Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (mental process) without significantly more.
Independent claims:
The claim(s) 1 recite(s):
“acquire feature amount data including a feature amount extracted from sensor data related to foot motion of a user, the feature amount being used to estimate lower limb muscle power of the user;”
“input the acquired feature amount data to the estimation model and estimate the lower limb muscle power of the user according to the lower limb muscle power index output from the estimation model; and”
“output information related to the estimated lower limb muscle power of the user”
This is a mental process because the human mind is fully capable of observing (i.e. acquire feature) and then mentally inputting the data into a simple function (i.e. input into estimation model), performing simply calculation to then determine an output (i.e. output information related to lower limb muscle power)
This judicial exception is not integrated into a practical application because the additional limitations amount to at most mere instructions to apply an exception and automated analysis that is insignificant extra-solution activity. And, as ruled by Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) in MPEP 2106.05(f) such mere instructions to apply the judicial exception does not integrate the judicial exception into a practical application and, as ruled by Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978) in MPEP 2106.05(g) such insignificant extra-solution activity does not integrate the judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim only recites structures of a processor and a memory/storage. These elements are well known and conventional as evidenced by disclosure in Agrawal et al (US 20170027803) hereafter known as Agrawal [see Fig. 15B and para 298… “The controller 360 has a processor 410 with data storage that may include non-volatile data storage and random access memory elements (Stor.).”] and Saporito et al (EP 3461403) hereafter known as Saporito [see Fig. 1 and paras 45-46… “Figure 1 shows an apparatus 2 for assessing the mobility of a subject according to an embodiment. The apparatus 2 comprises a processing unit 4 that controls the operation of the apparatus 2 and generally implements the method according to the invention.” And “The processing unit 4 may be implemented as a combination of dedicated hardware to perform some functions (e.g. amplifiers, pre-amplifiers, analog-to-digital convertors (ADCs) and/or digital-to-analog convertors (DACs)) and a processor (e.g., one or more programmed microprocessors, controllers, DSPs and associated circuitry) to perform other functions.” And “In various implementations, the processing unit 4 may be associated with or comprise one or more memory units 6 such as volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The processing unit 4 or associated memory unit 6 can also be used for storing program code that can be executed”] Thus, because these additional elements are well known and conventional these structures don’t amount to significantly more than the judicial exception. Therefore, as the mental process (i.e. the judicial exception) is not integrated into a practical application and the additional structures do not amount to significantly more than the judicial exception. Thus, claim 1 is rejected under 101.
The claim(s) 12 recite(s):
“acquiring feature amount data that includes a feature amount extracted from
sensor data related to foot motion of a user, the feature amount being used to estimate
lower limb muscle power of the user;”
“inputting the acquired feature amount data to an estimation model for outputting a lower limb muscle power index corresponding to input of the feature amount data;”
“estimating the lower limb muscle power of the user according to the lower limb
muscle power index output from the estimation model;”
“outputting information related to the estimated lower limb muscle power of the
user.”
This judicial exception is not integrated into a practical application because the additional limitations amount to at most mere instructions to apply an exception and automated analysis that is insignificant extra-solution activity. And, as ruled by Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) in MPEP 2106.05(f) such mere instructions to apply the judicial exception does not integrate the judicial exception into a practical application and, as ruled by Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978) in MPEP 2106.05(g) such insignificant extra-solution activity does not integrate the judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim only recites a computer which is not even positively recited in the body of the claim. This element is well known and conventional as evidenced by disclosure in Agrawal [see Fig. 15B and para 298… “The controller 360 has a processor 410 with data storage that may include non-volatile data storage and random access memory elements (Stor.).”] and Saporito [see Fig. 1 and paras 45-46… “Figure 1 shows an apparatus 2 for assessing the mobility of a subject according to an embodiment. The apparatus 2 comprises a processing unit 4 that controls the operation of the apparatus 2 and generally implements the method according to the invention.” And “The processing unit 4 may be implemented as a combination of dedicated hardware to perform some functions (e.g. amplifiers, pre-amplifiers, analog-to-digital convertors (ADCs) and/or digital-to-analog convertors (DACs)) and a processor (e.g., one or more programmed microprocessors, controllers, DSPs and associated circuitry) to perform other functions.” And “In various implementations, the processing unit 4 may be associated with or comprise one or more memory units 6 such as volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The processing unit 4 or associated memory unit 6 can also be used for storing program code that can be executed”]. Please note that a processor and memory are understood to recite a computer. Thus, because this additional element is well known and conventional this structure doesn’t amount to significantly more than the judicial exception Therefore, as the mental process (i.e. the judicial exception) is not integrated into a practical application and the additional structures do not amount to significantly more than the judicial exception. Thus, claim 12 is rejected under 101.
The claim(s) 13 recite(s):
processing of acquiring feature amount data that includes a feature amount
extracted from sensor data related to foot motion of a user, the feature amount being
used to estimate lower limb muscle power of the user,
processing of inputting the acquired feature amount data to an estimation model
for outputting a lower limb muscle power index corresponding to input of the feature
amount data,
processing of estimating the lower limb muscle power of the user according to
the lower limb muscle power index output from the estimation model, and
processing of outputting information related to the estimated lower limb muscle
power of the user.
This judicial exception is not integrated into a practical application because the additional limitations amount to at most mere instructions to apply an exception and automated analysis that is insignificant extra-solution activity. And, as ruled by Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) in MPEP 2106.05(f) such mere instructions to apply the judicial exception does not integrate the judicial exception into a practical application and, as ruled by Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978) in MPEP 2106.05(g) such insignificant extra-solution activity does not integrate the judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim only recites a computer which is not even positively recited in the body of the claim. This element is well known and conventional as evidenced by disclosure in Agrawal [see Fig. 15B and para 298… “The controller 360 has a processor 410 with data storage that may include non-volatile data storage and random access memory elements (Stor.).”] and Saporito [see Fig. 1 and paras 45-46… “Figure 1 shows an apparatus 2 for assessing the mobility of a subject according to an embodiment. The apparatus 2 comprises a processing unit 4 that controls the operation of the apparatus 2 and generally implements the method according to the invention.” And “The processing unit 4 may be implemented as a combination of dedicated hardware to perform some functions (e.g. amplifiers, pre-amplifiers, analog-to-digital convertors (ADCs) and/or digital-to-analog convertors (DACs)) and a processor (e.g., one or more programmed microprocessors, controllers, DSPs and associated circuitry) to perform other functions.” And “In various implementations, the processing unit 4 may be associated with or comprise one or more memory units 6 such as volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The processing unit 4 or associated memory unit 6 can also be used for storing program code that can be executed”]. Please note that a processor and memory are understood to recite a computer. Thus, because this additional element is well known and conventional this structure doesn’t amount to significantly more than the judicial exception Therefore, as the mental process (i.e. the judicial exception) is not integrated into a practical application and the additional structures do not amount to significantly more than the judicial exception. Thus, claim 13 is rejected under 101.
Dependent claims:
Regarding claims 2-7, these claims only further describe the mental process and do not further integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Thus, 2-7 are rejected under 35 USC 101 for similar reasons as claims 1 above.
Regarding claims 8-11 and 14: these claims further describe the mental process and do not further integrate the judicial exception into a practical application. However, claim 8 further discloses a sensor that measures spatial acceleration and spatial angular velocity and claim 9 discloses a screen, but these elements are well known and conventional as evidenced by disclosure of Lo et al (US 20190159719) hereafter known as Lo [see para 6… “The posture calculation program is configured to calculate a posture data cluster based on an acceleration signal and an angular velocity signal that is detected by an inertial sensor disposed on at least one part of a human body.” And para 27… “The display 25 has the function of providing display data to the user, and may be one of various types of screens”] and Saporito [see Fig. 1 element 10 and 8 and para 47… “The user interface 8 can therefore be or comprise a display screen or one or more other elements (e.g. lights or LEDs) for providing a visual output,” and para 50… “The one or more sensors in the sensor unit 10 can include any sensor for measuring the movements of a subject, and can include any one or more of an accelerometer 12, a gyroscope 14,”]. Therefore, for similar reasons as claim 1, claims 8-11 and 14 are rejected 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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shiotani et al (Paper entitled Estimation model for lower extremity strength using gait movement measured with inertial sensor considering difference of sex and environment) hereafter known as Shiotani in view of Agrawal et al (US 20170027803) hereafter known as Agrawal.
Independent claims:
Regarding claim 1:
Shiotani discloses:
A lower limb muscle power estimation device [see abstract… “In this study, we propose a method for estimating lower extremity strength from daily gait movement.” And see pg. 3922-3923… under 3.1 section entitled “Gait event detection”….“We based our study on a previous study assessing gait event using data from inertial sensors attached to the lower leg [17,18] to assess gait event using angular velocity in the directions of hip and knee flexion/extension. First, the DC components were removed from the
acceleration signal data using a high-pass filter (cutoff frequency, 0.2 Hz), and a low-pass filter (cutoff frequency, 15 Hz) was applied to smooth the waveforms.” Which implies some device that performs measurement and analysis related to limb muscle power] comprising:
an estimation model that outputs a lower limb muscle power index corresponding to input of feature amount data used for estimating lower limb muscle power [see pgs. 3924-3925 under section entitled “IV Correlation between gait features and lower extremity strength” which describes an estimation model as claimed]
acquire feature amount data including a feature amount extracted from sensor data related to foot motion of a user, the feature amount being used to estimate lower limb muscle power of the user [see pg. 3923-3924…section entitled “3.2 extraction of gait features”];
input the acquired feature amount data to the estimation model and estimate the lower limb muscle power of the user according to the lower limb muscle power index output from the estimation model [see pgs. 3923-3924 section entitled “3.2 extraction of gait features”… see “Table 1 shows the types of gait features used in this study.” and Fig. 5 and Table 1]; and
output information related to the estimated lower limb muscle power of the user [see pgs. 3923-3924… IV Correlation Between Gait features and lower extremity strength”…. See “In this study, the correlation coefficient of lower extremity strength and each gait features were calculated.” Correlation between gait features and lower extremity strength is understood to recite outputting information regarding lower limb muscle strength]
However, Shiotani is silent as all the details of the device including whether or not the models used are performed using storage, memory and processor. Therefore, Shiotani fails to explicitly fully disclose “a storage configured to store an estimation model”, “a memory storing instructions” or a “a processor connected to the memory and configured to execute the instructions”
Agrawal discloses in the analogous art of gait treatment/ diagnostics [see abstract… “Systems for machine-based rehabilitation of movement disorders including gait therapy applications can apply controlled forces to the pelvis and/or other body parts including knee and ankle joints.” And para 187… “The following describes tests, evaluation, control aspects for an A-TPAD embodiment for treatment, research, and diagnosis of locomotor adaptation in healthy adults when an asymmetric force vector is applied on the pelvis directed along the right leg.”] a controller that includes a processor, data storage and random memory elements (i.e. data storage and memory) is a known structure used for operating the rest of a gait treatment/diagnostics device [see Fig. 15B and para 298… “FIG. 15B shows a controller 350 for the movement training apparatus 299 that may be used for the leg actuators 300 and the trolley platform 102 as well as any the A-TPAD and CDPW embodiments. The controller 360 has a processor 410 with data storage that may include non-volatile data storage and random access memory elements (Stor.).”]
Since Shiotani is silent as to the how exactly how the device is controlled and Agrawal discloses that a controller that includes a processor, data storage, memory is a known way to control a device related to gait treatment/diagnostics, it would have been obvious to one having ordinary skill to configure the device to operate with a processor, data storage and memory as claimed as this is a known structure for controlling a device in the field of gait treatment/diagnostics.
Regarding claim 12:
Shiotani discloses:
A lower limb muscle power estimation method [see abstract… “In this study, we propose a method for estimating lower extremity strength from daily gait movement.”], the method comprising:
acquiring feature amount data that includes a feature amount extracted from sensor data related to foot motion of a user, the feature amount being used to estimate lower limb muscle power of the user [see pg. 3923-3924…section entitled “3.2 extraction of gait features”];
inputting the acquired feature amount data to an estimation model for outputting a lower limb muscle power index corresponding to input of the feature amount data [see pgs. 3923-3924 section entitled “3.2 extraction of gait features”… see “Table 1 shows the types of gait features used in this study.” and Fig. 5 and Table 1];
estimating the lower limb muscle power of the user according to the lower limb muscle power index output from the estimation model [see pgs. 3924-3925 section entitled “IV Correlation between gait features and lower extremity strength” which describes the estimation model being used as claimed]; and
outputting information related to the estimated lower limb muscle power of the
user [see pgs. 3923-3924… section entitled IV Correlation Between Gait features and lower extremity strength”…. See “In this study, the correlation coefficient of lower extremity strength and each gait features were calculated.” Correlation between gait features and lower extremity strength is understood to recite outputting information regarding lower limb muscle strength].
However, Shiotani is silent as to whether the steps are performed by a user or a computer. Therefore, Shiotani fails fully to disclose “the method executed by a computer”.
Agrawal discloses in the analogous art of gait treatment/ diagnostics [see abstract… “Systems for machine-based rehabilitation of movement disorders including gait therapy applications can apply controlled forces to the pelvis and/or other body parts including knee and ankle joints.” And para 187… “The following describes tests, evaluation, control aspects for an A-TPAD embodiment for treatment, research, and diagnosis of locomotor adaptation in healthy adults when an asymmetric force vector is applied on the pelvis directed along the right leg.”] a controller that includes a processor, data storage and random memory elements (i.e. a computer) is a known structure used for operating the rest of a gait treatment/diagnostics device [see Fig. 15B and para 298… “FIG. 15B shows a controller 350 for the movement training apparatus 299 that may be used for the leg actuators 300 and the trolley platform 102 as well as any the A-TPAD and CDPW embodiments. The controller 360 has a processor 410 with data storage that may include non-volatile data storage and random access memory elements (Stor.).”]
Since Shiotani is silent as to the how exactly what performs the steps of the method and Agrawal discloses that a controller that includes a computer is a known way to control the steps of using a method related to gait treatment/diagnostics, it would have been obvious to one having ordinary skill to operate the steps with a computer as claimed as this is a known structure for performing steps gait analysis in the field of gait treatment/diagnostics.
Regarding claim 13:
Shiotani discloses:
A device [see abstract… “In this study, we propose a method for estimating lower extremity strength from daily gait movement.” And see pg. 3922-3923… under 3.1 section entitled “Gait event detection”….“We based our study on a previous study assessing gait event using data from inertial sensors attached to the lower leg [17,18] to assess gait event using angular velocity in the directions of hip and knee flexion/extension. First, the DC components were removed from the
acceleration signal data using a high-pass filter (cutoff frequency, 0.2 Hz), and a low-pass filter (cutoff frequency, 15 Hz) was applied to smooth the waveforms.” Which implies some device that performs measurement and analysis related to limb muscle power] that performs the steps of:
processing of acquiring feature amount data that includes a feature amount extracted from sensor data related to foot motion of a user, the feature amount being used to estimate lower limb muscle power of the user [see pg. 3923-3924…section entitled “3.2 extraction of gait features”],
processing of inputting the acquired feature amount data to an estimation model
for outputting a lower limb muscle power index corresponding to input of the feature
amount data [see pgs. 3923-3924 section entitled “3.2 extraction of gait features”… see “Table 1 shows the types of gait features used in this study.” and Fig. 5 and Table 1],
processing of estimating the lower limb muscle power of the user according to the lower limb muscle power index output from the estimation model [see pgs. 3924-3925 section entitled “IV Correlation between gait features and lower extremity strength” which describes the estimation model being used as claimed], and processing of outputting information related to the estimated lower limb muscle power of the user [see pgs. 3923-3924… IV Correlation Between Gait features and lower extremity strength”…. See “In this study, the correlation coefficient of lower extremity strength and each gait features were calculated.” Correlation between gait features and lower extremity strength is understood to recite outputting information regarding lower limb muscle strength].
However, Shinto is silent as to whether these steps are performed by a user or a computer. Therefore, Shinto fails fully to disclose “non-transitory recording medium recording a program that causes a computer to execute”
Agrawal discloses in the analogous art of gait treatment/ diagnostics [see abstract… “Systems for machine-based rehabilitation of movement disorders including gait therapy applications can apply controlled forces to the pelvis and/or other body parts including knee and ankle joints.” And para 187… “The following describes tests, evaluation, control aspects for an A-TPAD embodiment for treatment, research, and diagnosis of locomotor adaptation in healthy adults when an asymmetric force vector is applied on the pelvis directed along the right leg.”] a controller that includes a processor, random memory and a CPU that executes programmatic instructions (i.e. non-transitory recording medium recording a program that causes a computer to execute a series of steps) are known structures used for performing gait analysis [see Fig. 15B and para 298… “FIG. 15B shows a controller 350 for the movement training apparatus 299 that may be used for the leg actuators 300 and the trolley platform 102 as well as any the A-TPAD and CDPW embodiments. The controller 360 has a processor 410 with data storage that may include non-volatile data storage and random access memory elements (Stor.).” and “It may have a processing unit (CPU) for numerical computation and execution of programmatic instructions.”]
Since Shiotani is silent as to the how exactly how the steps of gait analysis are being performed and Agrawal discloses that a controller with non-transitory recording medium recording a program that includes a computer to execute a series of steps is a known way to control a device related to gait treatment/ diagnostics, it would have been obvious to one having ordinary skill at the time the invention was filed to configure the device to operate the steps with a controller with non-transitory recording medium recording a program that includes a computer as claimed as this is a known structure for performing steps of diagnosis in the field of gait treatment/ diagnostics.
Dependent claims:
Regarding claim 2:
Shinotani discloses the invention substantially as claimed including all the limitations of claim 1 as outlined above and wherein the processor is configured to
execute the instructions to acquire the feature amount data including a feature amount
extracted from gait waveform data generated using time-series data in the sensor data
related to the foot motion [see pg. 3922-3923… under section “2.3 Measuring acceleration signals and angular velocity during indoor/outdoor gaits using inertial sensors”…. “Gait measurements were performed using inertial sensors (TSND121, ATR-Promotions).” And “First, the DC components were removed from the acceleration signal data using a high-pass filter (cutoff frequency, 0.2 Hz), and a low-pass filter (cutoff frequency, 15 Hz) was applied to smooth the waveforms.”].
However, Shinotani fails to disclose “the feature amount being used to estimate a performance value of a sit-to-stand test as the lower limb muscle power index.”
Shinotani further discloses that a sit-to-stand test is a known way to provide data as to a user’s balance and risk of falling [see pg. 3921 under section I Introduction… “For example, some previous studies have used the five-times sit-to-stand test to determine balance ability, risk of falling, and lower extremity strength [1-3] and examined the correlation between maximum walking speed and lower extremity strength [4].”
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify Shinotani’s processor use the feature amount to estimate a performance value of a sit-to-stand test because as explained by Shinotani this is a known way obtain additional pertinent information including an individual’s balance ability and risk of falling, thereby providing a more accurate analysis of gait.
Claim(s) 3 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shiotani in view of Agrawal as applied to claims 1-2 above, and further in view of Mantovani et al (US 20200215324) hereafter known as Mantovani.
Shiotani in view of Agrawal discloses the invention substantially as claimed including all the limitations of claims 1-2.
However, Shiotani in view of Agrawal fails to disclose:
“wherein the storage means stores the estimation model generated by machine learning using teacher data with a feature amount used to estimate the lower limb muscle power index for each of a plurality of subjects as an explanatory variable and the lower limb muscle power index of each of the plurality of subjects as an objective variable” or “the processor is configured to execute the instructions to input the feature amount data acquired for the user to the estimation model, and estimate the lower limb muscle power of the user according to the lower limb muscle power index of the user output from the estimation model.”.
Mantovani discloses in the analogous art of gait diagnostics/ treatment [see para 2… “This disclosure relates generally to the field of orthotics, more specifically, to improved devices, systems, and methods for real-time gait modulation.”] that a known way of inputting inertia data and collecting gait data is via a machine learning algorithm that is trained on correlating inertia data (i.e. machine learning using teacher data) [see abstract…. “The processors can be programmed to execute instructions to retrieve readings from the IMU, calculate a gait cycle percentage by inputting at least the IMU readings into a machine learning algorithm” and para 93… “The machine learning algorithm 700 can be trained or optimized by correlating IMU readings (e.g., gyroscope readings and/or accelerometer readings) with three-dimensional (3D) kinematic data obtained from computer vision. This can be done prior to usage of the FES device 100 during a training or data collection phase.”]
Since Shiotani in view of Agrawal has inertial sensors and uses data from these sensors for the gait analysis [see pg. 3922-3923 of Shiotani… under 3.1 section entitled “Gait event detection”….“We based our study on a previous study assessing gait event using data from inertial sensors attached to the lower leg [17,18] to assess gait event using angular velocity in the directions of hip and knee flexion/extension.] and Mantovani discloses that a known way to input inertia data into a processor in the field of gait diagnostics is to use machine learning that is trained on correlating inertia data, it would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify Shiotani in view of Agrawal by including machine learning that is trained on correlating inertia data similar to that disclosed by Mantovani (i.e. thereby reciting claim 3).
Examiner Note
While, all claims have been rejected, no prior art rejection was found to reject dependent claims 4-6, 8-11 and 14. However, claims 4-6, 8-11 and 14 are rejected under 35 USC 101 for the reasons outlined above.
Conclusion
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SEBASTIAN X LUKJAN
/SXL/Examiner, Art Unit 3792
/William J Levicky/Primary Examiner, Art Unit 3796
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