DETAILED ACTION
This action is pursuant to claims filed on 01/12/2024. Claims 1-10 are pending. A first action on the merits of claims 1-10 is as follows.
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 Objections
Claims 1-7 and 9-10 are objected to because of the following informalities:
In claim 1, line 11, “an machine learning model” should read “a machine learning model”
In claim 2, line 5, a colon should be inserted after “execute the instructions to”
In claim 3, line 5, a colon should be inserted after “execute the instructions to”
In claim 4, line 5, a colon should be inserted after “execute the instructions to”
In claim 5, line 5, a colon should be inserted after “execute the instructions to”
In claim 6, line 7, a colon should be inserted after “execute the instructions to”
In claim 7, line 2, a colon should be inserted after “execute the instructions to”
In claim 9, line 8, “an machine learning model” should read “a machine learning model”
In claim 10, line 9, “an machine learning model” should read “a machine learning model”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the claim recites the limitation “an input of the feature amount included in the feature amount data” in lines 12-13. It is unclear if this limitation is meant to refer to the input of the feature amount included in the acquired feature amount data from lines 10-11, or a different input. If it is referring to the input from lines 10-11, it needs to refer back to it. If it is referring to a different input, it needs to be distinguished from the input from lines 10-11. For purposes of examination, it is being interpreted as referring to the input from lines 10-11. Claims 2-8 are also rejected due to their dependence on claim 1.
Further regarding claim 1, the claim recites the limitation “a pelvic inclination” in line 14. It is unclear if this is meant to refer to the pelvic inclination in line 6, or a different pelvic inclination. If it is referring to the pelvic inclination from line 6, it needs to refer back to it. If it is referring to a different pelvic inclination, it needs to be distinguished from the pelvic inclination from line 6. For purposes of examination, it is being interpreted as referring to the pelvic inclination from line 6. Claims 2-8 are also rejected due to their dependence on claim 1.
Regarding claim 2, the claim recites the limitation “the gait parameter” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear if this is meant to refer to the gait waveform from claim 1, line 7, or a different gait parameter. If it is meant to refer to the gait waveform, it should read as “the gait parameter”. If it is referring to a different parameter, it should read “a gait parameter”. For purposes of examination, it is being interpreted as referring to the gait waveform from claim 1. Claims 3-5 are also rejected due to their dependence on claim 2.
Further regarding claim 2, the claim recites the limitation “gait parameters” in line 6. It is unclear if this is meant to refer to the gait parameter from lines 3-4, or different gait parameters. If it is referring to the gait parameter from lines 3-4, it needs to refer back to it. If it is referring to different gait parameters, it needs to be distinguished from the gait parameter from lines 3-4. For purposes of examination, it is being interpreted as referring to the gait parameter from lines 3-4. Claims 3-5 are also rejected due to their dependence on claim 2.
Further regarding claim 2, the claim recites the limitation “a gait waveform” in line 7. It is unclear if this limitation is meant to refer to the gait waveform from claim 1, line 7, or a different gait waveform. If it is referring to the gait waveform from claim 1, it needs to refer back to it. If it is referring to a different gait waveform, it needs to be distinguished from the gait waveform from claim 1. For purposes of examination, it is being interpreted as referring to the gait waveform from claim 1. Claims 3-5 are also rejected due to their dependence on claim 2.
Regarding claim 3, the claim recites the limitation “an estimation value related to the pelvic inclination” in lines 2-3. It is unclear if this limitation is meant to refer to the estimation value related to the pelvic inclination from claim 1, lines 11-12, or a different estimation value. If it is referring to the estimation value from claim 1, it needs to refer back to it. If it is referring to a different estimation value, it needs to be distinguished from the estimation value from claim 1. For purposes of examination, it is being interpreted as referring to the estimation value from claim 1. Claims 4-5 are also rejected due to their dependence on claim 3.
Further regarding claim 3, the claim recites the limitation “the first feature amount included in the feature amount data” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear if this limitation is meant to refer to the feature amount included in the feature amount data from claim 1, lines 5-6, or a different feature amount. If it is referring to the feature amount from claim 1, it needs to refer back to it. If it is referring to a different feature amount, it needs to be distinguished from the feature amount from claim 1. For purposes of examination, it is being interpreted as referring to the feature amount from claim 1. Claims 4-5 are also rejected due to their dependence on claim 3.
Further regarding claim 3, the claim recites the limitation “a first feature amount” from line 6. It is unclear if this is meant to refer to the first feature amount from lines 3-4, or a different first feature amount. If it is referring to the first feature amount from lines 3-4, it needs to refer back to it. If it is referring to a different first feature amount, it needs to be distinguished from the first feature amount from lines 3-4. For purposes of examination, it is being interpreted as referring to the first feature amount from lines 3-4. Claims 4-5 are also rejected due to their dependence on claim 3.
Regarding claim 4, the claim recites the limitation “an estimation value related to the pelvic inclination” in lines 2-3. It is unclear if this is meant to refer to the estimation value related to the pelvic inclination from claim 1, lines 11-12, the estimation value related to the pelvic inclination from claim 3, or a different estimation value. If it is meant to refer to any of the previously introduced estimation values, it needs to refer back to them. If it is referring to a different estimation value, it needs to be distinguished from all of the previously presented estimation values. For purposes of examination, it is being interpreted as referring to the estimation value from claim 1. Claim 5 is also rejected due to its dependence on claim 4.
Further regarding claim 4, the claim recites the limitation “an input of the first feature amount included in the feature amount data” in lines 3-4. It is unclear if this limitation is meant to refer to the input of the first feature amount included in the feature amount data from claim 3, lines 3-4, or a different input. If it is referring to the input from claim 3, it needs to refer back to it. If it is referring to a different input, it needs to be distinguished from the input from claim 3. For purposes of examination, it is being interpreted as referring to the input from claim 3. Claim 5 is also rejected due to its dependence on claim 4.
Further regarding claim 4, the claim recites the limitation “the gait parameters used for estimation of the pelvic inclination among the first feature amounts and the gait parameters for both feet of the user” in lines 7-9. It is unclear if this limitation is meant to refer to the first feature amount for each gait phase cluster extracted from the gait waveform from claim 3, lines 6-8, the gait parameter from claim 2, lines 3-4, the gait parameters from claim 2, line 6, or different gait parameters. If it is referring to any of the previously introduced parameters, it needs to clearly refer back to them. If it is referring to different gait parameters, then there is improper antecedent basis for these limitations. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as referring to any of the previously presented parameters. Claim 5 is also rejected due to its dependence on claim 4.
Regarding claim 5, the claim recites the limitation “an estimation value related to the pelvic inclination” in lines 2-3. . It is unclear if this is meant to refer to the estimation value related to the pelvic inclination from claim 1, lines 11-12, the estimation value related to the pelvic inclination from claim 3, the estimation value related to the pelvic inclination from claim 4, or a different estimation value. If it is meant to refer to any of the previously introduced estimation values, it needs to refer back to them. If it is referring to a different estimation value, it needs to be distinguished from all of the previously presented estimation values. For purposes of examination, it is being interpreted as referring to the estimation value from claim 1.
Regarding claim 6, the claim recites the limitation “an estimation value related to the pelvic inclination” in lines 4-5. It is unclear if this is meant to refer to the estimation value related to the pelvic inclination from claim 1, lines 11-12, or a different estimation value. If it is referring to the estimation value from claim 1, it needs to refer back to it. If it is referring to a different estimation value, it needs to be distinguished from the estimation value from claim 1. For purposes of examination, it is being interpreted as referring to the estimation value from claim 1.
Further regarding claim 6, the claim recites the limitation “a feature amount” in line 8. It is unclear if this limitation is meant to refer to the feature amount from claim 1, line 5, or a different feature amount. If it is referring to the feature amount from claim 1, it needs to refer back to it. If it is referring to a different feature amount, it needs to be distinguished from the feature amount from claim 1. For purposes of examination, it is being interpreted as referring to the feature amount from claim 1.
Further regarding claim 6, the claim recites the limitation “a variation width” in lines 10-11. It is unclear if this is meant to refer to the at least one variation width in lines 2-3, or a different variation width. If it is referring to the at least one variation width from lines 2-3, it needs to refer back to it. If it is referring to a different variation width, it needs to be distinguished from the variation width from lines 2-3. For purposes of examination, it is being interpreted as referring to the at least one variation width from lines 2-3.
Regarding claim 8, the claim recites the limitation “a spatial acceleration” in lines 3-4. It is unclear if this is meant to refer to the spatial acceleration from claim 1, lines 7-8, or a different spatial acceleration. If it is referring to the spatial acceleration from claim 1, it needs to refer back to it. If it is referring to a different spatial acceleration, it needs to be distinguished from the spatial acceleration from claim 1. For purposes of examination, it is being interpreted as referring to the spatial acceleration from claim 1.
Further regarding claim 8, the claim recites the limitation “a spatial angular velocity” in line 4. It is unclear if this is meant to refer to the spatial angular velocity from claim 1, line 8, or a different spatial angular velocity. If it is referring to the spatial angular velocity from claim 1, it needs to refer back to it. If it is referring to a different spatial angular velocity, it needs to be distinguished from the spatial angular velocity from claim 1. For purposes of examination, it is being interpreted as referring to the spatial angular velocity from claim 1.
Further regarding claim 8, the claim recites the limitation “feature amount data” in line 6. It is unclear if this is meant to refer to the feature amount data from claim 1, line 5, or different feature amount data. If it is referring to the feature amount data from claim 1, it needs to refer back to it. If it is referring to a different feature amount data, it needs to be distinguished from the feature amount data from claim 1. For purposes of examination, it is being interpreted as referring to the feature amount data from claim 1.
Further regarding claim 8, the claim recites the limitation “a feature amount” in line 6. It is unclear if this is meant to refer to the feature amount from claim 1, line 5, or a different feature amount. If it is referring to the feature amount from claim 1, it needs to refer back to it. If it is meant to refer to a different feature amount, it needs to be distinguished from the feature amount from claim 1. For purposes of examination, it is being interpreted as referring to the feature amount from claim 1.
Further regarding claim 8, the claim recites the limitation “a pelvic inclination” in line 7. It is unclear if this is meant to refer to the pelvic inclination from claim 1, line 6, or a different pelvic inclination. If it is referring to the pelvic inclination from claim 1, it needs to refer back to it. If it is referring to a different pelvic inclination, it needs to be distinguished from the pelvic inclination from claim 1. For purposes of examination, it is being interpreted as referring to the pelvic inclination from claim 1.
Regarding claim 9, the claim recites the limitation “an input of the feature amount included in the feature amount data” in lines 9-10. It is unclear if this limitation is meant to refer to the input of the feature amount included in the acquired feature amount data from lines 7-8, or a different input. If it is referring to the input from lines 7-8, it needs to refer back to it. If it is referring to a different input, it needs to be distinguished from the input from lines 7-8. For purposes of examination, it is being interpreted as referring to the input from lines 7-8.
Further regarding claim 9, the claim recites the limitation “a pelvic inclination” in line 11. It is unclear if this is meant to refer to the pelvic inclination from line 3, or a different pelvic inclination. If it is referring to the pelvic inclination from line 3, it needs to refer back to it. If it is referring to a different pelvic inclination, it needs to be distinguished from the pelvic inclination from line 3. For purposes of examination, it is being interpreted as referring to the pelvic inclination from line 3.
Regarding claim 10, the claim recites the limitation “an input of the feature amount included in the feature amount data” in lines 10-11. It is unclear if this limitation is meant to refer to the input of the feature amount included in the acquired feature amount data from lines 8-9, or a different input. If it is referring to the input from lines 8-9, it needs to refer back to it. If it is referring to a different input, it needs to be distinguished from the input from lines 8-9. For purposes of examination, it is being interpreted as referring to the input from lines 8-9.
Further regarding claim 10, the claim recites the limitation “a pelvic inclination” in line 12. It is unclear if this is meant to refer to the pelvic inclination in line 4, or a different pelvic inclination. If it is referring to the pelvic inclination from line 4, it needs to refer back to it. If it is referring to a different pelvic inclination, it needs to be distinguished from the pelvic inclination from line 4. For purposes of examination, it is being interpreted as referring to the pelvic inclination from line 4.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under the two-step 101 analysis, the claims fail to satisfy the criteria for subject matter eligibility.
Regarding Step 1, claims 1-10 are all within at least one of the four statutory categories.
Claim 1 and its dependent claims disclose a device (machine).
Claim 9 discloses a method (process).
Claim 10 discloses a recording medium (machine).
Regarding Step 2A, Prong One, the independent claims 1, 9, and 10 recite an abstract idea. In particular, the claims generally recite the following:
acquire feature amount data including a feature amount to be used for estimation of a pelvic inclination that is an index related to movement of a waist, the feature amount being extracted from a gait waveform of a spatial acceleration and a spatial angular velocity included in sensor data related to movement of a foot of a user;
input the feature amount included in the acquired feature amount data to a machine learning model that outputs an estimation value related to the pelvic inclination in response to an input of the feature amount included in the feature amount data;
estimate a pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model;
These elements recited in claims 1, 9, and 10 are drawn to abstract ideas since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgement, and opinion and using pen and paper.
Acquiring feature amount data including a feature amount to be used for estimation of a pelvic inclination that is an index related to movement of a waist, the feature amount being extracted from a gait waveform of a spatial acceleration and a spatial angular velocity included in sensor data related to movement of a foot of a user is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably acquire the feature amount data on paper. There is nothing to suggest an undue level of complexity in acquiring feature amount data including a feature amount to be used for estimation of a pelvic inclination that is an index related to movement of a waist, the feature amount being extracted from a gait waveform of a spatial acceleration and a spatial angular velocity included in sensor data related to movement of a foot of a user.
Inputting the feature amount included in the acquired feature amount data to a machine learning model that outputs an estimation value related to the pelvic inclination in response to an input of the feature amount included in the feature amount data is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably take the feature amount and perform the calculations necessary to estimate a value related to the pelvic inclination mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The mathematics of estimating pelvic inclination based on the feature amount are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in inputting the feature amount included in the acquired feature amount data to a machine learning model that outputs an estimation value related to the pelvic inclination in response to an input of the feature amount included in the feature amount data.
Estimate a pelvic inclination of the subject according to the estimation value related to the pelvic inclination output from the machine learning model is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably estimate a pelvic inclination of a subject according to the estimation value mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The mathematics of estimating the pelvic inclination according to the estimation value are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. Additionally, this process also involves evaluation and judgement, which can be performed mentally. There is nothing to suggest an undue level of complexity in estimating a pelvic inclination of the subject according to the estimation value related to the pelvic inclination output from the machine learning model.
Regarding Step 2A, Prong Two, claims 1, 9, and 10 do not recite additional elements that integrate the exception into a practical application. Therefore, the claims are directed to the abstract idea. The additional elements merely:
Recite the words “apply it” or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “at least one memory storing instructions”, “at least one processor”, and “a non-transitory program recording medium”)
Add insignificant extra-solution activity (the post-solution activity of: displaying information on a screen (e.g., “display a video containing recommended training according to the estimation result of the pelvic inclination of the user on a screen of a mobile terminal used by the user”)).
As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Regarding Step 2B, claims 1, 9, and 10 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above.
The elements of “at least one memory storing instructions”, “at least one processor”, and “a non-transitory program recording medium” do not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Also, the recitation “display a video containing recommended training according to the estimation result of the pelvic inclination of the user on a screen of a mobile terminal used by the user” is merely insignificant extrasolution activity to the judicial exception, e.g., simply displaying the results of the algorithm that uses conventional, routine, and well known elements.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above judicial exception. Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Regarding the dependent claims, claims 2-8 depend on claim 1. The dependent claims merely further define the abstract idea or are additional data output that is well-understood, routine, and previously known to the industry.
For example, the following are dependent claims reciting abstract ideas and can be performed in the human mind or are extra-solution activity:
(Claim 2): “wherein the machine learning model is trained to output the estimation value related to the pelvic inclination according to an input of the gait parameter included in the feature amount data, the processor is configured to execute the instructions to acquire the feature amount data including gait parameters extracted from a gait waveform of the spatial acceleration and the spatial angular velocity included in the sensor data, input the gait parameters included in the acquired feature amount data to the machine learning model, and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model” ” is based in mathematical concept that can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The analysis involved with these techniques are based in receiving data and performing calculations and then using evaluation and judgement to determine the result. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 3): “wherein the machine learning model is trained to output an estimation value related to the pelvic inclination according to an input of the first feature amount included in the feature amount data, the processor is configured to execute the instructions to acquire the feature amount data including a first feature amount for each gait phase cluster extracted from the gait waveform of the spatial acceleration and the spatial angular velocity included in the sensor data, input the first feature amount included in the acquired feature amount data to the machine learning model, and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model” is based in mathematical concept that can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The analysis involved with these techniques are based in receiving data and performing calculations and then using evaluation and judgement to determine the result. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 4): “wherein the machine learning model is trained to output an estimation value related to the pelvic inclination according to an input of the first feature amount included in the feature amount data, the processor is configured to execute the instructions to calculate, as a second feature amount, an average value and a difference of the first feature amounts and the gait parameters used for estimation of the pelvic inclination among the first feature amounts and the gait parameters for both feet of the user, input the calculated second feature amount to the machine learning model, and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model” is based in mathematical concept that can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The analysis involved with these techniques are based in receiving data and performing calculations and then using evaluation and judgement to determine the result. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 5): “wherein the machine learning model is trained to output an estimation value related to the pelvic inclination according to an input of an attribute of the user and the second feature amount, the processor is configured to execute the instructions to input the attribute of the user and the second feature amount input to the machine learning model, and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model” is based in mathematical concept that can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The analysis involved with these techniques are based in receiving data and performing calculations and then using evaluation and judgement to determine the result. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 6): “wherein the machine learning model is trained to output at least one variation width of the pelvic inclination related to three axes of a traveling axis, a left-right axis, and a vertical axis in one gait cycle as an estimation value related to the pelvic inclination according to the input of the feature amount included in the feature amount data, the processor is configured to execute the instructions to input a feature amount included in the acquired feature amount data to the machine learning model, and estimate the pelvic inclination of the user according to a variation width of at least one of the pelvic inclinations in the three axes of the traveling axis, the left-right axis, and the vertical axis output from the machine learning model” is based in mathematical concept that can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The analysis involved with these techniques are based in receiving data and performing calculations and then using evaluation and judgement to determine the result. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 7): “wherein the processor is configured to execute the instructions to display recommendation information according to the estimation result of the pelvic inclination of the user on the screen of the mobile terminal used by the user with content optimized for healthcare application” is insignificant post-solution activity;
(Claim 8): “an estimation system comprising: the pelvic inclination estimation device according to claim 1; and a measurement device including a sensor that measures a spatial acceleration and a spatial angular velocity, and generates the sensor data based on the spatial acceleration and the spatial angular velocity, and configured to generate feature amount data including a feature amount used for estimating a pelvic inclination using the sensor data” is the insignificant pre-solution activity of generic data gathering using conventional means, as evidenced by:
US Patent Application 20210299517 (Chuang) discloses a sensor that records acceleration and angular velocity as conventional (Chuang, [0004]);
US Patent Application 20200383609 (Aoki) discloses a conventional sensor measuring angular velocity and acceleration (Aoki, [0004]);
US Patent Application 20170191830 (Maeda) discloses a conventional sensor that measures acceleration and angular velocity (Maeda, [0115]).
The dependent claims do not recite significantly more than the abstract ideas. Therefore, claims 1-10 are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kubo (WO 2014181602) in view of Chang (US 20170188894) and Roche (US 20220202369). Citations to WO 2014181602 will refer to the English Machine Translation that accompanies this Office Action.
Regarding independent claim 1, Kubo teaches a pelvic inclination estimation device (Page 1: “The present invention relates to a walking posture meter, and more particularly to a walking posture meter that quantitatively evaluates whether a person's walking posture is correct or not.”) comprising:
a memory storing instructions (Page 10: “Memory 120 includes ROM (Read Only Memory) and RAM (Random Access Memory). The ROM stores data of a program for controlling the activity meter 100 . The RAM also stores setting data for setting various functions of the activity meter 100, data on acceleration measurement results and calculation results, and the like.”), and
a processor connected to the memory and configured to execute the instructions to (Page 10: “The control unit 110 includes a CPU (Central Processing Unit) that operates based on the clock signal, and controls each part of the activity meter 100 (including the memory 120, the display unit 140, and the BLE communication unit 180) based on the detection signal from the acceleration sensor 112 in accordance with a program for controlling the activity meter 100 stored in the memory 120. ) to control the control unit 110 includes a signal processing system capable of processing time series data of at least the vertical axis acceleration and the longitudinal axis acceleration.”):
acquire feature amount data including a feature amount to be used for estimation of a pelvic inclination that is an index related to movement of a waist (Page 12: “when this walking posture meter 1 is used by, for example, a user, a subject 90, the activity meter 100 is attached to the back side of the waist on the midline 91 of the subject 90 using an attachment clip 100C”), the feature amount being extracted from a gait waveform of a spatial acceleration included in sensor data related to movement of a foot of a user (Page 15: “FIG. 6 is a diagram showing the relationship between a human gait and a typical example of the time-varying waveform of vertical axis acceleration (acceleration in the Z-axis direction, with the vertical upward direction being positive) output from acceleration sensor 112 of activity meter 100 worn on the waist during a reference period (T7 (=Step T) in the figure) corresponding to one step in a walking cycle”; Fig. 6; Page 33-34: “the inventors experimentally discovered that the result of weighting and adding one or more feature quantities (feature parameters) that capture the characteristics of the waveform shape of the acceleration change over time output from an acceleration sensor attached to the subject's waist has a good correlation with the tilt angle of the pelvis when the subject is walking”).
However, Kobo does not disclose measuring a spatial angular velocity.
Chang discloses a system and method for sensing fatigue by measuring user’s steps. Specifically, Chang teaches measuring a spatial angular velocity ([0055]: “The kinematic measurements collected by the activity monitoring device are preferably along a set of orthonormal axes (e.g., an x, y, z coordinate system). The axis of measurements may not be aligned with a preferred or assumed coordinate system of the activity. Accordingly, the axis of measurement by one or more sensor(s) may be calibrated for analysis. One, two, or all three axes may share some or all features of the calibration, or be calibrated independently. The kinematic measurements can include acceleration, velocity, displacement, force, angular velocity, angular displacement, tilt/angle, and/or any suitable metric corresponding to a kinematic property or dynamic property of an activity. Preferably, a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes. The set of kinematic data streams preferably includes acceleration in any orthonormal set of axes in three-dimensional space, herein denoted as x, y, z axes, and angular velocity about the x, y, and z axes”; [0027]: “the system and method may be applied to activity use-cases such as gait-analysis”). Kubo and Chang are analogous arts as they are both related to devices that measure a user’s steps and gait pattern to analyze the user’s health.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the measurement of angular velocity from Chang into the device from Kubo as it allows the device to determine another important spatial measurement, which can provide a more comprehensive and accurate analysis.
The Kubo/Chang combination teaches input the feature amount included in the acquired feature amount data to a model that outputs an estimation value related to the pelvic inclination in response to an input of the feature amount included in the feature amount data, estimating a pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the model (Kubo, Page 13-14: “FIG. 12 shows an operation flow of the control unit 110 of the activity meter 100. When the power is turned on, the control unit 110 of the activity meter 100 waits for an instruction to start measurement from the smartphone 200, as shown in step S1. When an instruction to start measurement is received from the smartphone 200 (YES in step S1), the control unit 110 acquires the output of the acceleration in the three-axis directions from the acceleration sensor 112, as shown in step S2. The output of the acceleration sensor 112 is acquired for a predetermined period (for example, 14 seconds) which is a period including acceleration timeseries data for 10 steps in this example. The acquired time series data of acceleration is temporarily stored in memory 120 . Next, the control unit 110 waits for an instruction to start measurement from the smartphone 200, as shown in step S3. When a calculation instruction is received from smartphone 200 (YES in step S3), control unit 110 calculates the amount corresponding to the tilt angle of the pelvis, as shown in step S4. Then, as shown in step S5, the control unit 110 functions as an evaluation unit and uses the calculation result (pelvic tilt angle estimation result) to evaluate the degree of forward/backward tilt of the subject's posture while walking in multiple stages. Thereafter, as shown in step S6, the evaluation result is output (transmitted) to the smartphone 200. Note that the control unit 110 may execute the process of step S4 as soon as acceleration time-series data for at least one step is obtained”).
However, the Kubo/Chang combination does not teach using a machine learning model.
Chang teaches using a machine learning model ([0044]: “the method can use a machine intelligence approach”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the machine learning from Chang into the Kubo/Chang combination as it allows the device to process the information in a quick, adaptable way, which can improve the performance of the device.
The Kubo/Chang combination teaches display information according to the estimation result of the pelvic inclination of the user on a screen of a mobile terminal used by the user (Kubo, Page 11: “The control unit 210 includes a CPU and its auxiliary circuits, controls each unit of the smartphone 200 , and executes processing in accordance with the programs and data stored in the memory 220 . That is, it processes data input from the operation unit 230 and the communication units 280 and 290, and stores the processed data in the memory 220, displays it on the display unit 240, and outputs it from the communication units 280 and 290.”).
However, the Kubo/Chang combination does not teach displaying a video containing recommended training according to the estimation result of the pelvic inclination of the user on a screen of the mobile terminal used by the user.
Roche teaches a system to monitor a user’s musculoskeletal system. Specifically, Roche teaches displaying a video containing recommended training ([0032]: “videos can be provided on the application with detailed instructions on everything from … exercise programs”). Kubo, Chang, and Roche are analogous arts as they are all related to devices that measure a user’s steps and gait pattern to analyze the user’s health.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the recommended training video from Roche into the Kubo/Chang combination as it allows the device to recommend specific training to the user that can improve their health condition.
Regarding claim 2, the Kubo/Chang/Roche combination teaches the pelvic inclination estimation device according to claim 1, wherein the machine learning model is trained to output the estimation value related to the pelvic inclination according to an input of the gait parameter included in the feature amount data (Kubo, Page 15: “FIG. 6 is a diagram showing the relationship between a human gait and a typical example of the time-varying waveform of vertical axis acceleration (acceleration in the Z-axis direction, with the vertical upward direction being positive) output from acceleration sensor 112 of activity meter 100 worn on the waist during a reference period (T7 (=Step T) in the figure) corresponding to one step in a walking cycle”; Fig. 6; Page 33-34: “the inventors experimentally discovered that the result of weighting and adding one or more feature quantities (feature parameters) that capture the characteristics of the waveform shape of the acceleration change over time output from an acceleration sensor attached to the subject's waist has a good correlation with the tilt angle of the pelvis when the subject is walking”; Page 11: “The control unit 210 includes a CPU and its auxiliary circuits, controls each unit of the smartphone 200 , and executes processing in accordance with the programs and data stored in the memory 220 . That is, it processes data input from the operation unit 230 and the communication units 280 and 290, and stores the processed data in the memory 220, displays it on the display unit 240, and outputs it from the communication units 280 and 290.”), the processor is configured to execute the instructions to acquire the feature amount data including gait parameters extracted from a gait waveform of the spatial acceleration and the spatial angular velocity included in the sensor data (Kubo, Page 15: “FIG. 6 is a diagram showing the relationship between a human gait and a typical example of the time-varying waveform of vertical axis acceleration (acceleration in the Z-axis direction, with the vertical upward direction being positive) output from acceleration sensor 112 of activity meter 100 worn on the waist during a reference period (T7 (=Step T) in the figure) corresponding to one step in a walking cycle”; Fig. 6; Page 33-34: “the inventors experimentally discovered that the result of weighting and adding one or more feature quantities (feature parameters) that capture the characteristics of the waveform shape of the acceleration change over time output from an acceleration sensor attached to the subject's waist has a good correlation with the tilt angle of the pelvis when the subject is walking”; Chang, [0055]: “The kinematic measurements collected by the activity monitoring device are preferably along a set of orthonormal axes (e.g., an x, y, z coordinate system). The axis of measurements may not be aligned with a preferred or assumed coordinate system of the activity. Accordingly, the axis of measurement by one or more sensor(s) may be calibrated for analysis. One, two, or all three axes may share some or all features of the calibration, or be calibrated independently. The kinematic measurements can include acceleration, velocity, displacement, force, angular velocity, angular displacement, tilt/angle, and/or any suitable metric corresponding to a kinematic property or dynamic property of an activity. Preferably, a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes. The set of kinematic data streams preferably includes acceleration in any orthonormal set of axes in three-dimensional space, herein denoted as x, y, z axes, and angular velocity about the x, y, and z axes”; [0027]: “the system and method may be applied to activity use-cases such as gait-analysis”), input the gait parameters included in the acquired feature amount data to the machine learning model, and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model (Kubo, Page 13-14: “FIG. 12 shows an operation flow of the control unit 110 of the activity meter 100. When the power is turned on, the control unit 110 of the activity meter 100 waits for an instruction to start measurement from the smartphone 200, as shown in step S1. When an instruction to start measurement is received from the smartphone 200 (YES in step S1), the control unit 110 acquires the output of the acceleration in the three-axis directions from the acceleration sensor 112, as shown in step S2. The output of the acceleration sensor 112 is acquired for a predetermined period (for example, 14 seconds) which is a period including acceleration timeseries data for 10 steps in this example. The acquired time series data of acceleration is temporarily stored in memory 120 . Next, the control unit 110 waits for an instruction to start measurement from the smartphone 200, as shown in step S3. When a calculation instruction is received from smartphone 200 (YES in step S3), control unit 110 calculates the amount corresponding to the tilt angle of the pelvis, as shown in step S4. Then, as shown in step S5, the control unit 110 functions as an evaluation unit and uses the calculation result (pelvic tilt angle estimation result) to evaluate the degree of forward/backward tilt of the subject's posture while walking in multiple stages. Thereafter, as shown in step S6, the evaluation result is output (transmitted) to the smartphone 200. Note that the control unit 110 may execute the process of step S4 as soon as acceleration time-series data for at least one step is obtained”).
Regarding claim 3, the Kubo/Chang/Roche combination teaches the pelvic inclination estimation device according to claim 2, wherein the machine learning model is trained to output an estimation value related to the pelvic inclination according to an input of the first feature amount included in the feature amount data (Kubo, Page 15: “FIG. 6 is a diagram showing the relationship between a human gait and a typical example of the time-varying waveform of vertical axis acceleration (acceleration in the Z-axis direction, with the vertical upward direction being positive) output from acceleration sensor 112 of activity meter 100 worn on the waist during a reference period (T7 (=Step T) in the figure) corresponding to one step in a walking cycle”; Fig. 6; Page 33-34: “the inventors experimentally discovered that the result of weighting and adding one or more feature quantities (feature parameters) that capture the characteristics of the waveform shape of the acceleration change over time output from an acceleration sensor attached to the subject's waist has a good correlation with the tilt angle of the pelvis when the subject is walking”; Page 11: “The control unit 210 includes a CPU and its auxiliary circuits, controls each unit of the smartphone 200 , and executes processing in accordance with the programs and data stored in the memory 220 . That is, it processes data input from the operation unit 230 and the communication units 280 and 290, and stores the processed data in the memory 220, displays it on the display unit 240, and outputs it from the communication units 280 and 290.”), the processor is configured to execute the instructions to acquire the feature amount data including a first feature amount for each gait phase cluster extracted from the gait waveform of the spatial acceleration and the spatial angular velocity included in the sensor data (Kubo, Page 31: “control unit 110 uses the vertical axis acceleration time series data (FIG. 7) to determine a reference period corresponding to one step in the walking cycle of the person being measured. The control unit 110 may obtain, for example, the time interval between a zero crossing point where the signal changes from negative to positive and another zero crossing point where the signal changes from negative to positive, that is, the reference period StepT”; Page 14: “the control unit 110 functions as an evaluation unit and uses the calculation result (pelvic tilt angle estimation result) to evaluate the degree of forward/backward tilt of the subject's posture while walking in multiple stages”; Chang, [0055]: “The kinematic measurements collected by the activity monitoring device are preferably along a set of orthonormal axes (e.g., an x, y, z coordinate system). The axis of measurements may not be aligned with a preferred or assumed coordinate system of the activity. Accordingly, the axis of measurement by one or more sensor(s) may be calibrated for analysis. One, two, or all three axes may share some or all features of the calibration, or be calibrated independently. The kinematic measurements can include acceleration, velocity, displacement, force, angular velocity, angular displacement, tilt/angle, and/or any suitable metric corresponding to a kinematic property or dynamic property of an activity. Preferably, a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes. The set of kinematic data streams preferably includes acceleration in any orthonormal set of axes in three-dimensional space, herein denoted as x, y, z axes, and angular velocity about the x, y, and z axes”; [0027]: “the system and method may be applied to activity use-cases such as gait-analysis”), input the first feature amount included in the acquired feature amount data to the machine learning model, and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model (Kubo, Page 13-14: “FIG. 12 shows an operation flow of the control unit 110 of the activity meter 100. When the power is turned on, the control unit 110 of the activity meter 100 waits for an instruction to start measurement from the smartphone 200, as shown in step S1. When an instruction to start measurement is received from the smartphone 200 (YES in step S1), the control unit 110 acquires the output of the acceleration in the three-axis directions from the acceleration sensor 112, as shown in step S2. The output of the acceleration sensor 112 is acquired for a predetermined period (for example, 14 seconds) which is a period including acceleration timeseries data for 10 steps in this example. The acquired time series data of acceleration is temporarily stored in memory 120 . Next, the control unit 110 waits for an instruction to start measurement from the smartphone 200, as shown in step S3. When a calculation instruction is received from smartphone 200 (YES in step S3), control unit 110 calculates the amount corresponding to the tilt angle of the pelvis, as shown in step S4. Then, as shown in step S5, the control unit 110 functions as an evaluation unit and uses the calculation result (pelvic tilt angle estimation result) to evaluate the degree of forward/backward tilt of the subject's posture while walking in multiple stages. Thereafter, as shown in step S6, the evaluation result is output (transmitted) to the smartphone 200. Note that the control unit 110 may execute the process of step S4 as soon as acceleration time-series data for at least one step is obtained”).
Regarding claim 4, the Kubo/Chang/Roche combination teaches the pelvic inclination estimation device according to claim 3, wherein the machine learning model is trained to output an estimation value related to the pelvic inclination according to an input of the first feature amount included in the feature amount data (Kubo, Page 15: “FIG. 6 is a diagram showing the relationship between a human gait and a typical example of the time-varying waveform of vertical axis acceleration (acceleration in the Z-axis direction, with the vertical upward direction being positive) output from acceleration sensor 112 of activity meter 100 worn on the waist during a reference period (T7 (=Step T) in the figure) corresponding to one step in a walking cycle”; Fig. 6; Page 33-34: “the inventors experimentally discovered that the result of weighting and adding one or more feature quantities (feature parameters) that capture the characteristics of the waveform shape of the acceleration change over time output from an acceleration sensor attached to the subject's waist has a good correlation with the tilt angle of the pelvis when the subject is walking”; Page 11: “The control unit 210 includes a CPU and its auxiliary circuits, controls each unit of the smartphone 200 , and executes processing in accordance with the programs and data stored in the memory 220 . That is, it processes data input from the operation unit 230 and the communication units 280 and 290, and stores the processed data in the memory 220, displays it on the display unit 240, and outputs it from the communication units 280 and 290.”), the processor is configured to execute the instructions to calculate, as a second feature amount, an average value and a difference of the first feature amounts and the gait parameters used for estimation of the pelvic inclination among the first feature amounts and the gait parameters for both feet of the user (Kubo, Page 22-23: “The characteristic parameter PRM10 is a parameter relating to the difference between the magnitude of the first maximum point that appears when the first peak in the reference period for the vertical axis acceleration, i.e., the zero crossing point where the acceleration changes from negative to positive, is used as the reference, and the magnitude of the minimum point in the same reference period. For example, PRM10 is ZAP1-ZAMN (unit: [m/sec<sup>２</sup>]). Regarding the eleventh characteristic parameter PRM11 (difference between the second maximum point and the minimum point of the Z-axis acceleration): The characteristic parameter PRM11 is a parameter relating to the difference between the magnitude of the second maximum point that appears when the second peak in the reference period for the vertical axis acceleration, i.e., the zero crossing point where the acceleration changes from negative to positive, is used as the reference point, and the magnitude of the minimum point in the same reference period. For example, PRM11 is ZAP2-ZAMN (unit: [m/sec<sup>２< /sup>]). Regarding the twelfth characteristic parameter PRM12 (difference between the third maximum point and the minimum point of the Z-axis acceleration): The characteristic parameter PRM12 is a parameter relating to the difference between the magnitude of the third maximum point that appears when the third peak in the reference period for the vertical axis acceleration, i.e., the zero crossing point where the acceleration changes from negative to positive, is used as the reference point, and the magnitude of the minimum point in the same reference period. For example, PRM12 is ZAP3-ZAMN (unit: [m/sec<sup>２</sup>]).”; [0056]: “The biomechanical signals can reflect ranges in observed metrics and/or maximum, minimum, or average metric values”), input the calculated second feature amount to the machine learning model, and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model (Kubo, Page 13-14: “FIG. 12 shows an operation flow of the control unit 110 of the activity meter 100. When the power is turned on, the control unit 110 of the activity meter 100 waits for an instruction to start measurement from the smartphone 200, as shown in step S1. When an instruction to start measurement is received from the smartphone 200 (YES in step S1), the control unit 110 acquires the output of the acceleration in the three-axis directions from the acceleration sensor 112, as shown in step S2. The output of the acceleration sensor 112 is acquired for a predetermined period (for example, 14 seconds) which is a period including acceleration timeseries data for 10 steps in this example. The acquired time series data of acceleration is temporarily stored in memory 120 . Next, the control unit 110 waits for an instruction to start measurement from the smartphone 200, as shown in step S3. When a calculation instruction is received from smartphone 200 (YES in step S3), control unit 110 calculates the amount corresponding to the tilt angle of the pelvis, as shown in step S4. Then, as shown in step S5, the control unit 110 functions as an evaluation unit and uses the calculation result (pelvic tilt angle estimation result) to evaluate the degree of forward/backward tilt of the subject's posture while walking in multiple stages. Thereafter, as shown in step S6, the evaluation result is output (transmitted) to the smartphone 200. Note that the control unit 110 may execute the process of step S4 as soon as acceleration time-series data for at least one step is obtained”).
Regarding claim 5, the Kubo/Chang/Roche combination teaches the pelvic inclination estimation device according to claim 4, wherein the machine learning model is trained to output an estimation value related to the pelvic inclination according to an input of an attribute of the user and the second feature amount (Kubo, Page 15: “FIG. 6 is a diagram showing the relationship between a human gait and a typical example of the time-varying waveform of vertical axis acceleration (acceleration in the Z-axis direction, with the vertical upward direction being positive) output from acceleration sensor 112 of activity meter 100 worn on the waist during a reference period (T7 (=Step T) in the figure) corresponding to one step in a walking cycle”; Fig. 6; Page 33-34: “the inventors experimentally discovered that the result of weighting and adding one or more feature quantities (feature parameters) that capture the characteristics of the waveform shape of the acceleration change over time output from an acceleration sensor attached to the subject's waist has a good correlation with the tilt angle of the pelvis when the subject is walking”; Page 11: “The control unit 210 includes a CPU and its auxiliary circuits, controls each unit of the smartphone 200, and executes processing in accordance with the programs and data stored in the memory 220 . That is, it processes data input from the operation unit 230 and the communication units 280 and 290, and stores the processed data in the memory 220, displays it on the display unit 240, and outputs it from the communication units 280 and 290.”), the processor is configured to execute the instructions to input the attribute of the user and the second feature amount input to the machine learning model, and estimate the pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the machine learning model (Kubo, Page 13-14: “FIG. 12 shows an operation flow of the control unit 110 of the activity meter 100. When the power is turned on, the control unit 110 of the activity meter 100 waits for an instruction to start measurement from the smartphone 200, as shown in step S1. When an instruction to start measurement is received from the smartphone 200 (YES in step S1), the control unit 110 acquires the output of the acceleration in the three-axis directions from the acceleration sensor 112, as shown in step S2. The output of the acceleration sensor 112 is acquired for a predetermined period (for example, 14 seconds) which is a period including acceleration timeseries data for 10 steps in this example. The acquired time series data of acceleration is temporarily stored in memory 120 . Next, the control unit 110 waits for an instruction to start measurement from the smartphone 200, as shown in step S3. When a calculation instruction is received from smartphone 200 (YES in step S3), control unit 110 calculates the amount corresponding to the tilt angle of the pelvis, as shown in step S4. Then, as shown in step S5, the control unit 110 functions as an evaluation unit and uses the calculation result (pelvic tilt angle estimation result) to evaluate the degree of forward/backward tilt of the subject's posture while walking in multiple stages. Thereafter, as shown in step S6, the evaluation result is output (transmitted) to the smartphone 200. Note that the control unit 110 may execute the process of step S4 as soon as acceleration time-series data for at least one step is obtained”).
Regarding claim 6, the Kubo/Chang/Roche combination teaches the pelvic inclination estimation device according to claim 1, wherein the machine learning model is trained to output at least one variation width of the pelvic inclination related to three axes of a traveling axis, a left-right axis, and a vertical axis in one gait cycle as an estimation value related to the pelvic inclination according to the input of the feature amount included in the feature amount data (Kubo, Page 11: “The control unit 210 includes a CPU and its auxiliary circuits, controls each unit of the smartphone 200 , and executes processing in accordance with the programs and data stored in the memory 220 . That is, it processes data input from the operation unit 230 and the communication units 280 and 290, and stores the processed data in the memory 220, displays it on the display unit 240, and outputs it from the communication units 280 and 290”; Page 9: “The acceleration sensor 112 detects the acceleration in each of the three axes (three directions)”; Page 17: “In the processing of step S4 in Figure 12 (pelvic tilt angle estimation processing), the abovementioned control unit 110 calculates a quantity corresponding to the forward/backward tilt of the subject's posture while walking using the time-varying waveform of the vertical axis acceleration and the time-varying waveform of the anterior-posterior axis acceleration over at least one reference period. Here, the average pelvic tilt angle θ (θ = (θ<sub>１</sub> + θ<sub>２</sub>)/2) of the subject's pelvic tilt angle θ<sub>１</sub> at the time of heel contact and the subject's pelvic tilt angle θ<sub>２</sub> at the time of mid-stance during the same reference period is used as the quantity corresponding to the degree of forward/backward tilt in the direction of travel. The amount corresponding to the degree of tilt in the front-to-back direction is not limited to the average pelvic tilt angle θ.”), the processor is configured to execute the instructions to input a feature amount included in the acquired feature amount data to the machine learning model, and estimate the pelvic inclination of the user according to a variation width of at least one of the pelvic inclinations in the three axes of the traveling axis, the left-right axis, and the vertical axis output from the machine learning model (Kubo, Page 13-14: “FIG. 12 shows an operation flow of the control unit 110 of the activity meter 100. When the power is turned on, the control unit 110 of the activity meter 100 waits for an instruction to start measurement from the smartphone 200, as shown in step S1. When an instruction to start measurement is received from the smartphone 200 (YES in step S1), the control unit 110 acquires the output of the acceleration in the three-axis directions from the acceleration sensor 112, as shown in step S2. The output of the acceleration sensor 112 is acquired for a predetermined period (for example, 14 seconds) which is a period including acceleration timeseries data for 10 steps in this example. The acquired time series data of acceleration is temporarily stored in memory 120 . Next, the control unit 110 waits for an instruction to start measurement from the smartphone 200, as shown in step S3. When a calculation instruction is received from smartphone 200 (YES in step S3), control unit 110 calculates the amount corresponding to the tilt angle of the pelvis, as shown in step S4. Then, as shown in step S5, the control unit 110 functions as an evaluation unit and uses the calculation result (pelvic tilt angle estimation result) to evaluate the degree of forward/backward tilt of the subject's posture while walking in multiple stages. Thereafter, as shown in step S6, the evaluation result is output (transmitted) to the smartphone 200. Note that the control unit 110 may execute the process of step S4 as soon as acceleration time-series data for at least one step is obtained”).
Regarding claim 7, the Kubo/Chang/Roche combination teaches the pelvic inclination estimation device according to claim 1, wherein the processor is configured to execute the instructions to display recommendation information according to the estimation result of the pelvic inclination of the user on the screen of the mobile terminal used by the user with content optimized for healthcare application (Kubo, Page 11: “The control unit 210 includes a CPU and its auxiliary circuits, controls each unit of the smartphone 200 , and executes processing in accordance with the programs and data stored in the memory 220 . That is, it processes data input from the operation unit 230 and the communication units 280 and 290, and stores the processed data in the memory 220, displays it on the display unit 240, and outputs it from the communication units 280 and 290.”; Roche, [0032]: “videos can be provided on the application with detailed instructions on everything from … exercise programs”).
Regarding claim 8, the Kubo/Chang/Roche combination teaches an estimation system (Kubo, Page 35: “the walking posture monitor of the present invention is configured as a system including the activity monitor 100 and the smartphone 200, but the present invention is not limited to this”) comprising: the pelvic inclination estimation device according to claim 1 (see rejection of claim 1 above); and a measurement device including a sensor that measures a spatial acceleration (Kubo, Page 35: “the walking posture monitor of the present invention is configured as a system including the activity monitor 100”; Page 9: “the activity meter 100 includes a casing 100M, and a control unit 110, an oscillation unit 111, an acceleration sensor 112 … The acceleration sensor 112 detects the acceleration in each of the three axes (three directions) that the casing 100M receives, and outputs the detected acceleration to the control unit 110. The acceleration sensor 112 may be a module chip of a three-axis acceleration sensor.”) and a spatial angular velocity (Chang, [0081]: “Gyroscopic data providing angular velocity around a vertical axis through the sensor towards earth can be used”), and generates the sensor data based on the spatial acceleration and the spatial angular velocity (Kubo, Page 15: “FIG. 6 is a diagram showing the relationship between a human gait and a typical example of the time-varying waveform of vertical axis acceleration (acceleration in the Z-axis direction, with the vertical upward direction being positive) output from acceleration sensor 112 of activity meter 100 worn on the waist during a reference period (T7 (=Step T) in the figure) corresponding to one step in a walking cycle”; Fig. 6; Page 33-34: “the inventors experimentally discovered that the result of weighting and adding one or more feature quantities (feature parameters) that capture the characteristics of the waveform shape of the acceleration change over time output from an acceleration sensor attached to the subject's waist has a good correlation with the tilt angle of the pelvis when the subject is walking”; Chang, [0055]: “The kinematic measurements collected by the activity monitoring device are preferably along a set of orthonormal axes (e.g., an x, y, z coordinate system). The axis of measurements may not be aligned with a preferred or assumed coordinate system of the activity. Accordingly, the axis of measurement by one or more sensor(s) may be calibrated for analysis. One, two, or all three axes may share some or all features of the calibration, or be calibrated independently. The kinematic measurements can include acceleration, velocity, displacement, force, angular velocity, angular displacement, tilt/angle, and/or any suitable metric corresponding to a kinematic property or dynamic property of an activity. Preferably, a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes. The set of kinematic data streams preferably includes acceleration in any orthonormal set of axes in three-dimensional space, herein denoted as x, y, z axes, and angular velocity about the x, y, and z axes”; [0027]: “the system and method may be applied to activity use-cases such as gait-analysis”), and configured to generate feature amount data including a feature amount used for estimating a pelvic inclination using the sensor data (Kubo, Page 11: “The control unit 210 includes a CPU and its auxiliary circuits, controls each unit of the smartphone 200 , and executes processing in accordance with the programs and data stored in the memory 220 . That is, it processes data input from the operation unit 230 and the communication units 280 and 290, and stores the processed data in the memory 220, displays it on the display unit 240, and outputs it from the communication units 280 and 290.”).
Regarding independent claim 9, Kubo teaches an estimation method executed by a computer (Page 1: “The present invention also relates to a program for causing a computer to execute a method for quantitatively evaluating whether a person's walking posture is correct or not.”), the method comprising:
acquiring feature amount data including a feature amount to be used for estimation of a pelvic inclination that is an index related to movement of a waist (Page 12: “when this walking posture meter 1 is used by, for example, a user, a subject 90, the activity meter 100 is attached to the back side of the waist on the midline 91 of the subject 90 using an attachment clip 100C”), the feature amount being extracted from a gait waveform of a spatial acceleration included in sensor data related to movement of a foot of a user (Page 15: “FIG. 6 is a diagram showing the relationship between a human gait and a typical example of the time-varying waveform of vertical axis acceleration (acceleration in the Z-axis direction, with the vertical upward direction being positive) output from acceleration sensor 112 of activity meter 100 worn on the waist during a reference period (T7 (=Step T) in the figure) corresponding to one step in a walking cycle”; Fig. 6; Page 33-34: “the inventors experimentally discovered that the result of weighting and adding one or more feature quantities (feature parameters) that capture the characteristics of the waveform shape of the acceleration change over time output from an acceleration sensor attached to the subject's waist has a good correlation with the tilt angle of the pelvis when the subject is walking”).
However, Kobo does not disclose measuring a spatial angular velocity.
Chang discloses a system and method for sensing fatigue by measuring user’s steps. Specifically, Chang teaches measuring a spatial angular velocity ([0055]: “The kinematic measurements collected by the activity monitoring device are preferably along a set of orthonormal axes (e.g., an x, y, z coordinate system). The axis of measurements may not be aligned with a preferred or assumed coordinate system of the activity. Accordingly, the axis of measurement by one or more sensor(s) may be calibrated for analysis. One, two, or all three axes may share some or all features of the calibration, or be calibrated independently. The kinematic measurements can include acceleration, velocity, displacement, force, angular velocity, angular displacement, tilt/angle, and/or any suitable metric corresponding to a kinematic property or dynamic property of an activity. Preferably, a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes. The set of kinematic data streams preferably includes acceleration in any orthonormal set of axes in three-dimensional space, herein denoted as x, y, z axes, and angular velocity about the x, y, and z axes”; [0027]: “the system and method may be applied to activity use-cases such as gait-analysis”). Kubo and Chang are analogous arts as they are both related to devices that measure a user’s steps and gait pattern to analyze the user’s health.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the measurement of angular velocity from Chang into the method from Kubo as it allows the method to determine another important spatial measurement, which can provide a more comprehensive and accurate analysis.
The Kubo/Chang combination teaches inputting the feature amount included in the acquired feature amount data to a model that outputs an estimation value related to the pelvic inclination in response to an input of the feature amount included in the feature amount data and estimating a pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the model (Kubo, Page 13-14: “FIG. 12 shows an operation flow of the control unit 110 of the activity meter 100. When the power is turned on, the control unit 110 of the activity meter 100 waits for an instruction to start measurement from the smartphone 200, as shown in step S1. When an instruction to start measurement is received from the smartphone 200 (YES in step S1), the control unit 110 acquires the output of the acceleration in the three-axis directions from the acceleration sensor 112, as shown in step S2. The output of the acceleration sensor 112 is acquired for a predetermined period (for example, 14 seconds) which is a period including acceleration timeseries data for 10 steps in this example. The acquired time series data of acceleration is temporarily stored in memory 120 . Next, the control unit 110 waits for an instruction to start measurement from the smartphone 200, as shown in step S3. When a calculation instruction is received from smartphone 200 (YES in step S3), control unit 110 calculates the amount corresponding to the tilt angle of the pelvis, as shown in step S4. Then, as shown in step S5, the control unit 110 functions as an evaluation unit and uses the calculation result (pelvic tilt angle estimation result) to evaluate the degree of forward/backward tilt of the subject's posture while walking in multiple stages. Thereafter, as shown in step S6, the evaluation result is output (transmitted) to the smartphone 200. Note that the control unit 110 may execute the process of step S4 as soon as acceleration time-series data for at least one step is obtained”).
However, the Kubo/Chang combination does not teach using a machine learning model.
Chang teaches using a machine learning model ([0044]: “the method can use a machine intelligence approach”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the machine learning from Chang into the Kubo/Chang combination as it allows the method to process the information in a quick, adaptable way, which can improve the performance of the method.
The Kubo/Chang combination teaches display information according to the estimation result of the pelvic inclination of the user on a screen of a mobile terminal used by the user (Kubo, Page 11: “The control unit 210 includes a CPU and its auxiliary circuits, controls each unit of the smartphone 200 , and executes processing in accordance with the programs and data stored in the memory 220 . That is, it processes data input from the operation unit 230 and the communication units 280 and 290, and stores the processed data in the memory 220, displays it on the display unit 240, and outputs it from the communication units 280 and 290.”).
However, the Kubo/Chang combination does not teach displaying a video containing recommended training according to the estimation result of the pelvic inclination of the user on a screen of the mobile terminal used by the user.
Roche teaches a system to monitor a user’s musculoskeletal system. Specifically, Roche teaches displaying a video containing recommended training ([0032]: “videos can be provided on the application with detailed instructions on everything from … exercise programs”). Kubo, Chang, and Roche are analogous arts as they are all related to devices that measure a user’s steps and gait pattern to analyze the user’s health.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the recommended training video from Roche into the Kubo/Chang combination as it allows the method to recommend specific training to the user that can improve their health condition.
Regarding independent claim 10, Kubo teaches a non-transitory program recording medium recorded with a program causing a computer to perform the following processes (Page 10: “Memory 120 includes ROM (Read Only Memory) and RAM (Random Access Memory). The ROM stores data of a program for controlling the activity meter 100 . The RAM also stores setting data for setting various functions of the activity meter 100, data on acceleration measurement results and calculation results, and the like.”):
acquiring feature amount data including a feature amount to be used for estimation of a pelvic inclination that is an index related to movement of a waist (Page 12: “when this walking posture meter 1 is used by, for example, a user, a subject 90, the activity meter 100 is attached to the back side of the waist on the midline 91 of the subject 90 using an attachment clip 100C”), the feature amount being extracted from a gait waveform of a spatial included in sensor data related to movement of a foot of a user (Page 15: “FIG. 6 is a diagram showing the relationship between a human gait and a typical example of the time-varying waveform of vertical axis acceleration (acceleration in the Z-axis direction, with the vertical upward direction being positive) output from acceleration sensor 112 of activity meter 100 worn on the waist during a reference period (T7 (=Step T) in the figure) corresponding to one step in a walking cycle”; Fig. 6; Page 33-34: “the inventors experimentally discovered that the result of weighting and adding one or more feature quantities (feature parameters) that capture the characteristics of the waveform shape of the acceleration change over time output from an acceleration sensor attached to the subject's waist has a good correlation with the tilt angle of the pelvis when the subject is walking”).
However, Kobo does not disclose measuring a spatial angular velocity.
Chang discloses a system and method for sensing fatigue by measuring user’s steps. Specifically, Chang teaches measuring a spatial angular velocity ([0055]: “The kinematic measurements collected by the activity monitoring device are preferably along a set of orthonormal axes (e.g., an x, y, z coordinate system). The axis of measurements may not be aligned with a preferred or assumed coordinate system of the activity. Accordingly, the axis of measurement by one or more sensor(s) may be calibrated for analysis. One, two, or all three axes may share some or all features of the calibration, or be calibrated independently. The kinematic measurements can include acceleration, velocity, displacement, force, angular velocity, angular displacement, tilt/angle, and/or any suitable metric corresponding to a kinematic property or dynamic property of an activity. Preferably, a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes. The set of kinematic data streams preferably includes acceleration in any orthonormal set of axes in three-dimensional space, herein denoted as x, y, z axes, and angular velocity about the x, y, and z axes”; [0027]: “the system and method may be applied to activity use-cases such as gait-analysis”). Kubo and Chang are analogous arts as they are both related to devices that measure a user’s steps and gait pattern to analyze the user’s health.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the measurement of angular velocity from Chang into the method from Kubo as it allows the device to determine another important spatial measurement, which can provide a more comprehensive and accurate analysis.
The Kubo/Chang combination teaches inputting the feature amount included in the acquired feature amount data to a model that outputs an estimation value related to the pelvic inclination in response to an input of the feature amount included in the feature amount data and estimating a pelvic inclination of the user according to the estimation value related to the pelvic inclination output from the model (Kubo, Page 13-14: “FIG. 12 shows an operation flow of the control unit 110 of the activity meter 100. When the power is turned on, the control unit 110 of the activity meter 100 waits for an instruction to start measurement from the smartphone 200, as shown in step S1. When an instruction to start measurement is received from the smartphone 200 (YES in step S1), the control unit 110 acquires the output of the acceleration in the three-axis directions from the acceleration sensor 112, as shown in step S2. The output of the acceleration sensor 112 is acquired for a predetermined period (for example, 14 seconds) which is a period including acceleration timeseries data for 10 steps in this example. The acquired time series data of acceleration is temporarily stored in memory 120 . Next, the control unit 110 waits for an instruction to start measurement from the smartphone 200, as shown in step S3. When a calculation instruction is received from smartphone 200 (YES in step S3), control unit 110 calculates the amount corresponding to the tilt angle of the pelvis, as shown in step S4. Then, as shown in step S5, the control unit 110 functions as an evaluation unit and uses the calculation result (pelvic tilt angle estimation result) to evaluate the degree of forward/backward tilt of the subject's posture while walking in multiple stages. Thereafter, as shown in step S6, the evaluation result is output (transmitted) to the smartphone 200. Note that the control unit 110 may execute the process of step S4 as soon as acceleration time-series data for at least one step is obtained”).
However, the Kubo/Chang combination does not teach using a machine learning model.
Chang teaches using a machine learning model ([0044]: “the method can use a machine intelligence approach”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the machine learning from Chang into the Kubo/Chang combination as it allows the device to process the information in a quick, adaptable way, which can improve the performance of the device.
The Kubo/Chang combination teaches display information according to the estimation result of the pelvic inclination of the user on a screen of a mobile terminal used by the user (Kubo, Page 11: “The control unit 210 includes a CPU and its auxiliary circuits, controls each unit of the smartphone 200 , and executes processing in accordance with the programs and data stored in the memory 220 . That is, it processes data input from the operation unit 230 and the communication units 280 and 290, and stores the processed data in the memory 220, displays it on the display unit 240, and outputs it from the communication units 280 and 290.”).
However, the Kubo/Chang combination does not teach displaying a video containing recommended training according to the estimation result of the pelvic inclination of the user on a screen of the mobile terminal used by the user.
Roche teaches a system to monitor a user’s musculoskeletal system. Specifically, Roche teaches displaying a video containing recommended training ([0032]: “videos can be provided on the application with detailed instructions on everything from … exercise programs”). Kubo, Chang, and Roche are analogous arts as they are all related to devices that measure a user’s steps and gait pattern to analyze the user’s health.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the recommended training video from Roche into the Kubo/Chang combination as it allows the device to recommend specific training to the user that can improve their health condition.
Conclusion
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/E.K.M./ Examiner, Art Unit 3791
/MATTHEW KREMER/ Primary Examiner, Art Unit 3791