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 .
Response to Amendment
The amendments filed 30 September 2025 have been entered. Applicant’s amendments have overcome each and every objection to the drawings, except where noted below previously set forth in the Office Action mailed 03 June 2025.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 142 (see Fig. 1D, appears to be incorrect and should be replaced with “140”).
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Utilizing the two step process adopted by the Supreme Court (Alice Corp vs CLS Bank Int'l, US
Supreme Court, 110 USPQ2d 1976 (2014) and the recent 101 guideline Federal Register Vol. 84, No., Jan
2019)), determination of the subject matter eligibility under the 35 U.S.C. 101 is as follows: Specifically, the Step 1 requires claim belongs to one of the four statutory categories (process, machine, manufacture, or composition of matter). If Step 1 is satisfied, then in the first part of Step 2A (Prong One), identification of any judicial recognized exceptions in the claim is made. If any limitation in the claim is identified as judicial recognized exception, then in the second part of Step 2A (Prong Two), determination is made whether the identified judicial exception is being integrated into practical application. If the identified judicial exception is not integrated into a practical application, then in Step 2B, the claim is further evaluated to see if the additional elements, individually and in combination provide "inventive concept" that would amount to significantly more than the judicial exception. If the element and combination of elements do not amount to significantly more than the judicial recognized exception itself, then the claim is ineligible under the 35 U.S.C. 101.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in this case an abstract idea, without significantly more. The claim recite(s) "inputting the sensor data into a machine-learned exercise identification model comprising one or more neural networks trained to identify one or more upper-body exercises based on the sensor data; and generating, as an output of the machine-learned exercise identification model, exercise identification data that identifies the one or more lower body movements performed by the user as one or more upper-body exercises performed by the user and detected by the footwear device, the exercise identification data comprising one or more exercise characteristics associated with each upper-body exercise of the one or more upper-body exercises performed by the user". This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 1 satisfies Step 1, namely the claim is directed to one of the four statutory classes, machine. Following Step 2A Prong one, any judicial exceptions are identified in the claims. In claim 1, the limitations "inputting the sensor data into a machine-learned exercise identification model comprising one or more neural networks trained to identify one or more upper-body exercises based on the sensor data; and generating, as an output of the machine-learned exercise identification model, exercise identification data that identifies the one or more lower body movements performed by the user as one or more upper-body exercises performed by the user and detected by the footwear device, the exercise identification data comprising one or more exercise characteristics associated with each upper-body exercise of the one or more upper-body exercises performed by the user" are abstract ideas as they are directed to a mental process and mathematical calculations. With the identification of an abstract idea, the next phase is to proceed Step 2A, Prong Two, wherewith additional elements and taken as a whole, evaluation occurs of whether the identified abstract idea is integrated into a practical application.
In Step 2A, Prong Two, the claim does not recite any additional elements or evidence that amounts to significantly more than the judicial exception. Besides the abstract idea, the claim recites the additional elements “one or more processors; and one or more non-transitory computer-readable media that store instructions that when executed by the one or more processors cause the computing system to perform operations, the operations comprising: obtaining sensor data generated by one or more sensors of a footwear device worn by a user, the one or more sensors positioned in one or more positions of the footwear device, the sensor data associated with detection of one or more lower body movements performed by the user”. However, these components may be seen as the use of well-understood, routine, or conventional elements to perform a non-mental process in order to gather data for the mental process step, much like the example given in MPEP 2106.04(d)(2)(c), such that these limitations are extra-solution activity and thus do not integrate the judicial exception into a practical application. The data obtaining step leads to the final limitation of “generating” such that the end result of use of the system is only the generic output generated by the exercise identification model which may be any generic output, or no output at all. As this output is not defined as requiring any further action, such as a form of prophylaxis or treatment or an improvement to a computer or other technology, the claim limitations constitute mere generation of data, in this case the measurement of data relating to movements performed by the user, such that the claim does not integrate the judicial exception into any practical application. Regarding “one or more processors”, the limitation amounts to nothing more than an instruction to apply the abstract idea using a generic computer, which does not render an abstract idea eligible. The steps performed by the one or more processors are, as claimed, capable of being performed in the human mind similar to the examples given in MPEP 2106.04(a)(2)(III)(A)-(C), wherein it is described that “a claim to ‘collecting information, analyzing it, and displaying certain results of the collection and analysis’ where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind” recites a mental process and that claims which merely use a computer as a tool to perform a mental process are not eligible when “there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper” such as “mental processes of parsing and comparing data” when the steps are recited at a high level of generality and a computer is used merely as a tool to perform the processes. Furthermore, the recitation of a machine-learning model comprising one or more neural networks trained to identify one or more upper body exercises based on the sensor data recites the neural network to generally apply the abstract idea without placing any limits on how the neural network functions. It is additionally noted that the abstract idea itself may not provide an improvement; only additional elements can provide a practical application under Step 2A, Prong 2 or significantly more under Step 2B according to Genetic Technologies Limited v. Merial LLC (Fed Cir., 2016) which relays that the inventive concept of step 2 of the Alice/Mayo analysis cannot be supplied by the abstract idea. The inventive concept necessary at step two of the Mayo/Alice analysis cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself. That is, under the Mayo/Alice framework, a claim directed to a newly discovered law of nature (or natural phenomenon or abstract idea) cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility; instead, the application must provide something inventive, beyond mere “well-understood, routine, conventional activity.” Mayo, 132 S. Ct. at 1294; see also Myriad, 133 S. Ct. at 2117; Ariosa, 788 F.3d at 1379. Under the broadest reasonable interpretation, the claim elements are recited with a high level of generality (as written, no particular details have been provided which would preclude the input and output of data and results from a machine-learned model from being performed in the human mind by a person in an undefined manner) that there are no meaningful limitations to the abstract idea. Consequently, with the identified abstract idea not being integrated into a practical application, the next step is Step 2B, evaluating whether the additional elements provide "inventive concept" that would amount to significantly more than the abstract idea.
In Step 2B, claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation of “one or more processors; and one or more non-transitory computer-readable media that store instructions that when executed by the one or more processors cause the computing system to perform operations, the operations comprising: obtaining sensor data generated by one or more sensors of a footwear device worn by a user, the one or more sensors positioned in one or more positions of the footwear device, the sensor data associated with detection of one or more lower body movements performed by the user” constitutes extra-solution activity to the judicial exception, which does not amount to an inventive concept when the activity is well-understood, routine, or conventional, and are thus not indicative of integration into a practical application. The claim limitation constitutes adding a generic processor and sensors, which Sazonov (US 20110054359 A1) describes as well-understood, routine, or conventional in its description of a “The processing device 105 may be a dedicated electronic device or a ubiquitous electronic device that is configured to perform other functions. Some examples of electronic devices that may be used in conjunction with the disclosed embodiments include, but are not limited to, a personal computer, such as a laptop, tablet PC or a handheld PC, a PDA, a mobile telephone, a media player, such as an MP3 player, or a television receiver” (Paragraph 0035, 0047-0048) as well as “a three-dimensional accelerometer that may be used in conjunction with the disclosed embodiments is an LIS3L02AS4 MEMS accelerometer” (Paragraph 0038-0039, 0041). As discussed above with respect to integration of the abstract idea into a practical application, the present elements amount to no more than mere indications to apply the exception.
In Summary, Claim 1 recites abstract idea without being integrated into a practical application, and does not provide additional elements that would amount to significantly more. As such, taken as a whole, the claim and is ineligible under the 35 U.S.C. 101.
Claims 14 and 20 are rejected under 35 U.S.C. 101 for similar reasons.
Claims 2-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in this case an abstract idea, without significantly more. As each of these claims depends from claim 1, which was rejected under 35 U.S.C. 101 in paragraph 7 of this action, these claims must be evaluated on whether they sufficiently add to the practical application of claim 1, or comprise significantly more than the limitations of claim 1.
Besides the abstract idea of claim 1, claims 2-7 and 11-13 recite further limitations of the abstract idea which are additionally abstract; claims 8-10 recite further limitations of the additional elements which are additionally well-understood, routine, or conventional elements which constitute extra-solution activity and which may be seen in the above-cited portions of Sazonov. These limitations provide no practical application, nor do they provide meaningful limitations to the abstract idea.
Claims 15-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in this case an abstract idea, without significantly more. As each of these claims depends from claim 14, which was rejected under 35 U.S.C. 101 for the same reasons explained in paragraph 11 of this action, these claims must be evaluated on whether they sufficiently add to the practical application of claim 14, or comprise significantly more than the limitations of claim 14.
Besides the abstract idea of claim 14, claims 15-19 recite further limitations of the additional elements which are additionally well-understood, routine, or conventional elements which constitute extra-solution activity and which may be seen in the above-cited portions of Sazonov. These limitations provide no practical application, nor do they provide meaningful limitations to the abstract idea.
Claim Rejections - 35 USC § 103
Claim(s) 1-12, 14-15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sazonov (US 20110054359 A1) in view of Jeong (US 20210016150 A1).
Regarding claim 1, 14, and 20, Sazonov discloses a computing system (Paragraph 0035--processing device may be any electronic device having data processing capabilities), comprising:
one or more processors (Processor 120 of monitoring system 100, 200, Figs 1A-1C; paragraph 0042); and
one or more non-transitory computer-readable media that store instructions that when executed by the one or more processors cause the computing system to perform operations (Paragraph 0042—the processor 120 may be configured to sample and process the data…), the operations comprising:
obtaining sensor data generated by one or more sensors of a footwear device worn by a user (Paragraph 0033, 0036-0039, Figs. 1A-1B-- the wearable monitoring system may include an accelerometer and a pressure sensor that is integrated into an insole. An "insole," as used herein, is a member that sits beneath a foot. For example, an insole may include the interior bottom of a shoe, a foot-bed, or a removable insert that may be positioned in a shoe or in a sock), the one or more sensors positioned in one or more positions of the footwear device (Figs. 1A-1B—accelerometer 101, 201 positioned in a first position and pressure sensor 103, 203 positioned in a second position; paragraph 0037), the sensor data associated with detection of one or more lower body movements performed by the user (Paragraph 0024-- The wearable monitoring system may be based on a combination of multiple sensor modalities, including acceleration and pressure readings from the accelerometer and pressure sensor. The combination of these two modalities may identify many metabolically significant postures and activities; paragraph 0038-0040-- gait cycle identification and loading profiles obtained from the pressure sensor may be used to classify the type of motion-based activity that the user is performing (e.g., walking vs. running), quantify the amount of body motion in static postures (e.g., shifts in body weight while standing), and distinguish between movement performed along a level surface from movement performed along an inclined (i.e., uphill or downhill) surface, such as a gradually inclined surface, stairs, etc.);
inputting the sensor data into a machine-learned exercise identification model comprising one or more neural networks trained to identify one or more exercises involving the upper body based on the sensor data (Paragraph 0065--The pattern recognition module 452 may receive signals from the pressure sensor, accelerometer, and/or physiological sensor to recognize postures, for example, whether the user is sitting, standing, or in another posture, and movement-based activities, such as whether a user is walking, jogging, cycling, and so on; Paragraph 0081-0082, 0158, Block 613 and 615-- In the operation of block 613, the processed signals may be further processed to use pattern recognition to recognize various postures and/or movement-based activities of the user. For example, the processing device may be configured to determine whether the user is sitting, standing, walking, etc. by applying pattern recognition algorithms to the received data signals. Pattern recognition algorithms that may be used include artificial neural networks, for example, multi-layer perceptron, or other classification algorithms, such as support vector machines, Bayesian classifiers, etc.); and
generating, as an output of the machine-learned exercise identification model, exercise identification data that identifies the one or more lower body movements performed by the user and detected by the footwear device as one or more exercises performed by the user (Paragraph 0035-- the processing device may be configured to automatically determine the user's posture or activity based on the received data; paragraph 0034-- the combination of pressure and acceleration data allows for differentiation between major classes of metabolically significant activities, including sitting, standing, walking, jogging, cycling, ascending stairs, descending stairs, household chores, and so on; Paragraph 0065--The pattern recognition module 452 may receive signals from the pressure sensor, accelerometer, and/or physiological sensor to recognize postures, for example, whether the user is sitting, standing, or in another posture, and movement-based activities, such as whether a user is walking, jogging, cycling, and so on; paragraph 0081, 0158--A feature vector may be presented to the pattern recognition algorithm, which may assign it to one of the classes ('sitting', `standing`, etc) based on previously learned examples),
the exercise identification data comprising one or more exercise characteristics (Paragraph 0035--compute energy expenditure based on the type and intensity of the activity, compute performance metrics for different exercise activities (e.g., number of steps, distance for walking or jogging); Paragraph 0040-- gait cycle identification and loading profiles obtained from the pressure sensor may be used to classify the type of motion-based activity that the user is performing (e.g., walking vs. running), quantify the amount of body motion in static postures (e.g., shifts in body weight while standing), and distinguish between movement performed along a level surface from movement performed along an inclined (i.e., uphill or downhill) surface, such as a gradually inclined surface, stairs, etc. The gait cycle identification and loading profiles may also be used to detect asymmetries in the gait pattern indicating fatigue or potential development of injury. Additionally, data regarding key temporal and spatial gait parameters, including, but not limited to, cadence, stride length, and stance time, may be extracted from the pressure and/or acceleration data and used to characterize the user's movement-based activities and provide feedback to the user. For example, the feedback may include the number of steps taken by the user, distance walked, cadence, etc.); Paragraph 0081-- low values of acceleration combined with a pressure reading that is less than the user's body weight indicate that the user is sitting, while low acceleration values combined with a pressure reading that is substantially equal to the user's body weight indicate that the user is standing. Walking may be characterized by horizontal and vertical acceleration patterns that exhibit low cycle-to-cycle variability, combined with pressure changes that alternate between high and low (stance/swing) and travel from heel to toe; paragraph 0083-- may log the time spent in the first posture and compute the estimated energy expended by the user in the first posture…) associated with each exercise of the one or more exercises performed by the user.
However, while Sazonov discloses that the identified exercises may include a number of different activities which may include upper-body involvement (Paragraph 0034--sitting, standing, walking, jogging, cycling, ascending stairs, descending stairs, household chores, and so on), Sazonov does not explicitly disclose that the one or more exercises are upper-body exercises.
Jeong, in the same field of endeavor of monitoring exercise via sensors (Abstract) discloses a system wherein the system utilizes sensor measurements including pressure measured at the feet of the user (Paragraph 0006, 0033-0034) to determine exercise information, including determining upper-body exercises performed by a user (Paragraph 0036-0038, Figs. 6-7—free weight training exercises). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Sazonov to additionally identify and monitor upper body exercises as Sazonov discloses that various activities may be monitored which include some upper body component, and the inclusion of further exercises which Jeong discloses as being capable of being monitored via pressure at the foot of the user would predictably improve the ability of the system to be used in additional monitoring capacities such as monitoring weight lifting or rehabilitation progress.
Regarding claim 2, the combination of Sazonov and Jeong discloses the system of claim 1. Sazonov additionally discloses wherein the one or more exercise characteristics comprises an amount of time spent performing each upper-body exercise of the one or more upper-body exercises performed by the user (Paragraph 0040--Additionally, data regarding key temporal and spatial gait parameters, including, but not limited to, cadence, stride length, and stance time, may be extracted from the pressure and/or acceleration data and used to characterize the user's movement-based activities and provide feedback to the user; paragraph 0068-- In one embodiment, the energy expenditure estimation module may also be configured to monitor the time that a user is performing a particular activity or holding a particular posture ; paragraph 0083-- may log the time spent in the first posture and compute the estimated energy expended by the user in the first posture…).
Regarding claim 3, the combination of Sazonov and Jeong discloses the system of claim 1. Sazonov additionally discloses wherein the one or more exercise characteristics comprises an exercise categorization that further categorizes at least one exercise of the one or more upper-body exercises performed by the user as a full-body exercise (Paragraph 0035-- the processing device may be configured to automatically determine the user's posture or activity based on the received data; paragraph 0034-- the combination of pressure and acceleration data allows for differentiation between major classes of metabolically significant activities, including sitting, standing, walking, jogging, cycling, ascending stairs, descending stairs, household chores, and so on; Paragraph 0065--The pattern recognition module 452 may receive signals from the pressure sensor, accelerometer, and/or physiological sensor to recognize postures, for example, whether the user is sitting, standing, or in another posture, and movement-based activities, such as whether a user is walking, jogging, cycling, and so on). Jeong additionally discloses wherein the one or more exercise characteristics comprises an exercise categorization that further categorizes at least one exercise of the one or more upper-body exercises performed by the user as a full-body exercise (Figs. 4-5, 8-11, 16-17, in particular demonstrate full-body exercises). It is noted that an upper-body exercise is generally understood to refer to an exercise which focuses on training muscles of the upper body, but such an exercise does typically include a plurality of muscles which are not the focus of the exercise. A full-body exercise as claimed in claim 3 is thus interpreted as referring to an exercise which targets muscles of the upper body and muscles of some other part of the body, such as the back, core, or legs, which may include activities like various household chores or other exercises as disclosed by Sazonov, and specific exercises like dead lifts, bench presses, bent over dumbbell rows, and barbell squats as disclosed by Jeong.
Regarding claim 4, the combination of Sazonov and Jeong discloses the system of claim 1. Jeong additionally discloses wherein at least one of the one or more exercise characteristics comprises a number of sets performed for each upper-body exercise of the one or more upper-body exercises performed by the user (Paragraph 0038).
Regarding claim 5, the combination of Sazonov and Jeong discloses the system of claim 4. Jeong additionally discloses wherein the one or more exercise characteristics comprises a number of repetitions performed for each set performed for each upper-body exercise of the one or more upper-body exercises performed by the user (Paragraph 0038).
Regarding claim 6, the combination of Sazonov and Jeong discloses the system of claim 1. Jeong additionally discloses wherein the one or more exercise characteristics comprises a weight used for each upper-body exercise of the one or more upper-body exercises performed by the user (Paragraph 0038).
Regarding claim 7, the combination of Sazonov and Jeong discloses the system of claim 1. Sazonov additionally discloses determining, based on the sensor data and the exercise identification data, one or more exercise form deficiencies associated with the user's performance of at least one of the one or more upper-body exercises (Paragraph 0040--The gait cycle identification and loading profiles may also be used to detect asymmetries in the gait pattern indicating fatigue or potential development of injury), and generating one or more user form corrections for at least one exercise form deficiency of the one or more exercise form deficiencies (Paragraph 0040, 0065--Additionally, data regarding key temporal and spatial gait parameters, including, but not limited to, cadence, stride length, and stance time, may be extracted from the pressure and/or acceleration data and used to characterize the user's movement-based activities and provide feedback to the user). However, Sazonov fails to explicitly disclose the one or more exercise form deficiencies comprising a difference between the user's performance of the at least one of the one or more upper-body exercises and an optimal performance of the at least one of the one or more upper-body exercises.
Jeong additionally discloses determining, based on the sensor data and the exercise identification data, one or more exercise form deficiencies associated with the user's performance of at least one of the one or more upper-body exercises (Paragraph 0006, 0010, 0202-- comparing standard posture information according to exercise types pre-configured per piece of exercise equipment, previously stored in a storage unit, with the identified user body information, joint motion information, weight of the piece of exercise equipment according to the exercise type and repeat count, and foot position and per-foot section pressure distribution),
the one or more exercise form deficiencies comprising a difference between the user's performance of the at least one of the one or more upper-body exercises and an optimal performance of the at least one of the one or more upper-body exercises (Paragraph 0011, 0202, 0207, 0257-- comparison image information for standard posture information and the foot position and the motion of each joint and/or body of the captured user… Feedback for the user's body information, reeducation of exercise technique, and partial reinforcing exercise may be provided via comparison between pre-stored pieces of information related to the free weight training motion corresponding to an athlete with superior performance and the optimal exercise course with pieces of information gathered in relation to the free weight training motion the user is doing using the piece of exercise equipment); and
generating one or more user form corrections for at least one exercise form deficiency of the one or more exercise form deficiencies (Paragraph 0006, 0010-0011, 0202-0203, 0257-- generating feedback information related to the user's free weight training motion according to a result of the comparison). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Sazonov with the particular exercise deficiency determination and feedback of Jeong in order to predictably provide additional feedback to enable a user to improve their performance of measured activities.
Regarding claim 8, the combination of Sazonov and Jeong discloses the system of claim 7. Sazonov additionally discloses wherein the operations further comprise: providing the one or more user form corrections for display to the user (Paragraph 0040, 0065, Fig. 5—feedback is provided to the user). Jeong additionally discloses providing the one or more user form corrections for display to the user (Paragraph 0006, 0010-0011, 0202-0203, 0257-- generating feedback information related to the user's free weight training motion according to a result of the comparison).
Regarding claim 9, the combination of Sazonov and Jeong discloses the system of claim 1. Sazonov additionally discloses wherein the one or more sensors comprise at least one of: a pressure sensor (Figs. 1A-1B—pressure sensor 103, 203 positioned in a second position; paragraph 0037); an accelerometer (Figs. 1A-1B—accelerometer 101, 201 positioned in a first position; paragraph 0037); a gyroscope; an inertial measurement unit; or a force-sensitive resistor (Paragraph 0039-- the pressure sensor 101, 201 may be a capacitive sensor or a force-sensitive resistor sensor).
Regarding claim 10, the combination of Sazonov and Jeong discloses the system of claim 9. Sazonov additionally discloses wherein the pressure sensor comprises a barometer (Paragraph 0017, 0021, 0039-- As will be further described with respect to FIGS. 2A-2D and FIG. 3, the pressure sensor 101, 201 may be a capacitive sensor or a force-sensitive resistor sensor) and a rubber layer, the barometer positioned at least one of below or inside (Paragraph 0036-0037, 0039—pressure sensor 103, 203 may be integrated into the insole 107, 207 of the user's shoe 109, 209…may be connected to or integrated into one or both of the user’s shoes) the rubber layer (Paragraph 0052—top insole layer 301, which is positioned between the plates 305, 307 and the user’s foot 302 may function as a dielectric layer, and may be formed from rubber foam, or some other non-conductive material).
Regarding claim 11, the combination of Sazonov and Jeong discloses the system of claim 1. Sazonov additionally discloses the operations further comprising:
evaluating a loss function that evaluates a difference between the exercise identification data and actual exercise data (Paragraph 0183-0185-- the minimum mean squared error, MSE and the minimum mean absolute error, MAE); and
modifying values for one or more parameters of the machine-learned exercise identification model based on the loss function (Paragraph 0183-0184-- Within each model there were several activities performed by each subject, all of such experiments related to the same subject were removed from the training set. A model (coefficients) computed using the rest of the subjects was then used to predict the EE for all experiments of the left out subject. The best set of predictors had to provide the best fit (by producing the maximum adjusted coefficient of determination, R.sup.2.sub.adj and the minimum Akaike Information Criterion, AIC) in the training step and the best predictive performance (the minimum mean squared error, MSE and the minimum mean absolute error, MAE) in the verification step; paragraph 0187-0199—development of models).
Regarding claim 12, the combination of Sazonov and Jeong discloses the system of claim 11. Sazonov additionally discloses wherein the difference between the exercise identification data and the actual exercise data comprises at least one of a difference in pressure or a difference in force (Paragraph 0184—model construction utilizes inputs from the sensors, which measure pressure or force): and
wherein the actual exercise data is provided by the user (Paragraph 0189-0193-- error is computed as the difference between model predicted EE and the measured EE for each experiment).
Regarding claim 15, the combination of Sazonov and Jeong discloses the system of claim 14. Sazonov additionally discloses wherein the one or more sensors comprise a pressure sensor that comprises a barometer (Paragraph 0017, 0021, 0039-- As will be further described with respect to FIGS. 2A-2D and FIG. 3, the pressure sensor 101, 201 may be a capacitive sensor or a force-sensitive resistor sensor) and a rubber layer, the barometer positioned at least one of below or inside (Paragraph 0036-0037, 0039—pressure sensor 103, 203 may be integrated into the insole 107, 207 of the user's shoe 109, 209…may be connected to or integrated into one or both of the user’s shoes) the rubber layer (Paragraph 0052—top insole layer 301, which is positioned between the plates 305, 307 and the user’s foot 302 may function as a dielectric layer, and may be formed from rubber foam, or some other non-conductive material).
Regarding claim 16, the combination of Sazonov and Jeong discloses the system of claim 15. Sazonov additionally discloses wherein the sensor data comprises a combination of pressure data and force data (Paragraph 0017, 0021, 0039-- As will be further described with respect to FIGS. 2A-2D and FIG. 3, the pressure sensor 101, 201 may be a capacitive sensor or a force-sensitive resistor sensor; Figs. 1A-1B and paragraph 0037—accelerometer 101, 201, where it is known in the art that F=MA such that acceleration may also be considered force data).
Claim(s) 13 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sazonov in view of Jeong, further in view of Agrawal (US 20200000373 A1).
Regarding claim 13, the combination of Sazonov and Jeong discloses the system of claim 1. Sazonov additionally discloses wherein the machine-learned exercise identification model includes a neural network (Paragraph 0074, 0081, 0158-- other algorithms may include artificial neural networks, support vector machines, and other classification algorithms… Pattern recognition algorithms that may be used include artificial neural networks, for example, multi-layer perceptron, or other classification algorithms). However, Sazonov does not explicitly disclose wherein the machine-learned exercise identification model includes a convolutional neural network. Agrawal, in the same field of endeavor of instrumented footwear for monitoring motion of a user (Abstract), discloses that a convolution neural network may be used to process data from a user, including pressure and acceleration data (Paragraph 0053), to identify human motion (Paragraphs 0209-0211). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Sazonov via a simple substitution to utilize a convolution neural network in place of the broadly disclosed “neural network…or other classification algorithm” as Agrawal discloses it is known in the art at that a convolution neural network may be utilized to identify human motion such as exercise.
Regarding claim 17, the combination of Sazonov and Jeong discloses the system of claim 16. Sazonov additionally discloses wherein the one or more sensors comprise at least four pressure sensors (Paragraph 0059, 0062 Fig. 4--As shown in FIG. 4, the insole 403 may include a flexible printed circuit board 407 that support multiple force sensitive resistors 409, Fig. 4 demonstrates positioning of the pressure sensors-0063) and at least one acceleration sensor (Figs. 1A-1B and paragraph 0037—accelerometer 101, 201). Sazonov does not explicitly disclose the acceleration sensor is an inertial measurement unit sensor. However, it is known in the art according to Agrawal that an IMU is a typical sensor type for measuring acceleration (Paragraph 0061, 0065, 0079, 0082, 0084--An IMU may be mounted at various locations of the footwear unit… Additionally, IMU sensors 340 allow estimation of the orientation and of the position of the foot in real time… The foot IMU returns the components of the acceleration vector). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Sazonov to utilize an IMU for measuring acceleration via a simple substitution for the IMU of Agrawal as Agrawal discloses it is known in the art that an IMU may be used to measure acceleration.
Regarding claim 18, the combination of Sazonov and Jeong discloses the system of claim 17. Sazonov additionally discloses wherein the footwear device comprises a shoe, a sock, an insertable insole, or an external shoe covering (Paragraph 0033, 0036-0037, 0043, 0059-- insole 103, 203 may be a flexible insole, and may be configured as a removable insert, incorporated into user's socks, e.g., using a polymer backing or a conductive thread, or attached to the user's shoe 109, 209).
Regarding claim 19, the combination of Sazonov and Jeong discloses the system of claim 18. Sazonov additionally discloses wherein four pressure sensors of the at least four pressure sensors are respectively placed at an anterior toe area of the footwear device, a posterior heel area of the footwear device, a left-side area of the footwear device and a right-side rea of the footwear device (Paragraph 0059, 0062, Fig. 4--As shown in FIG. 4, the insole 403 may include a flexible printed circuit board 407 that support multiple force sensitive resistors 409, Fig. 4 demonstrates positioning of the pressure sensors). While Sazonov does not disclose an exact layout of an embodiment utilizing barometric pressure sensors (e.g., Figs. 1A-1B do not show the positioning of the pressure sensors beyond placement at the sole of the footwear), it is clear from the further disclosure of the alternate pressure-sensing embodiment (wherein the pressure sensors are embodied using force-sensitive resistors rather than capacitive sensors) that it is at least obvious that the capacitive sensor embodiment of Sazonov may be modified to follow the same layout as a matter of simple substitution that would retain the sensors in the same positioning in or on an insole and which would allow for monitoring of pressure at different points of the foot/shoe in order to permit monitoring of each part of the foot during exercise as Sazonov broadly states that the layout of sensors is not dependent on the sensor type (Paragraph 0063).
Response to Arguments
Applicant's arguments filed 30 September 2025 with respect to the rejection of the claims under 35 U.S.C. 101 have been fully considered but they are not persuasive.
The applicant argues that the claimed invention is directed to a practical application, namely that the claimed device allows for accurate tracking and identification of upper-body exercises without additional motion tracking device and significantly reduces user-device interactions required to manually enter exercise tracking information into a user device. The applicant additionally cites the August 4, 2025 memo.
However, per MPEP 2106.05(a)(I-II), the claims do not appear sufficient to show a practical application in the form of an improvement. In particular, the applicant’s cited paragraph referring to improving the technology via automatic collection of tracking data appears similar to examples (ii) and (iii) which relate to the accelerating of a process of analyzing data and mere automation of manual processes, which applicant’s own specification appears to refer to in presenting the claimed invention as a more efficient alternative to manually tracking information.
Furthermore, the current recitation of a neural network fails to integrate the improvements into a practical application. The rejection of the newly amended limitations of the independent claims relating to a neural network are supported by the guidance of the July 2024 Subject Matter
Eligibility Examples of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, specifically claim 2 of example 47 which relates to anomaly detection using a trained artificial neural network.
Furthermore, while the specification refers to detecting exercise form inefficiencies to prevent serious harm to users, there is no actual treatment or other practical application performed by the claimed invention. The current claims relate only to generating an output, similar to the example of “a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer Programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent” which is described in MPEP 2106.05(g).
The claims remain rejected under 35 U.S.C. 101.
Applicant's arguments filed 30 September 2025 with respect to the rejection of the claims under 35 U.S.C. 103 have been fully considered but they are not persuasive.
In particular, the applicant argues that the exercises determined by the pattern recognition algorithms of Sazonov are not upper-body exercises. Applicant additionally argues that Jeong fails to cure any deficiencies of Sazonov as Jeong “does not disclose or suggest that the device is a ‘footwear device’ or ‘generating, as an output of the machine-learned exercise identification model, exercise identification data that identifies the one or more lower body movements performed by the suer and detected by the footwear device as one or more upper-body exercises performed by the user’” as Jeong does not specifically disclose a footwear device.
However, as discussed above in the rejections of the claims, Sazonov discloses the use of the pattern recognition module to determine, from data of the footwear device, postures and movement-based activities, the examples of which do include some amount of upper-body involvement. The examples of these postures and activities are clearly not limiting, as shown by the inclusion of “for example” and “and so on” by Sazonov. Jeong, instead, has been cited to demonstrate that a system which includes measuring foot pressures may be used to determine an upper-body exercise from foot pressures, in order to motivate that Sazonov, which determines some postures and exercises from foot pressures, may similarly be used to determine specifically upper-body exercises. It is irrelevant that Jeong does or does not disclose a footwear device specifically.
The claims remain rejected under 35 U.S.C. 103.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
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/ANNA ROBERTS/Examiner, Art Unit 3791 /ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791