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 .
Election/Restrictions
Applicant’s election without traverse of Invention I, drawn to claims 1-11 and 13-19 in the reply filed on 7/28/2026 is acknowledged. Claim 12 is withdrawn from consideration.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –(a)(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 1-11, 13, 16 and 18-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tzvieli at. al. (U.S 20160046044, February 14, 2019)(hereinafter, “Tzvieli”).
Regarding Claim 1, Tzvieli teaches: A method for determining values of metabolic parameters of a user when performing physical activity on an exercise machine (“… a system that utilizes measurements indicative of respiration in order to estimate an aerobic activity parameter such as oxygen consumption (VO.sub.2), maximal oxygen consumption (VO.sub.2 max), and energy expenditure…” [0014]; “… the computer 445: (i) receives from a fitness app a certain number of breath cycles during which the user should perform a physical exercise, such as keeping a static yoga pose for a certain number of breath cycles, or riding a spin bike at a certain speed for a certain number of breath cycles. (ii) determines, based on m.sub.move, when the user performs the physical exercise, and (iii) counts, based on TH.sub.ROI, the number of breath cycles the user had while performing the physical exercise.” [0161]; “…various formulas and/or models were developed to estimate values of aerobic activity parameters from values such as heart rate…” [0249]), comprising:
acquiring, by a thermographic camera, at least one thermographic image of at least one portion of the body of the user when performing physical activity, said at least one portion of the body of the user comprising a body surface of the user (“…this system includes at least one inward-facing head-mounted thermal camera (CAM) and a computer. Each CAM, from among the at least one CAM, is configured to take thermal measurements of a region below the nostrils (THR.sub.RBN) of a user. TH.sub.RN are indicative of an exhale stream of the user, such as an exhale stream from one, or both, of the nostrils and/or the exhale from the mouth. The computer is configured to calculate, based on THR.sub.RBN, the aerobic activity parameter.” [0014]; “ The at least one CAM may include various combinations of one or more CAMs, as described in the various examples given in this disclosure of embodiments that include a single inward-facing head-mounted thermal camera that measures TH.sub.RBN (e.g., a single CAM coupled to the bottom of one of the sides of a frame worn by the user) or multiple CAMs…” [0243]);
identifying, by at least one data processing unit operatively connected to said thermographic camera, within the at least one thermographic image acquired by the thermographic camera, a first thermographic portion representative of the body surface of the user (“…this system includes at least one inward-facing head-mounted thermal camera (CAM) and a computer. Each CAM, from among the at least one CAM, is configured to take thermal measurements of a region below the nostrils (THR.sub.RBN) of a user. TH.sub.RN are indicative of an exhale stream of the user, such as an exhale stream from one, or both, of the nostrils and/or the exhale from the mouth. The computer is configured to calculate, based on THR.sub.RBN, the aerobic activity parameter.” [0014]; “ The at least one CAM may include various combinations of one or more CAMs, as described in the various examples given in this disclosure of embodiments that include a single inward-facing head-mounted thermal camera that measures TH.sub.RBN (e.g., a single CAM coupled to the bottom of one of the sides of a frame worn by the user) or multiple CAMs…” [0243]);
for said identified first thermographic portion, determining, by the at least one data processing unit, at least one group of thermal features representative of the identified first thermographic portion (“…to calculate, based on TH.sub.RBN…the aerobic activity parameter 688…the aerobic activity parameter 688 is indicative of one or more of the following values: oxygen consumption…maximal oxygen consumption…and energy expenditure…the computer 686 may utilize additional inputs to calculate the aerobic activity parameter such as measurements of the heart rate (HR) of the user, values of the activity level of the user, and/or various statistics about the user…” [0247]; “…the computer 686 generates feature values based on data comprising TH.sub.RBN, and utilizes a model 687 to calculate the aerobic activity parameter 688 based on the feature values.” [0251]);
estimating, by the at least one data processing unit, by executing an estimation algorithm based on a trained regression model, one or more values of metabolic parameters of the user when performing physical activity on the exercise machine based on said at least one determined group of thermal features representative of said identified first thermographic portion (“A value indicative of the physiological response may be obtained at the leaf node and/or based on calculations involving values on nodes and/or edges along the path; (b) the model comprises parameters of a regression model…the computer multiplies the feature values…with the parameters of the regression model in order to obtain the value indicative of the physiological response, and/or (c) the model comprises parameters of a neural network.” [0117]; “…the model 687 includes parameters of at least one of the following types of models: a regression model…” [0261]), said one or more values of metabolic parameters of the user comprising at least one of the following:
value of oxygen volume that the user is consuming when performing physical activity by the user (“… a system that utilizes measurements indicative of respiration in order to estimate an aerobic activity parameter such as oxygen consumption (VO.sub.2), maximal oxygen consumption (VO.sub.2 max), and energy expenditure…” [0014]; “Respiration volume” represents the volume of air breathed over a certain duration (usually per minute), the volume of air breathed during a certain breath, tidal volume, and/or the ratio between two or more breaths. For example, the respiration volume may indicate that a first breath was deeper than a second breath, or that breaths during a first minute were shallower than breaths during a second minute.” [0122]);
providing, by the at least one data processing unit, said estimated one or more values of metabolic parameters (“…a value such as the respiration rate can distinguish between non-metabolic (e.g., mental and non-exercise related physical stress) and metabolic (physical activity induced) increases in HR. Thus, for example, using respiration data in addition to other values (e.g., HR) may provide better estimations of the values of the aerobic activity parameters, compared to estimations that do not involve respiration data.” [0250]).
Regarding Claim 2, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: wherein the step of estimating, by the at least one data processing unit, by running a respective estimation algorithm based on a trained regression model, one or more values of metabolic parameters of the user when performing physical activity on the exercise machine is performed by the at least one data processing unit to estimate a first value of a first metabolic parameter and a second value of a second metabolic parameter of said one or more values of metabolic parameters, the method further comprising a step of determining, by the at least one data processing unit, a third value of a third metabolic parameter of said one or more values of metabolic parameters based on the estimated first value of the first metabolic parameter and the estimated second value of the second metabolic parameter of said one or more values of metabolic parameters (“…detecting a physiological response involves determining the extent of the physiological response, which may be expressed in various ways that are indicative of the extent of the physiological response, such as: (i) a binary value indicative of whether the user experienced, and/or is experiencing, the physiological response. (ii) a numerical value indicative of the magnitude of the physiological response. (iii) a categorial value indicative of the severity/extent of the physiological response, (iv) an expected change in thermal measurements of an ROI (denoted TH.sub.ROI or some variation thereof), and/or (v) rate of change in TH.sub.ROI.” [0099];“A value indicative of the physiological response may be obtained at the leaf node and/or based on calculations involving values on nodes and/or edges along the path; (b) the model comprises parameters of a regression model…the computer multiplies the feature values…with the parameters of the regression model in order to obtain the value indicative of the physiological response, and/or (c) the model comprises parameters of a neural network.” [0117]; “…the model 687 includes parameters of at least one of the following types of models: a regression model…” [0261]).
Regarding Claim 3, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: wherein said one or more values of metabolic parameters of the user further comprise values deriving from one or more of said one or more values of metabolic parameters (“…detecting a physiological response involves determining the extent of the physiological response, which may be expressed in various ways that are indicative of the extent of the physiological response, such as: (i) a binary value indicative of whether the user experienced, and/or is experiencing, the physiological response. (ii) a numerical value indicative of the magnitude of the physiological response. (iii) a categorial value indicative of the severity/extent of the physiological response, (iv) an expected change in thermal measurements of an ROI (denoted TH.sub.ROI or some variation thereof), and/or (v) rate of change in TH.sub.ROI.” [0099]; “A value indicative of the physiological response may be obtained at the leaf node and/or based on calculations involving values on nodes and/or edges along the path; (b) the model comprises parameters of a regression model…the computer multiplies the feature values…with the parameters of the regression model in order to obtain the value indicative of the physiological response, and/or (c) the model comprises parameters of a neural network.” [0117]; “…a value such as the respiration rate can distinguish between non-metabolic (e.g., mental and non-exercise related physical stress) and metabolic (physical activity induced) increases in HR. Thus, for example, using respiration data in addition to other values (e.g., HR) may provide better estimations of the values of the aerobic activity parameters, compared to estimations that do not involve respiration data.” [0250]).
Regarding Claim 4, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: wherein the estimation algorithm based on a trained regression model is based on the use of a regression technique for estimating said one or more values of metabolic parameters of the user when performing physical activity on the exercise machine (“…a machine learning-based model used to detect a physiological response is trained based on data that includes samples. Each sample includes feature values and a label... Labels of samples may be generated using various approaches, such as self-report by users, annotation by experts that analyze the training data, automatic annotation by a computer that analyzes the training data and/or analyzes additional data related to the training data, and/or utilizing additional sensors that provide data useful for generating the labels. It is to be noted that herein when it is stated that a model is trained based on certain measurements…it means that the model was trained on samples comprising feature values generated based on the certain measurements and labels corresponding to the certain measurements.” [0106]; “Training data used to train a model for detecting a physiological response may include, in some embodiments, a diverse set of samples corresponding to various conditions, some of which involve occurrence of confounding factors...” [0113]).
Regarding Claim 5, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: wherein the estimation algorithm based on a trained regression model is based on the execution of a neural network for estimating said one or more metabolic parameters of the user when performing physical activity on the exercise machine (“…a machine learning-based model used to detect a physiological response is trained based on data that includes samples. Each sample includes feature values and a label... Labels of samples may be generated using various approaches, such as self-report by users, annotation by experts that analyze the training data, automatic annotation by a computer that analyzes the training data and/or analyzes additional data related to the training data, and/or utilizing additional sensors that provide data useful for generating the labels. It is to be noted that herein when it is stated that a model is trained based on certain measurements…it means that the model was trained on samples comprising feature values generated based on the certain measurements and labels corresponding to the certain measurements.” [0106]; “Training data used to train a model for detecting a physiological response may include, in some embodiments, a diverse set of samples corresponding to various conditions, some of which involve occurrence of confounding factors...” [0113]; “A value indicative of the physiological response may be obtained at the leaf node and/or based on calculations involving values on nodes and/or edges along the path; (b) the model comprises parameters of a regression model…the computer multiplies the feature values…with the parameters of the regression model in order to obtain the value indicative of the physiological response, and/or (c) the model comprises parameters of a neural network.” [0117]).
Regarding Claim 6, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: wherein the step of estimating, by executing the estimation algorithm, said one or more values of metabolic parameters of the user when performing physical activity on the exercise machine is performed by the at least one data processing unit based on, in addition to the determined at least one group of determined thermal features representative of said identified first thermographic portion, first data representative of the physical exercise being performed by the user provided by the exercise machine and/or second anthropometric data of the user (“…detecting a physiological response involves determining the extent of the physiological response, which may be expressed in various ways that are indicative of the extent of the physiological response, such as: (i) a binary value indicative of whether the user experienced, and/or is experiencing, the physiological response. (ii) a numerical value indicative of the magnitude of the physiological response. (iii) a categorial value indicative of the severity/extent of the physiological response, (iv) an expected change in thermal measurements of an ROI (denoted TH.sub.ROI or some variation thereof), and/or (v) rate of change in TH.sub.ROI.” [0099]; “A value indicative of the physiological response may be obtained at the leaf node and/or based on calculations involving values on nodes and/or edges along the path; (b) the model comprises parameters of a regression model…the computer multiplies the feature values…with the parameters of the regression model in order to obtain the value indicative of the physiological response, and/or (c) the model comprises parameters of a neural network.” [0117]; “…a value such as the respiration rate can distinguish between non-metabolic (e.g., mental and non-exercise related physical stress) and metabolic (physical activity induced) increases in HR. Thus, for example, using respiration data in addition to other values (e.g., HR) may provide better estimations of the values of the aerobic activity parameters, compared to estimations that do not involve respiration data.” [0250]).
Regarding Claim 7, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: further comprises a step of storing, by the at least one data processing unit, inside at least one memory unit operatively connected to said at least one data processing unit, said determined groups of thermal features representative of said identified first thermographic portion (“…providing the model to the system involves forwarding the model to the system via a computer network and/or a shared computer storage medium…In another embodiment, providing the model to the system involves storing the model in a location from which the system can retrieve the model, such as a database and/or cloud-based storage from which the system may retrieve the model.” [0116]; “The computer 445 generates feature values based on TH.sub.ROI 443, and possibly other sources of data. Then the computer utilizes a model 442 to calculate, based on the feature values, a value 447 of the respiratory parameter. The value 447 may be indicative of at least one of the following: breathing rate, respiration volume, whether the user is breathing mainly through the mouth or through the nose, exhale (inhale) duration, post-exhale (post-inhale) breathing pause, a dominant nostril, a shape of the exhale stream, smoothness of the exhale stream, and/or temperature of the exhale stream.” [0138]).
Regarding Claim 8, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: further comprises a step of storing, by the at least one data processing unit, inside the at least one memory unit operatively connected to said at least one data processing unit, said estimated one or more values of metabolic parameters associated with the acquired at least one thermographic image (“…providing the model to the system involves forwarding the model to the system via a computer network and/or a shared computer storage medium (e.g., writing the model to a memory that may be accessed by the system that detects the physiological response). In another embodiment, providing the model to the system involves storing the model in a location from which the system can retrieve the model, such as a database and/or cloud-based storage from which the system may retrieve the model.” [0116]).
Regarding Claim 9, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: wherein the previous steps are performed by the at least one data processing unit in an instant of time of acquisition of a single thermographic image acquired by the thermographic camera and based on the processing of said single thermographic image acquired by the thermographic camera, said one or more values of metabolic parameters which can be estimated comprising: value of oxygen volume that the user is producing at the instant of time of acquisition of the at least one thermographic image; value of carbon dioxide volume that the user is producing at the instant of time of acquisition of the at least one thermographic image; value of respiratory quotient of the user in the instant of time of acquisition of at least one thermographic image (“…detecting a physiological response involves determining the extent of the physiological response, which may be expressed in various ways that are indicative of the extent of the physiological response, such as: (i) a binary value indicative of whether the user experienced, and/or is experiencing, the physiological response. (ii) a numerical value indicative of the magnitude of the physiological response. (iii) a categorial value indicative of the severity/extent of the physiological response, (iv) an expected change in thermal measurements of an ROI (denoted TH.sub.ROI or some variation thereof), and/or (v) rate of change in TH.sub.ROI.” [0099]; “The current smoothness is calculated in real-time based on TH.sub.ROI, and the target smoothness is calculated based on previous TH.sub.ROI of the user taken while the user was in a state considered better than the user's state while starting the breathing biofeedback session.” [0314]).
Regarding Claim 10, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: wherein the previous steps are repeated by the at least one data processing unit for a plurality of instants of time of acquisition included in a time interval and based on the processing of a plurality of thermographic images acquired by the thermographic camera in said time interval (“…detection of the physiological response is done by processing thermal measurements that fall within a certain window of time that characterizes the physiological response. For example, depending on the physiological response, the window may be five seconds long, thirty seconds long, two minutes long, five minutes long, fifteen minutes long, or one hour long. Detecting the physiological response may involve analysis of thermal measurements taken during multiple of the above-described windows, such as measurements taken during different days.” [0094]), a thermographic image of said plurality being acquired in an instant of time of acquisition of said plurality of instants of time of acquisition, said one or more values of metabolic parameters of the user also comprise: value of heart rate of the user when performing physical activity on the exercise machine; value of respiratory rate of the user when performing physical activity on the exercise machine (“…detecting a physiological response involves determining the extent of the physiological response, which may be expressed in various ways that are indicative of the extent of the physiological response, such as: (i) a binary value indicative of whether the user experienced, and/or is experiencing, the physiological response. (ii) a numerical value indicative of the magnitude of the physiological response. (iii) a categorial value indicative of the severity/extent of the physiological response, (iv) an expected change in thermal measurements of an ROI (denoted TH.sub.ROI or some variation thereof), and/or (v) rate of change in TH.sub.ROI.” [0099]; “The current smoothness is calculated in real-time based on TH.sub.ROI, and the target smoothness is calculated based on previous TH.sub.ROI of the user taken while the user was in a state considered better than the user's state while starting the breathing biofeedback session.” [0314]).
Regarding Claim 11, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: wherein the body surface of the user included in the at least one portion of the body of the user of the acquired at least one thermographic image is without clothing (“FIG. 6 illustrates a frame 35 that mounts inward-facing head-mounted cameras 30 and 31 that measure regions 32 and 33 on the forehead, respectively. FIG. 7 illustrates a frame 75 that mounts inward-facing head-mounted cameras 70 and 71 that measure regions 72 and 73 on the forehead, respectively, and inward-facing head-mounted cameras 76 and 77 that measure regions 78 and 79 on the upper lip, respectively. FIG. 8 illustrates a frame 84 that mounts inward-facing head-mounted cameras 80 and 81 that measure regions 82 and 83 on the sides of the nose, respectively. And FIG. 9 illustrates a frame 90 that includes (i) inward-facing head-mounted cameras 91 and 92 that are mounted to protruding arms and measure regions 97 and 98 on the forehead, respectively, (ii) inward-facing head-mounted cameras 95 and 96, which are also mounted to protruding arms, which measure regions 101 and 102 on the lower part of the face, respectively, and (iii) head-mounted cameras 93 and 94 that measure regions on the periorbital areas 99 and 100, respectively.” [0083]).
Regarding Claim 13, Tzvieli teaches: A system for determining metabolic parameters of a user when performing physical activity on an exercise machine (“… a system that utilizes measurements indicative of respiration in order to estimate an aerobic activity parameter such as oxygen consumption (VO.sub.2), maximal oxygen consumption (VO.sub.2 max), and energy expenditure…” [0014]; “… the computer 445: (i) receives from a fitness app a certain number of breath cycles during which the user should perform a physical exercise, such as keeping a static yoga pose for a certain number of breath cycles, or riding a spin bike at a certain speed for a certain number of breath cycles. (ii) determines, based on m.sub.move, when the user performs the physical exercise, and (iii) counts, based on TH.sub.ROI, the number of breath cycles the user had while performing the physical exercise.” [0161]; “…various formulas and/or models were developed to estimate values of aerobic activity parameters from values such as heart rate…” [0249]), comprising:
an exercise machine on which a user can perform physical activity (“… the computer 445: (i) receives from a fitness app a certain number of breath cycles during which the user should perform a physical exercise, such as keeping a static yoga pose for a certain number of breath cycles, or riding a spin bike at a certain speed for a certain number of breath cycles. (ii) determines, based on m.sub.move, when the user performs the physical exercise, and (iii) counts, based on TH.sub.ROI, the number of breath cycles the user had while performing the physical exercise.” [0161]; “…various formulas and/or models were developed to estimate values of aerobic activity parameters from values such as heart rate…” [0249]);
a thermographic camera configured to acquire thermographic images of at least one portion of the body of the user when performing physical activity on the exercise machine, said at least one portion of the body of the user comprising a body surface of the user (“…this system includes at least one inward-facing head-mounted thermal camera (CAM) and a computer. Each CAM, from among the at least one CAM, is configured to take thermal measurements of a region below the nostrils (THR.sub.RBN) of a user. TH.sub.RN are indicative of an exhale stream of the user, such as an exhale stream from one, or both, of the nostrils and/or the exhale from the mouth. The computer is configured to calculate, based on THR.sub.RBN, the aerobic activity parameter.” [0014]; “The at least one CAM may include various combinations of one or more CAMs, as described in the various examples given in this disclosure of embodiments that include a single inward-facing head-mounted thermal camera that measures TH.sub.RBN (e.g., a single CAM coupled to the bottom of one of the sides of a frame worn by the user) or multiple CAMs…” [0243]);
at least one data processing unit operatively connected to the thermographic camera (“HMSs equipped with head-mounted cameras. FIG. 1a illustrates various inward-facing head-mounted cameras coupled to an eyeglasses frame 15. Cameras 10 and 12 measure regions 11 and 13 on the forehead, respectively. Cameras 18 and 36 measure regions on the periorbital areas 19 and 37, respectively…computer 16, which may include a processor, memory, a battery and/or a communication module.” [0081]);
at least one memory unit operatively connected to the at least one data processing unit (“HMSs equipped with head-mounted cameras. FIG. 1a illustrates various inward-facing head-mounted cameras coupled to an eyeglasses frame 15. Cameras 10 and 12 measure regions 11 and 13 on the forehead, respectively. Cameras 18 and 36 measure regions on the periorbital areas 19 and 37, respectively…computer 16, which may include a processor, memory, a battery and/or a communication module.” [0081]), the at least one data processing unit being configured to:
identify, within the at least one thermographic image acquired by the thermographic camera, a first thermographic portion representative of the body surface of the user (“…this system includes at least one inward-facing head-mounted thermal camera (CAM) and a computer. Each CAM, from among the at least one CAM, is configured to take thermal measurements of a region below the nostrils (THR.sub.RBN) of a user. TH.sub.RN are indicative of an exhale stream of the user, such as an exhale stream from one, or both, of the nostrils and/or the exhale from the mouth. The computer is configured to calculate, based on THR.sub.RBN, the aerobic activity parameter.” [0014]; “ The at least one CAM may include various combinations of one or more CAMs, as described in the various examples given in this disclosure of embodiments that include a single inward-facing head-mounted thermal camera that measures TH.sub.RBN (e.g., a single CAM coupled to the bottom of one of the sides of a frame worn by the user) or multiple CAMs…” [0243]);
for said identified first thermographic portion, determine at least one group of thermal features representative of the identified first thermographic portion (“…to calculate, based on TH.sub.RBN…the aerobic activity parameter 688…the aerobic activity parameter 688 is indicative of one or more of the following values: oxygen consumption…maximal oxygen consumption…and energy expenditure…the computer 686 may utilize additional inputs to calculate the aerobic activity parameter such as measurements of the heart rate (HR) of the user, values of the activity level of the user, and/or various statistics about the user…” [0247]; “…the computer 686 generates feature values based on data comprising TH.sub.RBN, and utilizes a model 687 to calculate the aerobic activity parameter 688 based on the feature values.” [0251]);
estimate, by executing an estimation algorithm based on a trained regression model, one or more values of metabolic parameters of the user when performing physical activity on the exercise machine based on said at least one determined group of thermal features representative of said identified first thermographic portion (“A value indicative of the physiological response may be obtained at the leaf node and/or based on calculations involving values on nodes and/or edges along the path; (b) the model comprises parameters of a regression model…the computer multiplies the feature values…with the parameters of the regression model in order to obtain the value indicative of the physiological response, and/or (c) the model comprises parameters of a neural network.” [0117]; “…the model 687 includes parameters of at least one of the following types of models: a regression model…” [0261]), said one or more values of metabolic parameters of the user comprising at least one of the following:
value of oxygen volume that the user is consuming when performing physical activity by the user (“… a system that utilizes measurements indicative of respiration in order to estimate an aerobic activity parameter such as oxygen consumption (VO.sub.2), maximal oxygen consumption (VO.sub.2 max), and energy expenditure…” [0014]; “Respiration volume” represents the volume of air breathed over a certain duration (usually per minute), the volume of air breathed during a certain breath, tidal volume, and/or the ratio between two or more breaths. For example, the respiration volume may indicate that a first breath was deeper than a second breath, or that breaths during a first minute were shallower than breaths during a second minute.” [0122]);
provide said estimated one or more values of metabolic parameters (“…a value such as the respiration rate can distinguish between non-metabolic (e.g., mental and non-exercise related physical stress) and metabolic (physical activity induced) increases in HR. Thus, for example, using respiration data in addition to other values (e.g., HR) may provide better estimations of the values of the aerobic activity parameters, compared to estimations that do not involve respiration data.” [0250]).
Regarding Claim 16, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: wherein the thermographic camera comprises a thermal camera or an infrared camera (“…this system includes at least one inward-facing head-mounted thermal camera (CAM) and a computer. Each CAM, from among the at least one CAM, is configured to take thermal measurements of a region below the nostrils (THR.sub.RBN) of a user. TH.sub.RN are indicative of an exhale stream of the user, such as an exhale stream from one, or both, of the nostrils and/or the exhale from the mouth. The computer is configured to calculate, based on THR.sub.RBN, the aerobic activity parameter.” [0014]; “… CAM includes a focal-plane array (FPA) sensor and an infrared lens, and the FPA plane is tilted by more than 20 relative to the infrared lens plane according to the Scheimpflug principle in order to improve the sharpness of the image of ROI.sub.1…” [0408];“… the sensor includes a visible-light camera and/or a near-infrared camera, the system is powered by a battery, and the system may operate in a state belonging to a set comprising first and second states.” [0480]).
Regarding Claim 18, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: wherein the thermographic camera is separate from the exercise machine and is installed inside the training space in which the exercise machine usable by the user to perform physical activity is located, the thermographic camera being positioned and oriented so as to acquire the thermographic images of said at least one portion of the body of the user when performing physical activity on the exercise machine (“…a system that utilizes measurements indicative of respiration in order to estimate an aerobic activity parameter such as oxygen consumption (VO.sub.2), maximal oxygen consumption (VO.sub.2 max), and energy expenditure (EE). In one embodiment, this system includes at least one inward-facing head-mounted thermal camera (CAM) and a computer. Each CAM, from among the at least one CAM, is configured to take thermal measurements of a region below the nostrils (THR.sub.RBN) of a user. TH.sub.RN are indicative of an exhale stream of the user, such as an exhale stream from one, or both, of the nostrils and/or the exhale from the mouth. The computer is configured to calculate, based on THR.sub.RBN, the aerobic activity parameter.” [0014]; “A “thermal camera” refers herein to a non-contact device that measures electromagnetic radiation having wavelengths longer than 2500 nanometer (nm) and does not touch its region of interest (ROI). A thermal camera may include one sensing element (pixel), or multiple sensing elements that are also referred to herein as “sensing pixels”, “pixels”, and/or focal-plane array (FPA). A thermal camera may be based on an uncooled thermal sensor, such as a thermopile sensor, a microbolometer sensor…” [0072]; “… the computer 445: (i) receives from a fitness app a certain number of breath cycles during which the user should perform a physical exercise, such as keeping a static yoga pose for a certain number of breath cycles, or riding a spin bike at a certain speed for a certain number of breath cycles. (ii) determines, based on m.sub.move, when the user performs the physical exercise, and (iii) counts, based on TH.sub.ROI, the number of breath cycles the user had while performing the physical exercise.” [0161]; “…various formulas and/or models were developed to estimate values of aerobic activity parameters from values such as heart rate…” [0249]).
Regarding Claim 19, Tzvieli teaches the claim limitations as noted above.
Tzvieli further teaches: wherein the thermographic camera is separate from the exercise machine as it is integrated into a portable electronic device of the user present inside the training space in which the exercise machine usable by the user to perform physical activity is located, the thermographic camera being positioned and oriented so as to acquire the thermographic images of said at least one portion of the body of the user when performing physical activity on the exercise machine (“…a system that utilizes measurements indicative of respiration in order to estimate an aerobic activity parameter such as oxygen consumption (VO.sub.2), maximal oxygen consumption (VO.sub.2 max), and energy expenditure (EE). In one embodiment, this system includes at least one inward-facing head-mounted thermal camera (CAM) and a computer. Each CAM, from among the at least one CAM, is configured to take thermal measurements of a region below the nostrils (THR.sub.RBN) of a user. TH.sub.RN are indicative of an exhale stream of the user, such as an exhale stream from one, or both, of the nostrils and/or the exhale from the mouth. The computer is configured to calculate, based on THR.sub.RBN, the aerobic activity parameter.” [0014]; “A “thermal camera” refers herein to a non-contact device that measures electromagnetic radiation having wavelengths longer than 2500 nanometer (nm) and does not touch its region of interest (ROI). A thermal camera may include one sensing element (pixel), or multiple sensing elements that are also referred to herein as “sensing pixels”, “pixels”, and/or focal-plane array (FPA). A thermal camera may be based on an uncooled thermal sensor, such as a thermopile sensor, a microbolometer sensor…” [0072]; “… the computer 445: (i) receives from a fitness app a certain number of breath cycles during which the user should perform a physical exercise, such as keeping a static yoga pose for a certain number of breath cycles, or riding a spin bike at a certain speed for a certain number of breath cycles. (ii) determines, based on m.sub.move, when the user performs the physical exercise, and (iii) counts, based on TH.sub.ROI, the number of breath cycles the user had while performing the physical exercise.” [0161]; “…various formulas and/or models were developed to estimate values of aerobic activity parameters from values such as heart rate…” [0249]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 14-15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tzvieli in view of Ramp et. al. (U.S. 20230329636, October 19, 2023)(hereinafter, “Ramp”).
Regarding Claim 14, Tzvieli teaches the claim limitations as noted above.
With regards to limitations: wherein the at least one data processing unit and the at least one memory unit are integrated into the exercise machine, Tzvieli teaches: “… a system that utilizes measurements indicative of respiration in order to estimate an aerobic activity parameter such as oxygen consumption (VO.sub.2), maximal oxygen consumption (VO.sub.2 max), and energy expenditure…” [0014]; “… the computer 445: (i) receives from a fitness app a certain number of breath cycles during which the user should perform a physical exercise, such as keeping a static yoga pose for a certain number of breath cycles, or riding a spin bike at a certain speed for a certain number of breath cycles. (ii) determines, based on m.sub.move, when the user performs the physical exercise, and (iii) counts, based on TH.sub.ROI, the number of breath cycles the user had while performing the physical exercise.” [0161]; “…various formulas and/or models were developed to estimate values of aerobic activity parameters from values such as heart rate…” [0249].
Tzvieli does not explicitly teach the setup connection of the memory unit, thermographic camera and exercise machine together as recited in the disclosed claim.
Ramp in the field of monitoring based exercise systems teaches: “The user equipment 110, adhesive patch sensors 120, smart-glasses 130, and/or a smart-watch 140 may include other or additional sensors, such as the camera, microphone, IMU, clocks, electromyograms, gas sensors, pressure sensor, proximity/motion sensor, light sensor, and thermometer. Although three wearables 120, 130, 140 are illustrated in FIG. 1A, various embodiments may include fewer or a greater number of wearables and/or remote computing devices, including one or more different types of wearables and/or computing device not shown in the environment 100.” [0040].
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Tzvieli such that the at least one data processing unit and the at least one memory unit are integrated into the exercise machine as taught in Ramp for efficient high speed communications, storage, data processing and memory sharing.
Regarding Claim 15, Tzvieli teaches the claim limitations as noted above.
With regards to limitations: wherein the at least one data processing unit and the at least one memory unit reside in a remote electronic processor operatively connected to the thermographic camera and the exercise machine, Tzvieli teaches: “… a system that utilizes measurements indicative of respiration in order to estimate an aerobic activity parameter such as oxygen consumption (VO.sub.2), maximal oxygen consumption (VO.sub.2 max), and energy expenditure…” [0014]; “… the computer 445: (i) receives from a fitness app a certain number of breath cycles during which the user should perform a physical exercise, such as keeping a static yoga pose for a certain number of breath cycles, or riding a spin bike at a certain speed for a certain number of breath cycles. (ii) determines, based on m.sub.move, when the user performs the physical exercise, and (iii) counts, based on TH.sub.ROI, the number of breath cycles the user had while performing the physical exercise.” [0161]; “…various formulas and/or models were developed to estimate values of aerobic activity parameters from values such as heart rate…” [0249].
Tzvieli does not explicitly teach the setup connection of the memory unit, thermographic camera and exercise machine together as recited in the disclosed claim.
Ramp in the field of monitoring based exercise systems teaches: “The user equipment 110, adhesive patch sensors 120, smart-glasses 130, and/or a smart-watch 140 may include other or additional sensors, such as the camera, microphone, IMU, clocks, electromyograms, gas sensors, pressure sensor, proximity/motion sensor, light sensor, and thermometer. Although three wearables 120, 130, 140 are illustrated in FIG. 1A, various embodiments may include fewer or a greater number of wearables and/or remote computing devices, including one or more different types of wearables and/or computing device not shown in the environment 100.” [0040].
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Tzvieli such that the at least one data processing unit and the at least one memory unit reside in a remote electronic processor operatively connected to the thermographic camera and the exercise machine as taught in Ramp for efficient high speed communications, storage, data processing and memory sharing.
Regarding Claim 17, Tzvieli teaches the claim limitations as noted above.
With regards to limitations: wherein the thermographic camera is installed on or integrated into the exercise machine and is oriented so as to acquire the thermographic images of said at least one portion of the body of the user when performing physical activity on the exercise machine, Tzvieli teaches: “… a system that utilizes measurements indicative of respiration in order to estimate an aerobic activity parameter such as oxygen consumption (VO.sub.2), maximal oxygen consumption (VO.sub.2 max), and energy expenditure…” [0014]; “… the computer 445: (i) receives from a fitness app a certain number of breath cycles during which the user should perform a physical exercise, such as keeping a static yoga pose for a certain number of breath cycles, or riding a spin bike at a certain speed for a certain number of breath cycles. (ii) determines, based on m.sub.move, when the user performs the physical exercise, and (iii) counts, based on TH.sub.ROI, the number of breath cycles the user had while performing the physical exercise.” [0161]; “…various formulas and/or models were developed to estimate values of aerobic activity parameters from values such as heart rate…” [0249].
Tzvieli does not explicitly teach the setup connection of the memory unit, thermographic camera and exercise machine together as recited in the disclosed claim.
Ramp in the field of monitoring based exercise systems teaches: “The user equipment 110, adhesive patch sensors 120, smart-glasses 130, and/or a smart-watch 140 may include other or additional sensors, such as the camera, microphone, IMU, clocks, electromyograms, gas sensors, pressure sensor, proximity/motion sensor, light sensor, and thermometer. Although three wearables 120, 130, 140 are illustrated in FIG. 1A, various embodiments may include fewer or a greater number of wearables and/or remote computing devices, including one or more different types of wearables and/or computing device not shown in the environment 100.” [0040].
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Tzvieli such that the thermographic camera is installed on or integrated into the exercise machine as taught in Ramp for efficient high speed communications, storage, data processing and memory sharing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Blahnik et. al. U.S. 20180345078 teaches a portable physical activity monitoring system.
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/AMAL ALY FARAG/Primary Examiner, Art Unit 3798
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