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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 16, 2026 has been entered.
Status of Claims
This office action is in response to arguments and amendments entered on February 16, 2026 for the patent application 17/565,909 originally filed on December 30, 2021. Claims 1 and 17-21 are amended. Claims 9, 15, and 16 are canceled. Claims 1-8, 10-14, and 17-22 remain pending. The first office action of February 13, 2025 and the second office action of December 1, 2025 are fully incorporated by reference into this office action.
Response to Amendment
Applicant’s amendments to the claims have been noted by the Examiner.
The Applicant’s amendments are not sufficient to overcome the outstanding rejections under 35 USC 112(a) and 35 USC 112(b), for reasons set forth below.
Applicant’s amendments to the claims are not sufficient to overcome the 35 USC 101 rejections based on abstract idea, for reasons set forth below. However, Applicant’s amendments are sufficient to overcome the 35 USC 101 rejections based on claiming of non-statutory subject matter such as transitory signals.
Applicant’s amendments to the claims are not sufficient to overcome the 35 USC 103 rejections, for reasons set forth below.
Claim Rejections - 35 USC § 112
Claims rejected under § 112(a)
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.
Claims 1-8, 10-14, and 17-22 are rejected under 35 U.S.C. 112(a), 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, at the time the application was filed, had possession of the claimed invention.
Claim 1, and a substantially similar limitation in claim 22, contains the limitation “at least one property selected from the group consisting of a physiological property and a physical property of the user.” However, “a physical property” is not defined or disclosed anywhere in the disclosure. This limitation is not adequately described in the specification as originally filed and forms the basis of the rejection. As such, the claimed subject matter is 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, at the time the application was filed, had possession of the claimed invention. Therefore, claims 1 and 22 are deemed to recite new matter and is properly rejected under 35 U.S.C. §112(a). Claims 2-5 and 22 are also rejected under 35 U.S.C. §112(a) based on their respective dependencies to independent claim 1.
Claim 6, and a substantially similar limitation in claim 17, contains the limitation “at least one of physiological data and physical data.” However, “physical data” is not defined or disclosed anywhere in the disclosure. This limitation is not adequately described in the specification as originally filed and forms the basis of the rejection. As such, the claimed subject matter is 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, at the time the application was filed, had possession of the claimed invention. Therefore, claims 6 and 17 are deemed to recite new matter and is properly rejected under 35 U.S.C. §112(a). Claims 7, 8, 10-14, and 18-21 are also rejected under 35 U.S.C. §112(a) based on their respective dependencies to independent claims 6 and 17.
Claims rejected under § 112(b)
The following is a quotation of 35 U.S.C. § 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-8, 10-14, and 17-22 are rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1, and a substantially similar limitation in claim 22, contains the limitation “at least one property selected from the group consisting of a physiological property and a physical property of the user.” However, the term “physical property” is not defined in the instant disclosure, and its meaning is ambiguous in light of the disclosure. For example, it is unclear what the difference in scope is between “a physiological property” and “a physical property.” Therefore, claims 1 and 22 are rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-5 and 22 are also rejected under 35 U.S.C. §112(b) based on their respective dependencies to independent claim 1.
Claim 6, and a substantially similar limitation in claim 17, contains the limitation “at least one of physiological data and physical data.” However, the term “physical data” is not defined anywhere in the disclosure, and its meaning is ambiguous in light of the disclosure. For example, it is unclear what the difference in scope is between “physiological data” and “physical data.” It is also unclear how data can manifest itself in physical form, as its physical form is dependent on the medium upon which it is stored. Therefore, claims 6 and 17 are rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 7, 8, 10-14, and 18-21 are also rejected under 35 U.S.C. §112(b) based on their respective dependencies to independent claims 6 and 17.
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 1-8, 10-14, and 17-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is directed to “a physical activity measurement system” (i.e. a machine), claim 6 is directed to “a computer-implemented method” (i.e. a process), and claim 17 is directed to “a non-transitory computer-readable medium” (i.e. a machine), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
However, the claims are drawn to an abstract idea of “prediction relating to exercise,” in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion) which are “performed on a computer” (per MPEP 2106(III)(C) “A Claim That Requires a Computer May Still Recite a Mental Process”).
Regardless, the claims are reasonably understood as “mental processes,” which require the following limitations of claim 1:
“measure at least one property selected from the group consisting of a physiological property and a physical property of the user that is associated with exercise by the user…
said machine learning model… train[ing] itself locally… during the bout of exercise, and to make one or more exercise-related predictions to the user… during the bout of exercise.”
the following limitations of claim 6:
“providing a machine learning model;
collecting at least one of physiological data and physical data from the user…
adding said physiological data acquired by said collecting to the user's physiological data set;
training said machine learning model on the user's said physiological data set in realtime during the bout of exercise; and
making at least one prediction for the user based on the application of said machine learning model… during the bout of exercise.”
and the following limitations of claim 17:
“measuring at least one of physiological data and physical data associated with exercise by the user, during a bout of exercise…
adding said physiological and physical data acquired by said measuring to a user's physiological data set;
training a machine learning model only on the user's said physiological data set in realtime during the bout of exercise; and
making at least one prediction for the user based on the application of said machine learning model… during the bout of exercise.”
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that, e.g., “a physical activity measurement system,” “a processing unit,” “at least one input,” “at least one output,” “at least one storage device,” and “a non-transitory computer-readable medium” are claimed, as these are merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., data gathering) and/or do no more than generally link the use of a judicial exception to a particular technological environment or field of use. In other words, the claimed “prediction relating to exercise,” is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g., “a physical activity measurement system,” “a processing unit,” “at least one input,” “at least one output,” “at least one storage device,” and “a non-transitory computer-readable medium” are claimed these are all generic, well-known, and conventional computing elements. As evidence that these are generic, well-known, and conventional computing elements, Applicant’s specification discloses them in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a), which satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the Applicant’s claimed “physical activity measurement system” is not explicitly described in the disclosure, but is taken to encompass all of the devices required to satisfy the claims. Instant application paragraph [0018] describes such devices as potentially included in “a smartphone, a tablet computer, a wearable computer, or any other suitable device.” The Applicant’s claimed “a processing unit,” “at least one input,” “at least one output,” “at least one storage device” are all components of the “physical activity measurement system.” The disclosure describes the “at least one input” as possibly being “a touchscreen,” and it describes the “at least one output” as possibly being “a graphical display device.” Both “a touchscreen” and “a graphical display device” are conventional components of generic computing devices such as “a smartphone, a tablet computer, a wearable computer, or any other suitable device.”
The Applicant’s claimed “a non-transitory computer-readable medium” is described in instant application paragraphs [0090-0091] as “any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device… The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media.” The “non-transitory” designation in the claim only bars the computer-readable medium from being interpreted as a transitory signal such as a propagation medium. However, the computer-readable medium can still be interpreted as being a generic computer or generic computer component.
These elements are reasonably interpreted as a generic computer which provides no details of anything beyond ubiquitous standard equipment. As such, the claimed limitations of “a physical activity measurement system” and “a non-transitory computer-readable medium” are reasonably understood as not providing anything significantly more. Therefore, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 2-5, 7, 8, 10-14, and 18-22 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-5, 7, 8, 10-14, and 18-22 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to independent claims 1, 6, and 17.
Therefore, claims 1-8, 10-14, and 17-22 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over King et al. (hereinafter “King,” US 2017/0263147) in view of Olivier (US 2018/0140228), and in further view of Wu (US 2020/0320433).
Regarding claim 1, King discloses a physical activity measurement system usable by a user performing a bout of exercise, comprising
a processing unit (King [0041], “processors 112 may include one or more processing units”);
at least one input connected to said processing unit, at least one said input configured to measure at least one property selected from the group consisting of a physiological property and a physical property of the user that is associated with exercise by the user (King [0072], “an application executing on the user computing device may include an input by which the user can indicate that a given piece of workout equipment is busy”; also King [0061], “Sensors 104 may be configured to generate output signals conveying information related to the user. In some embodiments, sensors 104 may include audiovisual sensors, activity sensors, physiological sensors, biometric sensors, or other sensors. Examples of such sensors may include a heart rate sensor, a blood pressure sensor/monitor, a weight scale, motion sensors, an optical sensor, biometric sensors, a video sensor, an audio sensor, a color sensor, a blood glucose monitor, a blood oxygen saturation monitor (e.g., a pulse oximeter), a hydration monitor, a skin/body temperature thermometer, a respiration monitor, electroencephalogram (EEG) electrodes, accelerometers, activity sensors/trackers, a GPS sensor, or other sensors. These examples should not be considered limiting. Sensors 104 are configured to generate various output signals conveying information related to the user that allows computing environment 100 to function as described herein”);
at least one output connected to said processing unit (King [0096], “the gym-equipment-specific displays may execute an application configured to receive video instructions from the user computing devices that cause those displays to present a workout video obtained via the user computing device”);
at least one storage device connected to and local to said processing unit (see King Fig. 1, showing server 108 with processor 112, and server inherently requiring memory to function); and
a machine learning model stored in said at least one storage device (King [0119], “Other embodiments may iteratively adjust other machine learning models to reduce the error function, e.g., with a greedy algorithm that optimizes for the current iteration. The resulting, trained model, e.g., a vector of weights or thresholds, may be stored in memory”).
King does not teach every limitation of wherein said machine learning model utilizes said at least one input to train itself … utilizing the processing unit during the bout of exercise, and to make one or more exercise-related predictions to the user utilizing the processing unit during the bout of exercise.
King does disclose wherein said machine learning model utilizes said at least one input to train itself … and to make one or more exercise-related predictions to the user (King [0113], “a first model may be trained to select a workout instructor appearing in the blocks. Embodiments may ask users to rate their workout instructor and associate those ratings with the user's profiles. This data may be used as a training set. Embodiments may then determine weights or coefficients of a model, for instance a neural net or decision tree, by iteratively adjusting the coefficients, determining how closely the model describes the training set, and then adjusting the weights or coefficients in a direction in which the correspondence is expected to increase. The resulting model may then receive as an input a current user's profile and, based on the trained weights and coefficients, a score or selecting of candidate workout instructors may be determined to identify the best fit for the user based on the training data,” iteratively adjusting the model training set based on inputs; also King [0072], “an application executing on the user computing device may include an input by which the user can indicate that a given piece of workout equipment is busy, and some embodiments may dynamically recompose the workout by accessing the above-data structures, for instance, …by choosing alternative workout video block among the candidates that satisfy the set of criteria and, thus, are consistent with the user's goals and the workout template,” dynamically recomposing the workout based on user inputs). King does not teach that the machine learning model trains itself … utilizing the processing unit during the bout of exercise.
However, Olivier discloses the machine learning model trains itself … utilizing the processing unit during the bout of exercise (Olivier [0007], “simultaneously accrue multiple data metrics for an individual, including, but not limited to, fitness metrics, sleep patterns, heart rate, and heart rate variability during events such as, but not limited to, sleep, exercise and rest.”; also Olivier [0041], “incoming physiological data streams are mapped to a generated user profile by creating a list of features from the data streams associated with sleep, rest and free-living states through real time training of machine learning models such as, but not limited to, bidirectional neural networks”).
Olivier is analogous to King, as both are drawn to the art of machine learning model training. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by King, to include the machine learning model trains itself … utilizing the processing unit during the bout of exercise, as taught by Olivier, in order to accurately and reliably monitor a user in an unobtrusive and convenient manner (Olivier [0008]). Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
King in view of Olivier does not teach explicitly teach wherein said machine learning model utilizes said at least one input to train itself locally in realtime utilizing the processing unit.
However, Wu discloses wherein said machine learning model utilizes said at least one input to train itself locally in realtime utilizing the processing unit (Wu [0064], “Implementations may also allow the ability to run the machine learning model directly on a local personal machine. Not only the training can be performed inside local web browser, the running of the AI model learned on many unseen data can also be performed inside the operator's local web browser. While traditional machine learning needs huge computing power on cloud or on super-computer to apply the learned machine learning models, implementations of the disclosure require very low computing power to apply the model. A personal laptop with more than 1 core CPU will be enough to run the real-time machine learning model. It can be run from local personal machine while still generating the output at a high speed. The application of the machine learning model in local web browser can be performed in parallel by starting multiple browser tabs.”; also Wu [0016], “the processing device may continue to improve the machine learning model based on user's feedback(s) in the real-time training process,” the machine learning model can be trained locally in realtime).
Wu is analogous to King in view of Olivier, as both are drawn to the art of machine learning model training. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by King in view of Olivier, to include wherein said machine learning model utilizes said at least one input to train itself locally in realtime utilizing the processing unit, as taught by Wu, since providing the machine learning model directly on a local personal machine requires very low computing power (Wu [0064]). Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 2, King in view of Olivier and Wu discloses wherein at least one input is selected from the group consisting of a touchscreen, a GPS radio transceiver, an accelerometer, a gyroscope, a heart rate sensor, a pulse oximeter, a thermometer, a blood pressure sensor, a respiration rate sensor, a blood lactate sensor, an altimeter, a vibration sensor, a blood glucose sensor, a radar transceiver, a sonar transceiver, a weight sensor, and a clock (King [0061], “Sensors 104 may be configured to generate output signals conveying information related to the user. In some embodiments, sensors 104 may include audiovisual sensors, activity sensors, physiological sensors, biometric sensors, or other sensors. Examples of such sensors may include a heart rate sensor, a blood pressure sensor/monitor, a weight scale, motion sensors, an optical sensor, biometric sensors, a video sensor, an audio sensor, a color sensor, a blood glucose monitor, a blood oxygen saturation monitor (e.g., a pulse oximeter), a hydration monitor, a skin/body temperature thermometer, a respiration monitor, electroencephalogram (EEG) electrodes, accelerometers, activity sensors/trackers, a GPS sensor, or other sensors. These examples should not be considered limiting.”).
Regarding claim 3, King in view of Olivier and Wu discloses wherein at least one output is at least one of a touchscreen, a graphical display device, an audio output device, and a mechanical vibration device (King [0035], “Examples of interface devices suitable for inclusion in a physical interface of the client computing platforms 102 include one or more of a keypad, buttons, switches, a keyboard, knobs, levers, a display screen, a track pad, a touch screen (e.g., a force-sensitive touch screen), speakers, a microphone, an indicator light, an audible alarm, a printer, or other interfaces through which the user may provide or receive information.”; also King [0130], “some embodiments may select and provide exercise tutorial videos or audio files dynamically according to user profiles”).
Regarding claim 4, King in view of Olivier and Wu discloses at least one item of exercise equipment, wherein said at least one input is connected to said at least one item of exercise equipment and configured to measure a property of said exercise equipment when operated by the user (King [0058], “third-party API's for fitness trackers, networked scales, or networked fitness equipment may be accessed to retrieve metrics by which the profiles are enhanced. Profiles may include user feedback on particular exercises and instructors”; also King [0076], “some facilities may provide workout equipment with sensors by which the equipment registers as being in use with a central server that exposes the data via an API”).
Regarding claim 5, King in view of Olivier and Wu discloses wherein at least one input is selected from the group consisting of a torque sensor, an accelerometer, a vibration sensor, a gyroscope, a strain gauge, a force sensor, a velocity sensor, and an angular velocity sensor (King [0035], “Examples of interface devices suitable for inclusion in a physical interface of the client computing platforms 102 include one or more of… a touch screen (e.g., a force-sensitive touch screen)”; also King [0061], “Sensors 104 may be configured to generate output signals conveying information related to the user… Examples of such sensors may include… accelerometers”; also King [0097], “other sensors may be used to quantify or qualify the user's exercises. For example, some embodiments may obtain a stream of readings from an inertial measurement unit associated with a mobile computing device or a wearable computing device (e.g., one attached to the user) and extract features indicative of the quality of exercises. For example, some embodiments may execute a dynamic time warp algorithm to pattern match a stream of inertial measurement unit readings (e.g., with a sequence of time-stamped acceleration measurements in three or six dimensions from gyroscopes or accelerometers) to classify or extract features from an exercise being for performed, e.g., measure a range of movement in such an exercise, measure a frequency of such an exercise, or the like.”).
Regarding claim 22, King in view of Olivier and Wu discloses wherein at least one input is configured to measure directly at least one of a physiological property and a physical property of the user (King [0061], “In some embodiments, sensors 104 may include audiovisual sensors, activity sensors, physiological sensors, biometric sensors, or other sensors. Examples of such sensors may include a heart rate sensor, a blood pressure sensor/monitor, a weight scale, motion sensors, an optical sensor, biometric sensors, a video sensor, an audio sensor, a color sensor, a blood glucose monitor, a blood oxygen saturation monitor (e.g., a pulse oximeter), a hydration monitor, a skin/body temperature thermometer, a respiration monitor, electroencephalogram (EEG) electrodes, accelerometers, activity sensors/trackers, a GPS sensor, or other sensors. These examples should not be considered limiting. Sensors 104 are configured to generate various output signals conveying information related to the user that allows computing environment 100 to function as described herein.”).
Claims 6, 11, 17, 18, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over King in view of Olivier.
Regarding claim 6, and substantially similar limitations in claim 17, King discloses a computer-implemented method for machine learning and prediction relating to exercise by a user during a bout of exercise, comprising:
providing a physical activity measurement device that includes at least one input and at least one output (King [0098], “input may be provided via a wearable computing device or videos or audio may be presented via the wearable computing device,” wearable computing device receives input and provides output; also King [0072], “an application executing on the user computing device may include an input by which the user can indicate that a given piece of workout equipment is busy, and some embodiments may dynamically recompose the workout by accessing the above-data structures, for instance, …by choosing alternative workout video block among the candidates that satisfy the set of criteria and, thus, are consistent with the user's goals and the workout template,” when a user provides input to the workout equipment, the workout equipment outputs a busy status);
providing a machine learning model (King [0112], “a machine learning models may be trained to select video blocks”);
collecting at least one of physiological data and physical data from the user, via the at least one input (King [0061], “Sensors 104 may be configured to generate output signals conveying information related to the user. In some embodiments, sensors 104 may include audiovisual sensors, activity sensors, physiological sensors, biometric sensors, or other sensors.”);
adding said physiological data acquired by said collecting to the user's physiological data set (King [0061], “Sensors 104 are configured to generate various output signals conveying information related to the user that allows computing environment 100 to function as described herein”);
…
making at least one prediction for the user based on the application of said machine learning model to at least one input during the bout of exercise (King [0113], “a first model may be trained to select a workout instructor appearing in the blocks. Embodiments may ask users to rate their workout instructor and associate those ratings with the user's profiles. This data may be used as a training set. Embodiments may then determine weights or coefficients of a model, for instance a neural net or decision tree, by iteratively adjusting the coefficients, determining how closely the model describes the training set, and then adjusting the weights or coefficients in a direction in which the correspondence is expected to increase. The resulting model may then receive as an input a current user's profile and, based on the trained weights and coefficients, a score or selecting of candidate workout instructors may be determined to identify the best fit for the user based on the training data,” iteratively adjusting the model training set based on inputs; also King [0072], “an application executing on the user computing device may include an input by which the user can indicate that a given piece of workout equipment is busy, and some embodiments may dynamically recompose the workout by accessing the above-data structures, for instance, …by choosing alternative workout video block among the candidates that satisfy the set of criteria and, thus, are consistent with the user's goals and the workout template,” dynamically recomposing the workout based on user inputs).
King does not teach every limitation of training said machine learning model on the user's said physiological data set in realtime during the bout of exercise.
King does disclose training said machine learning model on the user’s said physiological data set (King [0120], “video blocks may be selected for a given user in a given session by training a machine learning model with a training set. Some embodiments may obtain a training set from previous sessions. Each record in the training set may include inputs to a given session, including: the user's profile, the user's previous workouts… each record may include feedback from the user indicative of the success of the workout in that session: … biometric feedback from one of the above-described sensors”). King, however, does not teach training said machine learning model on the user's said physiological data set in realtime during the bout of exercise.
However, Olivier discloses training said machine learning model on the user's said physiological data set in realtime during the bout of exercise (Olivier [0007], “simultaneously accrue multiple data metrics for an individual, including, but not limited to, fitness metrics, sleep patterns, heart rate, and heart rate variability during events such as, but not limited to, sleep, exercise and rest.”; also Olivier [0041], “incoming physiological data streams are mapped to a generated user profile by creating a list of features from the data streams associated with sleep, rest and free-living states through real time training of machine learning models such as, but not limited to, bidirectional neural networks”).
Olivier is analogous to King, as both are drawn to the art of machine learning model training. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by King, to include training said machine learning model on the user's said physiological data set in realtime during the bout of exercise, as taught by Olivier, in order to accurately and reliably monitor a user in an unobtrusive and convenient manner (Olivier [0008]). Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 11, and substantially similar limitations in claim 18, King in view of Olivier discloses communicating said at least one prediction to the user via said at least one output (King [0096], “the gym-equipment-specific displays may execute an application configured to receive video instructions from the user computing devices that cause those displays to present a workout video obtained via the user computing device”).
Regarding claim 21, King in view of Olivier discloses wherein said at least one prediction comprises at least one of onset of fatigue, maximum target heart rate, recommendation to stop the bout of exercise, recommendation to take a break from the bout of exercise, recommendation to hydrate, and recommendation to consume calories (King [0132], “Some embodiments may be configured to suggest food or drink for users to consume, for example, after a workout or between workouts. For example, some embodiments may present maps to particular grocery stores based on user's current location, suggest deals on products available at those grocery stores, and provide shopping lists for recipe selected in accordance with a user's workout goals and user profile. In some cases, the selections may be made dynamically responsive to how a user performed in a previous workout.”).
Claims 7, 10, 12, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over King in view of Olivier, and in further view of Po et al. (hereinafter “Po,” US 2021/0304001).
Regarding claim 7, King in view of Olivier does not explicitly teach storing said machine learning model on said physical activity measurement device.
However, Po discloses storing said machine learning model on said physical activity measurement device (Po [0005], “generating data representing estimates of multiple physiological signals, such as heart rate and respiratory rate, from an input in the form of RGB video frames of the face of a subject, e.g., captured by a smartphone camera”; also Po [0012], “the model is trained to predict at least two physiological signals”; also Po [0044], “the model can be stored and computed on a user's smartphone or other limited-resource system local to a user (e.g., a tablet, a laptop, etc.).”).
Po is analogous to King in view of Olivier, as both are drawn to the art of machine learning. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by King in view of Olivier, to include storing said machine learning model on said physical activity measurement device, as taught by Po, in order to protect user privacy and reduce bandwidth needed (Po [0044]). Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 10, King in view of Olivier does not explicitly teach wherein said making is performed locally.
However, Po discloses wherein said making is performed locally (Po [0018], “make two or more physiological predictions. Such predictions can be generated and reported locally on the smartphone”).
Po is analogous to King in view of Olivier, as both are drawn to the art of machine learning. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by King in view of Olivier, to include wherein said making is performed locally, as taught by Po, in order to protect user privacy and reduce bandwidth needed (Po [0044]). Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 12, King in view of Olivier does not explicitly teach wherein said making at least one prediction for the user comprises making a plurality of predictions for the user during a bout of exercise.
However, Po discloses wherein said making at least one prediction for the user comprises making a plurality of predictions for the user during a bout of exercise (Po [0018], “make two or more physiological predictions”).
Po is analogous to King in view of Olivier, as both are drawn to the art of machine learning. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by King in view of Olivier, to include wherein said making at least one prediction for the user comprises making a plurality of predictions for the user during a bout of exercise, as taught by Po, because it is a simple substitution of two predictions instead of one to obtain predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 19, King in view of Olivier does not explicitly teach wherein the computer-readable medium is a storage device in the physical activity measurement device.
However, Po discloses wherein the computer-readable medium is a storage device in the physical activity measurement device (Po [0005], “generating data representing estimates of multiple physiological signals, such as heart rate and respiratory rate, from an input in the form of RGB video frames of the face of a subject, e.g., captured by a smartphone camera”; also Po [0012], “the model is trained to predict at least two physiological signals”; also Po [0044], “the model can be stored and computed on a user's smartphone or other limited-resource system local to a user (e.g., a tablet, a laptop, etc.).”).
Po is analogous to King in view of Olivier, as both are drawn to the art of machine learning. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by King in view of Olivier, to include wherein the computer-readable medium is a storage device in the physical activity measurement device, as taught by Po, in order to protect user privacy and reduce bandwidth needed (Po [0044]). Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 20, King in view of Olivier does not explicitly teach wherein said training and said making are performed locally.
However, Po discloses wherein said training and said making are performed locally (Po [0005], “generating data representing estimates of multiple physiological signals, such as heart rate and respiratory rate, from an input in the form of RGB video frames of the face of a subject, e.g., captured by a smartphone camera”; also Po [0012], “the model is trained to predict at least two physiological signals”; also Po [0044], “the model can be stored and computed on a user's smartphone or other limited-resource system local to a user (e.g., a tablet, a laptop, etc.).”; also Po [0018], “make two or more physiological predictions. Such predictions can be generated and reported locally on the smartphone”).
Po is analogous to King in view of Olivier, as both are drawn to the art of machine learning. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by King in view of Olivier, to include wherein said training and said making are performed locally, as taught by Po, in order to protect user privacy and reduce bandwidth needed (Po [0044]). Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Claims 8 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over King in view of Olivier, and in further view of Shelton, IV et al. (hereinafter “Shelton,” US 2022/0238216).
Regarding claim 8, King in view of Olivier does not explicitly teach wherein said training is performed locally.
However, Shelton discloses wherein said training is performed locally (Shelton [0439], “such machine learning models may be created and trained locally at a computing system (e.g., a surgical hub) using data sources that may be connected with the computing system.”).
Shelton is analogous to King in view of Olivier, as both are drawn to the art of machine learning. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by King in view of Olivier, to include wherein said training is performed locally, as taught by Shelton, since it combines prior art elements according to known methods to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 14, King in view of Olivier does not explicitly teach wherein said training is performed on said physical activity measurement device.
However, Shelton discloses wherein said training is performed on said physical activity measurement device (Shelton [0439], “such machine learning models may be created and trained locally at a computing system (e.g., a surgical hub) using data sources that may be connected with the computing system.”).
Shelton is analogous to King in view of Olivier, as both are drawn to the art of machine learning. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by King in view of Olivier, to include wherein said training is performed on said physical activity measurement device, as taught by Shelton, since it combines prior art elements according to known methods to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over King in view of Olivier, and in further view of Walthers et al. (hereinafter “Walthers,” US 2019/0325323).
Regarding claim 13, King in view of Olivier does not explicitly teach before said making a prediction, determining whether said physiological data set is sufficiently large to allow said machine language model to perform said making a prediction.
However, Walthers discloses before said making a prediction, determining whether said physiological data set is sufficiently large to allow said machine language model to perform said making a prediction (Walthers [0058], “The algorithm to be used by prediction engine 425 may depend on the ‘signal’ that can be extracted from the data in the validation set built at block 520. For example, if fingerprint engine 422 is able to build a large validation set which includes enough training data where each incident class and knowledge element class is adequately represented (e.g., 2000-4000 samples for each incident and knowledge element class), prediction engine 425 may determine that the validation set meets predetermined conditions (YES at block 525), and operation proceeds to block 560, where prediction engine 425 may build a supervised machine learning model based on training data (training set) derived from the validation set. On the other hand, if prediction engine 425 determines that there is not enough training data in the validation set to build a supervised machine learning model, prediction engine 425 may determine that the validation set does not meet the predetermined conditions”).
Walthers is analogous to King in view of Olivier, as both are drawn to the art of machine learning. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by King in view of Olivier, to include before said making a prediction, determining whether said physiological data set is sufficiently large to allow said machine language model to perform said making a prediction, as taught by Walthers, so that each incident class and knowledge element class is adequately represented (Walthers [0058]). Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Response to Arguments
The Applicant’s arguments filed on February 16, 2026 have been fully considered, and Examiner’s responses to Applicant’s arguments are provided below. Applicant’s Declarations under 37 CFR 1.132 are also addressed in the arguments below.
35 USC 112
The Applicant respectfully argues that “[c]onsistent with MPEP 2163.02, the claimed ‘physical data’ is disclosed in the present specification, but not haec verba.” Applicant directs attention to the Mattis Declaration, in which Mr. Mattis states that “I understand a ‘physiological property’ of the user to relate to the dynamic biological functions and processes of their body. Further, I understand a ‘physical property’ of the user to relate to a property of the body that is not a dynamic biological function or process of the body.” (Applicant’s emphasis). The Applicant further respectfully argues that “[b]ased on the Mattis Declaration, its explanation of how the terms ‘physiological property’ and ‘physical property’ are defined and known in the art, and its showing of those items in the specification as filed, reversal of the rejection of claims 1-8, 10-14, and 17-22 as failing to comply with the written description requirement is respectfully requested.”
The Examiner has carefully reviewed the Applicant’s arguments and the Mattis Declaration, and respectfully disagrees. In MPEP 2163.02, the Examiner appreciates the statement that “The subject matter of the claim need not be described literally (i.e., using the same terms or in haec verba) in order for the disclosure to satisfy the description requirement.” However, the MPEP further states that “If a claim is amended to include subject matter, limitations, or terminology not present in the application as filed, involving a departure from, addition to, or deletion from the disclosure of the application as filed, the examiner should conclude that the claimed subject matter is not described in that application. This conclusion will result in the rejection of the claims affected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C.112, first paragraph - description requirement, or denial of the benefit of the filing date of a previously filed application, as appropriate.”
At issue is the amendment of claim 1 (and similar amendments to the other independent claims 6 and 17) filed August 8, 2025, where a limitation was amended as follows:
“at least one input connected to said processing unit, at least one said input configured to measure at least one property selected from the group consisting of a physiological property and a physical property of the user that is associated with exercise by the user;”
The wording of the claim establishes that “a physiological property” consists of a set of items exclusive to “a physical property.” It is important for properly interpreting the claims that the individual definitions of “a physiological property” and “a physical property” are adequately and concretely provided in the disclosure, so that the metes and bounds of the claims can be determined.
However, no explicit definition of “a physiological property” is provided in the specification, and “a physical property” is not disclosed at all in the original disclosure. In the Mattis Declaration, Mr. Mattis defines “a physiological property” as relating “to the dynamic biological functions and processes of their body.” The Examiner notes that this definition is not in the original disclosure. In fact, the original disclosure has no mention of “dynamic” or “biological.” “Functions” and “processes” are mentioned in the specification, but not in the context of the body. The original disclosure does not even hint at a distinction between “a physiological property” and another property, such as “a physical property.”
Therefore, even if one having ordinary skill in the art would clearly know the difference between “a physiological property” and “a physical property” (of which the Examiner is not convinced), the fact that “a physical property” is completely absent from the original disclosure and that it is now an essential component for determining the metes and bounds of the claims, points to the proper designation of “a physical property” under 35 USC 112(a) as “new matter”.
The Examiner notes that a declaration under 37 CFR 1.132 cannot be used to supply information that was required to be present in a patent application upon filing; it can only be used to provide facts about information already in the disclosure. In the present case, “a physical property” was not described in a manner compliant with 35 USC 112(a), and that defect cannot be remedied by the filing of a declaration.
The Examiner further notes that in the Mattis Declaration, Mr. Mattis wrote, “I understand a ‘physical property’ of the user to relate to a property of the body that is not a dynamic biological function or process of the body… The specification of the patent application describes measuring, among other things, location of a user (Specification, paragraph [0030]), altitude of a user (Specification, paragraph [0036]), weight of a user (Specification, paragraph [0041]), acceleration of a user (Specification, paragraph [0055]), and velocity of a user (Specification, paragraph [0059]). As one skilled in the art, I consider those to be physical properties, and measurements thereof. These properties relate to properties of the body that are not dynamical biological functions or processes of the body.” While the Examiner remains unconvinced that “a physical property” is adequately disclosed in the original disclosure, the Examiner notes that elements such as “location of a user,” “altitude of a user,” “weight of a user,” “acceleration of a user,” and “velocity of a user” do appear in the original disclosure and can be recited in the instant claims without issues of new matter.
For the above reasons, the outstanding rejections under 35 USC 112(a) and 35 USC 112(b) are maintained.
35 USC 101
The Applicant respectfully argues that “The Mattis SMED as a whole makes it clear that, as claimed, claim 1 is not drawn to an overbroad idea of ‘prediction relating to exercise,’ but rather a process where a machine learning model utilizes at least one input from a user to train itself locally in realtime utilizing the processing unit during the bout of exercise, and to make one or more exercise-related predictions to the user utilizing the processing unit during the bout of exercise.” (Applicant’s emphasis)
The Examiner respectfully disagrees. The instant claims recite training a machine learning model locally in realtime, and specifies when the training is performed (“during the bout of exercise”), but does not recite how the training is performed. This amounts to mere usage of a conventional machine learning model, with no improvements to technology or any other element which may amount to significantly more than the judicial exception. See also the argument below, “MPEP 2106.04(a)(2)(III)(C) states that a claim that requires a computer may still recite a mental process.”
The Applicant further respectfully argues (in reference to the Mattis SMED) that “Mr. Mattis describes why it is impossible for a human being to perform those calculations in realtime: ‘In the art of mobile and wearable devices for fitness and wellness that include sensors, many types of physiological and physical data are measured and transmitted by those sensors at a rate of one data point per second. This means that a person would have less than one second to complete all of the steps required to train and update the machine learning model, and form a prediction, before the next data point arrives. If the individual fails to complete all of those steps before the next data point arrives, the individual, once-per-second, data points will start to back up in a queue. However, all of those data points need to be processed to train the model and provide a prediction.’”
The Examiner respectfully disagrees. MPEP 2106.04(a)(2)(III)(C) states that a claim that requires a computer may still recite a mental process. It provides guidance for examiners to “review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept.” In the present case, the claimed invention is described as the concept of “prediction relating to exercise” that is performed on a generic computer or computer environment. This provision of the MPEP does not require that a human can perform calculations as fast as a computer. The claims may recite a mental process if the claimed invention can be performed at all by a human, regardless of speed, and is performed by a generic computer. The instant invention is entirely implemented using a generic computer or generic computing components (including generic use of conventional machine learning), and therefore still recites a mental process.
The Applicant also respectfully argues that “the 2024 Guidance reinforced the fact that handling data in realtime cannot be practically performed in the human mind, and thus does not merely recite a mental process.”
The Examiner respectfully disagrees. The cited portion of the 2024 Guidance does not state that handling data in realtime cannot be practically performed in the human mind. Instead, the recited portion is stating that “detect[ing] a source address associated with malicious network packets, drop[ping] the malicious network packets in real time, and block[ing] future traffic” cannot be practically performed in the human mind. The instant claims do not recite any manipulation of network packets, and therefore this portion of the 2024 Guidance is not analogous to the instant claimed invention.
The Applicant respectfully argues using support from the Mattis SMED that “[c]onventional wearable and mobile devices rely on cloud-based server systems to perform machine learning model training and predictions,” and that the instant claimed invention provides a technological improvement by “[t]raining a machine learning model locally” so as to provide “the ability to train that machine learning model in realtime.”
The Examiner respectfully disagrees. As evidenced by the abundance of prior art describing both cloud-based and local training of machine learning models, the use of remote machine learning models is more of a design decision made for the sake of efficiency than a problem in need of a solution. Both remote and local training of machine learning models were conventional at the time the invention was filed (for example, see the Wu reference in the 35 USC 103 rejections above). There are pros and cons to using remote training or local training of machine learning models, and deciding to use either one for a particular purpose does not improve upon the technology. In other words, the invention does not improve upon a conventional technology because providing for local machine learning model training is a conventional technology.
Particularly relevant is MPEP 2106.05(a)(I), stating “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: …Recording, transmitting, and archiving digital images by use of conventional or generic technology in a nascent but well-known environment, without any assertion that the invention reflects an inventive solution to any problem presented by combining a camera and a cellular telephone.” Also relevant is MPEP 2106.05(a)(II), stating “Examples that the courts have indicated may not be sufficient to show an improvement to technology include: … Gathering and analyzing information using conventional techniques and displaying the result.”
The Applicant further respectfully argues, “the Final Action admits that the limitations of claim 1 are only ‘partially analogous’ to the example of patent-ineligible ‘collecting information, analyzing it, and displaying certain results of the collection analysis.’ (Final Action, page 7; emphasis added). The Final Action provides no analysis as to why a set of claim limitations that are only ‘partially analogous’ to an example of a patent-ineligible process is itself patent ineligible. For this reason alone, the rejection of claims 1-5 and 22 as patent-ineligible should be withdrawn.”
The Examiner respectfully disagrees. The process of data gathering and manipulation is stated as being “partially analogous” only because the associated case law is taken from Electric Power Group, LLC, v. Alstom, which was directed to an interconnected electric power grid, and not a physical activity measurement system as in the instant invention. However, the “collecting information, analyzing it, and displaying certain results of the collection analysis” resulting from the precedential decision applies in the present case, as the physical activity measurement system of the instant claims essentially collects information, analyzes it, and displays certain results of the analysis.
For the above reasons, the 35 USC 101 rejections of the claims are maintained.
35 USC 103
Applicant respectfully argues that “Interpretation of the claim term ‘bout of exercise’ in the Final Action exceeds the limits of broadest reasonable interpretation… Olivier is utterly silent as to exercise at all, much less as to training a machine learning model during a bout of exercise. Olivier doesn't care about exercise… Such a bout of exercise is completely absent from Olivier.”
The Examiner respectfully disagrees. Olivier paragraph [0007] discloses, “simultaneously accrue multiple data metrics for an individual, including, but not limited to, fitness metrics, sleep patterns, heart rate, and heart rate variability during events such as, but not limited to, sleep, exercise and rest.” (emphasis added). Olivier paragraph [0041] then discloses, “incoming physiological data streams are mapped to a generated user profile by creating a list of features from the data streams associated with sleep, rest and free-living states through real time training of machine learning models such as, but not limited to, bidirectional neural networks.” That is, the machine learning model trains itself at all times during “sleep, rest and free-living states” which accrues data metrics “during events such as, but not limited to, sleep, exercise and rest.” Olivier contemplates training its machine learning model at all times while active, including “free-living states” in which the user may be engaging in “exercise.” Engaging in events such as “exercise” would reasonably include engaging in “a bout of exercise.”
The Applicant further respectfully argues that “not only did the Final Action fail to clearly articulate the reason(s) why the claimed invention would have been obvious, but also the Final Action failed to articulate any reason(s) at all why the claimed invention would have been obvious.”
The Examiner respectfully disagrees. Motivation is provided for combining King with Olivier in page 12 of the Final Action, “in order to accurately and reliably monitor a user in an unobtrusive and convenient manner (Olivier [0008]).” This rationale makes sense because Olivier is relied upon only for its teaching of the machine learning model training itself during use.
As to motivation for combining King in view of Olivier with Wu, the Final Action page 13 provides, “since providing the machine learning model directly on a local personal machine requires very low computing power (Wu [0064]).” This rationale also makes sense because Wu is relied upon for training a local machine learning model (not remotely on the cloud).
Both of the above motivations to combine are KSR Rationales providing some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
The Applicant also respectfully argues, “Because the Final Action provided no reason for combining the references, and merely made a conclusory assertion that they should be combined, the rejection of claims 1-5 and 22 as obvious by the Final Action was impermissible hindsight under TQ Delta. For this reason alone, the rejection of claims 1-5 and 22 as obvious should be reversed.”
The Examiner respectfully disagrees. As established above, each of the rejections under 35 USC 103 explicitly invokes a KSR rationale for combining, and therefore are not merely conclusory assertions.
The Applicant respectfully argues, “The combination of King and Olivier changes the principle of operation of each reference, and as a consequence that combination is not sufficient to render any of the claims prima facie obvious… The combination of King and Olivier renders each reference unsatisfactory for its intended purpose, and as a consequence that combination is not sufficient to render any of the claims prima facie obvious.”
The Examiner respectfully disagrees. The Examiner notes that Olivier is only relied upon for its teaching of the machine learning model training itself during usage of the device. King discloses using a machine learning model to make inferences based on user exercise data. Combining Olivier with King so that the machine learning model in King trains itself during exercise does not change the principle of operation of King’s invention, and certainly would not render King’s invention “unsatisfactory for its intended purpose.” If anything, more frequent training of the machine learning model would enhance King’s invention to make it more accurate in its inferences.
For the above reasons, the outstanding 35 USC 103 rejections will be maintained.
Conclusion
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/SA/Examiner, Art Unit 3715
/PETER S VASAT/Supervisory Patent Examiner, Art Unit 3715
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