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 .
Notice to Applicant
This communication is in response to the amendment filed 2/4/26. Claims 1, 8, 11, 18, and 20 have been amended. Claims 7 and 17 are cancelled. Claims 1-6, 8-16, and 18-20 are pending.
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 1-6, 8-16, and 18-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-6 and 8-10 are directed to a method (i.e., a process), claims 11-16, 18, and 19 are directed to a system (i.e., a machine), and claim 20 is directed to a non-transitory computer-readable medium (i.e., a machine). Accordingly, claims 1-6, 8-16, and 18-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts.
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
1. A method for providing remote physiotherapy sessions, the method comprising: capturing, by at least one camera, a first real-time video of a patient performing at least one predefined movement, wherein the at least one predefined movement is performed by the patient for diagnostic purpose and monitoring recovery status from a medical condition; processing in real-time, by a first Artificial Intelligence (AI) model, the first real-time video of the patient; determining a set of health parameters related to the medical condition of the patient, based on the at least one predefined movement performed by the patient; analyzing, by the first Al model, the set of health parameters and at least one of patient health records and demographic data to determine a current fitness state of the patient; identifying, by the first Al model, a set of exercises to be performed by the patient, based on the current fitness state of the patient; capturing, by the at least one camera, a second real-time video of the patient performing an exercise from the set of exercises, wherein the second real-time video comprises a stream of poses and movements made by the patient to perform the exercise; extracting a second Al model based on the current fitness state of the patient and the exercise being performed by the patient, wherein the second Al model is configured to determine a deviation of the patient from a plurality of expected movements associated with the exercise based on target exercise performance of a healthy specimen; processing in real-time, by the second Al model, the second real-time video of the patient to determine a set of patient mobility parameters based on current exercise performance of the patient; comparing, by the second Al model, the set of patient mobility parameters with a set of target mobility parameters, wherein the set of target mobility parameters corresponds to the healthy specimen; generating, by the second Al model, feedback for the patient based on the comparison of the set of patient mobility parameters with the set of target mobility parameters, wherein the feedback comprises at least one of corrective actions or alerts, and wherein the feedback comprises at least one of visual feedback, aural feedback, or haptic feedback; rendering, by the second Al model, the feedback on a rendering device; identifying, by the second Al model, a failure in completion of the exercise by the patient; and sending a reminder to the patient after expiry of a pre-defined time interval for completing of the exercise, in response to identifying the failure in completion of the exercise by the patient.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” because processing the first real-time video of the patient; determining a set of health parameters related to the medical condition of the patient, based on the at least one predefined movement performed by the patient; analyzing the set of health parameters and at least one of patient health records and demographic data to determine a current fitness state of the patient; identifying a set of exercises to be performed by the patient, based on the current fitness state of the patient; extracting a second model based on the current fitness state of the patient and the exercise being performed by the patient to determine a deviation of the patient from a plurality of expected movements associated with the exercise based on target exercise performance of a healthy specimen; processing the second real-time video of the patient to determine a set of patient mobility parameters based on current exercise performance of the patient; comparing the set of patient mobility parameters with a set of target mobility parameters; and identifying a failure in completion of the exercise by the patient amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or via pen and paper. The limitations regarding capturing videos, wherein the at least one predefined movement is performed by the patient for diagnostic purpose and monitoring recovery status from a medical condition, generating feedback for the patient based on the comparison of the set of patient mobility parameters with the set of target mobility parameters, wherein the feedback comprises at least one of corrective actions or alerts, rendering the feedback, and sending a reminder to the patient after expiry of a pre-defined time interval for completing of the exercise, in response to identifying the failure in completion of the exercise by the patient constitute “certain methods of organizing human activity“ because they amount to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), at the currently claimed high level of generality.
Accordingly, the claim recites at least one abstract idea.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 11, and 20, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and certain methods of organizing human activity but for the recitation of generic computer components. That is, other than reciting at least one camera, AI, a device, a processor, a memory, and a non-transitory computer-readable medium storing computer-executable instructions used to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind or certain methods of organizing human activity. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and certain methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Mental Processes” and “certain methods of organizing human activity“ groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the at least one camera, AI, device, processor, memory, and non-transitory computer-readable medium are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of capturing data, processing data, analyzing data, identifying data, extracting data, comparing data, generating data, rendering data, and sending data) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05). Their collective functions merely provide conventional computer implementation.
Claims 2-6, 8-10 and 12-16, 18, and 19 are ultimately dependent from Claim(s) 1 and 11 and include all the limitations of Claim(s) 1 and 11. Therefore, claim(s) 2-6, 8-10 and 12-16, 18, and 19 recite the same abstract idea. Claims 2-6, 8-10 and 12-16, 18, and 19 describe further limitations regarding overlaying with a pose skeletal model, overlaying one of at least one corrective action over the pose skeletal model; displaying the alerts; generating a warning to the patient rendering the set of exercises to the patient; receiving the exercise as patient selection; customizing the exercise for the patient; defining a number of repetitions and a number of sets of the exercise for the patient; selecting one of a plurality of modes for the exercise, based on the current fitness state of the patient; suggesting an alternative exercise instead of the exercise; monitoring each of the set of exercises being performed by the patient; generating a summarized report and rendering the summarized report to the patient; validating patient performance based on the summarized report; and providing an authorization to the patient to perform one or more action. These are all just further describing the abstract ideas recited in claims 1 and 11, without adding significantly more.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In particular, the graphical user interface (GUI) is recited at a high-level of generality (i.e., as a generic computer component performing the generic computer function of displaying data) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, independent claims 1, 11, and 20 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations directed to extracting a second Al model, all of which the Examiner submits merely add insignificant extra-solution activity to the abstract idea or are claimed in a merely generic manner (e.g., at a high level of generality), the Examiner further submits that such steps are not unconventional as they merely consist of storing and retrieving information in memory. See MPEP 2106.05(d)(II).
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-6, 8-16, and 18-20 are ineligible under 35 USC §101.
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.
Claim(s) 1, 5, 6, 8-11, 15, 16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asikainen et al. (US 2021/0008413 A1) in view of Kohli (US 2014/0172460 A1), and further in view of Sherpa (US 2020/0261019 A1).
(A) Referring to claim 1, Asikainen discloses A method for providing remote physiotherapy sessions, the method comprising (para. 46 & 66 of Asikainen):
capturing, by at least one camera, a first real-time video of a patient performing at least one predefined movement (para. 46 of Asikainen; As depicted, the example configuration includes the interactive personal training device 108 equipped with the sensor(s) 109 configured to capture a video of a scene in which user 106 is performing the exercise movement using the barbell equipment 134a. For example, the sensor(s) 109 may comprise one or more of a high definition (HD) camera, a regular 2D camera, a RGB camera, a multi-spectral camera, a structured light 3D camera, a time-of-flight 3D camera, a stereo camera, a radar sensor, a LiDAR scanner, an infrared sensor, or a combination of one or more of the foregoing sensors. The sensor(s) 109 comprising of one or more cameras may provide a wider field of view (e.g., field of view >120 degrees) for capturing the video of the scene in which user 106 is performing the exercise movement and acquiring depth information (R, G, B, X, Y, Z) from the scene. The interactive personal training device 108 is configured to process and analyze the stream of sensor data using trained machine learning algorithms and provide feedback in real time on the user 106 performing the exercise movement.);
processing in real-time, by a first Artificial Intelligence (AI) model, the first real-time video of the patient; determining a set of health parameters related to the medical condition of the patient, based on the at least one predefined movement performed by the patient (Fig. 2, para. 46, 66, 67, 70, 86, 102, & 116 of Asikainen; runs the model on one or more videos of users performing a repetition of the exercise movement. Although the example configuration in FIG. 1B is illustrated in the context of tracking physical activity of a user performing exercise movements and providing feedback and recommendations relating to performing the exercise movements, it should be understood that the configuration may apply to other contexts in vertical fields, such as medical diagnosis (e.g., health practitioner reviewing vital signs of a user, volumetric scanning, 3D imaging in medicine, etc.), physical therapy (e.g. physical therapist checking adherence to physio protocols during rehabilitation). The status monitor 312 receives the processed sensor data including the images and estimated 3D pose data from the pose estimator 302 for determining and tracking vital signs and health status of the user during and after the exercise movement.);
analyzing, by the first Al model, the set of health parameters and at least one of patient health records and demographic data to determine a current fitness state of the patient (para. 86, 90, & 91 of Asikainen; The recommendation engine 210 receives one or more of the 3D pose data, the exercise movements performed, the quality of exercise movements, the repetitions of the exercise movements, the vital signs and health status signals, performance data, object detection data, user profile, and other analyzed user data from the feedback engine 208 and the data processing engine 204 to compare a pattern of the user's workout with an aggregate user dataset (collected from multiple users) to identify a community of users with common characteristics. For example, the common characteristics may include an age group, gender, weight, height, fitness preference, similar performance and workout patterns. In one example, this community of users may be identified by comparing the estimated 3D pose data of users performing the exercise movements over a period of time. The recommendation engine 210 uses both individual user data and aggregate user data to analyze the individual user's workout pattern, user preferences, compare the user's performance data with other similarly performing users, and generate recommendations for users (e.g., novice, pro-athletes, etc.) in real time. The recommendation engine 210 uses the tagged sequences from the aggregate user dataset to train a machine learning model (e.g., CNN) to identify or predict a level of fatigue in the exercise movement.);
identifying, by the first Al model, a set of exercises to be performed by the patient, based on the current fitness state of the patient (para. 64-66, 92, & 102 of Asikainen; The recommendation engine 210 generates on-the-fly recommendation to modify or alter the user exercise workout based on the state or level of fatigue of the user. For example, the recommendation engine 210 may recommend to the user to push for As Many Repetitions As Possible (AMRAP) in the last set of an exercise movement if the level of fatigue of the user is low. In another example, the recommendation engine 210 may recommend to the user to reduce the number of repetitions from 10 to five on a set of exercise movements if the level of fatigue of the user is high. In another example, the recommendation engine 210 may recommend to the user to increase weight on the weight equipment by 10 pounds if the level of fatigue of the user is low. In yet another example, the recommendation engine 210 may recommend to the user to decrease weight on the weight equipment by 20 pounds if the level of fatigue of the user is high.);
capturing, by the at least one camera, a second real-time video of the patient performing an exercise from the set of exercises, wherein the second real-time video comprises a stream of poses and movements made by the patient to perform the exercise (Fig. 1B and para. 46 & 102 of Asikainen; The program enhancement engine 214 receives a video of a user performing one or more repetitions of an exercise movement in the new workout program. The enhancement engine 214 analyzes the video using the pose estimator 302 to estimate pose data relating to performing the exercise movement.);
extracting a second Al model, wherein the second Al model is configured to determine a deviation of the patient from a plurality of expected movements associated with the exercise based on target exercise performance of a healthy specimen (para. 84 & 85 of Asikainen; the movement adherence monitor 310 compares whether the user performance of the exercise movement in view of body mechanics associated with correctly performing the exercise movement falls within acceptable range or threshold for human joint positions and movements. In some implementations, the movement adherence monitor 310 uses a machine learning model, such as a convolutional neural network trained on a large set of ideal or correct repetitions of an exercise movement to determine a score or a quality of the exercise movement performed by the user based at least on the estimated 3D pose data and the consecutive repetitions of the exercise movement. For example, the score (e.g., 85%) may indicate the adherence to predefined conditions for correctly performing the exercise movement.);
processing in real-time, by the second Al model, the second real-time video of the patient to determine a set of patient mobility parameters based on current exercise performance of the patient (para. 46 & 102 of Asikainen; The program enhancement engine 214 receives a video of a user performing one or more repetitions of an exercise movement in the new workout program. The enhancement engine 214 analyzes the video using the pose estimator 302 to estimate pose data relating to performing the exercise movement. The enhancement engine 214 instructs the user interface engine 216 to generate a user interface to receive from the user an input (e.g., ground truth) indicating the position, the angle, and the relative distance between the detected keypoints in a segment of the video containing a repetition of the exercise movement from start to end. For example, the user uploads a video of the user performing a combination of a front squat movement and a standing overhead press movement. The user specifies the timestamps in the video segment that contain this new combination of exercise movement and sets conditions or acceptable thresholds for completing a repetition including angles and distance between keypoints, speed of movement, and range of movement. The enhancement engine 214 creates and trains a machine learning model for classifying the exercise movement using the user input as initial weights of the machine learning model and the video of the user performing the repetitions of the exercise movement. The enhancement engine 214 then applies this machine learning model on a plurality of videos of users performing repetitions of this exercise movement from the new workout program.);
comparing, by the second Al model, the set of patient mobility parameters with a set of target mobility parameters, wherein the set of target mobility parameters corresponds to the healthy specimen (para. 46, 84, 85, & 93-97 of Asikainen; the recommendation engine 210 analyzes the user profiles of similarly performing users who have done similar workouts, their ratings for the workouts, and their overall work capacity progress similar to the target user to generate recommendations. The feedback may include a comparison of the exercise form of the user 106 against conditions of an ideal or correct exercise form predefined for the exercise movement and providing a visual overlay on the interactive display of the interactive personal training device to guide the user 106 to perform the exercise movement correctly. The movement adherence monitor 310 compares whether the user performance of the exercise movement in view of body mechanics associated with correctly performing the exercise movement falls within acceptable range or threshold for human joint positions and movements.);
generating, by the second Al model, feedback for the patient based on the comparison of the set of patient mobility parameters with the set of target mobility parameters, wherein the feedback comprises at least one of corrective actions or alerts, and wherein the feedback comprises at least one of visual feedback, aural feedback, or haptic feedback (Fig. 10, para. 93 & 108 of Asikainen; the recommendation engine 210 receives actual motion path in association with a user using an equipment 134 performing an exercise movement from the movement adherence monitor 310 in the feedback engine 208. The recommendation engine 210 determines the direction of force used by the user in performing the exercise movement based on the actual motion path. If the percentage difference between the actual motion path and the correct motion path is not within a threshold limit, the recommendation engine 210 instructs the user interface engine 216 to generate an alert on the interactive screen of the interactive personal training device 108 informing the user to decrease force in the direction of the actual motion path to avoid injury.);
rendering, by the second Al model, the feedback on a rendering device (Fig. 10, para. 93 & 108 of Asikainen; FIG. 10 shows example graphical representations illustrating user interfaces 1000a-1000b for displaying real time feedback on the interactive personal training device 108. The user interface 1000a depicts an interactive screen on the interactive personal training device 108 displaying real time feedback 1001 and 1003.);
identifying, by the second Al model, a failure in completion of the exercise by the patient and sending a reminder to the patient after expiry of a pre-defined time interval for completing of the exercise, in response to identifying the failure in completion of the exercise by the patient (para. 88, 109, 84 and Fig. 11 of Asikainen; performance metrics and statistics include, but not limited to, total rest time, energy expenditure, current and average heart rate, historical workout data compared with current workout session, completed repetitions in an ongoing workout set, completed sets in an ongoing exercise movement, incomplete repetition, etc. The performance tracker 314 derives total exercise volume from individual workout sessions over a length of time, such as daily, weekly, monthly, and annually. The performance tracker 314 determines total time under tension expressed in seconds or milliseconds using active movement time and bodyweight or equipment weight. The performance tracker 314 determines a total time of exercise expressed in minutes as total length of workout not spent in recovery or rest. The performance tracker 314 determines total rest time from time spent in idle position, such as standing, lying down, hunched over, or sitting. The performance tracker 314 retrieves historical user performance of a workout similar to a current workout of the user and generates a summary comparing the historical performance metrics with the current workout as a percentage to indicate user progress. See Fig. 11, item 1107 which displays a progress bar showing a progress percentage for each muscle group on user interface 1100).
Asikainen does not expressly disclose wherein the at least one predefined movement is performed by the patient for diagnostic purpose and monitoring recovery status from a medical condition; and extracting an Al model based on the current fitness state of the patient and the exercise being performed by the patient.
Kohli discloses wherein the at least one predefined movement is performed by the patient for diagnostic purpose and monitoring recovery status from a medical condition (para. 40, 57, 84, and 86 of Kohli; assisting a clinician in diagnosing or treating a musculoskeletal condition of a patient, the method comprising: capturing an image of a patient's movement of one or more musculoskeletal joints and appendages while the patient is performing a diagnostic or rehabilitation exercise; Using the computer-program product of the present disclosure, a clinician is able to create new standard diagnostic and treatment exercise routines for a patient with a disorder related to a specific musculoskeletal anatomical location (see tab 320 of the Homepage shown in FIG. 3A, and step 320 in the flowchart of FIG. 3B). Alternatively, computer program product of the present disclosure provides the clinician preprogrammed exercise routines that may be downloaded from the system server database. The clinician, and the patient, may view the patient's rehabilitation progress on the system record (see FIG. 3B, steps 390, 400, and 410). The system generates a "History Graph", as illustrated in FIG. 10, which comprises the dates of the recorded exercise session (x-axis) versus the range of motion achieved on that date (y-axis), and wherein the boxes on the lines of the graph note the maximum range of motion (e.g. flexion) achieved on that particular date.)
Sherpa discloses extracting an Al model based on the current fitness state of the patient and the exercise being performed by the patient (para. 33, 37, 53, and 54 of Sherpa; Fitness state sorting module 120 includes a machine-learning user specific recommendation model 140 that relates fitness states to user specific recommendations, as described in more detail below. In an embodiment, fitness state sorting module 120 may be designed and configured to generate a current user fitness state using the fitness state model 136 and the at least a biological parameter and generate a user specific recommendation using the user specific recommendation model 140 and the at least a user input. As used herein, fitness-state includes any of the fitness states as used above and may include one of a strength state fitness module 124, an endurance state fitness module 128, and a well-being state fitness module 132. Strength state fitness module 124 may include any strength state as described above, including without limitation a state where a user is physically strong as demonstrated by muscularity, flexibility, and balance. Strength state fitness module 124 may include a state that is devoted to muscle building and muscle conditioning and may include user specific recommendations that highlight anaerobic activities such as weight-bearing exercises, weight-lifting, yoga, squats, lunges, planks, Pilates, push-ups, jumping jacks, rowing, and the like. Endurance state fitness module 128 may include a state where a user is able to endure cardiovascular exercise as demonstrated by stamina and resilience. In an embodiment, training set data may be utilized to create a machine-learning algorithm relating biological parameters to each fitness state including strength state fitness module 124, endurance state fitness module 128, and well-being state fitness module 132. Training set data utilized to generate mathematical algorithms for fitness state model and/or user specific recommendation model may be obtained from multiple sources such as data obtained from medical journals and medical studies. Sources may include data from fitness tracking equipment that a user may wear.).
Before the effective filing date of the claimed invention, it would have been obvious to person of ordinary skill in the art to combine the aforementioned features of Kohli and Sherpa within Asikainen. The motivation for doing so would have been to view the patient's rehabilitation progress (para. 86 of Kohli) and to suggest a goal (para. 37 of Sherpa).
(B) Referring to claim 5, Asikainen discloses further comprising: rendering, via a GUI, the set of exercises to the patient; and receiving, via the GUI, the exercise as patient selection (para. 66 & 98 and Figures 6 & 8 of Asikainen).
(C) Referring to claim 6, Asikainen discloses further comprising customizing, by the second Al model, the exercise for the patient, based on the comparison of the set of patient mobility parameters with the set of target mobility parameters, wherein customizing the exercise comprises: defining a number of repetitions and a number of sets of the exercise for the patient; and selecting one of a plurality of modes for the exercise, based on the current fitness state of the patient (Fig. 15, para. 65, 88, 90-92, 113, 114 of Asikainen).
(D) Referring to claim 8, Asikainen discloses further comprising: suggesting, by the second Al model, an alternative exercise instead of the exercise, in response to identified repeated failures in completion of the exercise by the patient (para. 88 & 92 of Asikainen).
(E) Referring to claim 9, Asikainen discloses further comprising: monitoring, by the second Al model, each of the set of exercises being performed by the patient based on the second real-time video of the patient; generating, by the second Al model, a summarized report corresponding to the patient based on the monitoring; and rendering, via a GUI, the summarized report to the patient (para. 37, 88, 99, 109, 110, & 116 and Fig. 11 of Asikainen).
(F) Referring to claim 10, Asikainen discloses further comprising: validating patient performance based on the summarized report; and providing an authorization to the patient to perform one or more actions, upon a successful validation (para. 44, 87, 88, 96, & 110 and Figures 11 & 12 of Asikainen).
(G) Claims 15, 16, 18 and 19 repeat substantially the same limitations as claims 5, 6, and 8-10, and are therefore rejected for the same reasons given above.
(H) Claim 11 differs from claim 1 by reciting “A system for providing remote physiotherapy sessions, the system comprising: a processor; and a memory coupled to the processor, wherein the memory stores processor executable instructions, which, on execution, causes the processor to:” (para. 33, 39, 46 & 66 of Asikainen). Claim 20 differs from claim 1 by reciting “A non-transitory computer-readable medium storing computer-executable instructions for providing remote physiotherapy sessions, the stored instructions, when executed by a processor, cause the processor to perform operations comprises…” (para. 46, 48-50, 59, 121, & 66 of Asikainen).
The remainder of claims 11 and 20 repeat substantially the same limitations as claim 1, and are therefore rejected for the same reasons given above.
Claim(s) 2-4 and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asikainen et al. (US 2021/0008413 A1) in view of Kohli (US 2014/0172460 A1), in view of Sherpa (US 2020/0261019 A1), and further in view of Kord (US 2015/0097937 A1).
(A) Referring to claim 2, Askikainen, Kohli, and Sherpa do not disclose further comprising overlaying, by the second Al model, the patient in the second real-time video with a pose skeletal model, wherein the pose skeletal model comprises a plurality of key points based on the exercise, and wherein each of the plurality of key points is overlayed over a corresponding joint of the patient in the second real-time video.
Kord discloses overlaying, by the second Al model, the patient in the second real-time video with a pose skeletal model, wherein the pose skeletal model comprises a plurality of key points based on the exercise, and wherein each of the plurality of key points is overlayed over a corresponding joint of the patient in the second real-time video (see Figures 2 & 3, para. 7, 12-15, 25, and 26 of Kord).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Kord within Askikainen, Kohli, and Sherpa. The motivation for doing so would have been to improve sports training or medical diagnosis (para. 2 of Kord).
(B) Referring to claim 3, Askikainen discloses displaying the alerts on a Graphical User Interface (GUI) of the rendering device; and outputting the aural feedback to the patient, via a speaker (para. 46 & 93 of Asikainen).
Askikainen, Kohli, and Sherpa do not disclose wherein rendering the feedback comprises: overlaying one of at least one corrective action over the pose skeletal model overlayed on the second real-time video of the patient.
Kord discloses wherein rendering the feedback comprises: overlaying one of at least one corrective action over the pose skeletal model overlayed on the second real-time video of the patient (see Figures 2 & 3, para. 2, 7, 12-15, 25, 26, and 42 of Kord).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Kord within Askikainen, Kohli, and Sherpa. The motivation for doing so would have been to improve sports training or medical diagnosis (para. 2 of Kord).
(C) Referring to claim 4, Asikainen discloses wherein the feedback comprises generating a warning to the patient comprising: indication for correcting a current pose of the patient; and indication for correcting motion associated with the current pose of the patient (para. 93 & 8 of Asikainen).
(D) Claims 12-14 repeat substantially the same limitations as claims 2-4 and are therefore rejected for the same reasons given above.
Response to Arguments
Applicant's arguments filed 2/4/26 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 2/4/26.
(1) Applicant requests reconsideration and withdrawal of the rejection of claims 1-20 under 35 U.S.C. § 101.
(2) Applicant argues that the cited references fail to teach, disclose or suggest extracting a second Al model based on the current fitness state of the patient and the exercise being performed by the patient, wherein the second Al model is configured to determine a deviation of the patient from a plurality of expected movements associated with the exercise based on target exercise performance of a healthy specimen and the cited references fail to teach, disclose or suggest identifying, by the second Al model, a failure in completion of the exercise by the patient; and sending a reminder to the patient after expiry of a pre-defined time interval for completing of the exercise, in response to identifying the failure in completion of the exercise by the patient.
(A) As per the first argument, see 101 rejection above. The Examiner submits that the foregoing underlined limitations in the 101 rejection above constitute “a mental process” because processing the first real-time video of the patient; determining a set of health parameters related to the medical condition of the patient, based on the at least one predefined movement performed by the patient; analyzing the set of health parameters and at least one of patient health records and demographic data to determine a current fitness state of the patient; identifying a set of exercises to be performed by the patient, based on the current fitness state of the patient; extracting a second model based on the current fitness state of the patient and the exercise being performed by the patient to determine a deviation of the patient from a plurality of expected movements associated with the exercise based on target exercise performance of a healthy specimen; processing the second real-time video of the patient to determine a set of patient mobility parameters based on current exercise performance of the patient; comparing the set of patient mobility parameters with a set of target mobility parameters; and identifying a failure in completion of the exercise by the patient amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or via pen and paper. The limitations regarding capturing videos, wherein the at least one predefined movement is performed by the patient for diagnostic purpose and monitoring recovery status from a medical condition, generating feedback for the patient based on the comparison of the set of patient mobility parameters with the set of target mobility parameters, wherein the feedback comprises at least one of corrective actions or alerts, rendering the feedback, and sending a reminder to the patient after expiry of a pre-defined time interval for completing of the exercise, in response to identifying the failure in completion of the exercise by the patient constitute “certain methods of organizing human activity“ because they amount to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), at the currently claimed high level of generality.
This judicial exception is not integrated into a practical application. In particular, the at least one camera, AI, device, processor, memory, and non-transitory computer-readable medium are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of capturing data, processing data, analyzing data, identifying data, extracting data, comparing data, generating data, rendering data, and sending data) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In addition, Applicant argues features that have not been claimed (e.g., pose-level representation extracted from video frames using computer vision techniques, multi-camera visual capture to eliminate occlusion and improve motion accuracy, a second Al model dynamically extracted, improves the functioning of image processing devices and database systems, etc.). Furthermore, the limitations regarding AI models do not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer.
Regarding McRo, the basis for the McRo court's decision was that the claims were directed to an improvement in computer-related technology (allowing computers to produce "accurate and realistic lip synchronization and facial expressions in animated characters" that previously could only be produced by human animators), and thus did not recite a concept similar to previously identified abstract ideas. Applicant's case is different because the claims recite a concept similar to previously identified abstract ideas and there is no improvement in computer-related technology. Regarding DDR, the patent at issue in DDR provided an Internet-based solution to solve a problem unique to the Internet that (1) did not foreclose other ways of solving the problem, and (2) recited a specific series of steps that resulted in a departure from the routine and conventional sequence of events after the click of a hyperlink advertisement. Id. at 1256–57, 1259. The patent claims here do not address problems unique to the Internet, so DDR has no applicability. Regarding BASCOM, the court agreed that the additional elements were generic computer, network, and Internet components that did not amount to significantly more when considered individually, but explained that the district court erred by failing to recognize that when combined, an inventive concept may be found in the non-conventional and non-generic arrangement of the additional elements, i.e., the installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user. It is unclear what is non-conventional and non-generic about the arrangement of the additional elements in Applicant’s case.
(B) As per the second argument, Examiner relied upon Asikainen to teach extracting a second Al model, wherein the second Al model is configured to determine a deviation of the patient from a plurality of expected movements associated with the exercise based on target exercise performance of a healthy specimen. See paragraphs 84 & 85 of Asikainen which disclose: the movement adherence monitor 310 compares whether the user performance of the exercise movement in view of body mechanics associated with correctly performing the exercise movement falls within acceptable range or threshold for human joint positions and movements. In some implementations, the movement adherence monitor 310 uses a machine learning model, such as a convolutional neural network trained on a large set of ideal or correct repetitions of an exercise movement to determine a score or a quality of the exercise movement performed by the user based at least on the estimated 3D pose data and the consecutive repetitions of the exercise movement. For example, the score (e.g., 85%) may indicate the adherence to predefined conditions for correctly performing the exercise movement.
Examiner relied upon Sherpa to teach extracting an Al model based on the current fitness state of the patient and the exercise being performed by the patient. See paragraphs 33, 37, 53, and 54 of Sherpa which disclose: Fitness state sorting module 120 includes a machine-learning user specific recommendation model 140 that relates fitness states to user specific recommendations, as described in more detail below. In an embodiment, fitness state sorting module 120 may be designed and configured to generate a current user fitness state using the fitness state model 136 and the at least a biological parameter and generate a user specific recommendation using the user specific recommendation model 140 and the at least a user input. As used herein, fitness-state includes any of the fitness states as used above and may include one of a strength state fitness module 124, an endurance state fitness module 128, and a well-being state fitness module 132. Strength state fitness module 124 may include any strength state as described above, including without limitation a state where a user is physically strong as demonstrated by muscularity, flexibility, and balance. Strength state fitness module 124 may include a state that is devoted to muscle building and muscle conditioning and may include user specific recommendations that highlight anaerobic activities such as weight-bearing exercises, weight-lifting, yoga, squats, lunges, planks, Pilates, push-ups, jumping jacks, rowing, and the like. Endurance state fitness module 128 may include a state where a user is able to endure cardiovascular exercise as demonstrated by stamina and resilience. In an embodiment, training set data may be utilized to create a machine-learning algorithm relating biological parameters to each fitness state including strength state fitness module 124, endurance state fitness module 128, and well-being state fitness module 132. Training set data utilized to generate mathematical algorithms for fitness state model and/or user specific recommendation model may be obtained from multiple sources such as data obtained from medical journals and medical studies. Sources may include data from fitness tracking equipment that a user may wear.
In response to applicant's argument that Sherpa does not extract or adapt an Al model for real-time biomechanical analysis or deviation determination during physical movement and Sherpa does not address movement deviation at all, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., dynamically selects and deploys a tailored second model to enable precise, state-aware deviation analysis against a healthy-specimen baseline) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Examiner disagrees that Asikainen does not disclose identifying, by the second Al model, a failure in completion of the exercise by the patient and sending a reminder to the patient after expiry of a pre-defined time interval for completing of the exercise, in response to identifying the failure in completion of the exercise by the patient. See paragraphs 88, 109, 84 and Fig. 11 of Asikainen which disclose: performance metrics and statistics include, but not limited to, total rest time, energy expenditure, current and average heart rate, historical workout data compared with current workout session, completed repetitions in an ongoing workout set, completed sets in an ongoing exercise movement, incomplete repetition, etc. The performance tracker 314 derives total exercise volume from individual workout sessions over a length of time, such as daily, weekly, monthly, and annually. The performance tracker 314 determines total time under tension expressed in seconds or milliseconds using active movement time and bodyweight or equipment weight. The performance tracker 314 determines a total time of exercise expressed in minutes as total length of workout not spent in recovery or rest. The performance tracker 314 determines total rest time from time spent in idle position, such as standing, lying down, hunched over, or sitting. The performance tracker 314 retrieves historical user performance of a workout similar to a current workout of the user and generates a summary comparing the historical performance metrics with the current workout as a percentage to indicate user progress. See Fig. 11, item 1107 which displays a progress bar showing a progress percentage for each muscle group on user interface 1100.
As such, it is unclear how the language of the claim differs from the applied prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The cited but not applied prior art teaches a method and system for using artificial intelligence to determine a user’s progress during interval training (US 2022/0016485 A1); and devices, systems, and methods for adaptive health monitoring using behavioral, psychological, and physiological changes of a body portion (WO 2019/075185 A1).
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 LENA NAJARIAN whose telephone number is (571)272-7072. The examiner can normally be reached Monday - Friday 9:30 am-6 pm.
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/LENA NAJARIAN/Primary Examiner, Art Unit 3687
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