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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The following title is suggested: GAIT DETERMINATION DEVICE, DETERMINATION METHOD, AND RECORDING.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under the two-step 101 analysis, the claims fail to satisfy the
criteria for subject matter eligibility.
Step 1: Claims 1-10 are within at least one of the four statutory categories.
Claim 1 and dependent claims 2-8 disclose an apparatus.
Claim 9 discloses a method.
Claim 10 discloses a product of manufacture (non-transitory recording medium).
Step 2A, Prong One: The independent claims 1, 9, and 10 recite limitations directed to an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
Mental Processes: Mental Processes can be practically performed in the human mind using mental steps, a pen and paper, or basic critical thinking/judgement -- types of activities that have been found by the courts to represent abstract ideas. See p. 7-8 of October 2019 Update: 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) for examples of ineligible claims that recite mental processes.
Claims 1, 9, and 10 recite the follow abstract ideas:
calculate a relative change value indicating a relative change from a traveling axis for each gait cycle using time-series data of the acquired sensor data
determine a gait situation using time-series data of the relative change value in a target period
These limitations describe a mental process as the skilled artisan is capable of performing the recited limitations and making a mental assessment thereafter. Calculating a relative change value and determining a gait situation based on the relative change value can be performed through an individual’s mental process and judgement with the aid of pen and paper.
Regarding the dependent claims, they are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine, and previously known to the industry:
Claim 2 further limits establishing start and terminal points for a gait cycle using the time-series data and calculating the relative change from the start point
Claims 3 and 4 further limit using a relative angle and displacement to calculate the relative change value and determine the gait situation
Claim 5 further limits determining and removing sensor data associated with exceptional gait
Claim 6 further limits tagging sensor data with a gait type selected by a user
Claim 7 further limits using machine learning to determine the gait situation based on the relative change value
Claim 8 further limits providing recommendation information based on the gait situation of a user
The dependent claims further limit the abstract ideas of independent claims 1, 9, and 10 and do not recite significantly more than the abstract ideas.
Step 2A, Prong Two: The judicial exceptions (abstract ideas) in claims 1-10 are not integrated into a practical application because:
The abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computing components for acquiring measurement data and outputting results merely invoke a computer as a tool.
The data-gathering step (acquiring measurement data) and the data-output step (outputting gait information and recommendations) do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II.
The claims recite a computer/processor that is used as a tool for acquiring sensor data and outputting gait information results.
The claims do not apply the abstract idea to affect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized for acquiring sensor data to provide the output of gait information.
The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computer/processor for acquiring measurement data and outputting results.
The additional elements are identified as follows:
Claims 1-8 – determination device
Claims 1 and 10 – memory/non-transitory recording medium
Claims 1-10 – processor/computer
Claims 8 – screen/mobile terminal
Step 2B: Claims 1-10 do not include additional elements that are sufficient to provide for an inventive concept nor amount to significantly more than the judicial exception.
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for acquiring measurement data and outputting results as demonstrated by the specification. The applicant discloses nothing unique about the determination device (Specification pg 34, lines 7-28), memory/non-transitory recording medium (Specification pg 48, lines 20-25 and pg 49, line 29 - pg 50, line 8), processor/computer (Specification pg 48, lines 4-19), or screen/mobile terminal (Specification pg 19, lines 11-13), configured to perform the generic computer functions (e.g., acquiring measurement data and outputting results) that are well-understood, routine, and conventional activities previously known to the pertinent industry.
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3. Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional generic computer components as those claimed. See option III. A. 2. in the Berkheimer memorandum.
When considered in combination, the additional elements (i.e., the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a determination device, memory/non-transitory recording medium, processor/computer, or screen/mobile terminal, or any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, claims 1-10 are directed to patent ineligible subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 9-10 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Tadano (US 20180220935 A1).
Regarding claim 1, Tadano teaches a determination device (gait analysis system 1) comprising:
a memory storing instructions; and a processor connected to the memory and configured to execute the instructions ([0124] “processing device 5… perform predetermined computation processing to indicate a predetermined gait parameter on the monitor screen 7 or output or record a predetermined gait parameter on a different device”) to:
acquire sensor data measured in accordance with a motion of a foot ([0125] “acquire acceleration data and angular velocity data from the sensor units 3… compute a lower limb posture (three-dimensional wire-frame model) during gait;” Fig. 12, sensor units 3; Fig. 11, segment posture computation portion 15, sensor posture estimation portion 13, a lower limb posture computation portion 17);
calculate a relative change value indicating a relative change from a traveling axis for each gait cycle using time-series data of the acquired sensor data (Figs. 25, 26, and 31. [0164] “The first novel gait parameter is a knee acceleration vector direction. FIG. 25 illustrates motion trajectories of joints (great trochanter, knee joint, ankle joint) in the sagittal plane. The original point is the midpoint of the right and left hip joint centers, and the rightward direction in the plane of paper is the walking direction. The arrow line supplementary indicated on the motion trajectory of the knee joint indicates the acceleration vector at the time of the heel contact. The parameter calculation portion 19 calculates an angle θ.sub.acc of the acceleration vector with regard to the knee joint trajectory as an assessment parameter.” [0165] “The other novel gait parameter is a trajectory angle in a knee joint horizontal plane. FIG. 26 illustrates motion trajectories of knee joints in the horizontal plane. The original point is the midpoint of the right and left hip joint centers, and the upward direction in the plane of paper is the walking direction. The left closed curve is the motion trajectory of the left knee joint, and the right closed curve is the motion trajectory of the right knee joint. In the drawing, straight lines extending substantially up and down in the motion trajectories are obtained as the motion trajectories are approximated to a straight line. The parameter calculation portion calculates an angle θ.sub.xy formed by the two straight lines as an assessment parameter.”);
determine a gait situation using time-series data of the relative change value in a target period ([0184] “the knee horizontal trajectory plane angle θ.sub.xy, a gait parameter calculated by the gait analysis system and the gait analysis method according to the present embodiment, exhibits a significant difference regarding both the right and left legs. In addition, although not exhibiting any significant difference regarding the right leg, the left leg, and the side with higher severity, the acceleration vector direction θ.sub.acc exhibits a significant difference regarding the side with lower severity, enabling quantitative assessment of a disease, e.g., knee OA.”) and
output gait information including the determined gait situation ([0124] “perform predetermined computation processing to indicate a predetermined gait parameter on the monitor screen 7 or output or record a predetermined gait parameter on a different device.”).
Regarding claim 9, Tadano teaches a determination method executed by a computer ([0124] “processing device 5 uses a personal computer… perform predetermined computation processing to indicate a predetermined gait parameter on the monitor screen 7 or output or record a predetermined gait parameter on a different device”), the method comprising:
acquiring sensor data measured in accordance with a motion of a foot ([0125] “acquire acceleration data and angular velocity data from the sensor units 3… compute a lower limb posture (three-dimensional wire-frame model) during gait;” Fig. 12, sensor units 3; Fig. 11, segment posture computation portion 15, sensor posture estimation portion 13, a lower limb posture computation portion 17);
calculating a relative change value indicating a relative change from a traveling axis for each gait cycle using time-series data of the acquired sensor data (Figs. 25, 26, and 31. [0164] “The first novel gait parameter is a knee acceleration vector direction. FIG. 25 illustrates motion trajectories of joints (great trochanter, knee joint, ankle joint) in the sagittal plane. The original point is the midpoint of the right and left hip joint centers, and the rightward direction in the plane of paper is the walking direction. The arrow line supplementary indicated on the motion trajectory of the knee joint indicates the acceleration vector at the time of the heel contact. The parameter calculation portion 19 calculates an angle θ.sub.acc of the acceleration vector with regard to the knee joint trajectory as an assessment parameter.” [0165] “The other novel gait parameter is a trajectory angle in a knee joint horizontal plane. FIG. 26 illustrates motion trajectories of knee joints in the horizontal plane. The original point is the midpoint of the right and left hip joint centers, and the upward direction in the plane of paper is the walking direction. The left closed curve is the motion trajectory of the left knee joint, and the right closed curve is the motion trajectory of the right knee joint. In the drawing, straight lines extending substantially up and down in the motion trajectories are obtained as the motion trajectories are approximated to a straight line. The parameter calculation portion calculates an angle θ.sub.xy formed by the two straight lines as an assessment parameter.”);
determining a gait situation using time-series data of the relative change value in a target period ([0184] “the knee horizontal trajectory plane angle θ.sub.xy, a gait parameter calculated by the gait analysis system and the gait analysis method according to the present embodiment, exhibits a significant difference regarding both the right and left legs. In addition, although not exhibiting any significant difference regarding the right leg, the left leg, and the side with higher severity, the acceleration vector direction θ.sub.acc exhibits a significant difference regarding the side with lower severity, enabling quantitative assessment of a disease, e.g., knee OA.”); and
outputting gait information including the determined gait situation ([0124] “perform predetermined computation processing to indicate a predetermined gait parameter on the monitor screen 7 or output or record a predetermined gait parameter on a different device.”).
Regarding claim 10, Tadano teaches a non-transitory recording medium recording a program for causing a computer to execute the steps ([0124] “processing device 5 uses a personal computer… perform predetermined computation processing to indicate a predetermined gait parameter on the monitor screen 7 or output or record a predetermined gait parameter on a different device”) of:
acquiring sensor data measured in accordance with a motion of a foot ([0125] “acquire acceleration data and angular velocity data from the sensor units 3… compute a lower limb posture (three-dimensional wire-frame model) during gait;” Fig. 12, sensor units 3; Fig. 11, segment posture computation portion 15, sensor posture estimation portion 13, a lower limb posture computation portion 17);
calculating a relative change value indicating a relative change from a traveling axis for each gait cycle using time-series data of the acquired sensor data (Figs. 25, 26, and 31. [0164] “The first novel gait parameter is a knee acceleration vector direction. FIG. 25 illustrates motion trajectories of joints (great trochanter, knee joint, ankle joint) in the sagittal plane. The original point is the midpoint of the right and left hip joint centers, and the rightward direction in the plane of paper is the walking direction. The arrow line supplementary indicated on the motion trajectory of the knee joint indicates the acceleration vector at the time of the heel contact. The parameter calculation portion 19 calculates an angle θ.sub.acc of the acceleration vector with regard to the knee joint trajectory as an assessment parameter.” [0165] “The other novel gait parameter is a trajectory angle in a knee joint horizontal plane. FIG. 26 illustrates motion trajectories of knee joints in the horizontal plane. The original point is the midpoint of the right and left hip joint centers, and the upward direction in the plane of paper is the walking direction. The left closed curve is the motion trajectory of the left knee joint, and the right closed curve is the motion trajectory of the right knee joint. In the drawing, straight lines extending substantially up and down in the motion trajectories are obtained as the motion trajectories are approximated to a straight line. The parameter calculation portion calculates an angle θ.sub.xy formed by the two straight lines as an assessment parameter.”);
determining a gait situation using time-series data of the relative change value in a target period ([0184] “the knee horizontal trajectory plane angle θ.sub.xy, a gait parameter calculated by the gait analysis system and the gait analysis method according to the present embodiment, exhibits a significant difference regarding both the right and left legs. In addition, although not exhibiting any significant difference regarding the right leg, the left leg, and the side with higher severity, the acceleration vector direction θ.sub.acc exhibits a significant difference regarding the side with lower severity, enabling quantitative assessment of a disease, e.g., knee OA.”); and
outputting gait information including the determined gait situation ([0124] “perform predetermined computation processing to indicate a predetermined gait parameter on the monitor screen 7 or output or record a predetermined gait parameter on a different device.”).
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.
Claim(s) 2-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tadano (US 20180220935 A1).
Regarding claim 2, Tadano discloses the determination device according to claim 1, wherein the processor (processing device 5) is configured to execute the instructions to:
extract end points set at a start point and a terminal point of a gait cycle from time-series data of the sensor data, set, to the start point of one gait cycle, an end point that precedes in time series among the two successive end points, set, to the terminal point of one gait cycle, an end point that follows in time series among the two successive end points ([0147] “The gait parameter computation portion 19 calculates various types of parameters required for gait assessment from the lower limb posture computed by the lower limb posture computation portion with a predetermined program and the timings of the heel contact and the toe off. The timings of the heel contact and the toe off can easily be determined by means of an optical system or a floor reaction force gauge. However, in the present embodiment, only the acceleration and angular velocity data are used for detection. Specifically, as described below, the timing of the heel contact is detected from the angular velocity data of the shank, and the timing of the toe off is detected from the relative positions of the right and left toes.” [0148] “As illustrated in FIG. 23, the angular velocity of the shank during gait is close to 0 degrees/second in the stance phase, and a relatively large angular velocity is generated in the swing phase because the shank is swung forward. Immediately before the heel contact, the shank swung forward is slightly pulled backward. Therefore, the peak value of the angular velocity appears in the direction opposite to that of the swing phase. This peak position is used for detection of the heel contact.” [0179] “The graphs are the average value of three gait cycles. FIGS. 30(b) and (c) illustrate the sagittal plane trajectory of the three gait cycles and the acceleration vector of the second step out of the three steps at the time of the heel contact. FIG. 30(d) indicates the knee joint horizontal plane trajectories of the three gait cycles.” ), and
calculate the relative change value indicating a relative change from a traveling axis at the start point (Figs. 25, 26, and 31. [0164] “The first novel gait parameter is a knee acceleration vector direction. FIG. 25 illustrates motion trajectories of joints (great trochanter, knee joint, ankle joint) in the sagittal plane. The original point is the midpoint of the right and left hip joint centers, and the rightward direction in the plane of paper is the walking direction. The arrow line supplementary indicated on the motion trajectory of the knee joint indicates the acceleration vector at the time of the heel contact. The parameter calculation portion 19 calculates an angle θ.sub.acc of the acceleration vector with regard to the knee joint trajectory as an assessment parameter.” [0165] “The other novel gait parameter is a trajectory angle in a knee joint horizontal plane. FIG. 26 illustrates motion trajectories of knee joints in the horizontal plane. The original point is the midpoint of the right and left hip joint centers, and the upward direction in the plane of paper is the walking direction. The left closed curve is the motion trajectory of the left knee joint, and the right closed curve is the motion trajectory of the right knee joint. In the drawing, straight lines extending substantially up and down in the motion trajectories are obtained as the motion trajectories are approximated to a straight line. The parameter calculation portion calculates an angle θ.sub.xy formed by the two straight lines as an assessment parameter.”).
Although Tadano discloses vertical and horizontal relative change values from the midpoint of the right and left hip joint centers throughout the gait cycle rather than from the start point of the gait cycle (Figs. 25, 26, and 31; [0164-0165]), it would be apparent to one of ordinary skill in the art that the two are functionally equivalent in the purpose of analyzing and diagnosing gait abnormalities as the relative change for the entirety of the gait cycle (including the start point) is provided and it would take simple addition/subtraction to change the point of reference.
Regarding claim 3, Tadano discloses the determination device according to claim 2, wherein the processor (processing device 5) is configured to execute the instructions to
calculate, as the relative change value, a relative angle corresponding to an angle formed by a straight line passing through the start point and the terminal point and the traveling axis at the start point on a horizontal plane and a relative displacement corresponding to a distance between the traveling axis at the start point and the terminal point on the horizontal plane ([0165] “The other novel gait parameter is a trajectory angle in a knee joint horizontal plane. FIG. 26 illustrates motion trajectories of knee joints in the horizontal plane. The original point is the midpoint of the right and left hip joint centers, and the upward direction in the plane of paper is the walking direction. The left closed curve is the motion trajectory of the left knee joint, and the right closed curve is the motion trajectory of the right knee joint. In the drawing, straight lines extending substantially up and down in the motion trajectories are obtained as the motion trajectories are approximated to a straight line. The parameter calculation portion calculates an angle θ.sub.xy formed by the two straight lines as an assessment parameter.”).
Although Tadano discloses an angle formed by the two straight lines through the left and right limb horizontal displacements, respectively, rather than from each limb’s straight line to the traveling axis (Fig. 26; [0165]), it would be apparent to one of ordinary skill in the art that the two are functionally equivalent in the purpose of analyzing and diagnosing gait abnormalities and, as the relative horizontal change from the traveling axis is provided for the entirety of the gait cycle, it would take simple addition/subtraction and trigonometry to determine an angle between each limb line and the traveling axis rather than the angle between the two limb lines.
Tadano further discloses and determine the gait situation using the calculated relative change value ([0184] “the knee horizontal trajectory plane angle θ.sub.xy, a gait parameter calculated by the gait analysis system and the gait analysis method according to the present embodiment, exhibits a significant difference regarding both the right and left legs. In addition, although not exhibiting any significant difference regarding the right leg, the left leg, and the side with higher severity, the acceleration vector direction θ.sub.acc exhibits a significant difference regarding the side with lower severity, enabling quantitative assessment of a disease, e.g., knee OA.”).
Regarding claim 4, Tadano discloses the determination device according to claim 2, wherein the processor (processing device 5) is configured to execute the instructions to
calculate, as the relative change value, a relative angle corresponding to an angle formed by a straight line passing through the start point and the terminal point and the traveling axis at the start point on a sagittal plane and a relative displacement corresponding to a distance between the traveling axis at the start point and the terminal point on the sagittal plane ([0164] “The first novel gait parameter is a knee acceleration vector direction. FIG. 25 illustrates motion trajectories of joints (great trochanter, knee joint, ankle joint) in the sagittal plane. The original point is the midpoint of the right and left hip joint centers, and the rightward direction in the plane of paper is the walking direction. The arrow line supplementary indicated on the motion trajectory of the knee joint indicates the acceleration vector at the time of the heel contact. The parameter calculation portion 19 calculates an angle θ.sub.acc of the acceleration vector with regard to the knee joint trajectory as an assessment parameter.”).
Although Tadano discloses an angle formed by straight lines representing the acceleration vector and motion/displacement trajectory at heel contact (start point), rather than from the displacement trajectory line to the traveling axis (Fig. 25; [0164]), it would be apparent to one of ordinary skill in the art that the two are functionally equivalent in the purpose of analyzing and diagnosing gait abnormalities and, as the relative vertical change from the traveling axis is provided for the entirety of the gait cycle, it would take simple addition/subtraction and trigonometry to determine an angle between a displacement trajectory line through the start and terminal points and the traveling axis rather than the acceleration vector and motion trajectory lines.
Tadano further discloses and determine the gait situation using the calculated relative change value ([0184] “the knee horizontal trajectory plane angle θ.sub.xy, a gait parameter calculated by the gait analysis system and the gait analysis method according to the present embodiment, exhibits a significant difference regarding both the right and left legs. In addition, although not exhibiting any significant difference regarding the right leg, the left leg, and the side with higher severity, the acceleration vector direction θ.sub.acc exhibits a significant difference regarding the side with lower severity, enabling quantitative assessment of a disease, e.g., knee OA.”).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tadano (US 20180220935 A1) in view of Li (CN 116758635 A).
Regarding claim 5, Tadano teaches the determination device according to any one of claim 1, wherein the processor (processing device 5). However, Tadano fails to disclose removing sensor data associated with exceptional gait data.
Li teaches a method and device for obtaining and analyzing gait data to determine fatigue state information of human body. Li discloses is configured to execute the instructions (pg 2 [1] “electronic device comprising a processor and a memory, the memory storing a program or instruction executable on the processor, the program or instruction when executed by the processor implementing the steps of the method”) to remove the sensor data measured in the target period in which the gait situation is determined to be an exceptional gait different from a normal gait (pg 2 [19] – pg 3 [1] “The data correction may be removing part of the gait data from the gait data set according to the target peak threshold, or increasing part of the gait data. The invention eliminates the interference of the abnormal condition”), determine that the gait situation is a normal gait in a case where the relative change value does not exceed a determination reference value, determine that the gait situation is an exceptional gait in a case where the relative change value exceeds the determination reference value, remove the sensor data in a time zone in which the gait situation is determined to be the exceptional gait (pg 3 [16-18] “the step of performing target processing on the gait data set based on the target peak threshold to obtain a target data set comprises: based on the target peak value threshold value, eliminating the invalid peak value in the gait data set or inserting the hidden peak value in the gait data set to obtain the target data set. invalid peak value, the hidden peak value is composed of noise, the data correction to the gait data set by the mode of eliminating the invalid peak value and inserting the hidden peak value can eliminate the interference to the gait data by the abnormal condition so as to improve the accuracy of the result of the subsequent analysis to the human gait and fatigue state.”), and
associate the sensor data in a time zone in which the gait situation is determined to be the normal gait with the gait situation (pg 2 [18] “step S4, based on the target peak value threshold value, performing target processing on the gait data set to obtain a target data set, the target processing is used for performing data correction on the gait data set, the gait data set is processed based on the target peak value threshold so as to improve the fitting degree of the processed data and the real human gait. Specifically, the gait data set is corrected to obtain a target data set.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Tadano to include removing sensor data associated with exceptional gait data as disclosed in Li to eliminate the interference of the abnormal condition to the gait data so as to improve the accuracy of the subsequent analysis result of the human gait and fatigue state (Li pg 3 [18]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tadano (US 20180220935 A1) in view of Lim (US 20180360347 A1) and Hyde (WO 2016069842 A1).
Regarding claim 6, Tadano teaches the determination device according to any one of claim 1, wherein the processor (processing device 5). However, Tadano fails to disclose setting and release signals selected by a user for indicating a gait type. Lim teaches a walking assistance method including measuring a current gait motion of a user, defining a state variable based on the current gait motion, setting a delay that is a feedback element for the state variable, and generating a torque profile based on the state variable and the delay.
Lim discloses is configured to execute the instructions to acquire a setting signal and a release signal of a tag indicating a gait type selected by a user ([0179] The remote controller 1810 may provide a user with a UI and/or a menu corresponding to a function for operating the walking assistance apparatus 100, using the display 1830. For example, the remote controller 1810 may be a device for a manual operation of a user. For example, a user may select a start, stop or end of gait assistance. Also, the user may select an abnormal gait type and may receive a gait assistance based on the selection.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Tadano to include setting and release signals selected by a user for indicating a gait type as disclosed in Lim to provide gait assistance based on the type of gait selected (Lim [0179]).
However, the combination of Tadano/Lim fails to disclose associating sensor data for a particular period with a tag indicating the gait situation. Hyde teaches a garment system that provides feedback based on a user’s sensed gait.
Hyde discloses and associate the sensor data measured in a period in which the tag is set with the gait situation indicated by the tag (pg 11 [4] “The garment systems disclosed herein can also be used in conjunction with a motion sensing system for monitoring, teaching, or correcting a subject's movement during different activities, such as walking, running, jumping, or specific sporting activities. For example, the one or more activity sensors 522 associated with the wearable device 520 can be configured to sense at least one characteristic associated with movement of the subject or at least one physiological characteristic of the subject, or track physical movement of the subject, such as motion of one or more limbs of the subject. For example, such physical movement can be sporting activities, such as a baseball bat swing, golf swing, tennis racquet swing, or other type of activity, or general movement such as walking or arm motion for physical therapy. … the user interface 514 is configured to allow a person to input sensing data into the memory 512 of the control system 508 and associate (e.g., directly designate or label the data set) the sensing data with one or more of the selected activities stored in the memory 512. In an embodiment, the control electrical circuitry 510 is configured to automatically associate or correlate the sensing data with one or more of the selected activities stored in the memory 512 based on recognized or template patterns of the sensing data stored in the memory 512 known to correspond to a particular one of the specific activities, by comparison therebetween. For example, the control electrical circuitry 510 can associate the pedometer data from a specific pattern of running with the specific activity of basketball based on comparison of stored baseline sensing data previously known to correlate with basketball.” Pg 11 [10] “ the at least one characteristic can include at least one of the a physical characteristic… gait of the subject, pace at which the subject moves”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Tadano/Lim to include associating sensor data for a particular period with a tag indicating the gait situation as disclosed in Hyde to direct, support, or aid the subject's movement to correspond to a stored movement, activity, or movement pattern in the memory of the control system (Hyde pg 11 [5]).
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tadano (US 20180220935 A1) in view of Dohrmann (KR 20200121832 A).
Regarding claim 7, Tadano teaches the determination device according to any one of claim 1, wherein the processor (processing device 5). However, Tadano fails to disclose determining the gait situation using a machine learning model.
Dohrmann teaches real-time comprehensive gait analysis and continuously provides guidance to reduce fall risk. Dohrmann discloses is configured to execute the instructions to determine the gait situation using a machine learning model that outputs the gait situation according to an input of time-series data of the relative change value (pg 3 [9] “The depth video is transmitted by kiosk 120 to gait analysis system 170 via communication network 140 in real time upon completion of each activity or upon completion of functional evaluation. … The motion parameter module 174 may process body tracking information generated by the body tracking module 172 to determine one or more spatiotemporal walking characteristics of the user 110.” pg 1 [8] “Gait analysis incorporates an interactive interface system, a depth camera, a tablet device, and algorithms and machine learning to calculate one or more gait characteristics of an individual and a fall risk score. Thereafter, the results of the comprehensive survey, gait characteristics, and fall risk score can be provided to the individual through an interactive interface and kiosk.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Tadano to include determining the gait situation using a machine learning model as disclosed in Dohrmann to determine one or more recommendations to the user based on the gait situation to reduce fall risk (Dohrmann pg 4 [3-4]).
Regarding claim 8, Tadano teaches the determination device according to any one of claim 1, wherein the processor (processing device 5). However, Tadano fails to disclose action recommendations based on the sensor data.
Dohrmann discloses is configured to execute the instructions to generate, using the sensor data associated with the gait situation (pg 2 [1] “kiosk 120 may include or be connected to a tablet device 120 and a depth camera system 150 … kiosk fall risk assessment system 100 may additionally include any number or combination of sensors including a video camera system, a motion sensor, a vibration sensor, and the like.” pg 2 [5] “The gait analysis system 170 is configured to evaluate and quantify a user's one or more spatiotemporal gait characteristics and completion abilities from one or more functional evaluations. Functional evaluation may include physical actions performed by the user such as walking, sitting, turning, and the like.”), action recommendation information optimized in accordance with the gait situation of a user and urging the user to make a decision (pg 4 [4] “8 and 9 illustrate providing a user with one or more recommendations based on a comprehensive fall risk assessment. FIG. 8 shows a graphical user interface 800 with recommendations 820 determined based on one or more survey responses provided to a user in a comprehensive fall risk assessment 810 and determined spatiotemporal gait characteristics. For example, the ECI avatar 830 recommends that the user considers arranging his or her home environment in order to reduce the risk of falling, based on the environmental score in the user's comprehensive fall risk assessment 810. Provides. The ECI avatar 830 likewise recommends that the user discuss with the health care provider how to increase the walking speed of the user based on the determined spatiotemporal gait characteristic, for example, which is less than the normative numerical threshold shown in FIG. 6. do. 9 shows a graphical user interface 900 with environmental questionnaires 910, recommendations 920, and an ECI avatar 930. As a result of the environmental survey 910, not only the user has cluttered floors, stairs, and landings because they are not removed, but also the user has a rug-liner under a rug, a double-sided tape, some without anti-slip feet. Based on user input that they have an area rug, the ECI avatar 930 provides a recommendation that the user's rug should be replaced with a non-slip rug to help reduce the risk of falls.”),
display the gait information including the action recommendation information on a screen of a mobile terminal used by the user (pg 4 [3] “the kiosk 120 determines that one or more recommendations received from the cloud-based normative data storage 700 related to a specific field are applicable to the user 110, and the user (110) can display one or more recommendations.” Fig. 8).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the modified the device of Tadano to include action recommendations based on the sensor data as disclosed in Dohrmann to reduce fall risk (Dohrmann pg 4 [3-4]).
Conclusion
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/M.H./Examiner, Art Unit 3791
/DEVIN B HENSON/Primary Examiner, Art Unit 3791
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