DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This office action is in response to arguments and amendments entered on September 5, 2025 for the patent application 18/461,144 originally filed on September 5, 2023. Claims 21, 33, and 34 are amended. Claims 1-20 are canceled. Claims 21-40 remain pending. The first office action of June 5, 2025 is fully incorporated by reference into this office action.
Response to Amendment
Applicant’s amendments to the claims have been noted by the Examiner.
Applicant’s amendments to the claims are sufficient to overcome the outstanding Double Patenting rejection. Accordingly, the Double Patenting rejection is withdrawn.
The Applicant’s amendments are sufficient to overcome the rejection under 35 USC 112. Therefore, the rejection under 35 USC 112 is withdrawn.
Applicant’s amendments are not sufficient to overcome the outstanding rejections under 35 USC 101, for reasons set forth below.
Applicant’s amendments are sufficient to overcome the outstanding rejections under 35 USC 103. However, new rejections under 35 USC 103 are applied in this office action, as set forth below.
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 21-40 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 21 is directed to “a method” (i.e. a process) and claim 33 is directed to “a system” (i.e. a machine), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
However, the claims are drawn to an abstract idea of “guiding movements of a subject” reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion) which are “performed on a computer” (per MPEP 2106(III)(C) “A Claim That Requires a Computer May Still Recite a Mental Process”).
Regardless, the claims are reasonably understood as “mental processes,” which require the following limitations:
“generating first sensor data…
tracking, based on the first sensor data… a performance of a first physical movement, the first physical movement being represented by the first sensor data;
generating a movement model based on the first sensor data… the movement model having combinations of formatted data values that were generated in real-time when the first physical movement was performed, the combinations of the formatted data values being stored in a repository that stores multiple movement models, the formatted data values including formatted data… the formatted data values being separated for each of an x-axis, a y-axis, and a z-axis and further separated… the formatted data having been formatted according to a kinematic chain that represents individual segments associated with one or multiple subject movements;
generating second sensor data… the second sensor data being generated during a performance of a second physical movement by a subject;
applying one or more data filters to the second sensor data, the formatted data values of the movement model being suitable for comparison with formatted data values derived from the filtered second sensor data;
determining a level of compliance of the second physical movement with the movement model by applying one or more modeling techniques to the filtered data; and
delivering feedback to the subject during or after the subject performs the second physical movement based on the level of compliance.”
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that, e.g., “motion sensors,” “a first wearable device,” “a gyroscope,” “an accelerometer,” “a magnetometer,” “a second wearable device,” and “a processor” are claimed, as these are merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., data gathering) and/or do no more than generally link the use of a judicial exception to a particular technological environment or field of use. In other words, the claimed “guiding movements of a subject,” is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g., “motion sensors,” “a first wearable device,” “a gyroscope,” “an accelerometer,” “a magnetometer,” “a second wearable device,” and “a processor” are claimed these are all generic, well-known, and conventional computing elements. As evidence that these are generic, well-known, and conventional computing elements, Applicant’s specification discloses them in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a), which satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the Applicant’s claimed “a first wearable device” and “a second wearable device” is described in paragraph [045] as follows: “’wearable device’ includes any form factor designed or intended to be worn by a person (e.g., personal equipment such as helmets, face guards, apparel, or the like; personal devices such as head-mounted electronic devices, wrist mounted devices, body-mounted devices, devices worn around the neck; or any form factor that, while not necessarily designed or intended to be worn by a person, may be adapted to be worn by a person (e.g., smartphones, tablet computers, and/or other digital processing devices).” That is, the wearable device may be a wrist mounted device, a smartphone, a tablet computer, or any type of digital processing device.
Regarding the wearable devices, instant application paragraph [046] further states, “Each wearable device may constitute a computing device including a processor, a memory storage, and a non-transitory computer-readable medium containing instructions that are executed by the processor. In addition, each of the wearable devices includes a plurality of motion and other types of sensors installed in the device and in communication with the processor. In one example, a wearable device, such as the first and second wearable devices 120, 130 illustrated in FIGs.1A-i and 1A-ii, may include at least a gyroscope, an accelerometer, and a magnetometer. In another example, a wearable device such as the first and second wearable devices 120, 130, may include a hall sensor, optical encoders, and/or any form of optical sensor for example.” That is, the other features of “motion sensors,” “a gyroscope,” “an accelerometer,” “a magnetometer,” and “a processor” are all included in the wearable devices. These features are standard features of conventional wrist mounted devices like smartwatches, smartphones, tablet computers, and other common digital processing devices.
These elements are reasonably interpreted as being generic computers or generic computing components, which provide no details of anything beyond ubiquitous standard equipment. As such, the claimed limitations are reasonably understood as not providing anything significantly more. Therefore, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 22-32 and 34-40 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 22-32 and 34-40 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to independent claims 21 and 33.
Therefore, claims 21-40 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21-27, 29, 30, 32-37, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (hereinafter “Chang,” US 2017/0344919) in view of Baker (US 2010/0015585).
Regarding claim 21, Chang discloses a method of generating a movement model, the method comprising:
generating first sensor data with one or more motion sensors of a first wearable device, the one or more motion sensors including one or more of a gyroscope, an accelerometer, or a magnetometer (Chang [0025], “the worker activity system 100 could be implemented on a personal computing device such as a mobile phone and/or a wearable computer.”; also Chang [0029], “The inertial measurement system 110 functions to measure multiple kinematic properties of an activity. The inertial measurement system 110 preferably includes at least one inertial measurement unit (IMU). An inertial measurement unit can include at least one accelerometer, gyroscope, magnetometer, or other suitable inertial sensor.”);
tracking, based on the first sensor data generated with the one or more motion sensors of the first wearable device, a performance of a first physical movement, the first physical movement being represented by the first sensor data (Chang [0029], “The inertial measurement unit preferably includes a set of sensors aligned for detection of kinematic properties along three perpendicular axes.”);
generating a movement model based on the first sensor data generated with the one or more motion sensors, the movement model having combinations of formatted data values that were generated in real-time when the first physical movement was performed, the combinations of the formatted data values being stored in a repository that stores multiple movement models, the formatted data values including formatted data for a plurality of sensors (Chang [0035], “displacements (e.g., vertical displacement, forward displacement, etc.) can be accounted for by multiple activity monitor devices. Modeled or predicted biomechanical motion can be generated using relative displacements. For example, if an activity monitor on the upper torso has a fourteen-inch vertical displacement and the pelvis has a one-inch displacement, the modeled biomechanical motion can indicate a lifting motion using the back.”; also Chang [0112], “generating an ergonomic model that associates task properties and kinematic activity, functions to analyze the collected observations to relate ergonomics with tasks. Generating the ergonomic model can include processing kinematic activity and task property data, and managing a data model of task associated kinematic activity data (e.g., biomechanical measurements). Generating an ergonomic model can include building the ergonomic model on a local device of the worker such as a phone, wearable device, or the worker activity system. Additionally or alternatively, the ergonomic model in part or whole could be stored and managed on a remote system such as in a remote internet-accessible computing system. The data model can be queried, analyzed, and/or otherwise used in driving various applications.” showing that Chang discloses storing biomechanical measurement data for future retrieval and comparison),
…
the formatted data having been formatted according to a kinematic chain that represents individual segments associated with one or multiple subject movements (Chang [0066], “normalizing the set of kinematic data streams can include adapting orientation of kinematic data sensing to a participant orientation, determining a base pelvic tilt position, and segmenting at least a subset of the kinematic data streams by detected steps,” the models including kinematic data streams);
generating second sensor data with a second wearable device, the second sensor data being generated during a performance of a second physical movement by a subject (Chang [0035], “In a variation with multiple sensing points, a set of signal processor modules 120 can be included in a plurality of activity monitor devices worn by a worker. The generation of the biomechanical signals can be completed on each of the activity monitor devices… an activity monitor device positioned on the pelvis or along the back can be used with one or more activity monitor devices on the thigh or below the hip to generate a hip rotation. In yet another variation, displacements (e.g., vertical displacement, forward displacement, etc.) can be accounted for by multiple activity monitor devices. Modeled or predicted biomechanical motion can be generated using relative displacements.”);
applying one or more data filters to the second sensor data, the formatted data values of the movement model beinq suitable for comparison with formatted data values derived from the filtered second sensor data (Chang [0063], “data can be filtered, error corrected, or otherwise transformed.”; also Chang [0084], “counting extrema exceeding a minimum amplitude requirement in the filtered, three-dimensional acceleration magnitude as measured by the sensor”; also Chang [0116], “Each lift event can be compared to all other previous lifts of the user or against an average good lift across the entire population”; also Chang [0119], “Irregular gait patterns could be detected by comparing current biomechanical measurements to historical gait related biomechanical measurements or averaged related biomechanical measurements across a larger group which can characterized in the gait ergonomic model.”);
determining a level of compliance of the second physical movement with the movement model by applying one or more modeling techniques to the filtered second sensor data (Chang [0035], “the modeled biomechanical motion can indicate a lifting motion using the back. Whereas if the upper torso has a fourteen inch vertical displacement and the pelvis similarly has a fourteen inch. As an exemplary scenario of monitoring lifting ergonomics, bad lifting ergonomics may be characterized as a larger angular displacement of the pelvis, a smaller vertical displacement of the pelvis, and/or a small angular displacement of the knee”); and
delivering feedback to the subject during or after the subject performs the second physical movement based on the level of compliance (Chang [0038], “if a set of workers has recently been using bad ergonomics when lifting items of a particular weight, a warning message may be played before lifting such an object”; also Chang [0122], “A posture ergonomic model functions to analyze the biomechanics and actions of a worker's posture while working. This could be particularly applicable when a worker is working on the factory floor. The model can analyze a worker's posture throughout the day and may provide haptic feedback to remind the worker if their posture is in a bad posture position for an extended period of time”).
Chang does not explicitly teach every limitation of the formatted data values being separated for each of an x-axis, a y-axis, and a z-axis and further separated for each sensor.
However, Baker discloses the formatted data values being separated for each of an x-axis, a y-axis, and a z-axis and further separated for each sensor (Baker [0079], “an object may have MEMs motion sensors attached or wear conductive movement sensing material or the like to which gives its position (x, y, and z coordinates), and the orientation (yaw, pitch, and roll) with respect to a reference point or state and as before the data obtained is stored and then transmitted to for storage in a database for analysis in a computer with the corresponding data of a preferred object whose data information is previously stored in the database. A typical object with which the apparatus may be used may comprise a golf club 26 (shown in dotted outline) which carries the sensors 13.”; also Baker [0035], “Each sensor 13 provides its own global orientation (3 degrees of freedom) and is physically and computationally independent,” showing that spatial position coordinates are separated for each sensor).
Baker is analogous to Chang, as both are drawn to the art of movement models. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by Chang, to include the formatted data values being separated for each of an x-axis, a y-axis, and a z-axis and further separated for each sensor, as taught by Baker, since it applies a known technique of data storage and retrieval to a known method ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 22, Chang in view of Baker discloses wherein the first wearable device and the second wearable device are different devices (Chang [0035], “In a variation with multiple sensing points, a set of signal processor modules 120 can be included in a plurality of activity monitor devices worn by a worker.”).
Regarding claim 23, Chang in view of Baker discloses wherein the feedback to correct the second physical movement identifies a movement adjustment for a first joint of the subject performing the second movement, the adjustment for the first joint also being associated with an adjustment of a position of a second joint (Chang [0068], “The biomechanical measurements may characterize the motion patterns of a particular body part. For example, the motion of a foot, a knee, pelvis, trunk or any suitable body part can be monitored and characterized in one or more biomechanical measurements. The biomechanical measurements may alternatively characterize the biomechanical relationship of multiple body parts. For example, the biomechanical measurements can show imbalances in a user's gait or a comparison of mobility between two joints. The biomechanical measurements may alternatively characterize the mobility quality of the particular body part, joint, or overall walking gait over time.”).
Regarding claim 24, Chang in view of Baker discloses wherein the subject is a second subject and the first physical movement is performed by a first subject that is different than the second subject (Chang [0018], “The worker activity systems 100 can be deployed to multiple active workers as shown in FIG. 2. In one variation, the worker activity systems can be distributed to a limited sampling of workers that make up a sub-portion of the workforce. In yet another version, a single worker activity system 100 may be shared by multiple workers”).
Regarding claim 25, and substantially similar limitations in claim 36, Chang in view of Baker discloses receiving a selection of a body portion that is tracked with the one or more motion sensors (Chang [0023], “The pelvis is used as a preferred reference point. The pelvis can have a strong correlation to lower body movements and can be more isolated from upper body movements such as turning of the head and swinging of the arms. An alternative sensing reference point can be used. The sensing point is preferably centrally positioned near the median plane in the trunk portion of the body. Additional sensing points or alternative sensing points may be used depending on the activity. The set of biomechanical signals may form a primitive set of signals from which a wide variety of activities can be monitored and analyzed,” body portion that is tracked is a sensing point).
Regarding claim 26, Chang in view of Baker discloses adjusting the one or more motion sensors based on the received selection (Chang [0024], “A biomechanical signal can be a function of time. The biomechanical signal can additionally be a real-time signal, wherein each data point is a value that corresponds to some instance of time. Real-time values can be for lifting motions, posture, walking gait strides, and/or any suitable type of biomechanical signals. The real-time value can be a current value for the biomechanical property or a running average. For example real-time stride time can be averaged over a window spanning a defined set of steps (e.g., 4 steps). Averaging, smoothing, and other error correcting processes may be applied to a real-time signal,” adjusting for a suitable type of biomechanical signals).
Regarding claim 27, and substantially similar limitations in claim 37, Chang in view of Baker discloses wherein the adjusting includes filtering performed on the first sensor data or the second sensor data (Chang [0063], “the data can be pre-processed. For example, data can be filtered, error corrected, or otherwise transformed.”).
Regarding claim 29, Chang in view of Baker discloses wherein the first sensor data is generated when an assessment movement is being performed (Chang [0115-0116], “There can be an ergonomic model for each class of kinematic activity measurement discussed above such as an action-specific ergonomic model (e.g., a lifting ergonomic model, a stair climbing model, etc.), gait ergonomic model, posture ergonomic model, and/or kinematic state or events ergonomic models (e.g., fall detection or balance ergonomic models)… In one preferred implementation, a lifting ergonomic model for analyzing lifting can be applied. The ergonomic model can assess the "goodness" or quality of the lift,” assessing the quality of the lift while it is being performed).
Regarding claim 30, Chang in view of Baker discloses wherein the feedback is delivered: at an end of a range of motion of the first physical movement or the second physical movement, or in response to identifying a deviation from the movement model in the first physical movement or the second physical movement, the deviation including a deviation in speed of movement, acceleration of movement, or orientation of a body part (Chang [0122], “A posture ergonomic model functions to analyze the biomechanics and actions of a worker's posture while working. This could be particularly applicable when a worker is working on the factory floor. The model can analyze a worker's posture throughout the day and may provide haptic feedback to remind the worker if their posture is in a bad posture position for an extended period of time. Proper posture is important while working on an assembly line or carrying a heavy package. The ergonomic model may quantify the angle of a worker's neck, upper back or lower back to make sure the spine is in a neutral spine posture. Overtime, the system may predict when a user will have back pain due to poor posture and mobility quality and may intervene to offer additional breaks or recommendations to the worker or team manager.”).
Regarding claim 32, and substantially similar limitations in claim 40, Chang in view of Baker discloses wherein the one or more motion sensors detect motion of the second physical movement within three dimensions (Chang [0065], “Preferably, a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes. The set of kinematic data streams preferably includes acceleration in any orthonormal set of axes in three-dimensional space, herein denoted as x, y, z axes, and angular velocity about the x, y, and z axes”).
Regarding claim 33, Chang discloses a system for modifying a movement model for guiding movements of a subject, the system comprising:
a first wearable device (Chang [0025], “the worker activity system 100 could be implemented on a personal computing device such as a mobile phone and/or a wearable computer.”);
a second wearable device (Chang [0035], “In a variation with multiple sensing points, a set of signal processor modules 120 can be included in a plurality of activity monitor devices worn by a worker. The generation of the biomechanical signals can be completed on each of the activity monitor devices”); and
a processor configured to execute instructions that enable the processor to perform functions (Chang [0149], “The computer-executable component can be a processor but any suitable dedicated hardware device can (alternatively or additionally) execute the instructions.”) including:
generating a movement model, the movement model representing a physical movement, the movement model configured to account for a kinematic chain that is involved with the physical movement, the movement model having combinations of formatted data values that were generated in real-time when the physical movement was performed, the combinations of the formatted data values being stored in a repository that stores multiple movement models, the formatted data values including formatted data for a plurality of sensors (Chang [0035], “displacements (e.g., vertical displacement, forward displacement, etc.) can be accounted for by multiple activity monitor devices. Modeled or predicted biomechanical motion can be generated using relative displacements. For example, if an activity monitor on the upper torso has a fourteen-inch vertical displacement and the pelvis has a one-inch displacement, the modeled biomechanical motion can indicate a lifting motion using the back.”; also Chang [0066], “normalizing the set of kinematic data streams can include adapting orientation of kinematic data sensing to a participant orientation, determining a base pelvic tilt position, and segmenting at least a subset of the kinematic data streams by detected steps,” the models including kinematic data streams; also Chang [0112], “generating an ergonomic model that associates task properties and kinematic activity, functions to analyze the collected observations to relate ergonomics with tasks. Generating the ergonomic model can include processing kinematic activity and task property data, and managing a data model of task associated kinematic activity data (e.g., biomechanical measurements). Generating an ergonomic model can include building the ergonomic model on a local device of the worker such as a phone, wearable device, or the worker activity system. Additionally or alternatively, the ergonomic model in part or whole could be stored and managed on a remote system such as in a remote internet-accessible computing system. The data model can be queried, analyzed, and/or otherwise used in driving various applications.” showing that Chang discloses storing biomechanical measurement data for future retrieval and comparison),
…
the formatted data having been formatted by a fast filtering technique, via a thresholding technique, or according to a machine learning technique (Chang [0063], “data can be filtered, error corrected, or otherwise transformed.”; also Chang [0084], “counting extrema exceeding a minimum amplitude requirement in the filtered, three-dimensional acceleration magnitude as measured by the sensor”; also Chang [0024], “Averaging, smoothing, and other error correcting processes may be applied to a real-time signal.”; also Chang [0113], “Ergonomic information can be analyzed based on location, task attributes (e.g., type, metadata related to the task), worker attributes, and/or other aspects in real-time and feedback (auditory, haptic, etc.) can be provided. The processing can include any suitable statistical analysis, machine learning/machine intelligence, deep learning, heuristic-based modeling, or other approaches to organizing the data”);
generating first sensor data with one or more motion sensors of the first wearable device during an actual physical movement by the subject, the one or more motion sensors including one or more of a gyroscope, an accelerometer, or a magnetometer (Chang [0025], “the worker activity system 100 could be implemented on a personal computing device such as a mobile phone and/or a wearable computer.”; also Chang [0029], “The inertial measurement system 110 functions to measure multiple kinematic properties of an activity. The inertial measurement system 110 preferably includes at least one inertial measurement unit (IMU). An inertial measurement unit can include at least one accelerometer, gyroscope, magnetometer, or other suitable inertial sensor.”);
creating a modified version of the movement model based on the first sensor data generated during the actual physical movement by the subject (Chang [0029], “The inertial measurement unit preferably includes a set of sensors aligned for detection of kinematic properties along three perpendicular axes.”);
generating second sensor data with the second wearable device, the second sensor data being generated during a performance of a second physical movement (Chang [0035], “In a variation with multiple sensing points, a set of signal processor modules 120 can be included in a plurality of activity monitor devices worn by a worker. The generation of the biomechanical signals can be completed on each of the activity monitor devices… an activity monitor device positioned on the pelvis or along the back can be used with one or more activity monitor devices on the thigh or below the hip to generate a hip rotation. In yet another variation, displacements (e.g., vertical displacement, forward displacement, etc.) can be accounted for by multiple activity monitor devices. Modeled or predicted biomechanical motion can be generated using relative displacements.”);
determining a level of compliance of the second actual physical movement with the modified version of the movement model by applying one or more modeling techniques to the second sensor data (Chang [0035], “the modeled biomechanical motion can indicate a lifting motion using the back. Whereas if the upper torso has a fourteen inch vertical displacement and the pelvis similarly has a fourteen inch. As an exemplary scenario of monitoring lifting ergonomics, bad lifting ergonomics may be characterized as a larger angular displacement of the pelvis, a smaller vertical displacement of the pelvis, and/or a small angular displacement of the knee”); and
delivering feedback during or after the second actual physical movement based on the level of compliance determined with the second sensor data (Chang [0038], “if a set of workers has recently been using bad ergonomics when lifting items of a particular weight, a warning message may be played before lifting such an object”; also Chang [0122], “A posture ergonomic model functions to analyze the biomechanics and actions of a worker's posture while working. This could be particularly applicable when a worker is working on the factory floor. The model can analyze a worker's posture throughout the day and may provide haptic feedback to remind the worker if their posture is in a bad posture position for an extended period of time”).
Chang does not explicitly teach every limitation of the formatted data values being separated for each of an x-axis, a y-axis, and a z-axis and further separated for each sensor.
However, Baker discloses the formatted data values being separated for each of an x-axis, a y-axis, and a z-axis and further separated for each sensor (Baker [0079], “an object may have MEMs motion sensors attached or wear conductive movement sensing material or the like to which gives its position (x, y, and z coordinates), and the orientation (yaw, pitch, and roll) with respect to a reference point or state and as before the data obtained is stored and then transmitted to for storage in a database for analysis in a computer with the corresponding data of a preferred object whose data information is previously stored in the database. A typical object with which the apparatus may be used may comprise a golf club 26 (shown in dotted outline) which carries the sensors 13.”; also Baker [0035], “Each sensor 13 provides its own global orientation (3 degrees of freedom) and is physically and computationally independent,” showing that spatial position coordinates are separated for each sensor).
Baker is analogous to Chang, as both are drawn to the art of movement models. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by Chang, to include the formatted data values being separated for each of an x-axis, a y-axis, and a z-axis and further separated for each sensor, as taught by Baker, since it applies a known technique of data storage and retrieval to a known method ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 34, Chang in view of Baker discloses wherein the first wearable device and the second wearable device are different wearable devices (Chang [0035], “In a variation with multiple sensing points, a set of signal processor modules 120 can be included in a plurality of activity monitor devices worn by a worker. The generation of the biomechanical signals can be completed on each of the activity monitor devices”).
Regarding claim 35, Chang in view of Baker discloses wherein the first sensor data is generated with the first wearable device and with the second wearable device (see Chang Fig. 4A, showing multiple wearable devices on a single worker; also Chang [0018], “The worker activity systems 100 can be deployed to multiple active workers as shown in FIG. 2. In one variation, the worker activity systems can be distributed to a limited sampling of workers that make up a sub-portion of the workforce. In yet another version, a single worker activity system 100 may be shared by multiple workers,” where a first set of data can be gathered by all the wearable devices worn by a first worker).
Claims 28, 31, 38, and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Chang in view Baker, and in further view of Dowling et al. (hereinafter “Dowling,” US 2013/0244211).
Regarding claim 28, and substantially similar limitations in claims 31, 38, and 39, Chang in view of Baker does not explicitly teach every limitation of adjusting the one or more motion sensors, the adjusting including setting a sensitivity, a sampling rate, a storage time, or a feedback trigger based on a calibration, the calibration being generated based on a library of designated movements.
Chang does disclose calibration of the sensors (Chang [0065], “the axis of measurement by one or more sensor(s) may be calibrated for analysis by calibrating the orientation of the kinematic data stream”), but does not specifically disclose calibrating the sensors based on a library of designated movements.
However, Dowling discloses adjusting the one or more motion sensors, the adjusting including setting a sensitivity, a sampling rate, a storage time, or a feedback trigger based on a calibration, the calibration being generated based on a library of designated movements (Dowling [0049], “Analysis of the data acquired in the Measurement Module of the invention typically involves both standard and custom algorithms. These algorithms include but are not limited to altering the measurement units; calibrating the signals to account for the location of the sensors on the body segments, on the objects, or in the environment; smoothing or filtering the signals; analyzing and combining the signals to obtain the specific descriptive parameters; extracting discrete metrics from the descriptive parameters; comparing the subject's movement (described by a set of discrete metrics) with a target movement using a comparison model to evaluate the risk of injury and, optionally, the performance, and to provide data on how the user can improve his or her technique”; also Dowling [0092], “The Processing Module includes calibration routines (as described in Example 1) to identify the locations of the sensors on the body and to align the sensors in relation to one another and to the global reference frame”).
Dowling is analogous to Chang in view of Baker, as both are drawn to the art of body movement analysis. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by Chang in view of Baker, to include adjusting the one or more motion sensors, the adjusting including setting a sensitivity, a sampling rate, a storage time, or a feedback trigger based on a calibration, the calibration being generated based on a library of designated movements, as taught by Dowling, in order to account for the location of the sensors on the body segments (Dowling [0049]). Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Response to Arguments
The Applicant’s arguments filed on September 5, 2025 have been fully considered and are addressed below.
Section 101 Rejections
The Applicant respectfully argues “Applicant has incorporated language that describe further technological processes that are unrelated to managing interactions between people and that are entirely incapable of being performed in the mind… These features are not directed to an abstract idea, especially when considered together with the other features present in independent claim 21… Independent claim 33, while of different scope, are not directed to an abstract idea for similar or additional reasons (e.g., due to the description of data analysis via a fast filtering technique, via a thresholding technique, or according to a machine learning technique).”
The Examiner respectfully disagrees. While the amended claims now describe limitations that are unrelated to managing interactions between people, the claims are still drawn to “mental processes.” MPEP 2106.04(a)(2)(III)(C) states that a claim that requires a computer may still recite a mental process. This section guides examiners to “review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.” In the present case, the invention is described as a concept of “guiding movements of a subject” performed on a generic computer or computer environment, and merely uses a computer as a tool to perform the concept. Because the claims only recite conventional computer processes performed on generic computing devices, they do not appear to describe significantly more than the judicial exception, and are thus considered abstract mental processes that are not eligible under 35 USC 101.
As such, the argument is not persuasive and the 35 USC 101 rejection of the claims will be maintained.
Art Rejections
Applicant’s arguments with respect to the claims 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:
Cho et al. (US 2006/0258194) Portable terminal having motion detection function and motion detection method therefor
McCartin (US 10,456,657) Method and electric device for identifying golf swings and tracking statistics during a golf round
Gray et al. (US 2018/0089280) Gait-based biometric data analysis system
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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/SA/Examiner, Art Unit 3715
/Robert P Bullington, Esq./Primary Examiner, Art Unit 3715