Notice of Pre-AIA or AIA Status
This action is made in response to the amendments/remarks filed on December 9, 2025. This action is made FINAL.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 12/09/2025 has been entered. Claims 1-17 and 19-20 remain pending in the application. Claim 18 has been cancelled. Claim 21 is newly added. Applicant’s amendments to the claims have overcome the 112(b) rejection and claim objections previously set forth in the Non-Final Office Action mailed 09/11/2025.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-17 and 19-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1, 11, and 20 recite the limitation “estimating a physical training index”. However, this limitation recites elements without support in the original disclosure (i.e., introduces new matter). The specification lacks support for a physical training index, and only shows support for a physical stress index and a training capability index. Therefore, this limitation is new matter. See MPEP 608.04.
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 1-17 and 19-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Step 1 analysis:
Claim 1 is drawn to a method (i.e., process), Claim 12 is drawn to an apparatus (i.e., machine), and Claim 20 is drawn to a non-transitory machine-readable storage medium (i.e., manufacture), which are all within the four statutory categories. (Step 1 – Yes, the claim falls into one of the statutory categories).
Step 2A analysis – Prong One:
Claim 1 recites:
A method of dynamically monitoring a health goal using a wearable device, comprising:
receiving, by a processor, user input associated with the wearable device worn by an individual to determine the health goal for the individual and a health plan associated with the health goal for the individual to reach the health goal;
obtaining, by the processor, health parameters associated with the individual from the wearable device and user feedback from the individual based on a physical condition of the individual associated with the health plan, wherein the health parameters comprises an exercising heart rate of the individual measured by one or more sensors of the wearable device;
evaluating, by the processor based on the health parameters and the user feedback, whether the health goal will be successfully reached by the individual following the health plan, comprising:
estimating a physical training index based on the exercising heart rate of the individual measured by the one or more sensors of the wearable device; and
determining whether the health goal will be successfully reached by the individual following the health plan by evaluating the physical training index; and
responsive to determining that the health goal will not be successfully reached by the individual following the health plan, dynamically adjusting the health plan based upon the health parameters and the user feedback to determine a modified health plan, wherein the modified health plan is provided to the individual on a display associated with the wearable device.
The series of steps as recited above describes managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity. Fundamentally, the method is that of a person receiving health data of another person and responding with a health goal and plan for the person to use. It also describes a person giving feedback and receiving an updated health plan in response to the feedback. These limitations encompass a person interacting with another individual including following rules or instructions and accordingly, the claim recites an abstract idea of managing interactions between people.
The series of steps as recited above also falls within the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Determining a health goal for an individual and a health plan associated with the health goal, evaluating whether a goal will be reached, estimating a physical training index based on the exercising heart rate, determining whether the health goal will be successfully reached by the individual following the health plan by evaluating the physical training index, and adjusting the health plan based on health parameters and user feedback are all processes that can be performed in the human mind with or without the use of a physical aid. Therefore, the claim recites an abstract idea of a mental process.
Claims 12 and 20 recite/describe nearly identical steps as claim 1 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Step 2A analysis – Prong 2:
This judicial exception is not integrated into a practical application. Specifically, independent claims 1, 12, and 20 recite the following additional elements beyond the abstract idea: a wearable device, a processor, one or more sensors, an apparatus, a non-transitory memory, and a non-transitory computer-readable storage medium configured to store computer programs, and a display. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Specifically, the wearable device can include a band, a ring, a strap, or wristwatch and can be configured for positioning at a user’s wrist, arm, finger, chest, another extremity of the user, or some other area of the user’s body (see specification par. 25). The processor refers to any logic processing unit such as CPUs, DSPs, ASICs, FPGAs, and similar devices (see applicant’s specification par. 43). The storage 230 may include any type of storage device configured to store information (see specification par. 46). Sensor array 155 including, but not limited to, one or more optical detectors 160, one or more light sources 165, one or more contact pressure/tonometry sensors 170, and at least one of the one or more gyroscopes or accelerometers 175. These sensors are only illustrative of the possibilities, however, and the lower module 150 may include additional or alternative sensors such as one or more acoustic sensors, electromagnetic sensors, ECG electrodes, bio impedance sensors, or galvanic skin response, or a combination thereof (See specification par. 31).
The limitation “the modified health plan is provided to the individual on a display associated with the wearable device” is mere data outputting recited at a high level of generality, and thus is insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”).
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, Claims 1, 12, and 20 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional elements are not integrated into a practical application).
Step 2B analysis:
As discussed above in “Step 2A analysis – Prong 2”, the identified additional elements in Independent Claims 1, 12, and 20 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Additional element of " the modified health plan is provided to the individual on a display associated with the wearable device" was found to be insignificant extra-solution activity in Step 2A, Prong Two, because it was determined to be an insignificant limitation as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of “well- understood, routine, [and] conventional activities previously known to the industry.” Further, “the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention.”
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Here, the claim limitations are similar to receiving and sending information over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OJP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); See MPEP 2106.05(d)(ll)(i)).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the steps for health goal monitoring amount to no more than using computer related devices to implement the abstract idea.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible. The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: Independent claims - NO).
Dependent Claims
Dependent Claims 2-11, 13-17, 19, and 21 are directed towards elements used to describe the user input, determining whether the health goal will be reached, determining the health plan, and modifying the health plan. These elements include: user input including at least one of a target distance, a level of skill, or a target date; adjusting the health plan comprises modifying at least one of a training intensity parameter, a training duration parameter, a training impulse or a rest period parameter, based on the physical training index and the user feedback; a current performance metric of the individual that is compared to the health goal to determine a goal gap; determining the health plan by evaluating the goal gap; at least one of a physical stress index or a training capability index estimated based on the exercising heart rate of the individual, wherein the physical stress index is indicative of a condition of physiological load of the individual, and the training capability index is indicative of how well the individual has followed the training plan; comparing values to estimate the physical stress index or the training capability index; estimating a training impulse value; dynamically adjusting the health plan based upon the health parameters and the user feedback, wherein the modified health plan is provided to the individual on the display associated with the wearable device comprises determining a training impulse adjustment ratio and determining the modified health plan; determining training load features from the exercising heart rate comprising a short-term training load and a long-term training load; evaluating whether the physical training index indicates that the health goal with be successfully reached, wherein the physical training index is determined based on the training load features; evaluate the physical training index and generating the modified health plan based on whether the physical training index indicates that the health goal will be successfully reached, wherein the user-specific physiological responses comprise at least one of user-specific stress responses or fatigue responses to training load; learn patterns based on the physical stress index and the training capability index; dynamically adjust one or more parameters of the health plan to determine the modified health plan; estimating a fatigue level and determine the modified health plan based on the fatigue level; and the user feedback comprises a subjective feedback provided by the individual after completing at least one training session of the health plan regarding a difficulty of the completed at least one training session. These elements describe managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity.
The elements as recited above also fall within the “mental processes” grouping of abstract
ideas, and describes concepts that can be performed in the human mind through observation,
evaluation, judgement, and opinion. Estimating, comparing, and evaluating steps are all tasks that can be performed in the human mind. Therefore, the dependent claims recite an abstract idea of a mental process.
This judicial exception is not integrated into a practical application. Specifically, the dependent
claims recite the following additional elements beyond the abstract idea: one or more sensors, the processor, a machine learning model, and a large language model (LLM). These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The machine learning model and large language model are mere instructions to apply the exception because they apply a mathematical algorithm to the abstract idea as in MPEP 2106.05(f)(2). The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Specifically, sensor array 155 including, but not limited to, one or more optical detectors 160, one or more light sources 165, one or more contact pressure/tonometry sensors 170, and at least one of the one or more gyroscopes or accelerometers 175. These sensors are only illustrative of the possibilities, however, and the lower module 150 may include additional or alternative sensors such as one or more acoustic sensors, electromagnetic sensors, ECG electrodes, bio impedance sensors, or
galvanic skin response, or a combination thereof (See specification par. 31).
The additional elements do not show an improvement to the functioning of a computer or to
any other technology, rather the additional elements perform general computing functions and do not
indicate how the particular combination improves any technology or provides a technical solution to a
technical problem. Accordingly, these additional elements, when considered separately and as an
ordered combination, do not integrate the abstract idea into a practical application because they do not
impose any meaningful limits on practicing the abstract idea. Therefore, the dependent claims are
directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional
elements are not integrated into a practical application).
As discussed above, the identified additional elements in Dependent Claims 2-11, 13-17, 19, and 21 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a
whole do not amount to significantly more than the judicial exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in
the context of this analysis, it must involve more than performance of “well-understood, routine, [and]
conventional activities previously known to the industry.” Further, “the mere recitation of a generic
computer cannot transform a patent ineligible abstract idea into a patent-eligible invention.”
The use of a computer or processor to merely automate or implement the abstract idea cannot
provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the
additional limitations alone or in combination improves the functioning of a computer or any other
technology, improves another technology or technical field, or effects a transformation or reduction of a
particular article to a different state or thing. Therefore, the claims are not patent eligible.
The Examiner has therefore determined that no additional element, or combination of
additional claims elements is/are sufficient to ensure the claims amount to significantly more than the
abstract idea identified above (Step 2B: Dependent claims - NO).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 4-5, 7-9, 12-14, 16-17, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Dixit (US 2022/0280105) in view of Sanders et al. (US 2017/0266501) (Hereinafter Sanders).
Regarding Claim 1, Dixit teaches:
A method of dynamically monitoring a health goal using a wearable device ([0002], [0019]: This invention relates generally to the field of health monitoring devices. A method and system for personalized biofeedback from a wearable device), comprising:
receiving, by a processor ([0155] The computing system can include a processor), user input associated with the wearable device worn by an individual ([0043] a wearable computing device such as a watch, health monitoring jewelry (e.g., a bracelet, ring, or necklace), patch, heart-rate monitoring band, smart headphones, smart glasses, and/or other suitable types of health monitoring devices can be used in collecting at least a portion of the biometric input data) to determine the health goal for the individual and a health plan associated with the health goal for the individual to reach the health goal ([0045], [0046], [0055]: determining a feedback treatment plan based on processing of the biometric inputs);
obtaining, by the processor ([0155] The computing system can include a processor), health parameters associated with the individual from the wearable device ([0063] Sensing data from a user may include sensing biometric physiological data (e.g., heart rate, breathing rate, temperature, perspiration rate, blinking rate, blood pressure, cranial electric activity, muscle twitch). The method may additionally or alternatively include collecting or sensing other types of user data (e.g., GPS position, rate of movement, direction of movement)) and user feedback from the individual based on a physical condition of the individual associated with the health plan ([0041], [0047]: behavioral intervention feedback can be delivered through user interface output of a computing device. How a biofeedback session is administered can automatically adjust and change according to the user's real-time response during that session), wherein the health parameters comprises an exercising heart rate of the individual measured by one or more sensors of the wearable device ([0063], [0066], [0070]: heart rate during increased activity (i.e., running) can be considered with monitoring movement, which are both sensed from a sensor on one or more devices worn by a user);
evaluating, by the processor based on the health parameters and the user feedback, whether the health goal will be successfully reached by the individual following the health plan ([0073], [0076] the feedback treatment plan is determined such that biofeedback is delivered so as to have the interventions target achieving real-time changes in physiological signals. breathing exercises guided through biofeedback are used to promote increases in HRV of a user. This may be used to keep the user in an HRV target range at all times or for some portion of time. Determining the feedback treatment plan may include analyzing the heart rate variability signal. Analyzing the heart rate variability signal may be used to evaluate the current level of HRV. In one variation, biofeedback treatment may be triggered based on a threshold of HRV value.)
responsive to determining that the health goal will not be successfully reached by the individual following the health plan, dynamically adjusting the health plan based upon the health parameters and the user feedback to determine a modified health plan, wherein the modified health plan is provided to the individual ([0046]-[0047]: determining an updated feedback treatment plan based on processing of biometric inputs that includes biometric inputs collected during and/or after delivering the feedback. Dynamic adjustment can be done in real-time based on inputs) on a display associated with the wearable device ([0124] Delivering feedback may be executed, for example, by activating a haptic feedback system (e.g., a vibrational motor, electrical stimulation, or other type of haptic engine), an audio feedback system (e.g., a speaker), and/or a visual feedback system (e.g., updating a display or visual indicator)).
However, Dixit does not disclose the following that is met by Sanders:
estimating a physical training index based on the exercising heart rate of the individual measured by the one or more sensors of the wearable device ([0117], [0127] activity sessions associated with a user based on received sensor data from one or more athletic sensor devices may be analyzed to determine a relative intensity. If it is available, heart rate data may be utilized to determine relative intensity. Personalized athletic constants, otherwise referred to as athletic constraints, may be generated based upon a level of activity of the user during a threshold amount of time prior to the current date, which may, in one example, relate to a predicted fitness level of the user); and
determining whether the health goal will be successfully reached by the individual following the health plan by evaluating the physical training index ([0173] For example, a prescribed activity may be 25% directed towards improving Endurance, thus that TRIMP count (e.g., 100 TRIMPS), may be multiplied by 0.25% to note that 25 TRIMPS may be quantified towards the plan goal of endurance improvement. Further, the user's actual performance may be considered. For example, if the user is the same plan as above (50% endurance and 50% mobility), however, is showing a need for more endurance or stability, then those may be increased by a factor. Thus, Examiner interprets this example as using the training capacity to evaluate whether the athlete will reach the goal of the plan); and
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Dixit to include the estimation and evaluation steps as taught by Sanders because by using the athlete’s training capabilities to evaluate the health plan, possible selections may be considered based upon their overall make up with respect to one or more individual focus areas or goals, which may be with respect to the athlete's desires, experience level, and/or actual performance, making the plan more personal and effective (See Sanders [0173]).
Regarding Claim 2, the combination of Dixit and Sanders teaches the method of claim 1, and Dixit further teaches:
The method of claim 1, wherein dynamically adjusting the health plan ([0047]: feedback can be dynamically adjusted in real-time based on real-time biometric inputs. For example, how a biofeedback session is administered can automatically adjust and change according to the user's real-time response during that session.) comprises modifying at least one of: a training intensity parameter, a training duration parameter, a training impulse or a rest period parameter, based on the user feedback ([0051], [0053], [0151]: The feedback treatment plan may be altered in a variety of ways, including session durations. One or more parameters of the feedback treatment plan may be controlled. Effectiveness to different biofeedback durations may change based on various factors which may be dynamically managed. The feedback is in the form of treatment recommendation, which directs application of a treatment. Treatment recommendation feedback can be a communication or directive specifying treatment parameters such as treatment timing, duration, dosage, type, and the like.).
However, Dixit do not discloses the following that is met by Sanders:
wherein the user input includes at least one of: a target distance ([0173] if a user selects a plan that intends to run a race of a specified distance, then running activities that are designed to progress the distance goal may be considered.), a level of skill ([0073]-[0074] an athlete may be categorized into an experience class based upon additional user input data which may range from beginner, intermediate, to advanced experience levels), or a target date ([0067] create a coaching plan that prescribes personalized athletic activities as a user trains towards a goal date. Received information may include an indication of a goal date, which may correspond to an intended end date of a coaching plan that may be adjustable in response to changes in a coaching plan. Further,)
wherein dynamically adjusting the health plan [comprises modifying at least one of: a training intensity parameter, a training duration parameter, a training impulse or a rest period parameter], based on the physical training index ([0127], [0217]: personalized athletic constants, otherwise referred to as athletic constraints, may be generated based upon a level of activity of the user during a threshold amount of time prior to the current date, which may, in one example, relate to a predicted fitness level of the user. Stored TRIMP (training impulse) data may be utilized to generate/assign new athletic constants to a user, and the time periods for training may be dynamically adjusted.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Dixit to include the user inputs and dynamic adjustment of the health plan based on the physical training index as taught by Sanders because by using the athlete’s training capabilities to evaluate and modify the health plan, possible selections may be considered based upon their overall make up with respect to one or more individual focus areas or goals, which may be with respect to the athlete's desires, experience level, and/or actual performance, making the plan more personal and effective (See [0173]).
Regarding Claim 4, the combination of Dixit and Sanders teaches the method of claim 1, and Sanders further teaches:
The method of claim 1, wherein the physical training index comprises at least one of a physical stress index or a training capability index estimated based on the exercising heart rate of the individual, wherein the physical stress index is indicative of a condition of physiological load of the individual ([0117], [0127], [0128], [0130] activity sessions associated with a user based on received sensor data from one or more athletic sensor devices may be analyzed to determine a relative intensity. If it is available, heart rate data may be utilized to determine relative intensity. Personalized athletic constants, otherwise referred to as athletic constraints, may be generated based upon a level of activity of the user during a threshold amount of time prior to the current date, which may, in one example, relate to a predicted fitness level of the user. The examiner interprets this as a physical stress index, which indicates the level of activity the user is doing. Decision block 1102 may execute one or more processes to determine whether calculated training impulse data, otherwise referred to as TRIMP data, meets one or more threshold levels associated with the assignment of athletic constants to the user. The examiner interprets this as an index of training impulse (TRIMP) data.), and the training capability index is indicative of how well the individual has followed the training plan ([0173] For example, a prescribed activity may be 25% directed towards improving Endurance, thus that TRIMP count (e.g., 100 TRIMPS), may be multiplied by 0.25% to note that 25 TRIMPS may be quantified towards the plan goal of endurance improvement. Further, the user's actual performance may be considered. For example, if the user is the same plan as above (50% endurance and 50% mobility), however, is showing a need for more endurance or stability, then those may be increased by a factor. Thus, Examiner interprets this example as using the training capacity to evaluate whether the athlete will reach the goal of the plan).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Dixit to include the estimation and evaluation of a physical stress index and a training impulse index as taught by Sanders because by using the athlete’s training capabilities to evaluate the health plan, possible selections may be considered based upon their overall make up with respect to one or more individual focus areas or goals, which may be with respect to the athlete's desires, experience level, and/or actual performance, making the plan more personal and effective (See Sanders [0173]).
Regarding Claim 5, the combination of Dixit and Sanders teaches the method of claim 4, and Sanders further teaches:
The method of claim 4, further comprising: estimating, by the processor, a training impulse value ([0087] Quantifying athletic activity or motions into TRIMPS may be utilized for one or more of: (1) quantifying prior athletic activity into TRIMPs; and (2) Determining the appropriate workout/athletic activity to prescribe for the athlete in an adaptive coaching system.) based on the exercising heart rate of the individual measured by the one or more sensors of the wearable device ([0051], [0117], [0120] Heart rate data collected by a sensor in a wearable device may be utilized to determine relative intensity. In one example, if heart rate may be utilized to compare one of the resting heart rate, maximum heart rate and/or average heart rate. Such information can collectively or individually be used in the calculation of TRIMPS),
wherein estimating the physical training index based on the exercising heart rate of the individual measured by the one or more sensors of the wearable device ([0127] personalized athletic constants, otherwise referred to as athletic constraints, may be generated based upon a level of activity of the user during a threshold amount of time prior to the current date, which may, in one example, relate to a predicted fitness level of the user), further comprises:
estimating the physical stress index by comparing an accumulation of the training impulse value in a first duration of time to an accumulation of the training impulse value in a second duration of time, wherein the first duration of time is less than the second duration of time, or estimating the training capability index by comparing the training impulse value to an expected training impulse value that is estimated based on the user input to determine the health plan ([0127] The TRIMP data that is calculated is compared to stored TRIMP data, which provides a threshold, to generate personalized constraints. The constraints may be generated based upon a level of activity of the user during a threshold amount of time prior to the current date, which may, in one example, relate to a predicted fitness level of the user).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Dixit and Sanders to include the training impulse value calculations (TRIMP) because by using the athlete’s training capabilities along with the TRIMP values to evaluate the health plan, possible selections may be considered based upon their overall make up with respect to one or more individual focus areas or goals, which may be with respect to the athlete's desires, experience level, and/or actual performance, making the plan more personal and effective (See Sanders [0173]).
Regarding Claim 7, the combination of Dixit and Sanders teaches the method of claim 1, and Dixit further teaches:
wherein evaluating, by the processor based on the health parameters and the user feedback, whether the health goal will be successfully reached by the individual following the health plan ([0073], [0076] the feedback treatment plan is determined such that biofeedback is delivered so as to have the interventions target achieving real-time changes in physiological signals. breathing exercises guided through biofeedback are used to promote increases in HRV of a user. This may be used to keep the user in an HRV target range at all times or for some portion of time. Determining the feedback treatment plan may include analyzing the heart rate variability signal. Analyzing the heart rate variability signal may be used to evaluate the current level of HRV. In one variation, biofeedback treatment may be triggered based on a threshold of HRV value.) further comprises:
However, Dixit does not teach the following that is met by Sanders:
determining, by the processor, training load features from the exercising heart rate ([0051], [0117], [0120] Heart rate data collected by a sensor in a wearable device may be utilized to determine relative intensity. In one example, if heart rate may be utilized to compare one of the resting heart rate, maximum heart rate and/or average heart rate.), the training load features comprising a short-term training load and a long-term training load ([0067] Received information may also include a number of days per week that the user intends to work out, a current level of activity of the user ( e.g. an average number of miles run by the user per week, or an average number of hours spent training per week). the eligibility window for stored running data may be two days, four days, five days, eight days, two weeks, among many others. Thus, the examiner interprets this as short and long term periods of training load.); and
evaluating, by the processor using a machine learning model, whether the physical training index indicates that the health goal will be successfully reached by the individual following the health plan, wherein the physical training index is determined based on the training load features ([0173], [0204] a prescribed activity may be 25% directed towards improving Endurance, thus that TRIMP count (e.g., 100 TRIMPS), may be multiplied by 0.25% to note that 25 TRIMPS may be quantified towards the plan goal of endurance improvement. Further, the user's actual performance may be considered. For example, if the user is the same plan as above (50% endurance and 50% mobility), however, is showing a need for more endurance or stability, then those may be increased by a factor. Thus, Examiner interprets this example as using the training index to evaluate whether the athlete will reach the goal of the plan. The machine learning methodology, as well as the neural network methodology, may be utilized to generate an adaptive athletic activity prescription and/or to adapt the prescription based on one or more athletic performances), and the machine learning model is trained to learn user-specific physiological responses to training load based on the physical training index and the user feedback ([0205]-[0208] machine learning methodology may, in certain examples, utilize a number of variable parameters in one or more calculations in identifying a model with a best predictive ability for workout recommendations. The parameters include workout data that describes the amount a person has completed and how long they worked out. The parameters also include dividing the data for training sets.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Dixit to include training load features and a machine learning model for evaluating the success of the health plan, as taught by Sanders, because by using the machine learning techniques, a dynamic recommendation can be implemented, which allows for a more personalized recommendation based on individual preferences and data available about the user (See Sanders [0202]-[0203]).
Regarding Claim 8, the combination of Dixit and Sanders teaches the method of claim 7, and Dixit further teaches:
The method of claim 7, wherein determining the modified health plan comprises: ([0107], [0114]: create and update a health model that processes biometric input data to develop a treatment model):
However, Dixit does not disclose the following that is met by Sanders:
applying, by the processor, the trained machine learning model to evaluate the physical training index ([0203] machine learning techniques may be utilized using this data to derive the intelligence behind workout recommendations.), and generating the modified health plan based on whether the physical training index indicates that the health goal will be successfully reached by the individual following the health plan, wherein the user-specific physiological responses comprise at least one of user-specific stress responses or fatigue responses to training load ([0075], [0076] a fitness gain, a fatigue gain, a fitness decay, and a fatigue decay. As will be described in further detail later, these athletic constants may be utilized to calculate a coaching plan for the user. An adaptive athletic activity prescription may be generated for a user based upon the categorized experience levels and a goal associated with the user.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the determination of the modified health plan, as taught by Dixit, with the machine learning model evaluating the physical training index and generating the modified health plan based on physiological responses, as taught by Sanders, because by using the machine learning techniques, a dynamic recommendation can be implemented, which allows for a more personalized recommendation based on individual preferences and data available about the user (See Sanders [0202]-[0203]).
Regarding Claim 9, the combination of Dixit and Sanders teaches the method of claim 4, and Dixit further teaches:
training, by the processor, a machine learning model with the health parameters and the user feedback ([0081] Determining the feedback treatment plan based on processing of the biometric inputs in some variations can include processing the biometric inputs as model inputs into a machine learning model. inputs such as a time series dataset of the HRV signal, past parameters of biofeedback sessions, pharmaceutical usage data, emotional input data, activity data, and/or other data inputs may be supplied to the machine learning model.) to learn patterns ([0085] develop a machine learning model to predict physiological responses to biofeedback of different people, and/or to learn patterns on human-managed administration of feedback treatment plans.)
However, Dixit does not disclose the following that is met by Sanders:
based on the physical stress index and the training capability index ([0117], [0127], [0128], [0130] Heart rate data may be utilized to determine relative intensity. Personalized athletic constants, otherwise referred to as athletic constraints, may be generated based upon a level of activity of the user during a threshold amount of time prior to the current date, which may, in one example, relate to a predicted fitness level of the user. The examiner interprets this as a physical stress index, which indicates the level of activity the user is doing. Decision block 1102 may execute one or more processes to determine whether calculated training impulse data, otherwise referred to as TRIMP data, meets one or more threshold levels associated with the assignment of athletic constants to the user. The examiner interprets this as an index of training impulse (TRIMP) data. The athletic constants use data for previous workout sessions and days, which is used for predicting fitness level of the user); and
applying, by the processor, the trained machine learning model to dynamically adjust one or more parameters of the health plan to determine the modified health plan ([0202]-[0203] calculation of an adaptive athletic prescription (which may include a running and/or training prescription) may utilize static or dynamic recommendations. A dynamic recommendation may allow for workouts or any sequence of athletic activity to be chosen and completed more frequently, and/or may be implemented such that users will enjoy them more because they are a better individual fit for that user. Machine learning techniques may be utilized using this data to derive the intelligence behind workout recommendations.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the training steps as taught by Dixit with the dynamic adjustment as taught by Sanders because, by implementing dynamic recommendations, a more personalized recommendation based on individual preferences and data available about the user can be utilized (See Sanders [0202]-[0203]).
Regarding Claim 12, Dixit teaches the following:
An apparatus for dynamically monitoring a health goal using a wearable device (See Fig. 12, [0002]: This invention relates generally to the field of health monitoring devices and more specifically to a new and useful system and method for personalized biofeedback from a wearable device), the apparatus comprising:
a non-transitory memory ([0159]: The computer-readable medium can be stored on any suitable computer readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device); and
a processor configured to execute instructions stored in the non-transitory memory ([0167]Data, databases, data records or other stored forms data created or used by the software programs can also be stored in the memory 1003, and such data is accessed by at least one of processors 1002A-1002N during execution of the machine-executable instructions of the software programs) to:
receive user input associated with the wearable device worn by an individual ([0043] a wearable computing device such as a watch, health monitoring jewelry (e.g., a bracelet, ring, or necklace), patch, heart-rate monitoring band, smart headphones, smart glasses, and/or other suitable types of health monitoring devices can be used in collecting at least a portion of the biometric input data) to determine the health goal for the individual and a health plan associated with the health goal for the individual to reach the health goal ([0045], [0046], [0055]: determining a feedback treatment plan based on processing of the biometric inputs);
obtain health parameters associated with the individual from the wearable device ([0063] Sensing data from a user may include sensing biometric physiological data (e.g., heart rate, breathing rate, temperature, perspiration rate, blinking rate, blood pressure, cranial electric activity, muscle twitch). The method may additionally or alternatively include collecting or sensing other types of user data (e.g., GPS position, rate of movement, direction of movement)) and user feedback from the individual based on a physical condition of the individual associated with the health plan ([0041], [0047]: behavioral intervention feedback can be delivered through user interface output of a computing device. How a biofeedback session is administered can automatically adjust and change according to the user's real-time response during that session), wherein the health parameters comprises an exercising heart rate of the individual measured by one or more sensors of the wearable device ([0063], [0066], [0070]: heart rate during increased activity (i.e., running) can be considered with monitoring movement, which are both sensed from a sensor on one or more devices worn by a user);
evaluate, based on the health parameters and the user feedback, whether the health goal will be successfully reached by the individual following the health plan ([0073], [0076] the feedback treatment plan is determined such that biofeedback is delivered so as to have the interventions target achieving real-time changes in physiological signals. breathing exercises guided through biofeedback are used to promote increases in HRV of a user. This may be used to keep the user in an HRV target range at all times or for some portion of time. Determining the feedback treatment plan may include analyzing the heart rate variability signal. Analyzing the heart rate variability signal may be used to evaluate the current level of HRV. In one variation, biofeedback treatment may be triggered based on a threshold of HRV value.), further comprising instructions to:
responsive to determining that the health goal will not be successfully reached by the individual following the health plan, dynamically adjust the health plan based upon the health parameters and the user feedback to determine a modified health plan and provide the modified health plan to the individual ([0046]-[0047]: determining an updated feedback treatment plan based on processing of biometric inputs that includes biometric inputs collected during and/or after delivering the feedback. Dynamic adjustment can be done in real-time based on inputs) on a display associated with the wearable device ([0124] Delivering feedback may be executed, for example, by activating a haptic feedback system (e.g., a vibrational motor, electrical stimulation, or other type of haptic engine), an audio feedback system (e.g., a speaker), and/or a visual feedback system (e.g., updating a display or visual indicator)).
However, Dixit does not teach the following that is met by Sanders:
estimate a physical training index based on the exercising heart rate of the individual measured by the one or more sensors of the wearable device ([0117], [0127] activity sessions associated with a user based on received sensor data from one or more athletic sensor devices may be analyzed to determine a relative intensity. If it is available, heart rate data may be utilized to determine relative intensity. Personalized athletic constants, otherwise referred to as athletic constraints, may be generated based upon a level of activity of the user during a threshold amount of time prior to the current date, which may, in one example, relate to a predicted fitness level of the user); and
determine whether the health goal will be successfully reached by the individual following the health plan by evaluating the physical training index ([0173] For example, a prescribed activity may be 25% directed towards improving Endurance, thus that TRIMP count (e.g., 100 TRIMPS), may be multiplied by 0.25% to note that 25 TRIMPS may be quantified towards the plan goal of endurance improvement. Further, the user's actual performance may be considered. For example, if the user is the same plan as above (50% endurance and 50% mobility), however, is showing a need for more endurance or stability, then those may be increased by a factor. Thus, Examiner interprets this example as using the training capacity to evaluate whether the athlete will reach the goal of the plan); and
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Dixit to include the estimation and evaluation steps as taught by Sanders because by using the athlete’s training capabilities to evaluate the health plan, possible selections may be considered based upon their overall make up with respect to one or more individual focus areas or goals, which may be with respect to the athlete's desires, experience level, and/or actual performance, making the plan more personal and effective (See [0173]).
Regarding Claim 13, the combination of Dixit and Sanders teaches the apparatus of claim 12, and Sanders further teaches:
The apparatus of claim 12, wherein the physical training index comprises at least one of a physical stress index or a training capability index based on the health parameters comprising the exercising heart rate of the individual ([0117], [0127], [0128], [0130] activity sessions associated with a user based on received sensor data from one or more athletic sensor devices may be analyzed to determine a relative intensity. If it is available, heart rate data may be utilized to determine relative intensity. Personalized athletic constants, otherwise referred to as athletic constraints, may be generated based upon a level of activity of the user during a threshold amount of time prior to the current date, which may, in one example, relate to a predicted fitness level of the user. The examiner interprets this as a physical stress index, which indicates the level of activity the user is doing. Decision block 1102 may execute one or more processes to determine whether calculated training impulse data, otherwise referred to as TRIMP data, meets one or more threshold levels associated with the assignment of athletic constants to the user. The examiner interprets this as an index of training impulse (TRIMP) data.);
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Dixit with the physical stress index and the training capability index as taught by Sanders because by using the athlete’s training capabilities to evaluate the health plan, possible selections may be considered based upon their overall make up with respect to one or more individual focus areas or goals, which may be with respect to the athlete's desires, experience level, and/or actual performance, making the plan more personal and effective (See [0173]).
Regarding Claim 14, the combination of Dixit and Sanders teaches the apparatus of claim 13, and Sanders further teaches:
The apparatus of claim 13, wherein the instructions further comprise instruction to: estimate a training impulse value (Quantifying athletic activity or motions into TRIMPS may be utilized for one or more of: (1) quantifying prior athletic activity into TRIMPs; and (2) Determining the appropriate workout/athletic activity to prescribe for the athlete in an adaptive coaching system.) based on the exercising heart rate of the individual measured by the one or more sensors of the wearable device ([0051], [0117], [0120] Heart rate data collected by a sensor in a wearable device may be utilized to determine relative intensity. In one example, if heart rate may be utilized to compare one of the resting heart rate, maximum heart rate and/or average heart rate. Such information can collectively or individually be used in the calculation of TRIMPS); and
The instructions to estimate the physical training index based on the exercising heart rate of the individual measured by the one or more sensors of the wearable device ([0127] personalized athletic constants, otherwise referred to as athletic constraints, may be generated based upon a level of activity of the user during a threshold amount of time prior to the current date, which may, in one example, relate to a predicted fitness level of the user) further comprise instructions to:
estimate the physical stress index by comparing an accumulation of the training impulse value in a first duration of time to an accumulation of the training impulse value in a second duration of time, wherein the first duration of time is less than the second duration of time, or estimate the training capability index by comparing the training impulse value to an expected training impulse value that is estimated based on the user input to determine the health plan (([0127] The TRIMP data that is calculated is compared to stored TRIMP data, which provides a threshold, to generate personalized constraints. The constraints may be generated based upon a level of activity of the user during a threshold amount of time prior to the current date, which may, in one example, relate to a predicted fitness level of the user).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Dixit and Sanders to include the training impulse value calculations (TRIMP) because by using the athlete’s training capabilities along with the TRIMP values to evaluate the health plan, possible selections may be considered based upon their overall make up with respect to one or more individual focus areas or goals, which may be with respect to the athlete's desires, experience level, and/or actual performance, making the plan more personal and effective (See [0173]).
Regarding Claim 16, the combination of Dixit and Sanders teaches the apparatus of claim 12, and Dixit further teaches:
wherein the instructions to evaluate, based on the health parameters and the user feedback, whether the health goal will be successfully reached by the individual following the health plan ([0073], [0076] the feedback treatment plan is determined such that biofeedback is delivered so as to have the interventions target achieving real-time changes in physiological signals. breathing exercises guided through biofeedback are used to promote increases in HRV of a user. This may be used to keep the user in an HRV target range at all times or for some portion of time. Determining the feedback treatment plan may include analyzing the heart rate variability signal. Analyzing the heart rate variability signal may be used to evaluate the current level of HRV. In one variation, biofeedback treatment may be triggered based on a threshold of HRV value.) further comprise instructions to:
However, Dixit does not teach the following that is met by Sanders:
determine training load features from the exercising heart rate ([0051], [0117], [0120] Heart rate data collected by a sensor in a wearable device may be utilized to determine relative intensity. In one example, if heart rate may be utilized to compare one of the resting heart rate, maximum heart rate and/or average heart rate.), the training load features comprising a short-term training load and a long-term training load ([0067] Received information may also include a number of days per week that the user intends to work out, a current level of activity of the user ( e.g. an average number of miles run by the user per week, or an average number of hours spent training per week). the eligibility window for stored running data may be two days, four days, five days, eight days, two weeks, among many others. Thus, the examiner interprets this as short and long term periods of training load.); and
evaluate, using a machine learning model, whether the physical training index indicates that the health goal will be successfully reached by the individual following the health plan, wherein the physical training index is determined based on the training load features ([0173], [0204] a prescribed activity may be 25% directed towards improving Endurance, thus that TRIMP count (e.g., 100 TRIMPS), may be multiplied by 0.25% to note that 25 TRIMPS may be quantified towards the plan goal of endurance improvement. Further, the user's actual performance may be considered. For example, if the user is the same plan as above (50% endurance and 50% mobility), however, is showing a need for more endurance or stability, then those may be increased by a factor. Thus, Examiner interprets this example as using the training index to evaluate whether the athlete will reach the goal of the plan. The machine learning methodology, as well as the neural network methodology, may be utilized to generate an adaptive athletic activity prescription and/or to adapt the prescription based on one or more athletic performances), and the machine learning model is trained to learn user-specific physiological responses to training load based on the physical training index and the user feedback ([0205]-[0208] machine learning methodology may, in certain examples, utilize a number of variable parameters in one or more calculations in identifying a model with a best predictive ability for workout recommendations. The parameters include workout data that describes the amount a person has completed and how long they worked out. The parameters also include dividing the data for training sets.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Dixit with training load features and a machine learning model for evaluating the success of the health plan, as taught by Sanders, because by using the machine learning techniques, a dynamic recommendation can be implemented, which allows for a more personalized recommendation based on individual preferences and data available about the user (See Sanders [0202]-[0203]).
Regarding Claim 17, the combination of Dixit and Sanders teaches the method of claim 16, and Sanders further teaches:
The apparatus of claim 16, wherein the instructions to determine the modified health plan comprise instructions to: apply the trained machine learning model to evaluate the physical training index ([0203] machine learning techniques may be utilized using this data to derive the intelligence behind workout recommendations.), and generate the modified health plan based on whether the physical training index indicates that the health goal will be successfully reached by the individual following the health plan, wherein the user-specific physiological responses comprise at least one of user-specific stress responses or fatigue responses to training load ([0075], [0076] a fitness gain, a fatigue gain, a fitness decay, and a fatigue decay. As will be described in further detail later, these athletic constants may be utilized to calculate a coaching plan for the user. An adaptive athletic activity prescription may be generated for a user based upon the categorized experience levels and a goal associated with the user.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the determination of the modified health plan, as taught by Dixit, with the machine learning model evaluating the physical training index and generating the modified health plan based on physiological responses, as taught by Sanders, because by using the machine learning techniques, a dynamic recommendation can be implemented, which allows for a more personalized recommendation based on individual preferences and data available about the user (See Sanders [0202]-[0203]).
Regarding Claim 20, Dixit teaches the following:
A non-transitory computer-readable storage medium ([0168] The processor-readable storage medium 1005 is one of (or a combination of two or more of) a hard drive, a flash drive, a DVD, a CD, an optical disk, a floppy disk, a flash storage, a solid-state drive, a ROM, an EEPROM, an electronic circuit, a semiconductor memory device, and the like.) configured to store computer programs for dynamically monitoring a health goal using a wearable device ([0002]: This invention relates generally to the field of health monitoring devices and more specifically to a new and useful system and method for personalized biofeedback from a wearable device, the computer programs comprising instructions executable by a processor ([0168] The processor-readable storage medium 1005 can include an operating system, software programs, device drivers, and/or other suitable sub-systems or software.) to:
receive user input associated with the wearable device worn by an individual ([0043] a wearable computing device such as a watch, health monitoring jewelry (e.g., a bracelet, ring, or necklace), patch, heart-rate monitoring band, smart headphones, smart glasses, and/or other suitable types of health monitoring devices can be used in collecting at least a portion of the biometric input data) to determine the health goal for the individual and a health plan associated with the health goal for the individual to reach the health goal ([0045], [0046], [0055]: determining a feedback treatment plan based on processing of the biometric inputs);
obtain health parameters associated with the individual from the wearable device ([0063] Sensing data from a user may include sensing biometric physiological data (e.g., heart rate, breathing rate, temperature, perspiration rate, blinking rate, blood pressure, cranial electric activity, muscle twitch). The method may additionally or alternatively include collecting or sensing other types of user data (e.g., GPS position, rate of movement, direction of movement)) and user feedback from the individual based on a physical condition of the individual associated with the health plan ([0041], [0047]: behavioral intervention feedback can be delivered through user interface output of a computing device. How a biofeedback session is administered can automatically adjust and change according to the user's real-time response during that session), wherein the health parameters comprises an exercising heart rate of the individual measured by one or more sensors of the wearable device ([0063], [0066], [0070]: heart rate during increased activity (i.e., running) can be considered with monitoring movement, which are both sensed from a sensor on one or more devices worn by a user);
evaluate, based on the health parameters and the user feedback, whether the health goal will be successfully reached by the individual following the health plan ([0073], [0076] the feedback treatment plan is determined such that biofeedback is delivered so as to have the interventions target achieving real-time changes in physiological signals. breathing exercises guided through biofeedback are used to promote increases in HRV of a user. This may be used to keep the user in an HRV target range at all times or for some portion of time. Determining the feedback treatment plan may include analyzing the heart rate variability signal. Analyzing the heart rate variability signal may be used to evaluate the current level of HRV. In one variation, biofeedback treatment may be triggered based on a threshold of HRV value.) by:
responsive to determining that the health goal will not be successfully reached by the individual following the health plan, dynamically adjust the health plan based upon the health parameters and the user feedback to determine a modified health plan and provide the modified health plan to the individual ([0046]-[0047]: determining an updated feedback treatment plan based on processing of biometric inputs that includes biometric inputs collected during and/or after delivering the feedback. Dynamic adjustment can be done in real-time based on inputs) on a display associated with the wearable device ([0124] Delivering feedback may be executed, for example, by activating a haptic feedback system (e.g., a vibrational motor, electrical stimulation, or other type of haptic engine), an audio feedback system (e.g., a speaker), and/or a visual feedback system (e.g., updating a display or visual indicator)).
However, Dixit does not disclose the following that is met by Sanders:
estimating a physical training index based on the exercising heart rate of the individual measured by the one or more sensors of the wearable device ([0117], [0127] activity sessions associated with a user based on received sensor data from one or more athletic sensor devices may be analyzed to determine a relative intensity. If it is available, heart rate data may be utilized to determine relative intensity. Personalized athletic constants, otherwise referred to as athletic constraints, may be generated based upon a level of activity of the user during a threshold amount of time prior to the current date, which may, in one example, relate to a predicted fitness level of the user); and
determining whether the health goal will be successfully reached by the individual following the health plan by evaluating the physical training index ([0173] For example, a prescribed activity may be 25% directed towards improving Endurance, thus that TRIMP count (e.g., 100 TRIMPS), may be multiplied by 0.25% to note that 25 TRIMPS may be quantified towards the plan goal of endurance improvement. Further, the user's actual performance may be considered. For example, if the user is the same plan as above (50% endurance and 50% mobility), however, is showing a need for more endurance or stability, then those may be increased by a factor. Thus, Examiner interprets this example as using the training capacity to evaluate whether the athlete will reach the goal of the plan); and
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Dixit to include the estimation and evaluation steps as taught by Sanders because by using the athlete’s training capabilities to evaluate the health plan, possible selections may be considered based upon their overall make up with respect to one or more individual focus areas or goals, which may be with respect to the athlete's desires, experience level, and/or actual performance, making the plan more personal and effective (See Sanders [0173]).
Regarding Claim 21, the combination of Dixit and Sanders teaches the method of claim 16, and Sanders further teaches:
The method of claim 1, wherein the user feedback comprises a subjective feedback provided by the individual after completing at least one training session of the health plan ([0217] a first time period of workout completion event data may be utilized to create a best model that evaluates recommendations for workouts going forward) regarding a difficulty of the completed at least one training session ([0218] the model may recommend workouts that a user is likely to complete (and/or determines if of an acceptable range of difficulty).
It would have been obvious to one of ordinary skill in the art to have modified the method as taught by Dixit and Sanders to include the subjective feedback after completion of at least one training session regarding the difficulty of the sessions, as taught by Sanders, because the feedback provides recommendations for best fit and more accurate workout plans/schedules moving forward through the collection of difficulty data (See Sanders [0217]-[0219]).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Dixit (US 2022/0280105) in view of Sanders et al. (US 2017/0266501) (Hereinafter Sanders), in further view of Quatrochi et al. (JP6021789B2) (Hereinafter Quatrochi).
Regarding Claim 3, the combination of Dixit and Sanders teaches the method of claim 2, and Quatrochi further teaches:
The method of claim 2, wherein the user input further includes a current performance metric of the individual (See pg. 25, par. 2: Data representing the performance of the user or exerciser may be collected as described with reference to FIG. 29 which shows the performance of a user or exerciser), and the method further comprises:
comparing the current performance metric of the individual to the health goal (See pg. 25, par. 2: the performance of a user or exerciser can be compared graphically with the goals of the training program on a daily basis) to determine a goal gap between the current performance metric and the health goal (See pg. 25, par. 1: the training program system may further provide an expected endpoint compared to a specified goal. In other words, if the user makes changes to the training program, the training program can indicate that the user is not expected to reach or exceed the set goal. Using such information, users can adjust other days, if desired, to make up for expected shortages); and
determining the health plan by evaluating the goal gap (See pg. 25, par. 1-2: the training program system may further provide an expected endpoint compared to a specified goal. In other words, if the user makes changes to the training program, the training program can indicate that the user is not expected to reach or exceed the set goal. Using such information, users can adjust other days, if desired, to make up for expected shortages. The training program system can also provide recommendations for tailoring the training program to reach a set goal,).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of Dixit/Sanders with the comparison step to determine a gap between current performance and expected performance because it allows users to adjust other days, if desired, to make up for expected shortages or to control the amount that users will exceed their goals (See Quatrochi Pg. 25, par. 1).
Relevant Prior Art of Record Not Currently Being Applied
The relevant art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Bin (KR20180059714) discloses a system for health care using a wearable device including a sensor unit for collecting physical activity information of a user.
WEN et al. (CN 114400093A) discloses a health plan generating method that includes adjusting the health plan if requirements are not successfully met.
Halson (Monitoring Training Load to Understand Fatigue in Athletes) discloses coaching/athletic planning research which includes the calculation of training impulses and discusses training loads.
Response to Arguments
Applicant's arguments filed 12/09/2025 have been fully considered but they are not persuasive. With respect to the previous 35 U.S.C. 101 rejection, Applicant argues the steps of obtaining exercising heart rate measured by one or more sensors of the wearable device, estimating a physical training index based on the sensor data, determining whether the individual will successfully reach a health goal by evaluating the physical training index, and dynamically adjusting the health plan are not generic computer functions and cannot be performed as a mental process. However, the examiner respectfully disagrees. A person could obtain heart rate data and analyze the data in their mind to estimate a training index and determine the success of the health plan. A person can also adjust the plan in their mind dynamically based on evaluating the data. The Applicant also argues the claimed subject matter represents a technological improvement to the real-time physiological monitoring capability and functional utility of a wearable device, however, the examiner respectfully disagrees. Automatically determining whether a health goal is attainable using a physical training index and obtaining the heart rate data from sensors is a part of the abstract ideas previously set forth, and therefore does not provide a technological improvement.
Applicant’s arguments, see pages 12-13 of Applicant’s Remarks, filed 12/09/2025, with respect to the rejections of claims 1-3, 7-9, 12, 16-18, and 20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Sanders et al. Dixit and Quatrochi do not explicitly disclose “estimating a physical training index based on the exercising heart rate of the individual measured by the one or more sensors of the wearable device” or “determining whether the health goal will be successfully reached by the individual following the health plan by evaluating the physical training index”, as recited in claim 1 and similarly in claims 12 and 20. However, Sanders does disclose these limitations by describing personalized athletic constants generated based on a level of activity of the user which gives insight into a training level the athlete will be classified into. The constants are used to provide an index of training levels, and the index can be used to determine whether the health goal will be successfully reached by the individual (See Sanders [0127], [0128], Table 1, Table 2, and [0173]).
Applicant's arguments on pages 14-15 of Applicant’s Remarks have been fully considered but they are not persuasive. Applicant argues neither Dixit nor Quatrochi discloses “responsive to determining that the health goal will not be successfully reached by the individual following the health plan, dynamically adjusting the health plan based upon the health parameters and the user feedback to determine a modified health plan, wherein the modified health plan is provided to the individual”, however, examiner respectfully disagrees. Dixit discloses dynamically adjusting the treatment plan in real time based on feedback from the health plan, and displaying the feedback (See Dixit [0046]-[0047] and [0124]).
Applicant also argues Sanders fails to disclose determining a physical training index based on the exercising heart rate measured by wearable sensors during execution of a health plan and “at least one of a physical stress index or a training capability index estimated based on the exercising heart rate of the individual”, however, Sanders discloses personalized athletic constants generated based on a level of activity of the user which gives insight into a training level the athlete will be classified into. The constants are used to provide an index of training levels, and the index can be used to determine whether the health goal will be successfully reached by the individual (See Sanders [0127], [0128], Table 1, Table 2, and [0173]). Sanders also discloses that heart rate data may be utilized to determine the relative intensity of the athletic constants (See Sanders [0117, [0127], [0128], [0130]). Sanders also discloses estimating the training capability index by comparing the training impulse value to an expected training impulse value that is estimated based on the user input to determine the health plan (See Sanders [0127]).
Applicant’s arguments, see page 15 of Applicant’s Remarks, with respect to Claim 10 have been fully considered and are persuasive. None of Dixit, Quatrochi, or Sanders teaches “estimating, by the processor, a fatigue level of the individual using a machine learning model trained to estimate the fatigue level of the individual based on the health parameters and the user feedback, wherein the health parameters comprise physiological parameters associated with the individual, and exercise performance parameters associated with completed exercise tasks of the individual, wherein the modified health plan is determined based upon the fatigue level estimated of the individual; and determining, by the processor using the trained machine learning model, the modified health plan based on the fatigue level of the individual estimated by the trained machine learning model”. Therefore, the rejection of claim 10 has been withdrawn.
Conclusion
THIS ACTION IS MADE FINAL. 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 ALEXIS K VAN DUZER whose telephone number is (571)270-5832. The examiner can normally be reached Monday thru Thursday 8-5 CT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.K.V./Examiner, Art Unit 3682
/EVANGELINE BARR/Primary Examiner, Art Unit 3682