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 .
DETAILED ACTION
Response to Amendment
In the preliminary amendment dated 10/30/2025, claims 1-3 and 6-8 have been amended.
Claims 1-20 are pending and have been examined.
Priority
Acknowledgement is made of applicant’s claim to priority under 35 U.S.C. 371 to PCT Application No. PCT/JP2023/023043 filed on 06/22/2023.
Information Disclosure Statement
The Information Disclosure Statement(s) (IDS)(s) submitted on 10/30/2025 follow(s) the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
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 1-20 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.
Claims 1, 9 and 15 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a device, method and CRM for disease risk estimation, which are within a statutory category.
Step 2A1
The limitations of acquiring sensor data measured in accordance with a movement of a foot of a subject who is an estimation target of a disease risk; estimating a disease risk related to a specific disease using the acquired sensor data; and outputting disease risk information relevant to the estimated disease risk, as drafted (claim 9 being representative), is a process that, under the broadest reasonable interpretation (BRI), covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to report an estimate of disease risk (see Spec. Para. 0002 describing gait analysis reporting as a human activity) in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “acquiring… estimating… and outputting…” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., a system implemented by a computer, the claimed invention amounts to managing personal behavior or interaction between people. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a disease risk estimation device/computer (claims 1, 9 and 15) comprising a memory and a processor (claim 1) and a computer-readable non-transitory recording medium (claim 15) that implement the identified abstract idea. The additional elements aforementioned are not described by the applicant and are recited at a high level of generality (i.e., each a generic computer or component performing generic computer functions that facilitate the identified abstract idea) such that this amounts no more than mere instructions to apply the exception using a generic computer component (see Spec. Para. 0027, 0120-0122). MPEP § 2106.04(d)(I). Accordingly, alone or in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a disease risk estimation device/computer comprising a memory and a processor and a computer-readable non-transitory recording medium to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using generic computers and/or generic computer components cannot provide an inventive concept (“significantly more”). MPEP § 2106.05(f).
Dependent claims 2-8, 10-14 and 16-20, when analyzed as a whole, are similarly rejected under 35 U.S.C. § 101 because they (1) further define/narrow the abstract idea and/or (2) do not further limit the claim to a practical application or provide an inventive concept, such that the claims are subject matter ineligible even when considered individually or as an ordered combination.
Claim(s) 2, 10 and 16 merely describe(s) calculating, inputting and outputting data, and estimating, which further define the abstract idea.
The claim(s) further recite(s) using “a disease risk estimation model” to input and output data. This represents the creation of mathematical interrelationships, formulas or equations, and calculations. See Specification, e.g., at para. 0088. That is, the claim(s) recite(s) a procedure for calculating a disease risk score that encompasses a mathematical concept. For example, the claim(s) encompass(es) applying data to a mathematical model and reporting the results. The Examiner notes that the mathematical concept need not be expressed in mathematical symbols. MPEP § 2106.04(a)(2)(I). If a claim limitation, under its BRI, encompasses a mathematical concept but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim(s) recite(s) an abstract idea. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
The claim(s) also include(s) the additional element(s) of the computer (claims 10 and 16) processor (claim 2) that represent mere instructions to apply the abstract idea. See analysis, supra.
Claim(s) 3, 11, and 17 merely describe(s) inputting the gait index to a physical ability estimation model, outputting a physical ability score, estimating physical ability information, which further defines the abstract idea. See analysis, supra.
The claim(s) further recite(s) using “a physical ability estimation model” and “the disease risk estimation model” to input and output data. This represents the creation of mathematical interrelationships, formulas or equations, and calculations. See Spec. Para. 0071 and 0088; see additionally Spec. Para. 0078, “linear regression algorithm”. The claim(s) recite(s) a procedure for calculating a physical ability score prior to calculating the disease risk score, and this encompasses a mathematical concept. See analysis, supra.
The claim(s) also include(s) the additional element(s) of the computer (claims 11 and 17) processor (claim 3) that represent mere instructions to apply the abstract idea. See analysis, supra.
Claim(s) 4, 12 and 18 merely describe(s) using the models (see analysis, supra) that “are models learned using a machine learning method”. The type of training utilized by the claimed invention is disclosed in the specification as being performed by a linear regression algorithm (see Spec. Para. 0083). The training of the machine learning models is considered to be part of the abstract idea because it/they fall(s) under data manipulations that humans perform (i.e., fitting a model to data) and thus are interpreted to be part of the abstraction—the rules or instructions that fall under Certain Methods of Organizing Human Activity. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 12 (Fed. Cir. April 18, 2025) (finding that “[i]terative training using selected training material…are incident to the very nature of machine learning.”). Alternately, the type of training utilized by the claimed invention, e.g., training of a machine learning model using a Gaussian process regression algorithm or a random forest algorithm (see Spec. Para. 0078, 0083), represents the creation of mathematical interrelationships between data. See, e.g., Example 47, Claim 2 of the Patents Subject Matter Eligibility Guidance. As such, the training of each machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra.
Claim(s) 4, 12 and 18 also include(s) the additional element of using the (learned) physical ability estimation model and the (learned) disease risk estimation model including an incomplete heterogeneous variational autoencoder to apply data to a model and report the results. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to input data and output the results merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (i.e., incomplete heterogeneous variational autoencoders) and thus fails to add an inventive concept to the claims.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning models (the learned physical ability estimation model and the learned disease risk estimation model including the incomplete heterogeneous variational autoencoder) to input data and report the results was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (the above-specified type of autoencoders). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Claims 5, 13 and 19 merely describe(s) inputting body information in addition to the gait index to the physical ability estimation model to output the physical ability score as well as inputting the physical ability score with the body information and the gait index to the disease risk estimation model to output the disease risk score, which further defines the abstract idea. See analysis, supra.
Claims 6, 14 and 20 merely describe(s) estimating, which further defines the abstract idea. See analysis, supra.
The claim(s) also include(s) the additional element(s) of the computer (claims 14 and 20) processor (claim 6) that represent mere instructions to apply the abstract idea. See analysis, supra.
Claim 7 merely recite(s) the additional element of the disease risk estimation device according to claim 1 (see analysis, supra).
Claim 7 also includes the additional element of a measurement device that is installed on footwear… includes a sensor that measures spatial acceleration and spatial angular velocity, which represents insignificant extra-solution activity. MPEP 2106.04(d)(I) indicates that extra-solution activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of a measurement device that is installed on footwear… includes a sensor that measures spatial acceleration and spatial angular velocity is considered extra solution activity. This additional element has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. The prior art of record indicates that sensing spatial acceleration and spatial angular velocity using a measurement device mounted to footwear (or otherwise attached in the location of a person’s foot) is well-understood, routine, and conventional in the field (see US 2022/0000430 to Fukushi et al. at para. 0034, 0049, 0051, 0059; WO 2023/023726 to Mobbs et al. at para. 046, 060, 148; US 2019/0150793 to Barth et al. at para. 0007-0008, 0035). The Examiner notes that there is no indication that the sensing feature of the measurement device or the mount is any different than how such sensors normally acquire movement data. Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, the additional elements do not provide significantly more.
Claim 7 also includes the additional element of the measurement device including a controller that implements the abstract idea (e.g., to generate the sensor data). The additional elements aforementioned are not described by the applicant and are recited at a high level of generality (i.e., each a generic computer or component performing generic computer functions that facilitate the identified abstract idea) such that this amounts no more than mere instructions to apply the exception using a generic computer component (see Spec. Fig. 2 and Para. 0027). See analysis, supra.
Claim 7 also includes the additional element of a measurement device including a transmitter as collecting, transmitting or outputting data. The additional element is recited at a high level of generality (i.e., general means of collecting, transmitting or outputting data) and amounts to a location from which data is received or to which data is transmitted or outputted, each of which represents insignificant extra-solution activity (e.g., mere data gathering and data output). MPEP § 2106.04(d)(I) indicates that extra-solution data gathering and data output activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Also discussed above with respect to integration of the abstract idea into a practical application, the additional element the measurement device including the transmitter (i.e., a device that collects, transmits or outputs data) is considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2106.05(d)(II) indicates that receiving, transmitting or outputting data over a network has been held by the courts to be well-understood, routine, conventional activity (citing TLI Communications, Symantec, OIP Techs., and buySAFE). See also MPEP 2106.05(g) (citing Cybersource, Mayo, OIP Techs.) Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such, the claim(s) is/are not patent eligible.
Claim 8 merely describe(s) displaying data optimized for the subject and browsable by the subject, which further defines the abstract idea. See analysis, supra.
The claim also includes the additional elements of the disease risk estimation device processor (see analysis, supra) and a terminal device including a screen that implements the abstract idea, which amounts to no more than mere instructions to apply the abstract idea (see Spec. Para. 0089-0090). See analysis, supra.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-11 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Morris et al. (US 2021/0059565; “Morris” herein) in view of Fukushi et al. (US 2022/0000430; “Fukushi” herein).
Re. Claim 1, Morris teaches a disease risk estimation device comprising ([0004], [0021] teach an approach for the gait-based assessment of neurodegenerative conditions and/or risk of developing neural degeneration):
a memory storing instructions (see Fig. 12 and [0051]-[0052].); and
a processor connected to the memory and configured to execute the instructions (see Fig. 12 and [0050]-[0051]) to:
acquire sensor data measured in accordance with a movement of a foot of a subject who is an estimation target of a disease risk ([0030] teaches patient-worn inertial sensors that measure accelerations and transmit their data wirelessly to a computer, may be used. [0003] teaches measuring footfalls as a person walks. Additionally, [0021] teaches the capturing (acquiring sensor data) and subsequent processing to derive motion data.);
estimate a disease risk related to a specific disease using the acquired sensor data (Fig. 1, [0021] teach the processing and subsequent analyzing to obtain a gait-based assessment of the patient’s neural condition and/or risk of developing neural degeneration. [0026], [0028] teach computing a predictive score 126 / probability associated with a certain neurodegenerative condition (e.g., Alzheimer’s disease) or a risk of developing symptoms associated with the condition.); and
output […] relevant to the estimated disease risk (Fig. 1, [0021] teach obtaining the gait-based assessment.)
Morris may not teach output of disease risk information (other than the estimate).
Fukushi teaches
output disease risk information ([0073] teaches outputting information that there is a risk for the left foot or the right foot to a display apparatus such as, for example, a liquid crystal screen (step S205)… Thereby, the user can learn that he/she is performing walking motion having a risk for the left foot or the right foot in his/her own walking motion. See also [0075], “outputs the determination result”.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the gait-based assessment of neurodegeneration of Morris to report the results of analyses and to use this information as part of an apparatus and method as taught by Fukushi, with the motivation of improving gait analysis, preventing/managing pathogenesis, mitigating disease risk/severity (see Fukushi at Para. 0003-0004).
Re. Claim 2, Morris/Fukushi teaches the disease risk estimation device according to claim 1, wherein the processor is configured to execute the instructions to:
calculate a gait index using the sensor data (Morris Fig. 1, [0022] teaches the preprocessed data is fed into a motion analysis model 110, which computes gait kinematic parameters. See also Morris [0023]-[0024]. Morris [0025] teaches further processing using a gait metric calculator 118 to compute one or more gait metrics 120 that can serve as biomarkers for neurodegeneration (gait indices).); and
input data including the gait index calculated using the sensor data to a disease risk estimation model that outputs a disease risk score indicating a degree of the disease risk related to the specific disease according to an input of the data including the gait index (Morris Fig. 1, [0026], [0028] teaches one or more gait metrics 120 computed for the patient… are used as input to a neurodegeneration prediction model 124, which computes a predictive score 126… associated with a neurodegenerative condition e.g., Alzheimer’s disease… provides the probability (degree) that the patient suffers from the certain condition.), and estimates disease risk information relevant to the disease risk score output from the disease risk estimation model (Morris Fig. 1, [0026] teaches the predictive score(s) 126 may also quantify… the level of that risk. Fukushi [0075] teaches determining whether the gait disorder risk is “low”, “medium”, or “high”.)
Re. Claim 3, Morris/Fukushi teaches the disease risk estimation device according to claim 2, wherein the processor is configured to execute the instructions to:
input data including the gait index calculated using the sensor data to a physical ability estimation model that outputs a physical ability score indicating a physical ability according to an input of data including the gait index (Fig. 1, [0021]-[0022] teach the preprocessed motion data is fed into a motion analysis model to compute certain gait kinematic parameters (gait indices calculated using the sensor data). Morris Fig. 1, [0004] teaches the gait metrics (physical ability scores) that flow as input into the model(s) may be derived (calculated) by... from gait kinematic data including time-series kinematic parameters of joints and body segments and/or spatiotemporal parameters derived from such time-series data (gait indices). Morris [0025] teaches using a gait metric calculator 118 (physical ability estimation model) to compute the one or more gait metrics 120.), and estimates physical ability information according to the physical ability score output from the physical ability estimation model ([0025] teaches the computed gait metrics can serve as biomarkers for neurodegeneration (physical ability information estimated).); and
input […] and the physical ability score estimated by the physical ability estimation unit (the gait metric) to the disease risk estimation model (Morris Fig. 1, [0026], [0028] teaches one or more gait metrics 120 computed for the patient, optionally along with… information, are used as input to a neurodegeneration prediction model 124, which computes a predictive score 126… that provides the probability that the patient suffers from the certain condition.), and estimates the disease risk information according to the disease risk score output from the disease risk estimation model (Morris Fig. 1, [0026] teaches the predictive score(s) 126 may also quantify… the level of that risk. Additionally, Fukushi [0075] teaches determining whether the gait disorder risk is “low”, “medium”, or “high”.)
Morris/Fukushi does not explicitly teach input the gait index calculated using the sensor data (i.e., the gait kinematic parameter.)
However, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to combine the noted features of Morris with teachings thereof, since the combination is merely combining prior art elements according to known methods to yield predictable results (KSR rational A). Each element claimed is presented in Morris and is further presented as a model input (see Morris at Fig. 1 and para. 0004 and 0025). Providing additional input to the neurodegeneration prediction model (as taught by Morris in para. 0026) does not change or affect the normal motion analysis model-related functionality of the system of Morris. Applying data to the models would be performed the same way even with the application of additional types of inputs. Since the functionalities of the elements in Morris do not interfere with each other, the results of the combination would be predictable.
Re. Claim 5, Morris/Fukushi teaches the disease risk estimation device according to claim 3,
wherein the physical ability estimation model (the gait metric calculator) is configured to execute to: output the physical ability score (the one or more gait metric) related to physical ability of at least one of grip strength, dynamic balance, lower limb muscle strength, movement ability, and static balance according to an input of body information of the subject and the gait index (Morris [0004], [0025] teaches the gait metrics that flow as input into the model(s) may be derived, using the gait metric calculator, from gait kinematic data including time-series kinematic parameters of joints and body segments and/or spatiotemporal parameters derived from such time-series data. Morris [0023] teaches the gait kinematic parameters are associated with joints and/or body segments and include linear and/or angular positions, velocities, and/or accelerations (body information), e.g., pelvic tilt, pelvic list, pelvic rotation, hip abduction, hip flexion, hip rotation, left and right knee angles, left and right ankle angles, Tl head neck axial rotation, Tl head neck flexion/extension, Tl head neck lateral bending, thoracic axial rotation, thoracic flexion/extension, thoracic lateral bending, left and right shoulder elevation, left and right elbow flexion, left and right wrist flexion), as well as spatiotemporal parameters (for movement ability) computed from the before-mentioned "raw" parameters, such as, e.g., stride length (defined as the distance between successive points of heel contact of the same foot), step length (defined as the distance between successive points of heel contact of opposite feet), average speed, step frequency, etc. Also, Morris [0044] teaches gait complexity provides a good biomarker for neurodegeneration (physical ability score related to dynamic balance).), and
the disease risk estimation model is configured to execute to: output the disease risk score related to the specific disease according to an input of the body information of the subject, the gait index, and the physical ability score (Morris Fig. 1, [0026], [0028] teaches the one or more gait metrics 120 (the physical ability score), optionally along with patient demographic data or personal health information, e.g., weight, height, etc. 122 (body information), are used as input to the neurodegeneration prediction model, which computes the predictive score(s) associated with the neurodegenerative condition (disease risk score(s)). The Examiner interprets the body information as including weight and height. Morris Fig. 1, [0025] teaches the gait kinematics parameters were input into the gait metric calculator 118 to derive the gait metrics (according to an input of the gait index).)
Re. Claim 6, Morris/Fukushi teaches the disease risk estimation device according to claim 3, wherein the processor execute is configured to execute the instructions to:
estimate body information using the gait index calculated using the sensor data (Fig. 1, [0021]-[0022] teach the preprocessed motion data is fed into a motion analysis model to compute certain gait kinematic parameters (calculated using the sensor data). Morris Fig. 1 and [0004], [0023] teaches the gait metrics (body information) that flow as input to the model(s) may be derived (estimated)... from gait kinematic data including time-series kinematic parameters of joints and body segments and/or spatiotemporal parameters (body information) derived (estimated) from such time-series data (using the gait index).); and
estimate the physical ability information and the disease risk information using the estimated body information (Morris Fig. 1 and [0024], [0025] teaches the one or more gait kinematics parameters are input to the gait metric calculator 118 to compute the gait metrics, which can serve as biomarkers for neurodegeneration (physical ability information). Morris Fig. 1, [0004], [0026], [0028] teaches the gait metrics, derived from the gait kinematics data/kinematics parameters of joints and body segments/spatiotemporal parameters derived from such data (using the estimated body information), are input to the neurodegeneration prediction model, which computes the predictive score 126 (disease risk information) … that provides the probability that the patient suffers from the certain condition… and may also quantify… the level of that risk. Additionally, Fukushi [0075] teaches determining whether the gait disorder risk is “low”, “medium”, or “high”.)
Re. Claim 7, Morris/Fukushi teaches a disease risk estimation system comprising: the disease risk estimation device according to claim 1 (see claim 1 prior art rejection); and a measurement device (2, 3) that is installed on footwear of a user who is an estimation target of the disease risk information, wherein the measurement device includes a sensor (207, 307) that measures spatial acceleration and spatial angular velocity, a controler that generates the sensor data using the measured spatial acceleration and spatial angular velocity (Fukushi Fig. 12, [0007] teaches a sensor apparatus provided in a shoe sole (measurement device includes a sensor), and the sensor apparatus includes: at least one memory configured to store instructions; and at least one processor (controller) configured to execute the instructions to: measure the acceleration or an angular velocity of a foot; and calculate a feature amount of a whipping motion of the foot on the basis of the acceleration or the angular velocity. See Applicant’s disclosure at para. 0016-0017.), and a transmitter (206, 306) that transmits the generated sensor data to the disease risk estimation device via wireless communication (Fukushi Fig. 13 and [0056] teaches the communication apparatus 206 of the first sensor apparatus 2… has a wireless communication function… to connect to other apparatuses and communicate with the other apparatuses. Morris Fig. 12, [0021], [0049] teaches the computer system receives sensor data, e.g., video data, via the wireless network connection.)
Re. Claim 8, Morris/Fukushi teaches the disease risk estimation system according to claim 7, wherein the processor of the disease risk estimation device is configured to execute the instructions to: display the disease risk information optimized for the subject on a screen of a terminal device browsable by the user (see claim 7 prior art rejection. Morris Fig. 1 and [0021], [0049] teaches the computer system 100… may include a handheld computational device / smartphone (terminal device). Fukushi Fig. 1, [0073] teaches the risk determination unit 13 outputs information… when the determination apparatus 1 is a smartphone, the information that there is a risk for the left foot or the right foot is displayed on the liquid crystal screen of the smartphone.)
Re. CLAIM 9, the subject matter of claim 9 is essentially defined in terms of a method, which is technically corresponding to system claim 1. Since claim 9 is analogous to claim 1, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 1. Further, Morris teaches a computer (1200) configured to execute the steps of the claim (see Fig. 12 and [0050]-[0051].)
Re. Claim 10, the subject matter of claim 10 is essentially defined in terms of a method, which is technically corresponding to system claim 2. Since claim 10 is analogous to claim 2, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 2.
Re. Claim 11, the subject matter of claim 11 is essentially defined in terms of a method, which is technically corresponding to system claim 3. Since claim 11 is analogous to claim 3, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 3.
Re. Claim 13, the subject matter of claim 13 is essentially defined in terms of a method, which is technically corresponding to system claim 5. Since claim 13 is analogous to claim 5, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 5.
Re. Claim 14, the subject matter of claim 14 is essentially defined in terms of a method, which is technically corresponding to system claim 6. Since claim 14 is analogous to claim 6, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 6.
Re. CLAIM 15, the subject matter of claim 15 is essentially defined in terms of a manufacture, which is technically corresponding to system claim 1. Since claim 15 is analogous to claim 1, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 1. Further, Morris teaches a computer-readable non-transitory recording medium having recorded therein a program for causing a computer (1200) to execute the steps of the claim (see Fig. 12 and [0051]-[0052].)
Re. Claim 16, the subject matter of claim 16 is essentially defined in terms of a manufacture, which is technically corresponding to system claim 2. Since claim 16 is analogous to claim 2, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 2.
Re. Claim 17, the subject matter of claim 17 is essentially defined in terms of a manufacture, which is technically corresponding to system claim 3. Since claim 17 is analogous to claim 3, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 3.
Re. Claim 19, the subject matter of claim 19 is essentially defined in terms of a manufacture, which is technically corresponding to system claim 5. Since claim 19 is analogous to claim 5, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 5.
Re. Claim 20, the subject matter of claim 20 is essentially defined in terms of a manufacture, which is technically corresponding to system claim 6. Since claim 20 is analogous to claim 6, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 6.
Claims 4, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Morris in view of Fukushi and Gootjes-Dreesbach et al. (2020) (“Variational Autoencoder Modular Bayesian Networks for Simulation of Heterogeneous Clinical Study Data”; “Gootjes-Dreesbach” herein).
Re. Claim 4, Morris/Fukushi teaches the disease risk estimation device according to claim 3, wherein […] and the disease risk estimation model (neurodegeneration prediction model) are models learned using a machine learning method, and the disease risk estimation model includes […] (see Morris [0025]-[0026]. Morris [0048] teaches, to train the neurodegeneration prediction model 124, one or more predictive scores 126 are computed from the gait metrics 120 provided as part of the training data… and evaluated against the cognitive and neuropathology scores 1102, 1104, using an evaluation module 1114… The prediction model 124 and its parameters (e.g., network weights of an artificial neural network model, or probabilities associated with the branches in a decision tree), as well as the selection of the gait metrics 120 to be used in the computation of the predictive score(s) 126 can be adjusted iteratively based on the evaluation (machine learning method)… The evaluation module 1114 may be part of, or accessed by, a suitable learning algorithm 1116 making the model adjustments. Additionally, Morris [0027] teaches principal component analysis may be used to reduce the number of parameters input.)
Morris/Fukushi does not explicitly teach the physical ability estimation model (i.e., the gait metric calculator) as also being learned using a machine learning method.
However, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to combine the noted features of Morris with teachings thereof, since the combination is merely combining prior art elements according to known methods to yield predictable results (KSR rational A). Each element claimed is presented in Morris (see Fig. 1 and para. 0025-0026). Providing machine learning technology and training (as taught by Morris in para. 0026 and 0048) does not change or affect the normal gait analysis- and risk analysis-related functionality of the system of Morris. Data modeling would be performed the same way even with the application of machine learning technology and training (to iteratively adjust, reduce, and apply data to an algorithm and evaluate the results). Since the functionalities of the elements in Morris do not interfere with each other, the results of the combination would be predictable.
Morris/Fukushi does not teach the disease risk estimation model includes an incomplete heterogeneous variational autoencoder.
Gootjes-Dreesbach teaches
an incomplete heterogeneous variational autoencoder (Abstract teaches a new machine learning approach [Variational Autoencoder Modular Bayesian Network (VAMBN)] to learn a generative model of longitudinal clinical study data. VAMBN considers typical key aspects of such data, namely limited sample size coupled with comparable many variables of different numerical scales and statistical properties (heterogeneous data), and many missing values (incomplete data) … with VAMBN, we can simulate virtual patients in a sufficiently realistic manner while making theoretical guarantees on data privacy.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the gait-based assessment of neurodegeneration of Morris/Fukushi to apply an incomplete heterogeneous variational autoencoder and to use this information as part of a method for simulation of heterogeneous (and incomplete) clinical study data as taught by Gootjes-Dreesbach, with the motivation of improving data modeling and data sharing while maintaining patient privacy (see Gootjes-Dreesbach at Abstract).
Re. Claim 12, the subject matter of claim 12 is essentially defined in terms of a method, which is technically corresponding to system claim 4. Since claim 12 is analogous to claim 4, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 4.
Re. Claim 18, the subject matter of claim 18 is essentially defined in terms of a manufacture, which is technically corresponding to system claim 4. Since claim 18 is analogous to claim 4, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bell et al. (WO 2023/168074) for teaching automated test of embodied cognition… predicting one or more neurological assessments or cognitive scores associated with the observed movements of a subject (see Abstract).
Barth et al. (US 2019/0150793) for teaching method and system for analyzing human gait.
Huang et al. (US 2022/0000434) for teaching basal metabolism estimation device, system, method, and program.
Huang et al. (US 2022/0000431) for the same.
Mobbs et al. (WO 2023/023726) for teaching the determination of a quantitative gait score, e.g., the Combined Mobility Score (CMoS), which is a comprehensive objective estimate of gait health representing both numerical/quantitative scoring (the recited “physical ability score”) and visual/quantitative gait information (the recited “physical ability information”)… takes into account the many aspects of gait including quality, quantity, consistency, and stability/balance (see para. 012, 169 and Table 17).
Nihey et al. (US 2024/0350034) for teaching estimation device, system, method, recording medium. See Fig. 13, e.g., step S16.
Otsuki et al. (US 2021/0005319) for teaching learning apparatus, rehabilitation support system, method, program, and trained model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jessica M Webb whose telephone number is (469)295-9173. The examiner can normally be reached Mon-Fri 9:00am-1:00pm CST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.M.W./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
Prosecution Insights