation measuring device 10 (or the sensing unit 700 of the multi-object thermal radiation measuring device 10) that is attached to the ceiling of the concentrated/sealed/crowded space and coupled to the motion controller 610 measures the thermal radiations of the multiple objects in the concentrated/sealed/crowded space while rotated in the top-to-bottom direction and the side-to-side direction. When the thermal radiation measurement result values are abnormal, the multi-object thermal radiation measuring device 10 detects a region in which an abnormal object is present, precisely and continuously monitors the behaviors of the abnormal object such as cough and sneeze in the region, and thus detects the symptoms of a respiratory infectious disease at the early stage.” [0051]).
Claim Rejections - 35 USC § 103
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.
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.
Claims 2, 6, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Jung as applied to claims 1, 3, and 13, and further in view of Markov et al (US 20220015644), hereinafter, Markov.
Regarding claim 2, Jung teaches the multi-object biometric information measurement device of claim 1, wherein the sensor module includes:
a first image sensor (“a thermal sensor” [0060]) that acquires a thermal image of the measurement target space;
a second image sensor (“an RGB sensor” [0060]) that acquires a real image of the measurement target space (“the sensing unit 700 may generate a thermal image or RGB image while rotated, and transfers the generated image to the object detection unit 200” [0062]);
a distance measurement sensor (“a depth sensor” [0060]) that measures a distance to the object (“The sensing unit 700 of the thermal radiation measuring device 10 includes a thermal sensor, an RGB sensor, and a depth sensor (or depth camera)” [0060]. “The sensing unit 700 generates a thermal image and RGB image on which multiple objects appear, by converting signals detected by the respective sensors included in the sensing unit 700, while the sensing unit 700 is rotated on the ceiling of the measurement target space by the driving unit 600, and measures the distances to the respective objects.” [0071]).
Jung does not teach an environmental sensor that measures an atmospheric air temperature of the measurement target space.
However, in the fever detection systems field of endeavor, Markov discloses a fever detector by distant multipixel thermal imaging, which is analogous art. Markov teaches an environmental sensor (120) that measures an atmospheric air temperature of the measurement target space (250) (“an ambient sensor module 120” [0019]; “the ambient conditions are measured by the ambient sensor module and include ambient temperature T.sub.A” [0031]; “the system is configured to also capture exterior ambient conditions, such as exterior temperature … from where the human subjects came. The exterior ambient conditions may be obtained by external sensor modules.” [0043]; “the sensors may be configured to capture … exterior temperature, …, from where the human subjects came.” [0074]; Figs. 1 - 2 ).
Therefore, based on Markov’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Jung to employ an environmental sensor that measures an atmospheric air temperature of the measurement target space, as taught by Markov, in order to improve body temperature estimations.
Regarding claim 6, Jung teaches the multi-object biometric information measurement device of claim 3, wherein the biometric information recognition unit detects a specific region in the detected object (“the face, forehead, and inner canthus” [0064]; Figs. 6A-B).
Jung does not teach that the biometric information recognition unit acquires a radiant heat measurement value of the specific region, and calculates a core temperature of the object based on the radiant heat measurement value.
However, in the fever detection systems field of endeavor, Markov discloses a fever detector by distant multipixel thermal imaging, which is analogous art. Markov teaches that the biometric information recognition unit (156) acquires a radiant heat measurement value of the specific region (“the heat transfer properties” [0026]; “the thermal image” [0082]. Note that the thermal image provides a radiant heat measurement value), and calculates a core temperature of the object based on the radiant heat measurement value (“The skin temperature calculator takes T.sub.S and determines a core body temperature T.sub.C using an empirical biophysical model. The biophysical model is employed to infer core body temperatures based on calculated skin temperature and the heat transfer properties of the human subject, accounting for the ambient conditions, such as the ambient temperature and humidity, which affect the heat transfer processes.” [0026]; “At 556, T.sub.S is employed to generate T.sub.C, which is estimated from the thermal image. Generating T.sub.C, for example, may be performed by the core temperature calculator. In one embodiment, T.sub.C is generated using a biophysical model, taking T.sub.S and ambient conditions” [0082]; Figs. 1 and 5-6).
Therefore, based on Markov’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Jung to employ the biometric information recognition unit that acquires a radiant heat measurement value of the specific region, and calculates a core temperature of the object based on the radiant heat measurement value, as taught by Markov, in order to improve body temperature estimations.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Jung as applied to claims 3 and 13, and further in view of Beall (US 20160295208), hereinafter, Beall.
Regarding claim 4, Jung teaches the multi-object biometric information measurement device of claim 3, wherein the images match (“When the thermal radiation measurement result values are abnormal, the multi-object thermal radiation measuring device 10 detects a region in which an abnormal object is present” [0051]; “the face, forehead, and inner canthus of the object may be detected by any one of the thermal sensor and the RGB sensor, or a combination of the two sensors.” [0064]; Figs. 1, 3, 5, and 6A-B), and
the object detection unit generates a bounding box of the detected object (seen in Fig. 3) to perform object tracking when the object corresponding to the preset class is present in the real image (“The multi-object thermal radiation measuring unit 400 detects the face, forehead, and inner canthus of an object in the thermal image and the RGB image by applying the image recognition technology to the images,” [0064]; Figs. 1, 5, and 6A-B).
Jung does not teach that the processor further includes an image correction unit that corrects the thermal image and the real image so that the images match.
However, in the thermal imaging systems field of endeavor, Beall discloses systems and approaches for repeated thermal imaging determinations, which is analogous art. Beall teaches that the processor further includes an image correction unit that corrects the thermal image and the real image so that the images match (“At step 3, and visualization C, a known transform is applied to the image obtained from the thermal image sensor 106 to obtain an expected location of the thermal markers 152 on the visible image using the controller 102. In other words, the non-thermal image field of view is matched to the thermal image field of view. As shown in visualization C, the estimated location of the thermal markers illustrated in broken lines does not match their location calculated from the visible camera. At step 6, an affine two-dimensional spatial transformation that matches these locations is performed by the controller 102. At step 7 and visualization D, the affine transformation is applied to the thermal image and results in a spatially calibrated thermal image matching the visible image field of view.” [0043]).
Therefore, based on Beall’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Jung to employ the processor that further includes an image correction unit that corrects the thermal image and the real image so that the images match, as taught by Beall, in order to improve thermal imaging of objects. In the combined invention, the real image is corrected as claimed.
Regarding claim 14, Jung teaches the method of claim 13.
Jung teaches the thermal image and the real image in the detecting of the object (“When the thermal radiation measurement result values are abnormal, the multi-object thermal radiation measuring device 10 detects a region in which an abnormal object is present” [0051]; “the face, forehead, and inner canthus of the object may be detected by any one of the thermal sensor and the RGB sensor, or a combination of the two sensors.” [0064]; Figs. 1, 3, 5, and 6A-B) and generating a bounding box of the detected object (seen in Fig. 3) to perform object tracking when the object corresponding to the preset class is present in the real image (“The multi-object thermal radiation measuring unit 400 detects the face, forehead, and inner canthus of an object in the thermal image and the RGB image by applying the image recognition technology to the images,” [0064]; Figs. 1, 5, and 6A-B).
Jung does not teach that the processor further includes an image correction unit that corrects the thermal image and the real image so that the images match.
However, in the fever detection systems field of endeavor, Beall discloses a fever detector by distant multipixel thermal imaging, which is analogous art. Beall teaches that the processor further includes an image correction unit that corrects the thermal image and the real image so that the images match (“At step 3, and visualization C, a known transform is applied to the image obtained from the thermal image sensor 106 to obtain an expected location of the thermal markers 152 on the visible image using the controller 102. In other words, the non-thermal image field of view is matched to the thermal image field of view. As shown in visualization C, the estimated location of the thermal markers illustrated in broken lines does not match their location calculated from the visible camera. At step 6, an affine two-dimensional spatial transformation that matches these locations is performed by the controller 102. At step 7 and visualization D, the affine transformation is applied to the thermal image and results in a spatially calibrated thermal image matching the visible image field of view.” [0043]).
Therefore, based on Beall’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Jung to employ the processor that further includes an image correction unit that corrects the thermal image and the real image so that the images match, as taught by Beall, in order to improve body temperature estimations. In the combined invention, the real image is corrected as claimed.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Jung and Markov as applied to claim 6, and further in view of Morgan et al (US 20090278728), hereinafter, Morgan.
Regarding claim 8, Jung modified by Markov teaches the multi-object biometric information measurement device of claim 6.
Jung modified by Markov does not teach that the biometric information recognition unit recognizes a heart rate and respiratory rate of the object by analyzing a micro-Doppler signal, which is a signal generated when an antenna radiation beam formed by a distance measurement sensor is reflected by the object and returned.
However, in the Doppler radar cardiopulmonary sensing field of endeavor, Morgan discloses Doppler radar cardiopulmonary sensor and signal processing system and method for use therewith, which is analogous art. Morgan teaches that the biometric information recognition unit recognizes a heart rate and respiratory rate of the object (140) (“estimation of respiration and heart rate from measurements of chest-wall dynamic motion.” [0141]) by analyzing a micro-Doppler signal (“the signal” [0051]), which is a signal generated when an antenna radiation beam formed by a distance measurement sensor (“Doppler radar CP sensing” [0141]) is reflected by the object and returned (“FIG. 1 shows a block diagram of a conventional Doppler radar. A continuous-wave (CW) source 110 feeds an antenna 120 through a circulator 130. The antenna 120 radiates to a desired object 140 in a field of view (not referenced) that experiences motion x(t). The object 140 reflects the signal back to the same antenna 120.” [0051] “Disclosed herein are signal processing systems and methods for Doppler radar CP sensing that enable estimation of respiration and heart rate from measurements of chest-wall dynamic motion.” [0141]; Fig. 1).
Therefore, based on Morgan’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Jung to employ the biometric information recognition unit that recognizes a heart rate and respiratory rate of the object by analyzing a micro-Doppler signal, which is a signal generated when an antenna radiation beam formed by a distance measurement sensor is reflected by the object and returned, as taught by Morgan, in order to improve biometric measurements of objects.
Claims 9, 11-12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Jung et al (US 20230148870), hereinafter Jung, in view of Bergmann-Good et al (US 20250135287), hereinafter, Bergmann-Good.
Regarding claim 9, Jung teaches the multi-object biometric information measurement device of claim 3, wherein the processor further includes a metadata generation unit (“the multi-object thermal radiation measuring device” [0018]) that generates biometric information including at least one of the recognized sneezing information, heart rate/respiratory rate information, and core temperature information of the object (“the metadata collected by the multi-object thermal radiation measuring device” [0018]; “metadata of the thermal radiation precise measurement values” [0044]; “the abnormal object behavior detection module 540 may track the location of an object who coughs or sneezes and the number of times that the object coughs or sneezes” [0085]; “the metadata on the abnormal object behavior detection result values,” [0087]) as metadata (“metadata” [0044]), and transmits the generated metadata to an external device (20) through a communication module (800) (“Furthermore, when the thermal radiation measuring devices installed across the country transmit, to a disease symptom early detection platform server, the disease symptom detection results and metadata of the thermal radiation precise measurement values and the abnormal object behavior detection result values, the platform server may analyze the disease risk based on the thermal radiation precise measurement values and the abnormal object behavior detection result values, and notice the disease symptoms and the disease risk to the whole nation, thereby supporting early blocking of the spread of the disease.” [0044]; Fig. 5).
Jung does not teach generating device state information of the multi-object biometric information measurement device as metadata.
However, in the wellness systems field of endeavor, Bergmann-Good discloses a method and system to provide individualized interventions based on a wellness model, which is analogous art. Bergmann-Good teaches generating device state information of the multi-object biometric information measurement device as metadata (16) (“The memory 13 can store device metadata 16 which can include available metadata for factors such as memory, processor speed, touch screen, resolution, camera, video camera, processor, device location, haptic input/output devices, augmented reality glasses, virtual reality headsets. The system 100 can determine device capacity for interaction or intervention types by evaluating the device metadata 16," [0178]; “the data records can involve … device metadata.” [0189]).
Therefore, based on Bergmann-Good’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Jung to generate device state information of the multi-object biometric information measurement device as metadata, as taught by Bergmann-Good, in order to facilitate access to information about the available resources of the device.
Regarding claim 11, Jung teaches Jung teaches a system (Figs. 3, 5, and 7-8) for detecting abnormal signs of disease (“provide an alarm service to inform the whole nation of the disease risk and the statistics of the disease symptom detection results” [0087]), comprising:
a plurality of multi-object biometric information measurement devices (10), each of which is installed in one measurement target space and recognizes at least one type of biometric information among sneezing information (“cough/sneeze recognition model” [0044]), heart rate/respiratory rate information, and core temperature information of an object that is present in the corresponding measurement target space (“the detection result” [0069]), and transmit metadata including the biometric information to a management server (20) (“The abnormal object thermal radiation precise measurement module 530 generates metadata on the abnormal object thermal radiation precise measurement value, for example, the maximum value, average value, and minimum value of the temperature of the corresponding abnormal object, and the abnormal object behavior detection module 540 generates metadata on the abnormal object behavior detection result value, for example, the average number of times that the abnormal object coughs per minute…The disease symptom early detection platform server 20 is a cloud-based service platform, and monitors data collected by the multi-object thermal radiation measuring devices 10 installed in main concentrated/sealed/crowded spaces across the country, evaluates the disease risk, and performs service management, thereby detecting the disease symptoms at the early stage, and providing an alarm service to notice the disease risk to the whole nation.” [0069]; Fig. 5); and
the management server that predicts a disease occurrence risk (“the disease risk” [0069]) based on the metadata from the plurality of multi-object biometric information measurement devices (“Furthermore, when the thermal radiation measuring devices installed across the country transmit, to a disease symptom early detection platform server, the disease symptom detection results and metadata of the thermal radiation precise measurement values and the abnormal object behavior detection result values, the platform server may analyze the disease risk based on the thermal radiation precise measurement values and the abnormal object behavior detection result values, and notice the disease symptoms and the disease risk to the whole nation, thereby supporting early blocking of the spread of the disease.” [0044]; Fig. 5).
Jung does not teach metadata including device state information.
However, in the wellness systems field of endeavor, Bergmann-Good discloses a method and system to provide individualized interventions based on a wellness model, which is analogous art. Bergmann-Good teaches metadata (16) including device state information (“The memory 13 can store device metadata 16 which can include available metadata for factors such as memory, processor speed, touch screen, resolution, camera, video camera, processor, device location, haptic input/output devices, augmented reality glasses, virtual reality headsets. The system 100 can determine device capacity for interaction or intervention types by evaluating the device metadata 16," [0178]; “the data records can involve … device metadata.” [0189]).
Therefore, based on Bergmann-Good’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Jung to employ metadata including device state information, as taught by Bergmann-Good, in order to facilitate access to information about the available resources of the device.
Regarding claim 12, Jung modified by Bergmann-Good teaches system of claim 11, wherein Jung teaches that the management server generates notification information including the predicted disease occurrence risk and notifies users of the notification information (“the platform server may analyze the disease risk based on the thermal radiation precise measurement values and the abnormal object behavior detection result values, and notice the disease symptoms and the disease risk to the whole nation, thereby supporting early blocking of the spread of the disease.” [0044]; “providing an alarm service to notice the disease risk to the whole nation.” [0069]).
Regarding claim 19, Jung teaches the method of claim 13, further comprising, after the recognizing of the biometric information, generating, by the processor, metadata including the biometric information and transmitting the metadata to a management server (20) (“The abnormal object thermal radiation precise measurement module 530 generates metadata on the abnormal object thermal radiation precise measurement value, for example, the maximum value, average value, and minimum value of the temperature of the corresponding abnormal object, and the abnormal object behavior detection module 540 generates metadata on the abnormal object behavior detection result value, for example, the average number of times that the abnormal object coughs per minute…The disease symptom early detection platform server 20 is a cloud-based service platform, and monitors data collected by the multi-object thermal radiation measuring devices 10 installed in main concentrated/sealed/crowded spaces across the country, evaluates the disease risk, and performs service management, thereby detecting the disease symptoms at the early stage, and providing an alarm service to notice the disease risk to the whole nation.” [0069]; Fig. 5).
Jung does not teach metadata including device state information.
However, in the wellness systems field of endeavor, Bergmann-Good discloses a method and system to provide individualized interventions based on a wellness model, which is analogous art. Bergmann-Good teaches metadata (16) including device state information (“The memory 13 can store device metadata 16 which can include available metadata for factors such as memory, processor speed, touch screen, resolution, camera, video camera, processor, device location, haptic input/output devices, augmented reality glasses, virtual reality headsets. The system 100 can determine device capacity for interaction or intervention types by evaluating the device metadata 16," [0178]; “the data records can involve … device metadata.” [0189]).
Therefore, based on Bergmann-Good’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Jung to employ metadata including device state information, as taught by Bergmann-Good, in order to facilitate access to information about the available resources of the device.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Jung as applied to claim 13, and further in view of Morgan et al (US 20090278728), hereinafter, Morgan.
Regarding claim 18, Jung teaches the method of claim 13.
Jung does not teach that the processor recognizes a heart rate and respiratory rate of the object by analyzing a micro-Doppler signal, which is a signal generated when an antenna radiation beam formed by a distance measurement sensor is reflected by the object and returned.
However, in the Doppler radar cardiopulmonary sensing field of endeavor, Morgan discloses Doppler radar cardiopulmonary sensor and signal processing system and method for use therewith, which is analogous art. Morgan teaches that the processor recognizes a heart rate and respiratory rate of the object (140) (“estimation of respiration and heart rate from measurements of chest-wall dynamic motion.” [0141]) by analyzing a micro-Doppler signal (“the signal” [0051]), which is a signal generated when an antenna radiation beam formed by a distance measurement sensor (“Doppler radar CP sensing” [0141]) is reflected by the object and returned (“FIG. 1 shows a block diagram of a conventional Doppler radar. A continuous-wave (CW) source 110 feeds an antenna 120 through a circulator 130. The antenna 120 radiates to a desired object 140 in a field of view (not referenced) that experiences motion x(t). The object 140 reflects the signal back to the same antenna 120.” [0051] “Disclosed herein are signal processing systems and methods for Doppler radar CP sensing that enable estimation of respiration and heart rate from measurements of chest-wall dynamic motion.” [0141]; Fig. 1).
Therefore, based on Morgan’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Jung to employ the processor that recognizes a heart rate and respiratory rate of the object by analyzing a micro-Doppler signal, which is a signal generated when an antenna radiation beam formed by a distance measurement sensor is reflected by the object and returned, as taught by Morgan, in order to improve biometric measurements of objects.
Allowable Subject Matter
Claims 7 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXEI BYKHOVSKI whose telephone number is (571)270-1556. The examiner can normally be reached on Monday-Friday: 8:30am - 5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pascal Bui Pho can be reached on 571-272-2714. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALEXEI BYKHOVSKI/
Primary Examiner, Art Unit 3798
Prosecution Insights