Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment Pursuant to 37 C.F.R. § 1.111
This is a final office action in response to communications filed on May 26, 2026.
Applicant amended claims 1, 8, 10-11, 13-14, 17, and 20 and cancelled claims 3, 6-7, and 19. Applicant added new claims 21-24. Claims 1-2, 4-5, 8-18, and 20-24 remain pending in the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-5, 8-18, and 20-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the claimed invention fall inside one of the four statutory categories (process, machine, manufacture, or composition of matter)? Yes for claims 1-2, 4-5, 8-18, and 20-24.
Claims 1-2, 4-5, 8-16, and 21-24 are drawn to a motion evaluation method (i.e., a process). Claims 17-18 and 20 are drawn to a method for motion evaluation and feedback (i.e., a process).
Step 2A - Prong One: Do the claims recite a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon)? Yes, for claims 1-2, 4-5, 8-18, and 20-24.
Claim 1 recites:
A motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject;
determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target motion signal includes at least one of a posture signal, an EMG signal, a mechanical signal, an electrocardiography signal, a respiratory signal, or a sweat signal, and the evaluation parameter standard includes a ratio threshold corresponding to a ratio between an amplitude of the target motion signal of the target part and an amplitude of a motion signal of a reference part;
obtaining the target motion signal of the target part;
obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including an error type;
and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part;
determining the ratio between the amplitude of the target motion signal of the target part and the amplitude of the motion signal of the reference part;
determining whether the ratio is less than the ratio threshold;
and in response to determining that the ratio is less than the ratio threshold, determining that the evaluation result includes a compensation error.
These steps amount to a form of mental process and organizing human activity (i.e., an abstract idea) because a human can obtain a motion state of a subject, determine evaluation criterion using a target, parameters, and various signals, obtain an evaluation result, and generate evaluation feedback using a reference, ratio calculations, and error calculations. Applicant discloses “the processing device … may obtain a user operation instruction … Exemplary operation instructions may include but not limited to setting user information (e.g., gender, age, height, weight, disease history, etc.), selecting a motion mode (e.g., running, rope skipping, swimming, muscle training, etc.), and setting a motion time.” [0054].
Independent claim 17 describes nearly identical steps as claim 1 (and therefore recite limitations that fall within this subject matter of grouping abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis. Dependent claims 2, 4-5, 8-16, 18, and 20-24 are directed towards mini-tasks (determining a feedback mode, determining an action type, and determining a ratio, etc.) for a motion evaluation and feedback method. Each claim amounts to a form of collecting, generating, and analyzing information, and therefore falls within the scope of a method for organizing human activity, (i.e., an abstract idea). As such, the Examiner concludes that claims 2, 4-5, 8-16, 18, and 20-24 recite an abstract idea.
Step 2A – Prong Two: Do the claims recite additional elements that integrate the exception into a practical application of the exception? No
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
Further, the additional limitations beyond the abstract idea identified above, serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
Dependent claims 2, 4-5, 8-16, 18, and 20-24 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified by the Examiner for each respective independent claim (i.e., they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? i.e., Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea? No
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an “inventive concept.” An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). As discussed above in “Step 2A – Prong Two”, the identified additional elements in independent claims 1 and 17 and dependent claims 2, 4-5, 8-16, 18, and 20-24 are equivalent to adding the words “apply it” on a computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Dependent claims 2, 4-5, 8-16, 18, and 20-24 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above. Therefore, claims 1-2, 4-5, 8-18, and 20-24 are not eligible subject matter under 35 USC 101.
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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4-5, 8-18, and 20-24 are rejected under 35 U.S.C. 103 as being unpatentable under US 20160081594 A1 (“Gaddipati”) in view of US 20210241464 A1 (“Uno”), US 20200409467 A1 (“Rispens”), and US 20200253541 A1 (“Okada”).
In regards to claim 1, Gaddipati discloses the following limitations with the exception of the underlined limitations.
A motion evaluation method, comprising: ([0012], “A … motion evaluation method comprising tracking … movement”) obtaining a motion signal of a subject, the motion signal representing a motion state of the subject ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”);
determining an evaluation criterion related to the motion signal ([0038], “The ROME ([0001], “range of motion evaluation (ROME)”) system can provide … evaluation … allowing … workers … to be trained to the same standards”), wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”), the target motion signal includes at least one of a posture signal, an EMG signal, a mechanical signal, an electrocardiography signal, a respiratory signal, or a sweat signal, and the evaluation parameter standard includes a ratio threshold corresponding to a ratio between an amplitude of the target motion signal of the target part ([0185], “The system … tracks the spine, hand … upper-lower body … and head”) and an amplitude of a motion signal of a reference part ([0233], “metrics are scored ... and ranked against population norms”);
obtaining the target motion signal of the target part ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”);
obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion ([0190], “activities that ROME can capture, analyze, and generate report include … bathing, dressing, transferring, using the toilet, continence, and eating” Examiner notes that analyzing data and generating a report is a method used to provide information to obtain an evaluation result.), the evaluation result including an error type;
and generating evaluation feedback based on the evaluation result ([0180], “ROME will automatically measure metrics real-time and provide constructive feedback”), wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part ([0236], “A log of the results in maintained and compared against the norms … with the same patient.”);
determining the ratio between the amplitude of the target motion signal of the target part ([0185], “The system … tracks the spine, hand … upper-lower body … and head”) and the amplitude of the motion signal of the reference part ([0233], “metrics are scored ... and ranked against population norms”);
determining whether the ratio is less than the ratio threshold;
and in response to determining that the ratio is less than the ratio threshold, determining that the evaluation result includes a compensation error.
Uno discloses
the target motion signal includes at least one of a posture signal, an EMG signal, a mechanical signal, an electrocardiography signal, a respiratory signal, or a sweat signal ([0041], “positions of the parts of a person's body are obtained by the sensor ... the posture ... unit … may acquire the coordinates for each person”)
Gaddipati and Uno are considered analogous to the claimed invention because they are in the field of motion evaluation and motion estimation systems and methods. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, the target motion signal includes at least one of a posture signal, an EMG signal, a mechanical signal, an electrocardiography signal, a respiratory signal, or a sweat signal, as disclosed by Uno, to provide a sensor and a posture unit for a motion estimation system, a motion estimation method, and a motion estimation program for estimating motions of a plurality of persons. One skilled in the art would recognize and value the addition of a sensor and a posture unit for a motion estimation system, a motion estimation method, and a motion estimation program for estimating motions of a plurality of persons.
Rispens discloses
and the evaluation parameter standard includes a ratio threshold corresponding to a ratio between an amplitude of the target motion signal of the and an amplitude of a motion signal of ([0090], “threshold ... can be found by observing the area under the curve ... by observing the TP ([0071], “TP (i.e. the fraction of fall events ...)”) at a ... FP ([0071], “FP (i.e. the fraction of non-fall events ...)”) ratio”)
determining the ratio between the amplitude of the target motion signal of and the amplitude of the motion signal of ([0067], “The logarithm of the ratio of two probabilities constitutes the Log Likelihood Ratio (LLR)”)
determining whether the ratio is less than the ratio threshold ([0068], “the fall detection algorithm determines that the feature values result … from a non-fall when the LLR is below the threshold”)
and in response to determining that the ratio is less than the ratio threshold ([0068], “the fall detection algorithm determines that the feature values result … from a non-fall when the LLR is below the threshold”)
Gaddipati and Rispens are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, and the evaluation parameter standard includes a ratio threshold corresponding to a ratio between an amplitude of the target motion signal of the and an amplitude of a motion signal of , determining the ratio between the amplitude of the target motion signal of and the amplitude of the motion signal of, determining whether the ratio is less than the ratio threshold, and in response to determining that the ratio is less than the ratio threshold, as disclosed by Rispens, to provide a threshold, a ratio, a logarithm, and a fall detection algorithm for a computer-implemented method, apparatus and computer program product for evaluating movement of a subject. One skilled in the art would recognize and value the addition of a threshold, a ratio, a logarithm, and a fall detection algorithm for a computer-implemented method, apparatus and computer program product for evaluating movement of a subject.
Okada discloses
an error type ([0060], “when the score of the superior movement data is lower than that of the inferior movement data, the difference is set as an error … when the difference of the scores is calculated and the calculated difference is equal to or larger than a predetermined value … the amount exceeding the predetermined value is set as an error”)
determining that the evaluation result includes a compensation error ([0060], “when the score of the superior movement data is lower than that of the inferior movement data, the difference is set as an error”).
Gaddipati and Okada are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, an error type determining that the evaluation result includes a compensation error, as disclosed by Okada, to provide a score, an error, and a predetermined value for a technique of evaluating the quality of a movement of a human. One skilled in the art would recognize and value the addition of a score, an error, and a predetermined value for a technique of evaluating the quality of a movement of a human.
In regards to claim 2, Gaddipati discloses
wherein the generating evaluation feedback based on the evaluation result includes ([0180], “ROME will automatically measure metrics real-time and provide constructive feedback”): determining a target feedback mode among a plurality of feedback modes based on the evaluation result or a user type of the subject, wherein the plurality of feedback modes notify the subject at different feedback times or in different feedback types ([0040], “Ability to … compare … movement from multiple time points”);
and generating feedback based on the target feedback mode ([0054]-[0055], “Using … information … to generate metrics …. Generating … evaluation reports” Examiner notes that evaluation reports are designed to provide feedback.).
In regards to claim 4, Gaddipati discloses
wherein the determining evaluation criterion related to the motion signal includes:
determining an action type of the subject by performing an action recognition operation on the subject based on the motion signal ([0174], “ROME can be used to capture the actions and movements of a person while performing a task”);
and determining the evaluation criterion related to the motion signal based on the action type ([0038], “The ROME ([0001], “range of motion evaluation (ROME)”) system can provide … evaluation … allowing … workers … to be trained to the same standards”).
In regards to claim 5, Gaddipati discloses
wherein the determining an action type of the subject by performing an action recognition operation on the subject based on the motion signal includes: for each frame of the motion signal, determining whether to perform the action recognition operation ([0190], “ROME, with its pattern recognition capability, can identify when a fall happens or when there is a reduced activity”);
and in response to a determination to perform the action recognition operation, determining the action type of the subject by performing the action recognition operation on one or more frames of the motion signal, the one or more frames at least including the frame ([0201], “An instantaneous linear velocity … for a single frame is estimated using numerical differentiation of the … position with respect to time”).
In regards to claim 8, Gaddipati discloses the following limitations with the exception of the underlined limitation.
wherein the evaluating the target motion signal of the target part ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”) based on the evaluation criterion includes ([0001], “range of motion evaluation (ROME)”) system can provide … evaluation … allowing … workers … to be trained to the same standards”): determining the amplitude of the target motion signal ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”);
determining whether the amplitude of the target motion signal is less than a first motion amplitude ([0180], “ROME will … measure metrics”);
and in response to determining that the amplitude of the target motion signal is less than the first motion amplitude ([0180], “ROME will … measure metrics”), determining that the evaluation result includes the compensation error.
Okada discloses
determining that the evaluation result includes the compensation error ([0060], “when the score of the superior movement data is lower than that of the inferior movement data, the difference is set as an error”).
Gaddipati and Okada are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, determining that the evaluation result includes the compensation error, as disclosed by Okada, to provide a score, an error, a predetermined value, and movement data for a technique of evaluating the quality of a movement of a human. One skilled in the art would recognize and value the addition of a score, an error, a predetermined value, and movement data for a technique of evaluating the quality of a movement of a human.
In regards to claim 9, Gaddipati discloses the following limitations with the exception of the underlined limitation.
wherein the evaluating the target motion signal of the target part ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”) based on the evaluation criterion includes ([0001], “range of motion evaluation (ROME)”) system can provide … evaluation … allowing … workers … to be trained to the same standards”): determining the amplitude of the target motion signal ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”);
determining whether the amplitude of the target motion signal is less than a second motion amplitude ([0180], “ROME will … measure metrics”);
and in response to determining that the amplitude of the target motion signal is less than the second motion amplitude ([0180], “ROME will … measure metrics”), determining that the evaluation result is an efficiency error.
Okada discloses
determining that the evaluation result is an efficiency error ([0060], “when the score of the superior movement data is lower than that of the inferior movement data, the difference is set as an error”).
Gaddipati and Okada are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, determining that the evaluation result is an efficiency error, as disclosed by Okada, to provide a score, an error, a predetermined value, and movement data for a technique of evaluating the quality of a movement of a human. One skilled in the art would recognize and value the addition of a score, an error, a predetermined value, and movement data for a technique of evaluating the quality of a movement of a human.
In regards to claim 10, Gaddipati discloses the following limitations with the exception of the underlined limitations.
wherein the target motion signal includes a first signal and a second signal ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”),
and the evaluating the target motion signal of the target part based on the evaluation criterion includes: identifying a first feature value of the first signal and a second feature value of the second signal ([0001], “range of motion evaluation (ROME)”) system can provide … evaluation … allowing … workers … to be trained to the same standards”);
determining a time difference between the first feature value of the first signal and the second feature value of the second signal ([0180], “ROME will … measure metrics”);
determining whether the time difference is greater than a time difference threshold;
and in response to determining that the time difference is greater than the time difference threshold, determining that the evaluation result includes an efficiency error.
Rispens discloses
determining whether the time difference is greater than a time difference threshold ([0068], “the fall detection algorithm determines that the feature values result … from a non-fall when the LLR is below the threshold”);
and in response to determining that the time difference is greater than the time difference threshold ([0068], “the fall detection algorithm determines that the feature values result … from a non-fall when the LLR is below the threshold”)
Gaddipati and Rispens are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, and the evaluation parameter standard includes a ratio threshold corresponding to a ratio between an amplitude of the target motion signal of the and an amplitude of a motion signal of , determining the ratio between the amplitude of the target motion signal of and the amplitude of the motion signal of, determining whether the ratio is less than the ratio threshold, and in response to determining that the ratio is less than the ratio threshold, determining whether the time difference is greater than a time difference threshold;
and in response to determining that the time difference is greater than the time difference threshold, as disclosed by Rispens, to provide a threshold, a ratio, a logarithm, and a fall detection algorithm for a computer-implemented method, apparatus and computer program product for evaluating movement of a subject. One skilled in the art would recognize and value the addition of a threshold, a ratio, a logarithm, and a fall detection algorithm for a computer-implemented method, apparatus and computer program product for evaluating movement of a subject.
Okada discloses
determining that the evaluation result includes an efficiency error
Gaddipati and Okada are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, determining that the evaluation result includes an efficiency error, as disclosed by Okada, to provide a score, an error, and a predetermined value for a technique of evaluating the quality of a movement of a human. One skilled in the art would recognize and value the addition of a score, an error, and a predetermined value for a technique of evaluating the quality of a movement of a human.
In regards to claim 11, Gaddipati discloses
wherein the target part includes at least two symmetrical parts of the subject ([0185], “The system … tracks … hand movement “), and the evaluating the motion signal based on the evaluation criterion includes:
obtaining target motion signals of the at least two symmetrical parts based on the motion signal ([0186], “When a patient is walking in the TUG ([0184], “Timed Up and Go”) test, ROME estimates … symmetry of placing legs”);
and evaluating the target motion signals of the at least two symmetrical parts based on the evaluation criterion ([0038], “The ROME ([0001], “range of motion evaluation (ROME)”) system can provide … evaluation … allowing … workers … to be trained to the same standards”).
In regards to claim 12, Gaddipati discloses the following limitations with the exception of the underlined limitations.
wherein the evaluating the target motion signals of the at least two symmetrical parts based on the evaluation criterion includes ([0186], “When a patient is walking in the TUG ([0184], “Timed Up and Go”) test, ROME estimates … symmetry of placing legs”): determining a signal difference between the target motion signals of the at least two symmetrical parts ([0180], “ROME will automatically measure metrics real-time”);
determining whether the signal difference is greater than a signal difference threshold;
and in response to determining that the signal difference is greater than the signal difference threshold, determining that the evaluation result includes a symmetry error.
Rispens discloses
determining whether the signal difference is greater than a signal difference threshold ([0068], “the fall detection algorithm determines that the feature values result … from a non-fall when the LLR is below the threshold”);
and in response to determining that the signal difference is greater than the signal difference threshold ([0068], “the fall detection algorithm determines that the feature values result … from a non-fall when the LLR is below the threshold”)
Gaddipati and Rispens are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, and the evaluation parameter standard includes a ratio threshold corresponding to a ratio between an amplitude of the target motion signal of the and an amplitude of a motion signal of , determining the ratio between the amplitude of the target motion signal of and the amplitude of the motion signal of, determining whether the ratio is less than the ratio threshold, and in response to determining that the ratio is less than the ratio threshold, determining whether the signal difference is greater than a signal difference threshold;
and in response to determining that the signal difference is greater than the signal difference threshold, as disclosed by Rispens, to provide a threshold, a ratio, a logarithm, and a fall detection algorithm for a computer-implemented method, apparatus and computer program product for evaluating movement of a subject. One skilled in the art would recognize and value the addition of a threshold, a ratio, a logarithm, and a fall detection algorithm for a computer-implemented method, apparatus and computer program product for evaluating movement of a subject.
Okada discloses
determining that the evaluation result includes a symmetry error
Gaddipati and Okada are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, determining that the evaluation result includes a symmetry error, as disclosed by Okada, to provide a score, an error, and a predetermined value for a technique of evaluating the quality of a movement of a human. One skilled in the art would recognize and value the addition of a score, an error, and a predetermined value for a technique of evaluating the quality of a movement of a human.
In regards to claim 13, Gaddipati discloses
wherein the evaluating the motion signal based on the evaluation criterion includes ([0001], “range of motion evaluation (ROME)”) system can provide … evaluation … allowing … workers … to be trained to the same standards”):
determining a frequency of the target motion signal of the target part ([0238], “ROME … can track … frequency”);
and determining a fatigue state of the target part based on the frequency and the evaluation criterion ([0200], “Data from ROME ... can ... be used to determine the root cause for injuries while working on a specific job tasks”).
In regards to claim 14, Gaddipati discloses
wherein the evaluating the motion signal based on the evaluation criterion includes ([0001], “range of motion evaluation (ROME)”) system can provide … evaluation … allowing … workers … to be trained to the same standards”): determining an evaluation parameter of the target part based on the target motion signal ([0188], “The ROME sensor detects and measures key parameters that include … motion”);
and determining an injury type or an injury level of the target part based on the evaluation parameter and the evaluation criterion ([0198], “Data from ROME can aid in ... quantifying the intensities of pain experienced while performing ... actions ... This data ... can be used to ... indicate ... injury”).
In regards to claim 15, Gaddipati discloses
wherein the evaluation parameter includes at least one of an internal rotation angle, an abduction angle, or a motion acceleration of the target part ([0185], “ROME … measures the sway, velocity and acceleration”).
In regards to claim 16, Gaddipati discloses
further comprising: evaluating the motion signal based on a motion evaluation model ([0012], “A … motion evaluation method comprising tracking … movement”).
In regards to claim 17, Gaddipati discloses the following limitations with the exception of the underlined limitations.
A method for motion evaluation and feedback, comprising ([0012], “A … motion evaluation method comprising tracking … movement”): obtaining a motion signal of a subject, the motion signal representing a motion state of the subject ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”);
determining an action type of the subject by performing an action recognition operation on the motion signal using an action recognition model ([0174], “ROME can be used to capture the actions and movements of a person while performing a task”), wherein the action recognition model is a trained machine learning model generated by training a machine learning model using a plurality of pieces of sample information, each of the plurality of pieces of sample information includes a sample motion signal ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”) and a labelled action type of the sample motion signal ([0174], “ROME can be used to capture the actions”);
determining an evaluation criterion related to the motion signal ([0038], “The ROME ([0001], “range of motion evaluation (ROME)”) system can provide … evaluation … allowing … workers … to be trained to the same standards”) base on the action type ([0174], “ROME can be used to capture the actions”), wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”), the target motion signal includes an EMG signal collected through one or more electrodes attached to different parts of the subject, and the evaluation parameter standard includes a ratio threshold corresponding to a ratio between an amplitude of the target motion signal of the target part ([0185], “The system … tracks the spine, hand … upper-lower body … and head”) and an amplitude of a motion signal of a reference part ([0233], “metrics are scored ... and ranked against population norms”);
obtaining the target motion signal of the target part ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”);
obtaining an evaluation result by evaluating the motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion related to the motion signal ([0190], “activities that ROME can capture, analyze, and generate report include … bathing, dressing, transferring, using the toilet, continence, and eating”);
determining a target feedback mode among a plurality of feedback modes based on the evaluation result, wherein the plurality of feedback modes notify the subject at different feedback times or in different feedback types ([0040], “Ability to … compare … movement from multiple time points”);
and generating evaluation feedback based on the target feedback mode ([0054]-[0055], “Using … information … to generate metrics …. Generating … evaluation reports”), the evaluation feedback ([0180], “ROME will automatically measure metrics real-time and provide constructive feedback”) including applying an electrical stimulation to a part with errors through electrodes to indicate that there are action errors in the corresponding part, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part;
determining the ratio between the amplitude of the target motion signal of the target part ([0185], “The system … tracks the spine, hand … upper-lower body … and head”) and the amplitude of the motion signal of the reference part ([0233], “metrics are scored ... and ranked against population norms”);
determining whether the ratio is less than the ratio threshold;
and in response to determining that the ratio is less than the ratio threshold, determining that the evaluation result includes a compensation error.
Okada discloses
wherein the action recognition model is a trained machine learning model generated by training a machine learning model using a plurality of pieces of sample information ([0069], “A text generating method can be performed by … using machine learning”)
the target motion signal includes an EMG signal collected through one or more electrodes attached to different parts of the subject including applying an electrical stimulation to a part ([0038], “The sensor … is … a wearable sensor which can be attached to the body of the user” Examiner notes that an EMG sensor is a wearable sensor and that an electrode can be considered a type of sensor.)
with errors through electrodes to indicate that there are action errors in the corresponding part ([0060], “when the score of the superior movement data is lower than that of the inferior movement data, the difference is set as an error … when the difference of the scores is calculated and the calculated difference is equal to or larger than a predetermined value … the amount exceeding the predetermined value is set as an error”)
determining that the evaluation result includes a compensation error ([0060], “when the score of the superior movement data is lower than that of the inferior movement data, the difference is set as an error”)
Gaddipati and Okada are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method for motion evaluation and feedback, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject, determining an action type of the subject by performing an action recognition operation on the motion signal using an action recognition model, each of the plurality of pieces of sample information includes a sample motion signal and a labelled action type of the sample motion signal, determining an evaluation criterion related to the motion signal base on the action type, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part, obtaining an evaluation result by evaluating the motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion related to the motion signal, determining a target feedback mode among a plurality of feedback modes based on the evaluation result, wherein the plurality of feedback modes notify the subject at different feedback times or in different feedback types, and generating evaluation feedback based on the target feedback mode, the evaluation feedback, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part,
the reference part, as disclosed by Gaddipati, wherein the action recognition model is a trained machine learning model generated by training a machine learning model using a plurality of pieces of sample information the target motion signal includes an EMG signal collected through one or more electrodes attached to different parts of the subject including applying an electrical stimulation to a part with errors through electrodes to indicate that there are action errors in the corresponding part determining that the evaluation result includes a compensation error, as disclosed by Okada, to provide a score, an error, a predetermined value, and machine learning for a technique of evaluating the quality of a movement of a human. One skilled in the art would recognize and value the addition of a score, an error, a predetermined value, and machine learning for a technique of evaluating the quality of a movement of a human.
Rispens discloses
and the evaluation parameter standard includes a ratio threshold corresponding to a ratio between an amplitude of the target motion signal of and an amplitude of a motion signal of ([0090], “threshold ... can be found by observing the area under the curve ... by observing the TP ([0071], “TP (i.e. the fraction of fall events ...)”) at a ... FP ([0071], “FP (i.e. the fraction of non-fall events ...)”) ratio”)
determining the ratio between the amplitude of the target motion signal of and the amplitude of the motion signal of ([0067], “The logarithm of the ratio of two probabilities constitutes the Log Likelihood Ratio (LLR)”)
determining whether the ratio is less than the ratio threshold ([0068], “the fall detection algorithm determines that the feature values result … from a non-fall when the LLR is below the threshold”);
and in response to determining that the ratio is less than the ratio threshold ([0068], “the fall detection algorithm determines that the feature values result … from a non-fall when the LLR is below the threshold”)
Gaddipati and Rispens are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method for motion evaluation and feedback, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject, determining an action type of the subject by performing an action recognition operation on the motion signal using an action recognition model, each of the plurality of pieces of sample information includes a sample motion signal and a labelled action type of the sample motion signal, determining an evaluation criterion related to the motion signal base on the action type, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part, obtaining an evaluation result by evaluating the motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion related to the motion signal, determining a target feedback mode among a plurality of feedback modes based on the evaluation result, wherein the plurality of feedback modes notify the subject at different feedback times or in different feedback types, and generating evaluation feedback based on the target feedback mode, the evaluation feedback, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part,
the reference part, as disclosed by Gaddipati, and the evaluation parameter standard includes a ratio threshold corresponding to a ratio between an amplitude of the target motion signal of and an amplitude of a motion signal of determining the ratio between the amplitude of the target motion signal of and the amplitude of the motion signal of
determining whether the ratio is less than the ratio threshold, and in response to determining that the ratio is less than the ratio threshold, as disclosed by Rispens, to provide a threshold, a ratio, a logarithm, and a fall detection algorithm for a computer-implemented method, apparatus and computer program product for evaluating movement of a subject. One skilled in the art would recognize and value the addition of a threshold, a ratio, a logarithm, and a fall detection algorithm for a computer-implemented method, apparatus and computer program product for evaluating movement of a subject.
In regards to claim 18, Gaddipati discloses
wherein the feedback times include immediate feedback or feedback after a motion ([0180], “ROME will automatically measure metrics real-time and provide constructive feedback”).
In regards to claim 20, Gaddipati discloses
wherein the determining a target feedback mode among a plurality of feedback modes based on the evaluation result includes ([0180], “ROME will automatically measure metrics real-time and provide constructive feedback”): determining the target feedback mode among the plurality of feedback modes based on at least one of: the action type, a user type of the subject, and the evaluation result ([0174], “ROME can be used to capture the actions and movements of a person while performing a task”).
In regards to claim 21, Gaddipati discloses the following limitations with the exception of the underlined limitation.
wherein the action recognition operation is performed using an action recognition model, the action recognition model is a trained machine learning model generated by training a machine learning model using a plurality of pieces of sample information, and each of the plurality of pieces of sample information includes a sample motion signal ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”) and a labelled action type of the sample motion signal ([0174], “ROME can be used to capture the actions”).
Okada discloses
wherein the action recognition operation is performed using an action recognition model, the action recognition model is a trained machine learning model generated by training a machine learning model using a plurality of pieces of sample information ([0069], “A text generating method can be performed by … using machine learning”)
Gaddipati and Okada are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, and each of the plurality of pieces of sample information includes a sample motion signal and a labelled action type of the sample motion signal, as disclosed by Gaddipati, an error type determining that the evaluation result includes a compensation error, wherein the action recognition operation is performed using an action recognition model, the action recognition model is a trained machine learning model generated by training a machine learning model using a plurality of pieces of sample information, as disclosed by Okada, to provide a score, an error, and a predetermined value for a technique of evaluating the quality of a movement of a human. One skilled in the art would recognize and value the addition of a score, an error, and a predetermined value for a technique of evaluating the quality of a movement of a human.
In regards to claim 22, Gaddipati discloses the following limitations with the exception of the underlined limitation.
wherein the evaluation criterion comprises ([0001], “range of motion evaluation (ROME)”) system can provide … evaluation … allowing … workers … to be trained to the same standards”) a time difference threshold corresponding to the time difference, the time difference is a difference between a signal acquisition time corresponding to the first feature value of the first signal and a signal acquisition time corresponding to the second feature value of the second signal ([0180], “ROME will … measure metrics”).
Rispens discloses
a time difference threshold corresponding to the time difference ([0068], “the fall detection algorithm determines that the feature values result … from a non-fall when the LLR is below the threshold”)
Gaddipati and Rispens are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, wherein the evaluation criterion comprises the time difference is a difference between a signal acquisition time corresponding to the first feature value of the first signal and a signal acquisition time corresponding to the second feature value of the second signal, as disclosed by Gaddipati, and the evaluation parameter standard includes a ratio threshold corresponding to a ratio between an amplitude of the target motion signal of the and an amplitude of a motion signal of , determining the ratio between the amplitude of the target motion signal of and the amplitude of the motion signal of, determining whether the ratio is less than the ratio threshold, and in response to determining that the ratio is less than the ratio threshold, determining whether the time difference is greater than a time difference threshold; and in response to determining that the time difference is greater than the time difference threshold, a time difference threshold corresponding to the time difference, as disclosed by Rispens, to provide a threshold, a ratio, a logarithm, and a fall detection algorithm for a computer-implemented method, apparatus and computer program product for evaluating movement of a subject. One skilled in the art would recognize and value the addition of a threshold, a ratio, a logarithm, and a fall detection algorithm for a computer-implemented method, apparatus and computer program product for evaluating movement of a subject.
In regards to claim 23, Gaddipati discloses the following limitations with the exception of the underlined limitations.
wherein the first signal is the EMG signal of the target part ([0185], “The system … tracks the spine, hand … upper-lower body … and head”), and the second signal is the posture signal of the target part ([0185], “The system … tracks the spine, hand … upper-lower body … and head”).
Okada discloses
wherein the first signal is the EMG signal of ([0038], “The sensor … is … a wearable sensor which can be attached to the body of the user” Examiner notes that an EMG sensor is a wearable sensor and that an electrode can be considered a type of sensor.)
Gaddipati and Okada are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, an error type determining that the evaluation result includes a compensation error, wherein the first signal is the EMG signal of, as disclosed by Okada, to provide a score, an error, a predetermined value, and a sensor for a technique of evaluating the quality of a movement of a human. One skilled in the art would recognize and value the addition of a score, an error, a predetermined value, and a sensor for a technique of evaluating the quality of a movement of a human.
Uno discloses
and the second signal is the posture signal of ([0041], “positions of the parts of a person's body are obtained by the sensor ... the posture ... unit … may acquire the coordinates for each person”)
Gaddipati and Uno are considered analogous to the claimed invention because they are in the field of motion evaluation and motion estimation systems and methods. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, the target motion signal includes at least one of a posture signal, an EMG signal, a mechanical signal, an electrocardiography signal, a respiratory signal, or a sweat signal, and the second signal is the posture signal of, as disclosed by Uno, to provide a sensor and a posture unit for a motion estimation system, a motion estimation method, and a motion estimation program for estimating motions of a plurality of persons. One skilled in the art would recognize and value the addition of a sensor and a posture unit for a motion estimation system, a motion estimation method, and a motion estimation program for estimating motions of a plurality of persons.
In regards to claim 24, Gaddipati does not disclose wherein the target motion signal comprises an EMG signal collected through one or more electrodes attached to different parts of the subject.
Okada discloses
wherein the target motion signal comprises an EMG signal collected through one or more electrodes attached to different parts of the subject ([0038], “The sensor … is … a wearable sensor which can be attached to the body of the user” Examiner notes that an EMG sensor is a wearable sensor and that an electrode can be considered a type of sensor.).
Gaddipati and Okada are considered analogous to the claimed invention because they are in the field of motion and movement evaluation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a motion evaluation method, comprising: obtaining a motion signal of a subject, the motion signal representing a motion state of the subject; determining an evaluation criterion related to the motion signal, wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal, the target part, a reference part, obtaining the target motion signal of the target part; obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion, the evaluation result including; and generating evaluation feedback based on the evaluation result, wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part, the target part, the reference part, as disclosed by Gaddipati, an error type determining that the evaluation result includes a compensation error, wherein the target motion signal comprises an EMG signal collected through one or more electrodes attached to different parts of the subject, as disclosed by Okada, to provide a score, an error, and a predetermined value for a technique of evaluating the quality of a movement of a human. One skilled in the art would recognize and value the addition of a score, an error, and a predetermined value for a technique of evaluating the quality of a movement of a human.
Response to Remarks
Applicant's arguments filed May 26, 2026 have been fully considered but they are not persuasive. Regarding the rejection under 35 U.S.C. § 101, Applicant submits that “the amended claim 1 is patent-eligible under the two-part test outlined in Alice Corp.” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 11, paragraph 2), “The Claimed Invention Is Not Directed to an Abstract Idea” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 11, paragraph 3), “amended independent claim 1 is not directed to any judicial exception” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 12, paragraph 1), “even if the amended independent claim 1 is directed to a judicial exception, the judicial exception is integrated into a practical application” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 12, paragraph 2), “The Claims Meet The ‘Significantly More’ Requirements”. (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 14, paragraph 2), and “the claims, as amended, are not directed to an abstract idea, and even assuming that the claims are directed to an abstract idea (which Applicant does not concede), the claims amount to significantly more than the abstract”. (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 19, paragraph 2). Examiner acknowledges Applicant’s remarks. With regards to the 101 rejection, the steps and limitations of claim 1 amount to a form of mental process and organizing human activity (i.e., an abstract idea) because a human can obtain a motion state of a subject, determine evaluation criterion using a target, parameters, and various signals, obtain an evaluation result, and generate evaluation feedback using a reference, ratio calculations, and error calculations. In the specifications, Applicant discloses that “the processing device … may obtain a user operation instruction … Exemplary operation instructions may include but not limited to setting user information (e.g., gender, age, height, weight, disease history, etc.), selecting a motion mode (e.g., running, rope skipping, swimming, muscle training, etc.), and setting a motion time.” [0054]. Independent claim 17 describes nearly identical steps and parallel limitations as claim 1 (and therefore fall within the subject matter of grouping abstract ideas), and is therefore determined to recite an abstract idea under the same analysis. Dependent claims 2, 4-5, 8-16, 18, and 20-24 are directed towards mini-tasks (determining a feedback mode, determining an action type, and determining a ratio, etc.) for a motion evaluation and feedback method. Each claim amounts to a form of collecting, generating, and analyzing information, and therefore falls within the scope of a method for organizing human activity, (i.e., an abstract idea). As such, the Examiner concludes that claims 2, 4-5, 8-16, 18, and 20-24 recite an abstract idea.
Examiner analyzed the claims to determine whether any additional element, or combination of additional elements, are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an “inventive concept.” An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). The identified additional elements in independent claims 1 and 17 and dependent claims 2, 4-5, 8-16, 18, and 20-24 are equivalent to adding the words “apply it” on a computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Dependent claims 2, 4-5, 8-16, 18, and 20-24 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above. Therefore, the 35 USC 101 ineligible subject matter rejections of claims 1-2, 4-5, 8-18, and 20-24 are maintained.
Regarding the rejections under 35 U.S.C. 102, Applicant submits that “Gaddipati does not disclose the ‘evaluation criterion’ as recited in amended claim 1” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 21, paragraph 1), “Gaddipati neither discloses any concept of a ‘target part’ (the term ‘target’ in paragraph [0236] of Gaddipati refers to a visual target on a screen, not an anatomical part of the human body), nor does Gaddipati disclose limiting, within an evaluation criterion, a specific motion signal or evaluation parameter standard for a target part” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 21, paragraph 2), “Gaddipati also does not disclose the subsequent steps of ‘obtaining the target motion signal of the target part’ and ‘evaluating the target motion signal based on the evaluation criterion.’ Gaddipati contains neither a teaching of dynamically determining an evaluation criterion based on a motion signal, nor a process of acquiring a motion signal for a specific target part and evaluating it.” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 22, paragraph 1), “Gaddipati completely lacks the technical feature of ‘identifying a compensation error based on a ratio of signal amplitudes between a target part and a reference part’ as recited in amended claim 1” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 22, paragraph 2), “The concept of a ‘reference part’ never appears in Gaddipati” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 22, paragraph 3), “Gaddipati in its entirety fails to disclose the evaluation criterion, the target part, the target motion signal, the ratio threshold, the reference part, and the compensation error.” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 25, paragraph 1), “Gaddipati does not explicitly disclose that the selection of a feedback mode based on the evaluation result, nor does it disclose a plurality of feedback modes distinguished by different feedback times or different feedback types.” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 25, paragraph 3 and page 26, lines 1-2), and “Gaddipati does not disclose any action recognition model, let alone a trained machine learning model for determining action types. Gaddipati lacks any teaching or suggestion of using machine learning to recognize action types from motion signals, and consequently does not disclose determining an evaluation criterion based on such an action type.” (See Amendment Pursuant to 37 C.F.R. § 1.111, Remarks, page 26, paragraph 1). Examiner acknowledges Applicant’s remarks. With regards to claim 1, Gaddipati discloses a motion evaluation method, comprising: ([0012], “A … motion evaluation method comprising tracking … movement”) obtaining a motion signal of a subject, the motion signal representing a motion state of the subject ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”); determining an evaluation criterion related to the motion signal ([0038], “The ROME ([0001], “range of motion evaluation (ROME)”) system can provide … evaluation … allowing … workers … to be trained to the same standards”), wherein the evaluation criterion includes a target part, a target motion signal corresponding to the target part, and an evaluation parameter standard corresponding to the target motion signal ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”), the target part ([0185], “The system … tracks the spine, hand … upper-lower body … and head”), a reference part ([0233], “metrics are scored ... and ranked against population norms”); obtaining the target motion signal of the target part ([0185], “The system … tracks the spine, hand movement, upper-lower body coordination and head position”); obtaining an evaluation result by evaluating the target motion signal based on the evaluation parameter standard corresponding to the target motion signal of the target part in the evaluation criterion ([0190], “activities that ROME can capture, analyze, and generate report include … bathing, dressing, transferring, using the toilet, continence, and eating” Examiner notes that analyzing data and generating a report is a method used to provide information to obtain an evaluation result.), the evaluation result including; and generating evaluation feedback based on the evaluation result ([0180], “ROME will automatically measure metrics real-time and provide constructive feedback”), wherein the evaluating the motion signal based on the evaluation criterion includes: determining the reference part based on the target part ([0236], “A log of the results in maintained and compared against the norms … with the same patient.”); the target part ([0185], “The system … tracks the spine, hand … upper-lower body … and head”) the reference part ([0233], “metrics are scored ... and ranked against population norms”), Uno discloses the target motion signal includes at least one of a posture signal, an EMG signal, a mechanical signal, an electrocardiography signal, a respiratory signal, or a sweat signal ([0041], “positions of the parts of a person's body are obtained by the sensor ... the posture ... unit … may acquire the coordinates for each person”), Rispens discloses and the evaluation parameter standard includes a ratio threshold corresponding to a ratio between an amplitude of the target motion signal of the and an amplitude of a motion signal of ([0090], “threshold ... can be found by observing the area under the curve ... by observing the TP ([0071], “TP (i.e. the fraction of fall events ...)”) at a ... FP ([0071], “FP (i.e. the fraction of non-fall events ...)”) ratio”)
determining the ratio between the amplitude of the target motion signal of and the amplitude of the motion signal of ([0067], “The logarithm of the ratio of two probabilities constitutes the Log Likelihood Ratio (LLR)”), determining whether the ratio is less than the ratio threshold ([0068], “the fall detection algorithm determines that the feature values result … from a non-fall when the LLR is below the threshold”) and in response to determining that the ratio is less than the ratio threshold ([0068], “the fall detection algorithm determines that the feature values result … from a non-fall when the LLR is below the threshold”), and Okada discloses an error type ([0060], “when the score of the superior movement data is lower than that of the inferior movement data, the difference is set as an error … when the difference of the scores is calculated and the calculated difference is equal to or larger than a predetermined value … the amount exceeding the predetermined value is set as an error”) and determining that the evaluation result includes a compensation error ([0060], “when the score of the superior movement data is lower than that of the inferior movement data, the difference is set as an error”).
MPEP § 2111 discusses proper claim interpretation, including giving claims their broadest reasonable interpretation (“BRI”) in light of the specification during examination. Under BRI, the words of a claim must be given their plain meaning unless such meaning is inconsistent with the specification, and it is improper to import claim limitations from the specification into the claim. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, claims 1 and 17 (which recites limitations parallel to claim 1), as obvious over Gaddipati in view of UnoRispens, and Okada, are rejected. Consequently, dependent claims 2, 4-5, 8-16, 18, and 20-24 are rejected.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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LISA H ANTOINE
Examiner
Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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