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 .
Status of Claims
The office action is in response to arguments and amendments entered on February 20, 2026 for the patent application 17/519,403 originally filled on March 5, 2025. Claims 1-20 are pending. Claims 1, 17, and 19 are amended. The third office action of November 20, 2025 is fully incorporated by reference into this final action.
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 1, 5-9, 14-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ilan et al. (Document ID US 20210074178 A1; 2021-03-11) in view of Karkanias et al. (Document ID US 20080300914 A1; 2008-12-04), Hacking et al. (Document ID US 20200289889 A1; 2020-09-17), and Omid-Zohoor et al. (Document ID US 20190224528 A1; 2019-07-25).
Regarding claims 1, 17, and 19 Ilan et al. teaches:A method for generating an exercise session for a user using an exercise machine, the method comprising: or A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to: or A system comprising: a memory device storing instructions; a processing device communicatively coupled to the memory device, the processing device executes the instructions to: receiving/receive a plurality of inputs, wherein the plurality of inputs comprise an indication of a level of pain of the user and a range of motion of a body part of the user; determining/determine, based on the plurality of inputs, an exercise level of the user (Para. [0157], shows that user information is gathered to determine the current state of the user—i.e. an exercise level—in order to achieve a physiological goal; Para. [0017], shows some examples of goals which include “increase range of motion” and inputs such as range-of-motion and pain would be classified as a “parameter directly or indirectly of relevance to the effect of the regimen or maneuver”; Para. [0076], shows some examples of goals which include treatment of pain and inputs such as range-of-motion and pain would be classified as a “parameter directly or indirectly of relevance to the effect of the regimen or maneuver”);
Reference is now made to FIG. 3, which schematically illustrates a flow chart of method 300 for continuously providing updated parameter generation of an alert for a better exercise/training/teaching/learning/education regimen or any type of device-generated maneuver/stimulation signal being generated, according to some embodiments. It schematically illustrates a functional block diagram of the subject-tailored continuously or semi continuously learning-closed loop system. A user related information is obtained (step 301). The user related information may include a sensor measurements, or more general information such as for a non-limiting example subject-related exercise/training/teaching/learning/education regimen-related, organ function-related, disease-related, biomarker-related, and/or any other parameter directly or indirectly of relevance to the effect of the regimen or maneuver on organ performance, such as concomitant medications, scores relevant to organ function, performance of subject, weight, gender, clinical history and the like or data which is specific for organ function or to the exercise…
…Sprinters on the USA Olympic team spend their training divided among running to build cardio capacity, strength-training to build muscle, plyometrics to increase range of motion and explosiveness, and rest time. Sprinters spend many practices running at half and three-quarter pace, in repetitive sets…
The above method may be applicable for any maneuver or exercise or nutrition relevant for treatment of neurological or mental disorders, and pain, as well as for any type of inborn error of metabolism; Peripheral or central neurological disorders: Huntington diseases; ALS; Dementia; Alzheimer's disease; treatment of genetic diseases; treatment of any endocrine disorder.
generating, using a machine learning model, the exercise session for the user by selecting, based on the exercise level of the user, one or more exercises to be performed by the user using an exercise machine (Para. [0157]);
Initial output exercise/training/teaching/learning/education regimen and/or device-generated maneuver/stimulation parameters are determined (step 303). The participant is provided with a new regimen and/or maneuver parameters based on specific target organs and/or regimen parameters and/or drug, disease, exercise-related parameters, and patient-related parameters (step 304) …
and causing initiation of the exercise session on the exercise machine and a virtual coach executed by a computing device associated with the exercise machine to provide instructions pertaining to the exercise session, wherein initiation of the exercise session comprises the machine learning model automatically controlling an operating parameter associated with a resistance provided by a pedal of the exercise machine and controlling the virtual coach during the exercise session (Para. [0167], shows that the continuous learning machine may be in communication with a treadmill, i.e. an exercise machine, and that the treadmill may receive stimulation parameters, i.e. operating parameters, from the continuous learning machine to adjust the exercise regimen to better achieve target goals; Para. [0224], additionally shows an algorithm controlling relevant parameters of a smart bicycle, i.e. an algorithm automatically controlling operating parameters of an exercising machine).
Reference is now made to FIG. 4, which schematically illustrates a person on a treadmill along with a muscle stimulator system 400, according to some embodiments. According to some embodiments, system 400 includes a stimulation device 401, configured to be inserted/introduced to a target area of a subject's legs, to induce stimulation thereto. The treadmill 402 is connected via wireless communication link to the control system 403. According to some embodiments, both the treadmill and the stimulation devices are in communication with an update module, such as a continuous or semi continuous learning machine 403 via wireless communication link, such as through antenna 404, for sending sensor information from treadmill and stimulation devices 401 and 402 to learning machine 403, and receiving updated algorithm-based new exercise regimen and/or stimulation parameters therefrom, 405, to adjust the exercise regimen and/or stimulations for achieving desired results towards reaching an improved target goal of a physiological feature and an ongoing improvement in the running capabilities, reducing complications from the use of a treadmill, and improving subject adherence to the training program.
The “smart bicycles” are connected via sensors to the subject. The predetermined range for all parameters relevant to the training session are inserted into the algorithm prior to starting to use it. The subject who is using the algorithm pushes activates a cell phone application which is connected to the bicycle, and provides a training algorithm that alters all of the relevant parameters such as speed, degree of strain and others, which are continuously/semi continuously being updated by the algorithm in real time or not, based on the input
Ilan et al. does not explicitly teach:
and causing initiation of the exercise session on the exercise machine and a virtual coach executed by a computing device associated with the exercise machine to provide instructions pertaining to the exercise session, wherein initiation of the exercise session comprises the machine learning model automatically controlling an operating parameter associated with a resistance provided by of a pedal of the exercise machine and controlling the virtual coach during the exercise session, wherein a motor is coupled to a wheel of the exercise machine and, based on the operating parameter, the machine learning model is trained to control the motor to regulate rotation of the wheel to cause the pedal to provide the resistance.
Karkanias et al. teaches:
and causing initiation of the exercise session on the exercise machine and a virtual coach executed by a computing device associated with the exercise machine to provide instructions pertaining to the exercise session, wherein initiation of the exercise session comprises the machine learning model automatically controlling an operating parameter associated with a resistance provided by a pedal of the exercise machine and controlling the virtual coach during the exercise session (Abstract and para. [0008] and para. [0058], show that the system may automatically calibrate an activity device, which may comprise a cycle, to attain the goals of the user, i.e. control an operating parameter of the exercise machine, and provide feedback, motivation, and commands to the user during an exercise regimen, i.e. function as a virtual coach that provides instruction; Para [0064], shows that the systems reasoning maybe controlled by a machine learning component).
A system that facilitates management of physical activity by dynamically compensating for current conditions is provided. A user profile can be employed to automatically calibrate an activity device (e.g., treadmill, cycle, haptic brace) based upon characteristics and/or limitations of a user. User activity and other data (e.g., physiological data, motion data, environmental data) can be monitored and employed to dynamically recalibrate the activity device in an effort to optimize performance. Additionally, a simulation profile can be employed as a benchmark for performance. For example, actual user activity can be contrasted against the simulation profile in order to provide feedback, motivation, or even to facilitate dynamic calibration of an activity device throughout an exercise regimen.
User activity and other data (e.g., physiological data, motion data, environmental data) can be monitored and employed to dynamically recalibrate the activity device in an effort to optimize performance. For example, by monitoring a user's performance and physiological characteristics, the activity apparatus can be dynamically adjusted to increase effort while maintaining a safe zone that minimizes probability of harm. Essentially, the dynamic compensation functionality can act as a virtual coach to promote rehabilitation, strength training, weight loss, stamina increase, or the like.
The simulation component 502 can be used to compare and/or contrast the user activity with the simulation or third party profile data. Thus, the simulation component 502 can essentially become a virtual coach by rendering the comparison and providing feedback to assist the user in reaching a desired goal, as defined by the profile(s). This feedback can be generated or delivered in most any manner including visual display or audible commands.
FIG. 9 illustrates a system 900 that employs a machine learning and reasoning (MLR) component 902 which facilitates automating one or more features in accordance with the subject innovation. The subject innovation (e.g., in connection with profile selection) can employ various AI-based schemes for carrying out various aspects thereof. For example, a process for determining which profile to select to achieve a goal can be facilitated via an automatic classifier system and process.
It would be obvious, before the effective filing date of the claimed invention, for someone of
ordinary skill to use the known techniques, regarding user feedback and automatic calibration of an activity device, of Karkanias et al. with the known device, a device for improving organ function by improved challenge-based training that may make use of an activity device, of Ilan et al. to yield predictable result of enhancing the device’s ability to guide the user through and exercise regimen. One of ordinary skill in the art would be motived to apply the known techniques of Karkanias et al. to the known device of Ilan et al. in order to improve the system’s ability to dynamically modify the user’s exercise regimen in response to feedback from the user.
Hacking et al. teaches:
Automatically controlling an operating parameter associated with a resistance provided by a pedal of the exercise machine (Para. [0006], shows that the system may dynamically update the amount of resistance an electric motor provides the pedals of an exercise machine),
…The one or more processing devices are configured to execute the instructions to (i) receive configuration information for a pedaling session; (ii) based on the configuration information for the pedaling session, set a resistance parameter and a maximum pedal force parameter; (iii) measure force applied to the one or more pedals of the electromechanical device as a user pedals the electromechanical device, wherein, based on the resistance parameter, the electric motor provides resistance during the pedaling session; (iv) determine whether the measured force exceeds a value of the maximum pedal force parameter; and (v) responsive to determining that the measured force exceeds the value of the maximum pedal force parameter, reduce the resistance parameter so the electric motor applies less resistance during the pedaling session to maintain a revolutions per time period threshold.
wherein a motor is coupled to a wheel of the exercise machine, and, based on the operating parameter, the machine learning model is trained to control the motor to regulate rotation of the wheel to cause the pedal to provide the resistance; (Para. [0059], shows that the electrical motor is coupled to and provides resistance to the rotation of the radially adjustable coupling; Para. [0073], shows that the radially adjustable couplings may comprises flywheels, i.e. a wheel; Para. [0070], shows that the systems treatment plans may be generated and controlled by machine learning models; Para. [0052], further shows that resistance provided by the electric motor may be a parameter of the treatment plan and that it may be modified in real time).
The electromechanical device 104 may be an adjustable pedaling device for exercising and rehabilitating arms and/or legs of a user. The electromechanical device 104 may include at least one or more motor controllers 120, one or more electric motors 122, and one or more radially-adjustable couplings 124. Two pedals 110 may be coupled to two radially-adjustable couplings 124 via left and right pedal assemblies that each include respective stepper motors. The motor controller 120 may be operatively coupled to the electric motor 122 and configured to provide commands to the electric motor 122 to control operation of the electric motor 122. The motor controller 120 may include any suitable microcontroller including a circuit board having one or more processing devices, one or more memory devices (e.g., read-only memory (ROM) and/or random access memory (RAM)), one or more network interface cards, and/or programmable input/output peripherals. The motor controller 120 may provide control signals or commands to drive the electric motor 122. The electric motor 122 may be powered to drive one or more radially-adjustable couplings 124 of the electromechanical device 104 in a rotational manner. The electric motor 122 may provide the driving force to rotate the radially-adjustable couplings 124 at configurable speeds. The couplings 124 are radially-adjustable in that a pedal 110 attached to the coupling 124 may be adjusted to a number of positions on the coupling 124 in a radial fashion. Further, the electromechanical device 104 may include a current shunt to provide resistance to dissipate energy from the electric motor 122. As such, the electric motor 122 may be configured to provide resistance to rotation of the radially-adjustable couplings 124.
The electromechanical device 104 includes a rotary device such as radially-adjustable couplings 124 or a flywheel or flywheels or the like rotatably mounted such as by a central hub to a frame 200 or other support….
In some embodiments, the cloud-based computing system 116 may include a training engine 130 capable of generating one or more machine learning models 132. The machine learning models 132 may be trained to generate treatment plans for the patients in response to receiving various inputs (e.g., a procedure performed on the patient, an affected body part on which the procedure was performed, other health characteristics or demographic attributes (e.g., age, race, fitness level, etc.)). The one or more machine learning models 132 may be generated by the training engine 130 and may be implemented in computer instructions executable by one or more processing devices of the training engine 130 and/or the servers 128…
In addition, the disclosed rehabilitation system may enable a physician to use the clinical portal to monitor the progress of the user in real-time. The clinical portal may present information pertaining to when the user is engaged in one or more sessions, statistics (e.g., speed, revolutions per minute, positions of pedals, forces on the pedals, vital signs, numbers of steps taken by user, ranges of motion, etc.) of the sessions, and the like. The clinical portal may also enable the physician to view before and after session images of the affected body part of the user to enable the physician to judge how well the treatment plan is working and/or to make adjustments to the treatment plan. The clinical portal may enable the physician, based on information received from the control system, to dynamically change a parameter (e.g., position of pedals, amount of resistance provided by electric motor, speed of the electric motor, duration of one of the modes, etc.) of the treatment plan in real-time.
It would be obvious, before the effective filing date of the claimed invention, for someone of
ordinary skill in the art to use the known techniques, regarding the automatic and dynamic modification of a cycling device’s pedaling resistance, of Hacking et al. with the known device, a device for improving organ function by improved challenge-based training that may make use of a cycling device, of Ilan et al. to yield predictable result of enhancing the device’s ability to dynamically adjust to the needs of the user. One of ordinary skill in the art would be motived to apply the known techniques of Hacking et al. to the known device of Ilan et al. in order to better the system’s ability to aid in a user’s rehabilitation as the dynamic control of the devices resistance would allow the system to meet a user’s needs in realtime.
Omid-Zohoor et al. teaches:
and causing/cause initiation of the exercise session on the exercise machine and a virtual coach executed by a computing device associated with the exercise machine to provide instructions pertaining to the exercise session, wherein initiation of the exercise session comprises the machine learning model controlling the virtual coach during the exercise session (Para. [0054], shows that the human motion monitoring system may function as a virtual coach provides instruction during the exercise routines; Para. [0050], additionally shows that the human motion monitoring system may utilize machine learning to analyze sensed user motions).
Embodiments of the invention may include a human motion monitoring system configured so that during exercise routines, real-time feedback or analysis may be provided to the user based on sensed data, including image data, about the user. In this manner, the system may function as a “virtual coach” to the user to help make exercising more interactive and help achieve results and goals of the user faster.
Embodiments of the invention may include a human motion monitoring system that applies machine learning techniques to learn relationships, functions, and categories associated with various analysis procedures, which may include modeling or scoring a particular motion gesture (e.g., golf swing) or exercise based on the sensor data. A supervised machine learning algorithm offers flexibility as it trains motion scoring models based on data, such as data contained in an exercise database, a participant database, an observer database, a motion database, and/or subsets thereof.
It would be obvious, before the effective filing date of the claimed invention, for someone of
ordinary skill to use the known techniques, regarding human motion analysis and instruction, of Omid-Zohoor et al. with the known device, a device for improving organ function by improved challenge-based training, of Ilan et al. to yield predictable result of enhancing the device’s ability to gather information from and present information to the user. One of ordinary skill in the art would be motived to apply the known techniques of Omid-Zohoor et al. to the known device of Ilan et al. in order to improve both the system’s ability to receive feedback/data from the users and the systems options for relaying feedback/instruction to the user.
Regarding claim 5, Ilan et al. further teaches:
The method of claim 1, wherein the plurality of inputs further comprise a plurality of characteristics of the user, and the plurality of characteristics of the user comprise: an age of the user, a height of the user, a weight of the user, a gender of the user, a condition that caused the pain in the body part, one or more procedures perform on the user, a goal of the user, whether the user is in a pre-procedure stage or a post-procedure stage, or some combination thereof (Para. [0098]).
According to some embodiments, the machine learning algorithm further considers personal data selected from a group consisting of: subject performance, cell/tissue/organ function-related scores, parameters relevant to cell/tissue/organ performance, age, weight, waist circumference, target organ, and other organs' function, caloric intake and output, gender, ethnicity, geography, pathological history/state, temperature, metabolic rate, brain function, health status, heart, lung muscle function, blood tests, and any physiological or pathological biomarkers, a subject's health related parameter or any combination thereof.
Regarding claim 6, Omid-Zohoor et al. further teaches:
The method of claim 1, further comprising: receiving, from the user while the user is performing an exercise of the one or more exercises, feedback pertaining to the exercise, wherein the feedback comprises an indication of a level of difficulty of the exercise; determining whether the feedback has been received more than a threshold number of times for the exercise; responsive to determining the feedback has been received more than the threshold number of times for the exercise, adjusting, in real-time or near real-time, the exercise session, wherein adjusting the exercise session comprises changing to another exercise, controlling the exercise machine to stop the exercise, removing the exercise from the exercise session, changing an intensity of the exercise, or some combination thereof; and causing the virtual coach to provide an indication of the adjustment (Para. [0301], shows that if the exercise is not being performed properly, i.e. is too difficult, the user is interrupted and the session is modified in real-time).
For example, such continual monitoring may be performed so that employers can ensure that their employees are complying with a particular training regime while in the workplace. Pre-determined movements of employees may be measured as they perform their regular day-to-day work tasks, such as, for example, lifting or walking. Such continual monitoring can be important to prevent injuries for employees performing repetitive tasks, such as those in hospitality (e.g., making beds), in a warehouse (e.g., lifting, pick and place movements), etc. Using the captured motion data, the motion instruction system 1900 assigns an exercise score for a particular exercise or movement being performed. When the exercise score is below a predetermined threshold or the assigned exercise or movement is not being performed, then the motion instruction system 1900 may transmit an alert to an observer (via text message, e-mail message, alert on web portal, etc.) to inform the employer that the employee is moving incorrectly (which puts them at risk of injury). Preferably, such alert is transmitted to the observer (employer) in real time so that the exercise regime may be revised or changed accordingly. Additionally, when the exercise score is below a predetermined threshold or the assigned exercise or movement is not being performed, then the motion instruction system 1900 may transmit an alert to the participant device so that the employee may have an opportunity to self-correct. If the employee fails to self-correct (i.e., the exercise score remains below the predetermined threshold), then the motion instruction system 1900 may send an alert to the participant device 1901 (as well as the observer device 1903) ordering the employee to stop and then guide the user through a protocol to activate muscles and remind them of the correct movement pattern via instructions (graphical, video, and/or textual) displayed on the participant device 1901 and/or the observer device 1903, or another display or recipient device configured to convey feedback to the employee.
Regarding claim 7, Omid-Zohoor et al. further teaches:
The method of claim 1, further comprising: receiving, from the user while the user is performing an exercise of the one or more exercises, feedback pertaining to the exercise, wherein the feedback comprises an indication that the exercise is too easy; responsive to receiving the feedback, causing an intensity of the exercise to increase; and causing the virtual coach to provide an indication of the increase of the intensity of the exercise (Para. [0300], shows that if a user is assigned an exercise score above a certain value, i.e. an exercise is too easy, the exercise can be appropriately revised).
According to one or more of the foregoing embodiments, the motion instruction system 1900 may be configured to continually monitor user compliance with a training regime. For example, in the factory worker example discussed above, user compliance with an exercise regime (e.g., assigned exercise score is above a predetermined threshold), or lack thereof (e.g., assigned exercise score is below a predetermined threshold, or the assigned exercise is not being performed), may be transmitted to an observer (via text message, e-mail message, alert on web portal, etc.). Preferably, such alert is transmitted to the coach or observer in real time so that the exercise regime may be revised or changed accordingly.
Regarding claim 8, Omid-Zohoor et al. further teaches:
The method of claim 7, further comprising: responsive to determining the feedback has been received more than a threshold number of times, controlling, in real-time or near real-time, the exercise machine to initiate a more advanced exercise than the exercise; and causing the virtual coach to provide an indication that the more advanced exercise has been initiated (Para. [0300], shows that if a user is assigned an exercise score above a certain value, i.e. an exercise is too easy, the exercise can be appropriately revised in real-time; Para. [0301], further shows that an exercise score may need to be within certain bounds for a threshold period before modification of the exercise takes place).
According to one or more of the foregoing embodiments, the motion instruction system 1900 may be configured to continually monitor user compliance with a training regime. For example, in the factory worker example discussed above, user compliance with an exercise regime (e.g., assigned exercise score is above a predetermined threshold), or lack thereof (e.g., assigned exercise score is below a predetermined threshold, or the assigned exercise is not being performed), may be transmitted to an observer (via text message, e-mail message, alert on web portal, etc.). Preferably, such alert is transmitted to the coach or observer in real time so that the exercise regime may be revised or changed accordingly.
…Additionally, when the exercise score is below a predetermined threshold or the assigned exercise or movement is not being performed, then the motion instruction system 1900 may transmit an alert to the participant device so that the employee may have an opportunity to self-correct. If the employee fails to self-correct (i.e., the exercise score remains below the predetermined threshold), then the motion instruction system 1900 may send an alert to the participant device 1901 (as well as the observer device 1903) ordering the employee to stop and then guide the user through a protocol to activate muscles and remind them of the correct movement pattern via instructions (graphical, video, and/or textual) displayed on the participant device 1901 and/or the observer device 1903, or another display or recipient device configured to convey feedback to the employee.
Regarding claim 9, Ilan et al. further teaches:
The method of claim 1, monitoring progress of the user while the user uses the exercise machine to perform the one or more exercises, wherein the progress comprises an amount of time the user performs the one or more exercises, the range of motion of the user while the user performs the one or more exercises, the level of pain of the user while the user performs the one or more exercises, whether the user completes the one or more exercises, an indication of the user of a level of difficulty of the one or more exercises, or some combination; and adjusting, by the machine learning model, a subsequent exercise session based on the progress of the user, wherein the adjusting is based on: advancing the exercise level of the user to a next exercise level, achieving a desired goal as defined by the user, a medical professional, or both, or some combination thereof (Para. [0048], shows that has the machine is updated with information that demonstrates progress towards the physiological goal, the user’s regimen will be updated accordingly; Para. [0157], shows that user information is gathered to determine the current state of the user—i.e. an exercise level—in order to achieve a physiological goal and updates to this information would be indicative of progress towards the physiological goal; Para. [0017], shows some examples of goals which include “increase range of motion” and inputs such as range-of-motion and pain would be classified as a “parameter directly or indirectly of relevance to the effect of the regimen or maneuver”; Para. [0076], shows some examples of goals which include treatment of pain and inputs such as range-of-motion and pain would be classified as a “parameter directly or indirectly of relevance to the effect of the regimen or maneuver”).
According to some embodiments, the user updates the machine with inputs indicative of progress towards the targeted physiological goal, and the learning machine provides an updated method of challenged-exercise/training/teaching/learning/education regimen or maneuver administration according to the tailored parameters relevant for the subject/procedure based on data learned from the user and/or other users.
Reference is now made to FIG. 3, which schematically illustrates a flow chart of method 300 for continuously providing updated parameter generation of an alert for a better exercise/training/teaching/learning/education regimen or any type of device-generated maneuver/stimulation signal being generated, according to some embodiments. It schematically illustrates a functional block diagram of the subject-tailored continuously or semi continuously learning-closed loop system. A user related information is obtained (step 301). The user related information may include a sensor measurements, or more general information such as for a non-limiting example subject-related exercise/training/teaching/learning/education regimen-related, organ function-related, disease-related, biomarker-related, and/or any other parameter directly or indirectly of relevance to the effect of the regimen or maneuver on organ performance, such as concomitant medications, scores relevant to organ function, performance of subject, weight, gender, clinical history and the like or data which is specific for organ function or to the exercise…
…Sprinters on the USA Olympic team spend their training divided among running to build cardio capacity, strength-training to build muscle, plyometrics to increase range of motion and explosiveness, and rest time. Sprinters spend many practices running at half and three-quarter pace, in repetitive sets…
The above method may be applicable for any maneuver or exercise or nutrition relevant for treatment of neurological or mental disorders, and pain, as well as for any type of inborn error of metabolism; Peripheral or central neurological disorders: Huntington diseases; ALS; Dementia; Alzheimer's disease; treatment of genetic diseases; treatment of any endocrine disorder.
Regarding claim 14, Omid-Zohoor et al. further teaches:
The method of claim 1, further comprising: determining, by the machine learning model (Para. [0304] and Para. [0306], show that the virtual coach is controlled by a machine learning model;),
According to one or more of the foregoing embodiments, motion instruction system 1900 may be configured so that during exercise routines, real-time feedback or analysis may be provided to the user based on sensed data, including image data, about the user. In this manner, the system 1900 may function as a “virtual coach” to the user to help make exercising more interactive and help achieve results and goals of the user faster.
According to one or more of the foregoing embodiments, the system 1900 may apply machine learning techniques to learn relationships, functions, and categories associated with various analysis procedures, which may include modeling or scoring a particular motion gesture (e.g., golf swing) or exercise based on the sensor data. A supervised machine learning algorithm offers flexibility as it trains motion scoring models based on data, such as data contained in exercise database 1905, participant database 1907, observer database 1913, a motion database 1915, and/or subsets thereof.
a plurality of audio segments for the virtual coach to say while the user performs the one or more exercises (Para. [0305]).
The virtual coach feature may operate by automatically generating useful tips and exercise lessons based on motion data from the sensors… These tips and lessons can be communicated to the user in real-time through text, audio, vibration, an animated coach avatar in the golf simulator, or any combination thereof. The same concept can be altered for other sports and activities, such as baseball, tennis, exercising, etc. Moreover, this concept may be extended to all forms of motion monitoring.
Regarding claim 15, Ilan et al. further teaches:
further comprising: determining, by the machine learning model (Para. [0049]),
For example, the goal may be avoiding development of adaptation to training aimed at improving the ability to run or study faster. In this case, such a goal may be avoiding tolerance to a certain challenged-exercise/training/teaching/learning/education/nutritional regimen by setting a deep-machine learning closed-loop individual based-algorithm that sets a new regimen for the subject. The new regimen is designed with or without setting a specific range as a target for parameter/value change
a schedule (i.e., a regimen) of a plurality of exercise sessions to be performed by the user to achieve a desired goal specified by the user, a medical professional, or both (Para. [0158]).
Initial output exercise/training/teaching/learning/education regimen and/or device-generated maneuver/stimulation parameters are determined (step 303). The participant is provided with a new regimen and/or maneuver parameters based on specific target organs and/or regimen parameters and/or drug, disease, exercise-related parameters, and patient-related parameters (step 304).
Regarding claim 16, Omid-Zohoor et al. further teaches:
wherein the virtual coach is controlled, in real-time or near real-time, (Para. [0305]).
The virtual coach feature may operate by automatically generating useful tips and exercise lessons based on motion data from the sensors… These tips and lessons can be communicated to the user in real-time through text, audio, vibration, an animated coach avatar in the golf simulator, or any combination thereof. The same concept can be altered for other sports and activities, such as baseball, tennis, exercising, etc. Moreover, this concept may be extended to all forms of motion monitoring.
by the machine learning model (Para. [0304] and Para. [0306], show that the virtual coach is controlled by a machine learning model;).
According to one or more of the foregoing embodiments, motion instruction system 1900 may be configured so that during exercise routines, real-time feedback or analysis may be provided to the user based on sensed data, including image data, about the user. In this manner, the system 1900 may function as a “virtual coach” to the user to help make exercising more interactive and help achieve results and goals of the user faster.
According to one or more of the foregoing embodiments, the system 1900 may apply machine learning techniques to learn relationships, functions, and categories associated with various analysis procedures, which may include modeling or scoring a particular motion gesture (e.g., golf swing) or exercise based on the sensor data. A supervised machine learning algorithm offers flexibility as it trains motion scoring models based on data, such as data contained in exercise database 1905, participant database 1907, observer database 1913, a motion database 1915, and/or subsets thereof.
Claims 2-4, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ilan et al. (Document ID US 20210074178 A1; 2021-03-11) in view of Karkanias et al. (Document ID US 20080300914 A1; 2008-12-04), Hacking et al. (Document ID US 20200289889 A1; 2020-09-17), and Omid-Zohoor et al. (Document ID US 20190224528 A1; 2019-07-25) and in further view of King et al. (Document ID US 20180036591 A1; 2018-02-08).
Regarding claims 2, 18, and 20 Ilan et al. in view of Karkanias et al., Hacking et al., and Omid-Zohoor et al. teaches:
The method of claim 1, or the computer-readable medium of claim 17, or the system of claim 19,
Ilan et al. in view of Karkanias et al., Hacking et al., and Omid-Zohoor et al. fails to explicitly teach:
wherein the selecting or to select, based on the exercise level of the user, the one or more exercises to be performed by the user using the exercise machine further comprises: tagging each exercise of a plurality of exercises with a respective user exercise level, wherein the plurality of exercises are specified in a data structure accessed by the machine learning model.
King et al. teaches:
wherein the selecting or to select, based on the exercise level of the user, the one or more exercises to be performed by the user using the exercise machine further comprises: tagging each exercise of a plurality of exercises with a respective user exercise level, wherein the plurality of exercises are specified in a data structure accessed by the machine learning model (Para. [0051], shows that exercises are rated/tagged by difficulty before being selected; Para [0117], shows that selection of exercises can be done by a machine learning model).
Each individual workout video block may include an exercise that is different from the exercises in other workout video blocks, or may include the same exercise (or a variant of the same exercise) as in other workout video blocks but at different levels of difficulty (e.g., hard, medium, easy, or level 1, 2, 3, etc.). In some embodiments, video block may have video segments, such as every minute or with bookmarked timestamps within the video, and some embodiments may dynamically terminate videos at the end of those segments, applying less than a full video block to adapt to user feedback more quickly rather than waiting for the entire video. Or some embodiments may adapt at the end of one video or upon receiving feedback.
Dynamic selection of video blocks may be performed with a variety of techniques… In some embodiments, a machine learning models may be trained to select video blocks. Due to the temporal sequence of video block selection, some embodiments may train other types of models, for instance, a Hidden Markov model or a recurrent neural net.
It would be obvious, before the effective filing date of the claimed invention, for someone of
ordinary skill to use the known techniques, regarding the event-based prescription of fitness-related activities, of King et al. with the known device, a device for improving organ function by improved challenge-based training, of Ilan et al. to yield predictable result of allowing the device to better utilize the gathered data to prescribe appropriate exercises. One of ordinary skill in the art would be motived to apply the known techniques of King et al. to the known device of Ilan et al. in order to improve the overall effectiveness of the user’s training regimen.
Regarding claim 3, King et al. further teaches:
The method of claim 2, further comprising filtering the plurality of exercises by: identifying, based on the tagging, a subset of exercises having the respective user exercise level that matches the exercise level of the user; and selecting the subset of exercises as the one or more exercises (Para [0053], shows that the exercises are broken into different difficulties which may be more appropriate depending on the fitness level of the user).
In some embodiments, the collection of workout video blocks may include a plurality of groupings of the workout video blocks (e.g., the groupings may be by body-region, by fitness goal, by physiological function, or other groupings.) Groupings may be expressed by associating a unique identifier of each video block file with a value corresponding to the grouping, e.g., a tag for “upper body” or “legs.” Video blocks may be associated with multiple tags to facilitate intelligent sequencing of videos according to multiple dimensions, in some embodiments… In some cases, a family 220 may include three, five, ten, or more blocks 210, each block containing a variation of one exercise (e.g., along a single dimension, like intensity, or along two or more dimensions, like intensity and range of movement). The blocks in the family may be ordered such that the first block in the family (in this case, the first block may be referred to as Level 1) is the easiest version of the exercise, and the last block in the order is the hardest version of the exercise. A user with low physical fitness may find Level 1 appropriate, where someone of high physical fitness would find a higher level more appropriate.
Regarding claim 4, Omid-Zohoor et al. further teaches:
The method of claim 3, identifying, using a historical performance of the user, a third subset of exercises that have been performed by the user less than a threshold number of times (Para. [0300], shows that an adherence to performing an assigned exercise a threshold number of times is required),
According to one or more of the foregoing embodiments, the motion instruction system 1900 may be configured to continually monitor user compliance with a training regime. For example, in the factory worker example discussed above, user compliance with an exercise regime (e.g., assigned exercise score is above a predetermined threshold), or lack thereof (e.g., assigned exercise score is below a predetermined threshold, or the assigned exercise is not being performed), may be transmitted to an observer (via text message, e-mail message, alert on web portal, etc.). Preferably, such alert is transmitted to the coach or observer in real time so that the exercise regime may be revised or changed accordingly.
King et al. further teaches:
further comprising filtering the plurality of exercises by: identifying a first subset of exercises having a respective section of a plurality of sections, wherein the plurality of sections comprise warm-up, cycling, strength, flexibility, or some combination thereof (Para. [0056], shows that exercises can be broken down based on if there are part of a warm-up, strength exercise, cardio, or something else),
In some cases, a group of sets 240 may be referred to as a stream 250, where a stream is made of sets among which selections are made to compose video blocks for a complete workout. The user may choose to play a stream from start to finish. The user may choose the length of the stream. For example a full body stream may include warm-up (1), warm-up(2), warm-up(3), lower, then upper, cardio, full, core, cardio, cooldown 1, and cooldown 2. Other examples of stream include tone down stream, endurance stream, strength stream, core stream, recovery stream, or other streams.
identifying a second subset of exercises that result in a desired outcome specified by a medical professional, wherein the desired outcome pertains to increasing a range of motion, mobility, strength, flexibility, or some combination thereof (Para. [0129], shows that the exercises can be constrained in order to achieve a goal in accordance to a medical professional; Para. [0064], shows that the desired outcome could pertain to a wide variety of goals that would encompass and surpass increasing a range of motion, mobility, strength, flexibility, or some combination thereof),
…For instance, some embodiments may train a model subject to the constraint of a knee injury, e.g., selecting records in the training set corresponding to this condition, and manually constraining candidate outputs based on instructions by a medical professional as to what is an appropriate candidate. Or some embodiments may train (and access to select video blocks) different models for different segments of users, e.g., those having particular attributes, like a gender, age range, preferred intensity, or the like.
…In some embodiments, the user profile may include information related to a fitness goal of the user. For example, improving a specific function, skill, or a physical attribute. In some cases, a fitness goal may indicate a general goal (e.g., improve physical, mental, emotional fitness, etc.)...
identifying, based on feedback from the user, a fourth subset of exercises that have been performed by the user and indicated as being too easy or too hard for the user (Para. [0128], shows that user feedback regarding difficulty is considered),
…In some embodiments, the models may then be accessed when selecting video blocks. A next video may be selected responsive to a variety of signals, e.g., in response to determining that a given video block that is playing reaching a threshold duration (like within 20 seconds of the video block ending), or in response to an event handler receiving user feedback (e.g., upon receiving a message from client computing device with a session identifier and an associated feedback value, like “favorite,” “dislike,” “easier,” or “harder” …
and selecting at least one of the subset of exercises, the first subset of exercises, the second subset of exercises, the third subset of exercises, or the fourth subset of exercises as the one or more exercises (Para. [0115], shows that some exercises are chosen based off of the aforementioned criteria).
At operation 912 a second workout video block may be selected from the collection. In some cases, a session record may be updated to indicate the user has advanced to a next stage of a workout stream, e.g., from legs to back. The second video block may be selected based on the feedback, the intensity of the second workout video block, a current state of the user in a workout stream, and a body-region grouping of the second video block. In some embodiments, operation 912 may be performed by a selection component the same as or similar to selection component 130 (shown in FIG. 1 and described herein).
Claims 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Ilan et al. (Document ID US 20210074178 A1; 2021-03-11) in view of Karkanias et al. (Document ID US 20080300914 A1; 2008-12-04), Hacking et al. (Document ID US 20200289889 A1; 2020-09-17), and Omid-Zohoor et al. (Document ID US 20190224528 A1; 2019-07-25) and in further view of Kaleal (Document ID US 9997082 B2; 2018-06-12).
Regarding claim 10, Ilan et al. in view of Karkanias et al., Hacking et al., and Omid-Zohoor et al. teaches:
The method of claim 1,
Ilan et al. in view of Karkanias et al., Hacking et al., and Omid-Zohoor et al. fails to explicitly teach:
further comprising: monitoring progress of the user while the user uses the exercise machine to perform the one or more exercises; and causing, based on the progress of the user, an incentive, reward, or both to be elicited by a computing device associated with the exercise machine, wherein the incentive, reward, or both comprise an animation, video, audio, haptic feedback, image, push notification, email, text, or some combination thereof; and causing the virtual coach to perform an encouraging action.
Kaleal further teaches:
further comprising: monitoring progress of the user while the user uses the exercise machine to perform the one or more exercises; and causing, based on the progress of the user, an incentive, reward, or both to be elicited by a computing device associated with the exercise machine, wherein the incentive, reward, or both comprise an animation, video, audio, haptic feedback, image, push notification, email, text, or some combination thereof; and causing the virtual coach to perform an encouraging action (Para. (226)).
During performance of a monitored program, information regarding one or more of the user's physiological state or condition, body movement/position, appearance, and context (e.g. location, time of day, people near the user), is dynamically received and analyzed in view of known requirements of the monitored program, and in some aspects user profile information, to determine or infer whether, how and to what degree the user deviates and/or is likely to deviate from the monitored program (in accordance with aspects described herein). Based on this analysis, reaction component 214 determines or infers a suitable reaction for manifestation by an avatar presented to the user and the avatar control component 208 and avatar generation component 226 cause the avatar presented to the user to perform the reaction. The reaction is configured to provide the user with guidance and/or motivation with adhering to the program and can include visual and audible reactions in the avatar (e.g., speaking, moving, facial expression, tone of voice, etc.). In some aspects, the reaction can include initiation of electronic communication (e.g., sending a notification, initiating an emergency call), and provision of external media (e.g., images, articles, a map, videos, songs, etc.) to the user to facilitate guiding the user with adherence to the program.
Regarding claim 11, Kaleal further teaches:
The method of claim 10, further comprising: determining when a number of incentives, rewards, or both elicited by the computing device satisfy a threshold value; and responsive to determining that threshold value is satisfied, causing a certificate to be transmitted to the computing device and associated with an account of the user using the exercise machine (Para. (226), shows that depending on how the user is performing, i.e. has received a threshold number of rewards, an appropriate form of encouragement is generated from the virtual coach, i.e. the provision of external media like a certificate).
During performance of a monitored program, information regarding one or more of the user's physiological state or condition, body movement/position, appearance, and context (e.g. location, time of day, people near the user), is dynamically received and analyzed in view of known requirements of the monitored program, and in some aspects user profile information, to determine or infer whether, how and to what degree the user deviates and/or is likely to deviate from the monitored program (in accordance with aspects described herein). Based on this analysis, reaction component 214 determines or infers a suitable reaction for manifestation by an avatar presented to the user and the avatar control component 208 and avatar generation component 226 cause the avatar presented to the user to perform the reaction. The reaction is configured to provide the user with guidance and/or motivation with adhering to the program and can include visual and audible reactions in the avatar (e.g., speaking, moving, facial expression, tone of voice, etc.). In some aspects, the reaction can include initiation of electronic communication (e.g., sending a notification, initiating an emergency call), and provision of external media (e.g., images, articles, a map, videos, songs, etc.) to the user to facilitate guiding the user with adherence to the program.
It would be obvious, before the effective filing date of the claimed invention, for someone of
ordinary skill to use the known techniques, regarding personalized avatars, of Kaleal with the known device, a device for improving organ function by improved challenge-based training, of Ilan et al. to yield predictable result of providing the device with new methods with which to present the user with information. One of ordinary skill in the art would be motived to apply the known techniques of Kaleal to the known device of Ilan et al. in order to improve user’s adherence to the exercise regimen and to provide the user with real-time guidance during performance of the exercise regimen.
Regarding claim 12, Omid-Zohoor et al. further teaches:
The method of claim 1, further comprising: selecting, for the coach, a persona from a plurality of personas; causing the coach to provide instructions as the user performs the one or more exercises; monitoring a parameter associated with the user while the user performs the one or more exercises, wherein the parameter pertains to a progress of the user, an indication of whether the user likes the persona of the coach, or both (Para. [0327]).
The workout may be generated in conjunction with an auto capture system; an autonomous training system; a dynamic motion scoring and training system; and/or a training motion scoring models with machine learning algorithms system, such as described herein, as well as biofeedback. Motion data may be transmitted to the observer/coach in real time or at the conclusion of the prescribed workout so that that the trainer/coach can provide feedback or additional coaching to the user. Furthermore, performance data from one or more users can be used to generate a leaderboard, points, competitions, etc. in conjunction with the prescribed workout of the day. The system may further include a database of observers/coaches such that the user may select an observer/coach from the database based on the user's preference (e.g., gender, age, intensity of workouts, music playlists, personality, etc.) …
Ilan et al. in view of Karkanias et al., Hacking et al., and Omid-Zohoor et al. fails to explicitly teach:
further comprising: selecting, for the virtual coach, a persona from a plurality of personas; causing the virtual coach to provide instructions as the user performs the one or more exercises; monitoring a parameter associated with the user while the user performs the one or more exercises, wherein the parameter pertains to a progress of the user, an indication of whether the user likes the persona of the virtual coach, or both; and selecting, based on the parameter, a subsequent persona for the virtual coach.
Kaleal teaches:
further comprising: selecting, for the virtual coach, a persona from a plurality of personas; causing the virtual coach to provide instructions as the user performs the one or more exercises (Para. (187));
Avatar customization component 410 allows a user to customize the avatar presented to the user to facilitate a fitness routine or activity. In an aspect, avatar customization component 410 can allow the user to manipulate variables to create an avatar that reflects the needs and tastes of the user. In particular, using avatar customization component 410, a user can select the avatar's appearance, demographics, voice and personality. For example, the age, gender, language or accent, dress, or other visual and/or audio characteristic of the avatar may be selected to motivate and/or comfort the user. In another aspect, avatar customization component 410 can provide predetermined character personas for the user to select and apply to his or her avatar. For example, the avatar can be selected from a familiar character set that includes known cartoon characters or people (e.g., famous actors, musicians, politicians, athletes, etc.) where such characters creators or persons have authorized usage of their persona. For example, a cartoon avatar may be suitable to lead a child user through an exercise regime, or a popular athlete or fitness trainer may motivate an adult user to adhere to a fitness program.
monitoring a parameter associated with the user while the user performs the one or more exercises, wherein the parameter pertains to a progress of the user, an indication of whether the user likes the persona of the virtual coach, or both (Para. (71));
…Avatar guidance platform 202 can provide monitoring and guidance in association with adherence to the program over the course of the program while tracking the user's progress throughout the program based in part on physical, physiological, image, motion, context and user profile data…
and selecting, based on the parameter, a subsequent persona for the virtual coach (Para. (188), shows that the avatar’s personality can be automatically designed based off of the user’s performance history and profile information).
In another aspect, avatar customization component 410 can automatically design an avatar to facilitate a user with a fitness routine or activity. According to this aspect, avatar customization component 410 can select the avatar's appearance, demographics, voice and personality based on one or more of: the fitness routine or activity selected for performance, profile information for the user regarding the user's preferences, the user's demographics, and the user's performance history with respect to monitored fitness routines or activities. For example, based on analysis of the user's preferences and demographics, avatar customization component 410 can determine or infer what type of avatar in terms of appearance and personality would best facilitate/motivate the user in association with performance of a selected fitness activity or routine (e.g., based on data relating avatar appearance and character traits to various aspects of user profile information).
Regarding claim 13, Omid-Zohoor et al. further teaches:
The method of claim 1, further comprising: selecting, for the coach, a persona from a plurality of personas; causing the coach to provide instructions as the user performs the one or more exercises; monitoring a parameter associated with the user while the user performs the one or more exercises, wherein the parameter pertains to a progress of the user, an indication of whether the user likes the persona of the coach, or both (Para. [0327]).
The workout may be generated in conjunction with an auto capture system; an autonomous training system; a dynamic motion scoring and training system; and/or a training motion scoring models with machine learning algorithms system, such as described herein, as well as biofeedback. Motion data may be transmitted to the observer/coach in real time or at the conclusion of the prescribed workout so that that the trainer/coach can provide feedback or additional coaching to the user. Furthermore, performance data from one or more users can be used to generate a leaderboard, points, competitions, etc. in conjunction with the prescribed workout of the day. The system may further include a database of observers/coaches such that the user may select an observer/coach from the database based on the user's preference (e.g., gender, age, intensity of workouts, music playlists, personality, etc.) …
Ilan et al. in view of Karkanias et al., Hacking et al., and Omid-Zohoor et al. fails to explicitly teach:
further comprising: selecting, for the virtual coach, a persona from a plurality of personas; causing the virtual coach to provide instructions as the user performs the one or more exercises; monitoring a parameter associated with the user while the user performs the one or more exercises, wherein the parameter pertains to a progress of the user, an indication of whether the user likes the persona of the virtual coach, or both; and switching, in real-time or near real-time, based on the parameter, a different persona for the virtual coach while the user performs the one or more exercises.
Kaleal teaches:
further comprising: selecting, for the virtual coach, a persona from a plurality of personas; causing the virtual coach to provide instructions as the user performs the one or more exercises (Para. (187));
Avatar customization component 410 allows a user to customize the avatar presented to the user to facilitate a fitness routine or activity. In an aspect, avatar customization component 410 can allow the user to manipulate variables to create an avatar that reflects the needs and tastes of the user. In particular, using avatar customization component 410, a user can select the avatar's appearance, demographics, voice and personality. For example, the age, gender, language or accent, dress, or other visual and/or audio characteristic of the avatar may be selected to motivate and/or comfort the user. In another aspect, avatar customization component 410 can provide predetermined character personas for the user to select and apply to his or her avatar. For example, the avatar can be selected from a familiar character set that includes known cartoon characters or people (e.g., famous actors, musicians, politicians, athletes, etc.) where such characters creators or persons have authorized usage of their persona. For example, a cartoon avatar may be suitable to lead a child user through an exercise regime, or a popular athlete or fitness trainer may motivate an adult user to adhere to a fitness program.
monitoring a parameter associated with the user while the user performs the one or more exercises, wherein the parameter pertains to a progress of the user, an indication of whether the user likes the persona of the virtual coach, or both (Para. (71));
…Avatar guidance platform 202 can provide monitoring and guidance in association with adherence to the program over the course of the program while tracking the user's progress throughout the program based in part on physical, physiological, image, motion, context and user profile data…
and switching, in real-time or near real-time, based on the parameter, a different persona for the virtual coach while the user performs the one or more exercises. (Para. (188), shows that the avatar’s personality can be automatically designed based off of the user’s performance history and profile information; Para. (213), shows that the avatar’s responses, i.e. persona, can change in real-time based on data gathered from the user and other context).
In another aspect, avatar customization component 410 can automatically design an avatar to facilitate a user with a fitness routine or activity. According to this aspect, avatar customization component 410 can select the avatar's appearance, demographics, voice and personality based on one or more of: the fitness routine or activity selected for performance, profile information for the user regarding the user's preferences, the user's demographics, and the user's performance history with respect to monitored fitness routines or activities. For example, based on analysis of the user's preferences and demographics, avatar customization component 410 can determine or infer what type of avatar in terms of appearance and personality would best facilitate/motivate the user in association with performance of a selected fitness activity or routine (e.g., based on data relating avatar appearance and character traits to various aspects of user profile information).
In an aspect, interface component 206 can generate interface 700 for presentation to a user (e.g., via rendering component 236) during performance of a selected fitness routine or activity. Interface 700 includes an avatar 712 displayed within a workout space 710. The appearance of the avatar and/or the workout space are customizable (e.g., via avatar customization component 410). In an aspect, as described above, the avatar 712 is configured to function as the user's personal trainer and provide instruction to the user regarding performance of the selected fitness routine. In particular, the avatar 712 is configured to respond in real-time to physical and physiological activity input received for the user as the user performs the fitness routine in accordance with aspects described herein. For example, as the user performs a fitness routine, the avatar 712 can provide various real-time reaction to the user's performance based on received and analyzed physical and physiological activity data for the user (e.g., in accordance with aspects described herein). For instance, the avatar can call out commands, tell the user how to correct certain physical deviations from the routine (e.g., based on physiological data and/or movement data for the user), provide physical demonstration of moves, motivate the user with facial and body movement expression, etc. As described above, these reactions can be specifically tailored to the user's personal tastes, goals and abilities (e.g., based on user profile information) and/or the user's current context (based on received context information). In some aspects, the avatar 712 is configured to perform the fitness routine for the user to follow. In other aspects, the avatar can perform parts of the fitness routine during performance of the fitness routine by the user when demonstration is necessary (e.g., to correct improper technique by the user).
Summary
No claim is allowed
Claims 1-20 are rejected under 35 USC § 103
Response to Arguments
The Applicants arguments filed on February 20, 2026 related to claims 1-20 are fully considered, but are not fully persuasive.
Rejections under 35 USC § 101
In light of the amendments to independent claims 1, 17, and 19, Examiner agrees that the limitations as a whole constitute a practical application, i.e. the utilization of machine learning to control a motor that is “coupled to a wheel of the exercise machine” and “regulates rotation of the wheel to cause the pedal to provide the resistance”, of the abstract idea. Respectfully, Examiner has removed the prior rejection under 35 USC § 101.
Rejections under 35 USC § 103
Examiner respectfully agrees that the art listed in the last office action fails to teach following limitations in the amended independent claims 1, 17, and 19: “wherein a motor is coupled to a wheel of the exercise machine and, based on the operating parameter, the machine learning model is trained to control the motor to regulate rotation of the wheel to cause the pedal to provide the resistance”. However, these limitations are taught by new art necessitated by Applicant’ amendments, see the new rejections under 35 USC § 103 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
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/A.J.B./Examiner, Art Unit 3715
/KANG HU/Supervisory Patent Examiner, Art Unit 3715