DETAILED ACTION
Applicant’s arguments, filed on 06/10/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 06/10/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-10 are the current claims hereby under examination.
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 .
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 4, the claim recites the limitation “an input of the first feature amount” in line 4. It is unclear if this limitation is meant to refer to the input of the first feature amount in claim 3, line 10, or a different input. If it is meant to refer to the input from claim 3, it needs to refer back to it. If it is meant to refer to a different input, it needs to be distinguished from the input from claim 3. For purposes of examination, it is being interpreted as referring to the input from claim 3. Claim 5 is also rejected due to its dependence on claim 4.
Further regarding claim 4, the claim recites the limitation “the gait parameters used for estimation of the pelvic inclination” in line 8. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear if these specific gait parameters are included in the gait parameters from claim 2 or are different gait parameters. If it is meant to refer to the gait parameters from claim 2, it needs to clearly refer back to it. If it is meant to refer to different gait parameters, it needs to be clearly distinguished from the gait parameters from claim 2. For purposes of examination, it is being interpreted as referring to the gait parameters from claim 2. Claim 5 is also rejected due to its dependence on claim 4.
Further regarding claim 4, the claim recites the limitation “the gait parameters for both feet of the user” in line 9. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear if these specific gait parameters are included in the gait parameters from claim 2 or are different gait parameters. If it is meant to refer to the gait parameters from claim 2, it needs to clearly refer back to it. If it is meant to refer to different gait parameters, it needs to be clearly distinguished from the gait parameters from claim 2. For purposes of examination, it is being interpreted as referring to the gait parameters from claim 2. Claim 5 is also rejected due to its dependence on claim 4.
Further regarding claim 4, the claim recites the limitation “first feature amounts” in line 8. It is unclear if this limitation is meant to refer to the first feature amount from line 4, or different first feature amounts. If it is meant to refer to the first feature amount from line 4, it needs to refer back to it. If it is meant to refer to different first feature amounts, it needs to be distinguished from the first feature amount from line 4. For purposes of examination, it is being interpreted as referring to the first feature amount from line 4. Claim 5 is also rejected due to its dependence on claim 4.
Regarding claim 6, the claim recites the limitation “a variation width” in lines 10-11. It is unclear if this is meant to refer to the at least one variation width in lines 2-3, or a different variation width. If it is referring to the at least one variation width from lines 2-3, it needs to refer back to it. If it is referring to a different variation width, it needs to be distinguished from the variation width from lines 2-3. For purposes of examination, it is being interpreted as referring to the at least one variation width from lines 2-3.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Chang (US 20170188894) in view of Murakami (US 20190224063) and Roche (US 20220202369).
Regarding independent claim 1, Chang teaches a pelvic inclination estimation device (Abstract: “A system and method for utilizing an activity monitoring device”) comprising:
a memory storing instructions, and a processor connected to the memory ([0031]: “The activity monitoring device 110 can additionally include any suitable components to support computational operation such as a processor, RAM, Flash memory”) and configured to execute the instructions to:
receive, from a measurement device disposed on footwear of a user ([0031]: “The activity monitoring device 110 is preferably small enough to be mounted to a participant in an unobtrusive way and may be integrated into a wearable such as … shoes”), feature amount data including a feature amount to be used for estimation of a pelvic inclination that is an index related to movement of a waist ([0022]: “The system and method preferably uses the motion of a participant as they perform an action to determine if the participant is fatigued. The kinematic motion can be characterized as biomechanical signals. The biomechanical signals are preferably for repeated actions such as … pelvic tilt”. The biomechanical signals are the feature amount data, and the pelvic tilt is the pelvic inclination that is an index related to movement of the waist.), the feature amount being extracted from motion path of a spatial acceleration and a spatial angular velocity included in sensor data ([0055]: “The kinematic measurements can include acceleration, velocity, displacement, force, angular velocity, angular displacement, tilt/angle, and/or any suitable metric corresponding to a kinematic property or dynamic property of an activity. Preferably, a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes”; [0084]: “a process for generating at least one set of motion paths functions to create a dimensional map of movement of at least one point of the body. The kinematic data can include multi-dimensional linear acceleration and angular velocity data, which can be converted to relative displacement or velocity for one to three dimensions as a function of time”) measured by a sensor included in the measurement device during walking of the user ([0032]: “The inertial measurement system 112 preferably includes at least one inertial measurement unit (IMU). An IMU can include at least one accelerometer, gyroscope, magnetometer, or other suitable inertial sensor.”; [0027]: “the system and method may be applied to activity use-cases such as gait-analysis, walking”).
However, Chang does not teach the motion path being a gait waveform.
Murakami discloses an apparatus that monitors a user’s walk. Specifically, Murakami teaches a gait waveform of a spatial acceleration and a spatial angular velocity ([0141]: “the gait timing detection unit 123 may estimate a gait cycle by using signal waveforms obtained from the acceleration sensor”. The acceleration sensor can measure both acceleration and angular velocity, therefore the gait waveform is made up from the acceleration and angular velocity.). Chang and Murakami are analogous art as they are related to the same field of endeavor of devices used to monitor a user while walking.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the motion path being a gait waveform from Murakami into the device from Chang as Chang is silent on the specific details of the motion path, and Murakami discloses a suitable type of motion path in an analogous device.
The Chang/Murakami combination teaches the features of inputting the feature amount included in the feature amount data to a machine learning model that outputs an estimation value related to the pelvic inclination in response to the input of the feature amount included in the feature amount data; receiving the estimation value related to the pelvic inclination from the machine learning model; and estimating the pelvic inclination of the user in real-time according to the estimation value related to the pelvic inclination output from the machine learning model (Chang, [0117]: “Detection of a change in biomechanical signals, such as ground contact time, sagittal tilt, cadence or motion paths, can additionally or alternatively use various machine learning techniques”; [0118]: “a hybrid implementation can use machine intelligence in combination with a heuristic approach. In one hybrid implementation, various heuristic-based analyses of biomechanical signals can be used as feature inputs into a machine learning algorithm as described above” [0066]: “Pelvic tilt (i.e., pitch) can be characterized as rotation in the sagittal plane (i.e., rotation about a lateral axis)”; [0084]: “Pattern recognition algorithms, computer vision, image recognition, neural networks, and/or other suitable machine intelligence techniques can be used to analyze and characterize the shapes and variability of the motion paths created by the runner”; [0056]: “The biomechanical signals for an activity are preferably a substantially real-time assessment of the biomechanical properties during the activity”. The analysis of the shapes and variability of motion paths are the estimation value related to the pelvic inclination, and the detection of sagittal tilt is the pelvic tilt (which is the pelvic inclination), since the pelvic tilt is determined by tilt in the sagittal plane.), and
displaying information according to the estimation result of the pelvic inclination of the user on a screen of a mobile terminal used by the user (Chang, [0120]: “Analysis can be provided by displaying information, generating a graphical representation (e.g., a chart, a graphical indicator, etc.), playing audio feedback (e.g., making an speech audio announcement concerning the changes), activating a haptic feedback device, or using any suitable mechanism to provide feedback.”; [0129]: “The instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a user computer or mobile device, wristband, smartphone, or any suitable combination thereof”).
However, the Chang/Murakami combination does not teach the displaying of a video containing recommended training.
Roche teaches a system to monitor a user’s musculoskeletal system. Specifically, Roche teaches displaying a video containing recommended training ([0032]: “videos can be provided on the application with detailed instructions on everything from … exercise programs”). Chang, Murakami, and Roche are analogous arts as they are all related to the same field of endeavor of devices that measure a user’s steps and gait pattern to analyze the user’s health.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the recommended training video from Roche into the Chang/Murakami combination as it allows the device to recommend specific training to the user that can improve their health condition.
Regarding claim 2, the Chang/Murakami/Roche combination teaches the pelvic inclination estimation device according to claim 1, wherein the machine learning model is trained to output the estimation value related to the pelvic inclination according to an input of gait parameters included in the feature amount data (Chang, [0044]: “the data used to train the machine intelligence is collected with particular conditions so as to be most applicable to a set of target audiences”; [0026]: “a biomechanical signal quantifies at least one aspect of motion that occurs once or repeatedly during a task. For example, in the case of walking or running, how a participant takes each step can be broken into several biomechanical signals. In a preferred implementation”. The biomechanical signals are the signals input into the machine learning, which include signals related to aspects of a walking or running motion, which are gait parameters.), the processor is configured to execute the instructions to: acquire the feature amount data including the gait parameters extracted from the gait waveform of the spatial acceleration and the spatial angular velocity included in the sensor data, input the gait parameters included in the feature amount data to the machine learning model, and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model (Chang, [0117]: “Detection of a change in biomechanical signals, such as ground contact time, sagittal tilt, cadence or motion paths, can additionally or alternatively use various machine learning techniques”; [0118]: “a hybrid implementation can use machine intelligence in combination with a heuristic approach. In one hybrid implementation, various heuristic-based analyses of biomechanical signals can be used as feature inputs into a machine learning algorithm as described above”; [0066]: “Pelvic tilt (i.e., pitch) can be characterized as rotation in the sagittal plane (i.e., rotation about a lateral axis)”; [0084]: “Pattern recognition algorithms, computer vision, image recognition, neural networks, and/or other suitable machine intelligence techniques can be used to analyze and characterize the shapes and variability of the motion paths created by the runner”. The analysis of the shapes and variability of motion paths are the estimation value related to the pelvic inclination, and the detection of sagittal tilt is the pelvic tilt (which is the pelvic inclination), since the pelvic tilt is determined by tilt in the sagittal plane.; Murakami, [0141]: “the gait timing detection unit 123 may estimate a gait cycle by using signal waveforms obtained from the acceleration sensor”. The acceleration sensor can measure both acceleration and angular velocity, therefore the gait waveform is made up from the acceleration and angular velocity.).
Regarding claim 3, the Chang/Murakami/Roche combination teaches the pelvic inclination estimation device according to claim 2, wherein the machine learning model is trained to output the estimation value related to the pelvic inclination according to an input of a first feature amount included in the feature amount data (Chang, [0084]: “a process for generating at least one set of motion paths functions to create a dimensional map of movement of at least one point of the body. The kinematic data can include multi-dimensional linear acceleration and angular velocity data, which can be converted to relative displacement or velocity for one to three dimensions as a function of time”. The first feature amount is one of the motion paths functions created from the acceleration and angular velocity data.), the processor is configured to execute the instructions to: acquire the feature amount data including the first feature amount for each gait phase cluster extracted from the gait waveform of the spatial acceleration and the spatial angular velocity included in the sensor data (Chang, [0032]: “The inertial measurement system 112 can additionally include an integrated processor that, among other functionality, provides sensor fusion, which effectively provides a separation of forces caused by gravity from forces caused by speed changes on the sensor. The integrated processor may additionally provide post processing of kinematic data”. The processor is used for post processing of the kinematic data, therefore the processor acquires the first feature amount data.), input the first feature amount included in the feature amount data to the machine learning model (Chang, [0059]: “Detecting an action pattern can additionally or alternatively use machine intelligence such as deep learning, machine learning, statistical methods, and/or other suitable algorithmic approaches to detecting an action”. The post processing of the kinematic data includes the machine learning model, therefore the first feature amount is input into the machine learning model.), and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model (Chang, [0117]: “Detection of a change in biomechanical signals, such as ground contact time, sagittal tilt, cadence or motion paths, can additionally or alternatively use various machine learning techniques”; [0118]: “a hybrid implementation can use machine intelligence in combination with a heuristic approach. In one hybrid implementation, various heuristic-based analyses of biomechanical signals can be used as feature inputs into a machine learning algorithm as described above”; [0066]: “Pelvic tilt (i.e., pitch) can be characterized as rotation in the sagittal plane (i.e., rotation about a lateral axis)”; [0084]: “Pattern recognition algorithms, computer vision, image recognition, neural networks, and/or other suitable machine intelligence techniques can be used to analyze and characterize the shapes and variability of the motion paths created by the runner”. The analysis of the shapes and variability of motion paths are the estimation value related to the pelvic inclination, and the detection of sagittal tilt is the pelvic tilt (which is the pelvic inclination), since the pelvic tilt is determined by tilt in the sagittal plane.).
Regarding claim 4, the Chang/Murakami/Roche combination teaches the pelvic inclination estimation device according to claim 3, wherein the machine learning model is trained to output the estimation value related to the pelvic inclination according to an input of the first feature amount and a second feature amount (Chang, [0084]: “a process for generating at least one set of motion paths functions to create a dimensional map of movement of at least one point of the body. The kinematic data can include multi-dimensional linear acceleration and angular velocity data, which can be converted to relative displacement or velocity for one to three dimensions as a function of time”. The processor is used for post processing of the kinematic data, therefore the processor acquires the first feature amount data and the second feature amount is another one of the motion paths functions created from the acceleration and angular velocity data.), the processor is configured to execute the instructions to: calculate, as the second feature amount, an average value and a difference of first feature amounts and the gait parameters used for estimation of the pelvic inclination among the first feature amounts and the gait parameters for both feet of the user (Chang, [0056]: “The biomechanical signals can reflect ranges in observed metrics and/or maximum, minimum, or average metric values”; [0075]: “the ground contact time can be estimated as a running average for both feet”. The motion path, which is the feature amount, includes the ground contact time, therefore the running average of ground time for both feet is the average value of the first feature amounts for both feet of the subject.; [0076]: “Determining ground contact time in a first variation includes segmenting the vertical velocity data by steps and taking the difference between the time of the maximum vertical velocity and the time of the minimum vertical velocity within each step cycle”. The motion path, which is the first feature amount, includes the ground contact time, therefore the difference used to determine the ground contact time is the difference between the first feature amounts.), input the second feature amount to the machine learning model (Chang, [0059]: “Detecting an action pattern can additionally or alternatively use machine intelligence such as deep learning, machine learning, statistical methods, and/or other suitable algorithmic approaches to detecting an action”. The post processing of the kinematic data includes the machine learning model, therefore the second feature amount is input into the machine learning model.), and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model (Chang, [0117]: “Detection of a change in biomechanical signals, such as ground contact time, sagittal tilt, cadence or motion paths, can additionally or alternatively use various machine learning techniques”; [0118]: “a hybrid implementation can use machine intelligence in combination with a heuristic approach. In one hybrid implementation, various heuristic-based analyses of biomechanical signals can be used as feature inputs into a machine learning algorithm as described above”; [0066]: “Pelvic tilt (i.e., pitch) can be characterized as rotation in the sagittal plane (i.e., rotation about a lateral axis)”; [0084]: “Pattern recognition algorithms, computer vision, image recognition, neural networks, and/or other suitable machine intelligence techniques can be used to analyze and characterize the shapes and variability of the motion paths created by the runner”. The analysis of the shapes and variability of motion paths are the estimation value related to the pelvic inclination, and the detection of sagittal tilt is the pelvic tilt (which is the pelvic inclination), since the pelvic tilt is determined by tilt in the sagittal plane.).
Regarding claim 5, the Chang/Murakami/Roche combination teaches the pelvic inclination estimation device according to claim 4, wherein the machine learning model is trained to output the estimation value related to the pelvic inclination according to an input of an attribute of the user and the second feature amount (Chang, [0103]: “Users could be given initially calibrated thresholds based on demographic information”; [0051]: “a base set of data can be characterized for a set of participants with different demographics and activity experience, and the data can be collected in controlled environments”. The demographic information about the user is the attribute of the subject that is used in the estimation process; [0084]: “a process for generating at least one set of motion paths functions to create a dimensional map of movement of at least one point of the body. The kinematic data can include multi-dimensional linear acceleration and angular velocity data, which can be converted to relative displacement or velocity for one to three dimensions as a function of time”. The second feature amount is one of the motion paths functions created from the acceleration and angular velocity data.), the processor is configured to execute the instructions to: input the attribute of the user and the second feature amount input to the machine learning model (Chang, [0059]: “Detecting an action pattern can additionally or alternatively use machine intelligence such as deep learning, machine learning, statistical methods, and/or other suitable algorithmic approaches to detecting an action”. The post processing of the kinematic data includes the machine learning model, therefore the second feature amount and the attribute of the subject are input into the machine learning model.), and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model (Chang, [0117]: “Detection of a change in biomechanical signals, such as ground contact time, sagittal tilt, cadence or motion paths, can additionally or alternatively use various machine learning techniques”; [0118]: “a hybrid implementation can use machine intelligence in combination with a heuristic approach. In one hybrid implementation, various heuristic-based analyses of biomechanical signals can be used as feature inputs into a machine learning algorithm as described above”; [0066]: “Pelvic tilt (i.e., pitch) can be characterized as rotation in the sagittal plane (i.e., rotation about a lateral axis)”; [0084]: “Pattern recognition algorithms, computer vision, image recognition, neural networks, and/or other suitable machine intelligence techniques can be used to analyze and characterize the shapes and variability of the motion paths created by the runner”. The analysis of the shapes and variability of motion paths are the estimation value related to the pelvic inclination, and the detection of sagittal tilt is the pelvic tilt (which is the pelvic inclination), since the pelvic tilt is determined by tilt in the sagittal plane.).
Regarding claim 6, the Chang/Murakami/Roche combination teaches the pelvic inclination estimation device according to claim 1, wherein the machine learning model is trained to output at least one variation width of the pelvic inclination related to three axes of a traveling axis, a left-right axis, and a vertical axis in one gait cycle as the estimation value related to the pelvic inclination according to the input of the feature amount included in the feature amount data (Chang, [0106]: “The x-offset and y-offset can relate to the amount of variation (i.e., wiggle) in the motion path”. Fig. 19 shows the x offset and y offset for each plane that show the amount of variation in a range, which is the variation width of each axis.), the processor is configured to execute the instructions to: input the feature amount included in the feature amount data to the machine learning model (Chang, [0059]: “Detecting an action pattern can additionally or alternatively use machine intelligence such as deep learning, machine learning, statistical methods, and/or other suitable algorithmic approaches to detecting an action”. The post processing of the kinematic data includes the machine learning model, therefore the feature amount is input into the machine learning model.), and estimate the pelvic inclination of the user according to a variation width of at least one of the pelvic inclinations in the three axes of the traveling axis, the left-right axis, and the vertical axis output from the machine learning model (Chang, [0117]: “Detection of a change in biomechanical signals, such as ground contact time, sagittal tilt, cadence or motion paths, can additionally or alternatively use various machine learning techniques”; [0118]: “a hybrid implementation can use machine intelligence in combination with a heuristic approach. In one hybrid implementation, various heuristic-based analyses of biomechanical signals can be used as feature inputs into a machine learning algorithm as described above”; [0066]: “Pelvic tilt (i.e., pitch) can be characterized as rotation in the sagittal plane (i.e., rotation about a lateral axis)”; [0084]: “Pattern recognition algorithms, computer vision, image recognition, neural networks, and/or other suitable machine intelligence techniques can be used to analyze and characterize the shapes and variability of the motion paths created by the runner”. The analysis of the shapes and variability of motion paths are the estimation value related to the pelvic inclination, and the detection of sagittal tilt is the pelvic tilt (which is the pelvic inclination), since the pelvic tilt is determined by tilt in the sagittal plane.).
Regarding claim 7, the Chang/Murakami/Roche combination teaches the pelvic inclination estimation device according to claim 1, wherein the processor is configured to execute the instructions to: display recommendation information according to the estimation result of the pelvic inclination of the user on the screen of the mobile terminal used by the user with content optimized for healthcare application (Chang, [0120]: “Analysis can be provided by displaying information, generating a graphical representation (e.g., a chart, a graphical indicator, etc.), playing audio feedback (e.g., making an speech audio announcement concerning the changes), activating a haptic feedback device, or using any suitable mechanism to provide feedback.”; [0129]: “The instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a user computer or mobile device, wristband, smartphone, or any suitable combination thereof”; Roche, [0032]: “videos can be provided on the application with detailed instructions on everything from … exercise programs”).
Regarding claim 8, the Chang/Murakami/Roche combination teaches an estimation system (Chang, Abstract: “A system and method for utilizing an activity monitoring device that includes, during a set of initial activity sessions, collecting the kinematic data from an activity monitoring device and generating a temporal record of at least one biomechanical signal that is calculated from the kinematic data”; Claim 1: “the at least one biomechanical signal and the at least one current biomechanical signal includes cadence, braking, pelvic rotation, pelvic tilt, and pelvic drop signals of a running activity.”) comprising: the pelvic inclination estimation device according to claim 1 (see rejection of claim 1 above); and the measurement device including the sensor that measures the spatial acceleration and the spatial angular velocity (Chang, [0032]: “The inertial measurement system 112 of the activity monitoring device 110 functions to measure multiple kinematic properties of an activity. The inertial measurement system 112 preferably includes at least one inertial measurement unit (IMU). An IMU can include at least one accelerometer, gyroscope, magnetometer, or other suitable inertial sensor”; [0055]: “a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes.”), and generates the sensor data based on the spatial acceleration and the spatial angular velocity (Chang, [0026]: “the system and method preferably operate with a set of biomechanical signals that can include ground contact time, braking, pelvic rotation, pelvic tilt, pelvic drop, vertical oscillation of the pelvis, forward oscillation, forward velocity properties of the pelvis, step duration, stride or step length, step impact or shock, and/or foot pronation. Additionally, the biomechanical signals can include left/right foot detection”), and configured to generate the feature amount data including the feature amount used for estimating the pelvic inclination using the sensor data (Chang, [0055]: “The kinematic measurements can include acceleration, velocity, displacement, force, angular velocity, angular displacement, tilt/angle, and/or any suitable metric corresponding to a kinematic property or dynamic property of an activity. Preferably, a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes”; [0084]: “a process for generating at least one set of motion paths functions to create a dimensional map of movement of at least one point of the body. The kinematic data can include multi-dimensional linear acceleration and angular velocity data, which can be converted to relative displacement or velocity for one to three dimensions as a function of time”; [0022]: “The system and method preferably uses the motion of a participant as they perform an action to determine if the participant is fatigued. The kinematic motion can be characterized as biomechanical signals. The biomechanical signals are preferably for repeated actions such as … pelvic tilt”. The biomechanical signals are the feature amount data, and the pelvic tilt is the pelvic inclination that is an index related to movement of the waist.).
Regarding independent claim 9, Chang teaches an estimation method executed by a computer, the estimation method comprising (Abstract: “A system and method for utilizing an activity monitoring device”):
receiving, from a measurement device disposed on footwear of a user ([0031]: “The activity monitoring device 110 is preferably small enough to be mounted to a participant in an unobtrusive way and may be integrated into a wearable such as … shoes”), feature amount data including a feature amount to be used for estimation of a pelvic inclination that is an index related to movement of a waist ([0022]: “The system and method preferably uses the motion of a participant as they perform an action to determine if the participant is fatigued. The kinematic motion can be characterized as biomechanical signals. The biomechanical signals are preferably for repeated actions such as … pelvic tilt”. The biomechanical signals are the feature amount data, and the pelvic tilt is the pelvic inclination that is an index related to movement of the waist.), the feature amount being extracted from motion path of a spatial acceleration and a spatial angular velocity included in sensor data ([0055]: “The kinematic measurements can include acceleration, velocity, displacement, force, angular velocity, angular displacement, tilt/angle, and/or any suitable metric corresponding to a kinematic property or dynamic property of an activity. Preferably, a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes”; [0084]: “a process for generating at least one set of motion paths functions to create a dimensional map of movement of at least one point of the body. The kinematic data can include multi-dimensional linear acceleration and angular velocity data, which can be converted to relative displacement or velocity for one to three dimensions as a function of time”) measured by a sensor included in the measurement device during walking of the user ([0032]: “The inertial measurement system 112 preferably includes at least one inertial measurement unit (IMU). An IMU can include at least one accelerometer, gyroscope, magnetometer, or other suitable inertial sensor.”; [0027]: “the system and method may be applied to activity use-cases such as gait-analysis, walking”).
However, Chang does not teach the motion path being a gait waveform.
Murakami discloses an apparatus that monitors a user’s walk. Specifically, Murakami teaches a gait waveform of a spatial acceleration and a spatial angular velocity ([0141]: “the gait timing detection unit 123 may estimate a gait cycle by using signal waveforms obtained from the acceleration sensor”. The acceleration sensor can measure both acceleration and angular velocity, therefore the gait waveform is made up from the acceleration and angular velocity.). Chang and Murakami are analogous art as they are related to the same field of endeavor of devices used to monitor a user while walking.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the motion path being a gait waveform from Murakami into the method from Chang as Chang is silent on the specific details of the motion path, and Murakami discloses a suitable type of motion path in an analogous device.
The Chang/Murakami combination teaches inputting the feature amount included in the feature amount data to a machine learning model that outputs an estimation value related to the pelvic inclination in response to the input of the feature amount included in the feature amount data; receiving the estimation value related to the pelvic inclination from the machine learning model; estimating the pelvic inclination of the user in real-time according to the estimation value related to the pelvic inclination output from the machine learning model (Chang, [0117]: “Detection of a change in biomechanical signals, such as ground contact time, sagittal tilt, cadence or motion paths, can additionally or alternatively use various machine learning techniques”; [0118]: “a hybrid implementation can use machine intelligence in combination with a heuristic approach. In one hybrid implementation, various heuristic-based analyses of biomechanical signals can be used as feature inputs into a machine learning algorithm as described above” [0066]: “Pelvic tilt (i.e., pitch) can be characterized as rotation in the sagittal plane (i.e., rotation about a lateral axis)”; [0084]: “Pattern recognition algorithms, computer vision, image recognition, neural networks, and/or other suitable machine intelligence techniques can be used to analyze and characterize the shapes and variability of the motion paths created by the runner”; [0056]: “The biomechanical signals for an activity are preferably a substantially real-time assessment of the biomechanical properties during the activity”. The analysis of the shapes and variability of motion paths are the estimation value related to the pelvic inclination, and the detection of sagittal tilt is the pelvic tilt (which is the pelvic inclination), since the pelvic tilt is determined by tilt in the sagittal plane.),
display information according to the estimation result of the pelvic inclination of the user on a screen of a mobile terminal used by the user (Chang, [0120]: “Analysis can be provided by displaying information, generating a graphical representation (e.g., a chart, a graphical indicator, etc.), playing audio feedback (e.g., making an speech audio announcement concerning the changes), activating a haptic feedback device, or using any suitable mechanism to provide feedback.”; [0129]: “The instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a user computer or mobile device, wristband, smartphone, or any suitable combination thereof”).
However, the Chang/Murakami combination does not teach displaying a video containing recommended training.
Roche teaches a system to monitor a user’s musculoskeletal system. Specifically, Roche teaches displaying a video containing recommended training ([0032]: “videos can be provided on the application with detailed instructions on everything from … exercise programs”). Chang, Murakami, and Roche are analogous arts as they are all related to the same field of endeavor of devices that measure a user’s steps and gait pattern to analyze the user’s health.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the recommended training video from Roche into the Chang/Murakami combination as it allows the device to recommend specific training to the user that can improve their health condition.
Regarding independent claim 10, Chang teaches a non-transitory recording medium recorded with a program causing a computer to perform ([0031]: “The activity monitoring device 110 can additionally include any suitable components to support computational operation such as a processor, RAM, Flash memory”):
receiving, from a measurement device disposed on footwear of a user ([0031]: “The activity monitoring device 110 is preferably small enough to be mounted to a participant in an unobtrusive way and may be integrated into a wearable such as … shoes”), feature amount data including a feature amount to be used for estimation of a pelvic inclination that is an index related to movement of a waist ([0022]: “The system and method preferably uses the motion of a participant as they perform an action to determine if the participant is fatigued. The kinematic motion can be characterized as biomechanical signals. The biomechanical signals are preferably for repeated actions such as … pelvic tilt”. The biomechanical signals are the feature amount data, and the pelvic tilt is the pelvic inclination that is an index related to movement of the waist.), the feature amount being extracted from motion path of a spatial acceleration and a spatial angular velocity included in sensor data ([0055]: “The kinematic measurements can include acceleration, velocity, displacement, force, angular velocity, angular displacement, tilt/angle, and/or any suitable metric corresponding to a kinematic property or dynamic property of an activity. Preferably, a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes”; [0084]: “a process for generating at least one set of motion paths functions to create a dimensional map of movement of at least one point of the body. The kinematic data can include multi-dimensional linear acceleration and angular velocity data, which can be converted to relative displacement or velocity for one to three dimensions as a function of time”) measured by a sensor included in the measurement device during walking of the user ([0032]: “The inertial measurement system 112 preferably includes at least one inertial measurement unit (IMU). An IMU can include at least one accelerometer, gyroscope, magnetometer, or other suitable inertial sensor.”; [0027]: “the system and method may be applied to activity use-cases such as gait-analysis, walking”).
However, Chang does not teach the motion path being a gait waveform.
Murakami discloses an apparatus that monitors a user’s walk. Specifically, Murakami teaches a gait waveform of a spatial acceleration and a spatial angular velocity ([0141]: “the gait timing detection unit 123 may estimate a gait cycle by using signal waveforms obtained from the acceleration sensor”. The acceleration sensor can measure both acceleration and angular velocity, therefore the gait waveform is made up from the acceleration and angular velocity.). Chang and Murakami are analogous art as they are related to the same field of endeavor of devices used to monitor a user while walking.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the motion path being a gait waveform from Murakami into the device from Chang as Chang is silent on the specific details of the motion path, and Murakami discloses a suitable type of motion path in an analogous device.
The Chang/Murakami combination teaches inputting the feature amount included in the feature amount data to a machine learning model that outputs an estimation value related to the pelvic inclination in response to the input of the feature amount included in the feature amount data; receiving the estimation value related to the pelvic inclination from the machine learning model; estimating the pelvic inclination of the user in real-time according to the estimation value related to the pelvic inclination output from the machine learning model (Chang, [0117]: “Detection of a change in biomechanical signals, such as ground contact time, sagittal tilt, cadence or motion paths, can additionally or alternatively use various machine learning techniques”; [0118]: “a hybrid implementation can use machine intelligence in combination with a heuristic approach. In one hybrid implementation, various heuristic-based analyses of biomechanical signals can be used as feature inputs into a machine learning algorithm as described above” [0066]: “Pelvic tilt (i.e., pitch) can be characterized as rotation in the sagittal plane (i.e., rotation about a lateral axis)”; [0084]: “Pattern recognition algorithms, computer vision, image recognition, neural networks, and/or other suitable machine intelligence techniques can be used to analyze and characterize the shapes and variability of the motion paths created by the runner”; [0056]: “The biomechanical signals for an activity are preferably a substantially real-time assessment of the biomechanical properties during the activity”. The analysis of the shapes and variability of motion paths are the estimation value related to the pelvic inclination, and the detection of sagittal tilt is the pelvic tilt (which is the pelvic inclination), since the pelvic tilt is determined by tilt in the sagittal plane.),
display information according to the estimation result of the pelvic inclination of the user on a screen of a mobile terminal used by the user (Chang, [0120]: “Analysis can be provided by displaying information, generating a graphical representation (e.g., a chart, a graphical indicator, etc.), playing audio feedback (e.g., making an speech audio announcement concerning the changes), activating a haptic feedback device, or using any suitable mechanism to provide feedback.”; [0129]: “The instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a user computer or mobile device, wristband, smartphone, or any suitable combination thereof”).
However, the Chang/Murakami combination does not teach displaying a video containing recommended training.
Roche teaches a system to monitor a user’s musculoskeletal system. Specifically, Roche teaches displaying a video containing recommended training ([0032]: “videos can be provided on the application with detailed instructions on everything from … exercise programs”). Chang, Murakami, and Roche are analogous arts as they are all related to the same field of endeavor of devices that measure a user’s steps and gait pattern to analyze the user’s health.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the recommended training video from Roche into the Chang/Murakami combination as it allows the device to recommend specific training to the user that can improve their health condition.
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant has amended the claims to overcome the claim objections and some 112(b) rejections, however some 112(b) rejections are reiterated, as the issues have not been overcome. Additionally, due to amendments, a new 112(b) rejection of claim 4 is introduced.
Applicant’s arguments with respect to the 103 rejections of claims 1-10 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.
Applicant's arguments with regards to Chang not having a sensor disposed on footwear have been fully considered but they are not persuasive. Chang does disclose the sensors disposed on footwear of a user for the purpose of estimating pelvic inclination ([0031]: “The activity monitoring device 110 is preferably small enough to be mounted to a participant in an unobtrusive way and may be integrated into a wearable such as … shoes”; [0022]: “The system and method preferably uses the motion of a participant as they perform an action to determine if the participant is fatigued. The kinematic motion can be characterized as biomechanical signals. The biomechanical signals are preferably for repeated actions such as … pelvic tilt”. The biomechanical signals are the feature amount data, and the pelvic tilt is the pelvic inclination that is an index related to movement of the waist), therefore this argument is not persuasive.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIN K MCCORMACK whose telephone number is (703)756-1886. The examiner can normally be reached Mon-Fri 7:30-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Sims can be reached at 5712727540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
Prosecution Insights