DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
Claims 1-20 are within the four statutory categories. However, as will be shown below, claims 1-20 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1 is representative of the inventive concept and recites:
A method for optimizing at least one exercise for a user, wherein an exercise apparatus is configured to enable the user to perform the at least one exercise, the method comprising:
receiving user data, wherein the user data includes attribute data associated with the user and outcome data associated with the exercise;
generating, based on the user data, initial target data wherein the initial target data is associated with at least one of the user, the exercise apparatus, and the exercise;
receiving measurement data associated with at least one of the user, the exercise apparatus, and the exercise, wherein the measurement data is associated with one or more sensors;
determining differential data, wherein the determining is based on one or more differences between the initial target data and the measurement data;
receiving, based on cohort users who perform the exercise, cohort data;
generating, via an artificial intelligence engine and based on the differential data, a machine learning model trained to generate message data based on a difference between the differential data and the cohort data, wherein the message data comprises one or more operating parameters of at least one pedal of the exercise apparatus;
transmitting, to an interface, a message based on the message data;
and based on the one or more operating parameters, controlling, via the machine learning model, operation of the at least one pedal of the exercise apparatus, wherein the controlling is effectuated by moving the pedal to a physical position that provides a desired range of motion.
*Claims 14 and 20 recite similar limitations as claim 1, but for a system and non-transitory computer-readable medium, respectively.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the highlighted components can practically be performed by the human mind (in this case, the process of receiving, generating, and determining) or using pen and paper. Other than reciting generic computer components/functions such as “artificial intelligence engine”, “trained machine learning model”, “interface”, “sensors”, and “apparatus”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 1, but for the device language, the claim encompasses attaining data and processing it. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping and thus, the claim recites a mental process. Additionally, the recitation of generic computer components and functions such generating as receiving, generating, and controlling covers behavioral or interactions between people (i.e. the computer), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case a person is able to physically follow to steps to receive and manage information), and hence falls under “Certain Methods of Organizing Human Activity”. In addition, the limitation “generate message data based on a difference between the differential data and the cohort data” recites a concept that falls into the “mathematical concept” group of abstract ideas.
Dependent claims 2-13 and 15-19 recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 2, reciting and additional function of the method, but for recitation of generic computer components/functions).
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional limitations:
Claim 1 recites “artificial intelligence engine”, “trained machine model”, “interface”, “sensor”, “transmitting, to an interface, a message based on the message data”, and “apparatus”
In particular, the additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations are recited as being performed by an “artificial intelligence engine”, “trained machine model”, “interface”, “sensors”, and “apparatus”. These limitations are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The model is used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the recitation of “transmitting, to an interface, a message based on the message data”.
Dependent claims 5 and 7 recite video.
In particular, the additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the recitation of video and virtual-reality environment
Dependent claims 2-4, 6, 8-13, and 15-19 do not include any additional elements beyond those already recited in independent claim 1, 14, and 20 and dependent claims 5 and 7, hence do not integrate the aforementioned abstract idea into a particular application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 14, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements: An apparatus in claim 1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields as demonstrated by the recitation of an additional element such as:
Transmit/transmission (in claims 1, 14, and 20), which is expressly sending or receiving data electronically (TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)) in a manner that would be well-understood, routine, and conventional
Video (in claims 5 and 17), which is expressly used to visualize data/information(Para 0024, Fisher discloses: “video”) in a manner that would be well-understood, routine, and conventional
Virtual-reality environment (in claims 5 and 17) which is expressly used as a computer-generated environment which make objects seem real(Para 0036, Fisher discloses: “virtual-reality”)) in a manner that would be well-understood, routine, and conventional
Dependent claims 2-4, 6, 8-13, and 15-19 do not include any additional elements beyond those already addressed above for dependent claims 5 and 17 and independent claims 1, 14, and 20. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 14, and 20 hence do not amount to “significantly more” than the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective function merely provide conventional computer implementation.
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 1-20 are rejected under 35 U.S.C. 103 is being unpatentable over Solomon(US8172724B2) in view of Bitran et al(US20180056130), Bowman(GB2485216A), and Hartman(US20170080281A1).
Claim 1
Solomon discloses:
A method for optimizing at least one exercise for a user, wherein an exercise apparatus is configured to enable the user to perform the at least one exercise, the method comprising: receiving user data(Figure 29, Solomon discloses: “The system collects data on an individual profile…” [INDIVIDUAL PROFILE DATA CAN BE USER DATA]), wherein the user data(Figure 29, Solomon discloses: “The system collects data on an individual profile…” [INDIVIDUAL PROFILE DATA CAN BE USER DATA]) includes attribute data(Figure 29, Solomon discloses: “The system collects data on an individual profile…” [INDIVIDUAL PROFILE DATA CAN BE ATTRIBUTE DATA]) associated with the user and outcome data (Figure 27, Solomon discloses: Exercise data [EXERCISE DATA CAN BE OUTCOME DATA]) associated with the exercise(Figure 5, Solomon discloses: exercise); generating, based on the user data(Figure 29, Solomon discloses: “The system collects data on an individual profile…” [INDIVIDUAL PROFILE DATA CAN BE USER DATA]), initial target data(Figure 29, Solomon discloses: “The system collects data on an individual profile and goals.” [GOAL DATA CAN BE INITIAL TARGET DATA]) wherein the initial target data(Figure 29, Solomon discloses: “The system collects data on an individual profile and goals.” [GOAL DATA CAN BE INITIAL TARGET DATA]) is associated with at least one of the user, the exercise apparatus(Figure 3, Solomon discloses: Exercise machine [Exercise machine can be an exercise apparatus]), and the exercise(Figure 5, Solomon discloses: exercise); receiving measurement data(Col. 5, Line 31, Solomon discloses: sensor data (SENSOR DATA CAN BE MEASUREMENT DATA]) associated with at least one of the user, the exercise apparatus(Figure 3, Solomon discloses: Exercise machine [Exercise machine can be an exercise apparatus]), and the exercise(Figure 5, Solomon discloses: exercise), wherein the measurement data(Col. 5, Line 31, Solomon discloses: sensor data (SENSOR DATA CAN BE MEASUREMENT DATA]) is associated with one or more sensors(Figure 4, Solomon discloses: sensors [ONE OR MORE SENSORS]); determining differential data(Figure 30, Solomon discloses a process by which the system is programmed to maintain heart rate based on a specified level [INITIAL TARGET DATA] and any variability from the specification [DIFFERENTIAL DATA] is considered feedback to the system to adjust the workout.), wherein the determining is based on one or more differences between the initial target data(Figure 29, Solomon discloses: “The system collects data on an individual profile and goals.” [GOAL DATA CAN BE INITIAL TARGET DATA]) and the measurement data(Col. 5, Line 31, Solomon discloses: sensor data (SENSOR DATA CAN BE MEASUREMENT DATA]); receiving, based on cohort users (Col. 11, Line 13, Solomon discloses: “group of individuals” [GROUP OF INDIVIDUALS CAN BE COHORT USERS]) who perform the exercise(Col. 5, Line 12, Solomon discloses: “an aggregate of a type of workout[WORKOUT CAN BE EXERCISE] of a group of individuals…” [COHORT USERS WHO PERFORM EXERCISE]), cohort data(Col. 10, Line 55, Solomon discloses: “Overall, a model is built based on the collection of information about a group of individual's workouts over time.” [INFORMATION ABOUT A GROUP OF INDIVIDUALS WORKOUTS CAN BE COHORT DATA]); generating,
Solomon does not explicitly disclose: artificial intelligence engine, machine learning model, message, parameters of a pedal, control operations of a pedal, and controlling is effectuated by moving the pedal to a physical position that provides a desired range of motion
Bitran discloses: artificial intelligence engine, machine learning model, and message
artificial intelligence engine(Para 0051, Bitran discloses: “support vector machines, … and symbolic computation engines.” [THESE CAN ALL BE FORMS OF AN ARTIFICIAL INTELLIGENCE ENGINE])
machine learning model(Para 0021, Bitran discloses: machine learning algorithm)
message(Para 0095, Bitran discloses: “messages”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the physical fitness optimization system of Solomon to add artificial intelligence engine, machine learning model, and message, as taught by Bitran. One of ordinary skill would have been so motivated to provide a means to process data to provide insights and communicate the insights to the user via message, but in this case, for a system created to provide insights based on health-related information(Para 0002, Bitran discloses: “Electronic devices, such as wearables and smart phones, may be configured to track and output information regarding physiological and behavioral characteristics of a person, such as health and fitness data.”).
Bitran does not explicitly disclose:
parameters of a pedal, control operations of a pedal, and controlling is effectuated by moving the pedal to a physical position that provides a desired range of motion
Bowman discloses:
one or more operating parameters of at least one pedal of the exercise apparatus(Page 6, Line 26, Bowman discloses a torque sensor which detects level of torque applied to pedals)
control operation of the at least one pedal of the exercise apparatus(Page 7, Lines 3-5, Bowman discloses adjusting pedaling resistance based on heart rate of the rider)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the physical fitness optimization system of Solomon to add operating parameters of a pedal and control operation of a pedal of an exercise apparatus, as taught by Bowman. One of ordinary skill would have been so motivated to provide a means to control parameters of an exercise apparatus based on sensor data, but in this case, for an electric pedal cycle with exercise program(Page 2, Lines 10-14, Bowman discloses: “However, the known control systems for the supplementary electrical power systems are designed for the purpose of increasing the speed and/or range of a rider, compared with a traditional pedal-only bicycle. Accordingly, there remains a need
for an improved electric pedal bicycle adapted to meet the needs of riders wishing to gain or improve their physical fitness.”)
Bowman does not explicitly disclose: controlling is effectuated by moving the pedal to a physical position that provides a desired range of motion
Hartman discloses:
controlling is effectuated by moving the pedal to a physical position that provides a desired range of motion(Para 0034, Hartman discloses the use of limiters to restrict pedals to provide a desired range of motion)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the physical fitness optimization system of Solomon to add controlling is effectuated by moving the pedal to a physical position that provides a desired range of motion, as taught by Hartman. One of ordinary skill would have been so motivated to provide a means to control parameters of an exercise apparatus via pedal positioning to better accommodate for range of motion for the patient, but in this case, for a reciprocating rehabilitation device(Pare 0010, Hartman discloses: “However, there still remains a need for a rehabilitation device that can be utilized by a patient with no or very little therapist aide.”)
Claim 2
Solomon discloses:
The method of claim 1, further comprising controlling, based on at least one of the message data(Col. 10, Line 55, Solomon discloses: “Overall, a model is built based on the collection of information about a group of individual's workouts over time. This aggregate data set is useful in order to compare individual exercise programs. The pattern of an individual's behavior is analyzed by comparing it to the pattern of group behavior.” [AGGREGATE DATA CAN BE MESSAGE DATA]) and the differential data(Figure 30, Solomon discloses a process by which the system is programmed to maintain heart rate based on a specified level [INITIAL TARGET DATA] and any variability from the specification [DIFFERENTIAL DATA] is considered feedback to the system to adjust the workout.), the exercise apparatus(Col. 14, Line 41, Solomon discloses: “with a goal to keep the user's heart rate at a specified level for a specified period of time. The system sends a wireless signal from the Web site to the exercise equipment to maintain a specific heart rate level. The system uses feedback from the exercise machine and fuzzy logic-based algorithms to continuously calculate the rate of change of activity in the machine in order to calibrate the heart rate to meet the goal. The equipment changes settings in order to keep the heart rate stable until the goal is met.” [CONTROLLING EXERCISE APPARATUS BASED ON DIFFERENTIAL DATA] ).
Claim 3
Solomon discloses:
The method of claim 1, wherein the message data(Col. 10, Line 55, Solomon discloses: “Overall, a model is built based on the collection of information about a group of individual's workouts over time. This aggregate data set is useful in order to compare individual exercise programs. The pattern of an individual's behavior is analyzed by comparing it to the pattern of group behavior.” [AGGREGATE DATA CAN BE MESSAGE DATA]) comprises at least one of audio data, visual data, and haptic data.
Solomon does not disclose: visual data*, haptic data*, audio data*
*art optional due to “at least one of” designation
Bitran discloses: visual data, haptic data
visual data (Para 0093, Bitran discloses: “visual representation of data” [VISUAL REPRESENTATION OF DATA CAN BE VISUAL DATA])
haptic data(Para 0028, Bitran discloses: “haptic output” [HAPTIC OUTPUT CAN BE HAPTIC DATA])
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the physical fitness optimization system of Solomon to add visual and haptic data, as taught by Bitran. One of ordinary skill would have been so motivated to be able to collect additional data types to help drive accurate insights, but in this case, for a system created to provide insights based on health-related information(Para 0002, Bitran discloses: “Electronic devices, such as wearables and smart phones, may be configured to track and output information regarding physiological and behavioral characteristics of a person, such as health and fitness data.”).
Claim 4
Art optional for audio data due to “at least one of” designation in claim 3.
Claim 5
Solomon discloses:
The method of claim 3, wherein the visual data includes a visual characteristic associated with at least one of a color, an image, a video, a text, a font type, a font style, a point size, a font modifier, a virtual-reality environment, and an illumination, wherein the visual characteristic is based on at least one of the cohort data(Col. 10, Line 55, Solomon discloses: “Overall, a model is built based on the collection of information about a group of individual's workouts over time.” [INFORMATION ABOUT A GROUP OF INDIVIDUALS WORKOUTS CAN BE COHORT DATA]) and the outcome data(Figure 27, Solomon discloses: Exercise data [EXERCISE DATA CAN BE OUTCOME DATA]).
Solomon does not disclose: color*, an image*, a video*, a text*, a font type*, a font style*, a point size*, a font modifier*, a virtual-reality environment*, and an illumination*
*art optional due to “at least one of” designation
Bitran discloses: text
text(Para 0082, Bitran discloses: “text message”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the physical fitness optimization system of Solomon to text, as taught by Bitran. One of ordinary skill would have been so motivated to be able to display data in various formats, but in this case, for a system created to provide insights based on health-related information(Para 0002, Bitran discloses: “Electronic devices, such as wearables and smart phones, may be configured to track and output information regarding physiological and behavioral characteristics of a person, such as health and fitness data.”).
Claim 6
Solomon discloses:
The method of claim 3, wherein the haptic data includes a haptic characteristic associated with at least one of a vibration, a force, a pressure, a torque, an intensity, a resistance, an electric stimulus, an ultrasonic frequency, a heat level, and a temperature, wherein the haptic characteristic is based on at least one of the cohort data(Col. 10, Line 55, Solomon discloses: “Overall, a model is built based on the collection of information about a group of individual's workouts over time.” [INFORMATION ABOUT A GROUP OF INDIVIDUALS WORKOUTS CAN BE COHORT DATA]) and the outcome data(Figure 27, Solomon discloses: Exercise data [EXERCISE DATA CAN BE OUTCOME DATA]).
Solomon does not disclose: haptic data*, vibration, a force*, a pressure*, a torque*, an intensity*, a resistance*, an electric stimulus*, an ultrasonic frequency*, a heat level*, temperature*
*art optional due to “at least one of” designation
Bitran discloses: haptic data, vibration
haptic data(Para 0028, Bitran discloses: “haptic output” [HAPTIC OUTPUT CAN BE HAPTIC DATA])
vibration(Para 0028, Bitran discloses: “(e.g. haptic outputs, such as piezoelectric vibrators[VIBRATION])”
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the physical fitness optimization system of Solomon to include haptic data and vibration, as taught by Bitran. One of ordinary skill would have been so motivated to be able to incorporate various data types for inclusion into health insights, but in this case, for a system created to provide insights based on health-related information(Para 0002, Bitran discloses: “Electronic devices, such as wearables and smart phones, may be configured to track and output information regarding physiological and behavioral characteristics of a person, such as health and fitness data.”).
Claim 7
Solomon discloses:
The method of claim 3, further comprising receiving, after transmission of the message, response data based on at least one of the message data(Col. 10, Line 55, Solomon discloses: “Overall, a model is built based on the collection of information about a group of individual's workouts over time. This aggregate data set is useful in order to compare individual exercise programs. The pattern of an individual's behavior is analyzed by comparing it to the pattern of group behavior.” [AGGREGATE DATA CAN BE MESSAGE DATA]) and measurement data(Col. 5, Line 31, Solomon discloses: sensor data (SENSOR DATA CAN BE MEASUREMENT DATA]); and writing to an associated memory, for access by the artificial intelligence engine, the response data(Para 0026, Bitran discloses: “The alert 310 [AN ALERT CAN BE A MESSAGE] may also provide selectable options regarding actions to take…” [CHOOSE OUT OF SELECTABLE OPTIONS TO RESPOND TO AN ALERT CAN BE RESPONSE DATA]).
Solomon does not disclose: message, response data, artificial intelligence engine
Bitran discloses: message, response data, artificial intelligence engine
message(Para 0095, Bitran discloses: “messages”)
response data (Para 0026, Bitran discloses: “The alert 310 [AN ALERT CAN BE A MESSAGE] may also provide selectable options regarding actions to take…” [CHOOSE OUT OF SELECTABLE OPTIONS TO RESPOND TO AN ALERT CAN BE RESPONSE DATA])
artificial intelligence engine(Para 0051, Bitran discloses: “support vector machines, … and symbolic computation engines.” [THESE CAN ALL BE FORMS OF AN ARTIFICIAL INTELLIGENCE ENGINE])
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the physical fitness optimization system of Solomon to include message, response data, and artificial intelligence engine, as taught by Bitran. One of ordinary skill would have been so motivated to be able to incorporate a method of processing accurate insights using additional data types, and communication of these insights to the user, but in this case, for a system created to provide insights based on health-related information(Para 0002, Bitran discloses: “Electronic devices, such as wearables and smart phones, may be configured to track and output information regarding physiological and behavioral characteristics of a person, such as health and fitness data.”).
Claim 8
Solomon discloses:
The method of claim 7, further comprising correlating the response data with at least one of audio data, visual data, and haptic data to generate, using optimized message data(Col. 10, Line 55, Solomon discloses: “Overall, a model is built based on the collection of information about a group of individual's workouts over time. This aggregate data set is useful in order to compare individual exercise programs. The pattern of an individual's behavior is analyzed by comparing it to the pattern of group behavior.” [AGGREGATE DATA CAN BE MESSAGE DATA]) associated with the differential data(Figure 30, Solomon discloses a process by which the system is programmed to maintain heart rate based on a specified level [INITIAL TARGET DATA] and any variability from the specification [DIFFERENTIAL DATA] is considered feedback to the system to adjust the workout.), an optimized message.
Solomon does not disclose: message, response data
Bitran discloses: message, response data
response data(Para 0026, Bitran discloses: “The alert 310 [AN ALERT CAN BE A MESSAGE] may also provide selectable options regarding actions to take…” [CHOOSE OUT OF SELECTABLE OPTIONS TO RESPOND TO AN ALERT CAN BE RESPONSE DATA])
message(Para 0095, Bitran discloses: “messages”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the physical fitness optimization system of Solomon to include message and response data, as taught by Bitran. One of ordinary skill would have been so motivated to be able to incorporate a method of processing accurate insights using additional data types, and communication of these insights to the user, but in this case, for a system created to provide insights based on health-related information(Para 0002, Bitran discloses: “Electronic devices, such as wearables and smart phones, may be configured to track and output information regarding physiological and behavioral characteristics of a person, such as health and fitness data.”).
Claim 9
Solomon discloses:
The method of claim 8, further comprising updating, based on the optimized message data(Col. 10, Line 55, Solomon discloses: “Overall, a model is built based on the collection of information about a group of individual's workouts over time. This aggregate data set is useful in order to compare individual exercise programs. The pattern of an individual's behavior is analyzed by comparing it to the pattern of group behavior.” [AGGREGATE DATA CAN BE MESSAGE DATA]), the cohort data(Col. 10, Line 55, Solomon discloses: “Overall, a model is built based on the collection of information about a group of individual's workouts over time.” [INFORMATION ABOUT A GROUP OF INDIVIDUALS WORKOUTS CAN BE COHORT DATA]).
Claim 10
Solomon discloses:
The method of claim 1, wherein the outcome data(Figure 27, Solomon discloses: Exercise data [EXERCISE DATA CAN BE OUTCOME DATA]) is based on a selection by the user.
Claim 11
Solomon discloses:
The method of claim 1, wherein the outcome data(Figure 27, Solomon discloses: Exercise data [EXERCISE DATA CAN BE OUTCOME DATA]) is generated via the machine learning model.
Solomon does not disclose: machine learning model
Bitran discloses: machine learning model
machine learning model(Para 0021, Bitran discloses: machine learning algorithm).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the physical fitness optimization system of Solomon to include machine learning model as taught by Bitran. One of ordinary skill would have been so motivated to be able to incorporate a method of providing accurate insights, but in this case, for a system created to provide insights based on health-related information(Para 0002, Bitran discloses: “Electronic devices, such as wearables and smart phones, may be configured to track and output information regarding physiological and behavioral characteristics of a person, such as health and fitness data.”).
Claim 12
Solomon discloses:
The method of claim 1, wherein the attribute data(Figure 29, Solomon discloses: “The system collects data on an individual profile…” [INDIVIDUAL PROFILE DATA CAN BE ATTRIBUTE DATA]) associated with the user comprises at least one of a measurement of a vital sign of the user, a respiration rate of the user, a heartrate (Col. 4, Line 10 Solomon, discloses: “heartrate”) of the user, a heart rhythm of a user, an oxygen saturation of the user, a sugar level of the user, a composition of blood of the user, cerebral activity of the user, cognitive activity of the user, a lung capacity of the user, a temperature of the user, a blood pressure of the user, an eye movement of the user, a degree of dilation of an eye of the user, a reaction time, a sound produced by the user, a perspiration rate of the user, an elapsed time of using the exercise apparatus, an amount of force exerted on a portion of the exercise apparatus, a range of motion achieved on the exercise apparatus, a movement speed of a portion of the exercise apparatus, a pressure exerted on a portion of the exercise apparatus, a movement acceleration of a portion of the exercise apparatus, a movement jerk of a portion of the exercise apparatus, a torque level of a portion of the exercise apparatus, and an indication of a plurality of pain levels experienced by the user when using the exercise apparatus(Figure 3, Solomon discloses: Exercise machine [Exercise machine can be an exercise apparatus]).
Solomon does not disclose: vital sign, respiration rate*, a heartrate*, a heart rhythm*, an oxygen saturation*, a sugar level*, a composition of blood*, cerebral activity*, cognitive activity*, a lung capacity*, a temperature*, a blood pressure*, an eye movement*, a degree of dilation of an eye*, a reaction time*, a sound produced*, a perspiration rate*, an elapsed time of using the exercise apparatus*, an amount of force exerted on a portion of the exercise apparatus*, a range of motion achieved on the exercise apparatus*, a movement speed of a portion of the exercise apparatus*, a pressure exerted on a portion of the exercise apparatus*, a movement acceleration of a portion of the exercise apparatus*, a movement jerk of a portion of the exercise apparatus*, a torque level of a portion of the exercise apparatus*, and an indication of a plurality of pain levels*
*art optional due to “at least one of” designation
Bitran discloses: vital sign, oxygen saturation, blood pressure
vital sign (Para 0018, Bitran discloses: “health metric” [HEALTH METRIC CAN BE VITAL SIGN])
oxygen saturation(Para 0018, Bitran discloses: “blood oxygenation”)
blood pressure (Para 0018, Bitran discloses: “blood pressure”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the physical fitness optimization system of Solomon to include vital sign, oxygen saturation, and blood pressure, as taught by Bitran. One of ordinary skill would have been so motivated to be able to incorporate vital signs into the assessment of the user to better provide insights, but in this case, for a system created to provide insights based on health-related information(Para 0002, Bitran discloses: “Electronic devices, such as wearables and smart phones, may be configured to track and output information regarding physiological and behavioral characteristics of a person, such as health and fitness data.”).
Claim 13
Solomon discloses:
The method of claim 1, wherein the measurement data(Col. 5, Line 31, Solomon discloses: sensor data (SENSOR DATA CAN BE MEASUREMENT DATA]) is sensor data received from one or more sensors(Figure 4, Solomon discloses: sensors [ONE OR MORE SENSORS]) associated with at least one of the user, the exercise apparatus(Figure 3, Solomon discloses: Exercise machine [Exercise machine can be an exercise apparatus]), and the exercise(Figure 5, Solomon discloses: exercise); wherein the measurement data(Col. 5, Line 31, Solomon discloses: sensor data (SENSOR DATA CAN BE MEASUREMENT DATA]) is received in real-time(Col. 14, Line 52, Solomon discloses: “fitness program is monitored and tracked in real time[REAL-TIME] and supplies feedback…”) or near real-time; and wherein the outcome data(Figure 27, Solomon discloses: Exercise data [EXERCISE DATA CAN BE OUTCOME DATA]) includes at least one of a duration of the exercise(Col. 12, Line 14, Solomon discloses: “amount of weight”), a duration of uninterrupted use, a weight(Col. 12, Line 12, Solomon discloses: “duration with each exercise”), a number of repetitions(Col. 12, Line 14, Solomon discloses: “number of repetitions”), a respiration rate of the user, a heartrate(Col. 4, Line 10 Solomon, discloses: “heartrate”) of the user, a reaction time, a perspiration rate of the user, an amount of force exerted on a portion of the exercise apparatus, a range of motion achieved on the exercise apparatus, a pressure exerted on a portion of the exercise apparatus, a movement speed of a portion of the exercise apparatus, a movement acceleration of a portion of the exercise apparatus, a movement jerk of a portion of the exercise apparatus, a torque level of a portion of the exercise apparatus(Figure 3, Solomon discloses: Exercise machine [Exercise machine can be an exercise apparatus]), or any combination thereof.
Claims 14 and 20
Claims 14 and 20 recite similar limitations to claim 1 . See claim 1 analysis.
Claim 15
Claim 15 recites similar limitations to claim 2 . See claim 2 analysis.
Claim 16
Claim 16 recites similar limitations to claim 3 . See claim 3 analysis.
Claim 17
Claim 17 recites similar limitations to claims 4, 5, and 6. See claim 4, 5, and 6 analysis.
Claim 18
Claim 18 recites similar limitations to claim 7. See claim 7 analysis.
Claim 19
Claim 19 recites similar limitations to claims 8 and 9. See claim 8 and 9 analysis.
Response to Arguments
35 U.S.C. 101
(Page 8) Regarding the amendment from transmitting to controlling using a machine learning model integrating an abstract idea into a practical application.
Applicant's arguments filed have been fully considered but they are not persuasive. Controlling would be considered abstract (human activity). Merely using a machine learning model to perform human activity without describing the technical details as to how this occurs would render this amendment abstract.
(Page 9) The pedal of the exercise apparatus claimed and described in the specification is “significantly more” because it “plays a significant part in permitting the claimed method to be performed” by moving to a physical position to provide a desired range of motion.
Applicant's arguments filed have been fully considered but they are not persuasive. Specification cannot be read into the claims. The pedal of the exercise apparatus as described in the claim is what is under scrutiny in regards to 101.
35 U.S.C. 103
(Pages 11-13) Regarding the assertion that the amendments applied to the independent claims are not taught by Solomon, Bitran, or Bowman.
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Corbalis et al (US9295878B2): Corbalis et al discloses a system that provides the ability to adjust a workout program in response to sensor data, similar to that of the invention disclosed in this application (Specification, Para 0122).
Foley et al (US10322315B2): Foley et al discloses a method for displaying live and archived exercise classes and tracks performance metrics through use of sensors and allows for comparison of gathered metrics between the user and related cohorts. The gathering and comparing of data is similar to that of the invention disclosed in this application (Specification, Para 0005, 0019).
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685