DETAILED ACTION
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 .
Priority
Applicant claims domestic priority benefit to the national phase application filed on 22 June 2023. However, it should be noted that Applicant does not claim foreign priority to the associated filed Japanese application.
Response to Amendment
Claims 1-20 were previously pending in this application. The preliminary amendment filed 31 October 2025 has been entered. Claims 1-10 & 12-13 have been amended. No Claims have been added or cancelled.
Claims 1-20 remain pending in the application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 31 October 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the Examiner in this Office Action.
Drawings
The drawings filed on 31 October 2025 and the amendments to the drawings received on 31 October 2025 are accepted.
Claim Objections
Claim 12 is objected to because of the following informalities:
Claim 12 recites “a controler that generates the sensor data using the measured spatial acceleration and spatial angular velocity” instead of “a controller that generates the sensor data using the measured spatial acceleration and spatial angular velocity”. Appropriate correction is required.
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.
The claims recite subject matter within a statutory category as a process (claims 14-19), machine (claims 1-13), and manufacture (claim 20) (Subject Matter Eligibility (SME) Test Step 1: Yes) which recite steps 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 reflecting a risk for each disease using the acquired sensor data; and
outputting disease risk information relevant to the estimated disease risk.
These steps of acquiring sensor data, estimating a disease risk, and outputting disease risk information, as drafted, under the broadest reasonable interpretation, includes performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the acquiring sensor data language, acquiring sensor data in the context of this claim encompasses a mental process of a person or user extracting sensor data from one or more sources, either by utilizing a measuring device to collect said data or acquiring said data from one or more data/health records. Similarly, the limitation of estimating a disease risk, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, such as the user or person applying one or more computational models or equations and plugging in/applying the acquired sensor data to estimate disease risk. For example, but for the outputting disease risk information language, outputting information in the context of this claim encompasses a mental process of the user displaying or reporting said results, such as in a report or other visual media/means. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
These steps 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 reflecting a risk for each disease using the acquired sensor data; and outputting disease risk information relevant to the estimated disease risk as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. MPEP 2106.04(a)(2)(II) sets forth various methods of organizing human activity, including concepts relating to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). These steps represent aspects of managing personal behavior or relationships or interactions between people, at least by applying the system for diagnosis interactions that exist between a provider and a patient. For instance, MPEP 2106.04(a)(2)(II)(C) sets forth examples of managing personal behavior including a mental process that a neurologist should follow when testing a patient for nervous system malfunctions. This example is substantially similar to the claimed steps found above regarding said steps relating to diagnosis interactions that exist between a provider and a patient.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 2-13 & 15-19, reciting particular aspects of how calculating gait metrics, estimating disease risks, or generating insurance policy/proposals in view of said calculations/estimations may be performed in the mind but for recitation of generic computer components, which directly relates to agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations) (SME Test Step 2A, Prong 1: Yes).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of a memory, a processor, a computer-readable non-transitory recording medium, and a computer amounts to invoking computers as a tool to perform the abstract idea, see Applicant’s Specification [0213] for a memory; Spec [0027] for a processor; Spec [0213] & [0220] for a computer-readable non-transitory recording medium; Spec [0027] for a computer; see MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea (such as recitation 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 amounts to mere data gathering; recitation of estimating a disease risk reflecting a risk for each disease using the acquired sensor data amounts to selecting a particular data source or type of data to be manipulated; recitation of estimating a disease risk reflecting a risk for each disease using the acquired sensor data and outputting disease risk information relevant to the estimated disease risk amounts to insignificant application, see MPEP 2106.05(g); output disease risk information relevant to the estimated disease risk, i.e. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, see MPEP 2106.05(a)(II));
generally link the abstract idea to a particular technological environment or field of use (such as recitation of a disease risk, i.e. applied to medical risk in particular, and/or receiving gait-related information from a subject for health risk determinations, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-13 & 15-19, which recite limitations relating to a processor, a disease risk estimation device, a measurement device, a controller, a transmitter, a computer, additional limitations which amount to invoking computers as a tool to perform the abstract idea, see Applicant’s Specification [0027] for a processor; Spec [0031] for a disease risk estimation device; Spec [0015] for a measurement device; Spec [0027] for a controller; Spec [0028] for a transmitter; Spec [0027] for a computer, see MPEP 2106.05(f); claims 2, 9-10, & 15 which recite limitations relating to collecting input data, additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering; claims 2-13 & 15-19, which recite limitations relating to calculating a gait index, calculating the disease risk score, determining a disease risk, such as determining the risk is high or low, generating proposal information, estimating the disease risk reflecting a risk for each disease using the acquired sensor data, applying a model to existing data, generating proposal information for insurance-related institutions, additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated; claims 2-13 & 15-19, which recite limitations relating to calculating a gait index, calculating the disease risk score, determining a disease risk, such as determining the risk is high or low, generating proposal information, estimating the disease risk reflecting a risk for each disease using the acquired sensor data, applying a model to existing data, generating proposal information for insurance-related institutions, additional limitations which amount to insignificant application; claims 2-13 & 15-19, which relate to additional limitations which generally link the abstract idea to a particular technological environment or field of use; claims 2, 9, & 15, which recite limitations relating to outputting said results, i.e. gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, see MPEP 2106.05(a)(II)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application (SME Test Step 2A, Prong 2: No).
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 discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as acquiring sensor data measured according to a movement of a foot of a subject, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); estimating a disease risk reflecting a risk for each disease using the acquired sensor data, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); updating disease risks and benchmarks for disease risk estimations, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); storing computerized instructions for performance of the steps recited such as in a memory or non-transitory computer readable storage medium, storing acquired sensor data, storing disease risk estimations, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); acquiring sensor data, which under BRI includes mere extraction from a physical or electronic document, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-13 & 15-19, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 2, 9-10, & 15 which recite limitations relating to collecting input data, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 2-13 & 15-19, which recite limitations relating to calculating a gait index, calculating the disease risk score, estimating the disease risk reflecting a risk for each disease using the acquired sensor data, applying a model to existing data, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 2, 9, & 15, which recite limitations relating to outputting said results, which includes outputting said results to one or more records, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); claims 2-13 & 15-19, which recite limitations relating to storing computerized instructions to perform the steps recited, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); claims 2, 9-10, & 15 which recite limitations relating to collecting input data, which under BRI includes acquiring said data from one or more documents that are physical or electronic, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation (SME Test Step 2B: No).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-9, 12-15, & 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Groteke et al. (U.S. Patent Publication No. 2023/0170069), hereinafter “Groteke”, in view of Coffey et al. (U.S. Patent Publication No. 2023/0000396), hereinafter “Coffey”.
Claim 1 –
Regarding Claim 1, Groteke discloses a disease risk estimation device comprising:
a memory storing instructions (See Groteke Par [0042] & [0167]); and
a processor connected to the memory and configured to execute the instructions (See Groteke Par [0042] & [0167]) 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 (See Groteke Par [0188]-[0194] which discloses gait analysis being performed, including monitoring various parts of walking/gait such as various heel strikes, foot raises, etc.; See Groteke Par [0220] which discloses various devices that can be used to measure and record their range of motion using computer, tablet, etc., or other device having a two-dimensional video camera (e.g. a camera configured to capture standard, two dimensional video footage), and while not explicitly mentioned as a “sensor” per se, an additional reference will be applied hereinafter for advancing prosecution; See Groteke Par [0326]-[0328] & [0334]-[0344] which discloses measuring angles, movement, and/or rotation of feet);
estimate a disease risk reflecting a risk for each disease using the acquired sensor data (See Groteke Par [0008] which discloses providing a risk value for quickly estimating and assessing a patient’s risk of experiencing musculoskeletal (MSK) injury for specific body regions or conditions; See Groteke Par [0146] which discloses an AI generated risk analysis care plan and evidence-based guidelines, including tracking of improvements against MSK risk factors; See Groteke Par [0111] which discloses containing a record of a patient’s MSK health over time, such as by an individual’s MSK tracking including a patient’s personal range of motion; See Groteke Par [0130] & [0136] which discloses extracting join angles from midstance frames gait phases including feet movement; See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions); and
output disease risk information relevant to the estimated disease risk (Without specifying “output”, “outputting” could simply include any means of generating said information, therefore see Groteke Par [0060] which discloses “providing” a quantitative value called a “injury risk index score”, i.e. a risk value, that is highly correlated to the risk of musculoskeletal injury, a value which takes into account both known, reported issues as well as asymptomatic, hidden medical conditions; See Groteke Par [0067] which discloses the injury risk index score may be reported as a diagnostic figure).
While Groteke generally discloses the use of a camera or other capturing device for monitoring various parts of walking/gait such as various heel strikes, foot raises, and/or range of motion, Groteke is generally silent on the use of sensors, e.g. biosensors, such as for sensing certain health metrics directly from the user.
Coffey discloses sensing certain health metrics directly from the user (See Coffey Par [0068] & [0072] which discloses a control system analyzing data from one or more sensors to determine a gait for one or more users; See Coffey Par [0072]-[0073] discloses that the one or more sensors generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time, and said sensors can be implemented into one or more devices to be used by the user). The disclosure of Coffey is directly applicable to the disclosure of Groteke because both disclosures share limitations and capabilities, such as being directed towards performing gait analysis on one or more users for health predictions.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Groteke which already discloses the use of a camera or other capturing device for monitoring various parts of walking/gait to further include sensors, e.g. biosensors, such as for sensing certain health metrics directly from the user, as disclosed by Coffey, because this allows for said sensors to be implemented into one or more devices to be used by the user that can generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time (See Coffey Par [0072]-[0073]).
Claim 2 –
Regarding Claim 2, Groteke and Coffey disclose the disease risk estimation device according to claim 1 in its entirety. Groteke further discloses a device, wherein:
the processor is configured to execute the instructions to:
calculate a gait index using the sensor data (See Groteke Par [0080] which discloses the AI utilizing angle-based and key-point detection data capture techniques, such as described in the gait analysis section, i.e. Par [0151]-[0157] which discloses gait analysis being performed, including monitoring various parts of walking/gait such as various heel strikes, foot raises, etc.); and
an estimation unit configured to 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 disease according to an input of data including the gait index (See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning, i.e. a model, to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; See Coffey Par [0068] & [0072] which discloses a control system analyzing data from one or more sensors to determine a gait for one or more users; See Coffey Par [0072]-[0073] discloses that the one or more sensors generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time, and said sensors can be implemented into one or more devices to be used by the user), and estimates disease risk information relevant to the disease risk score output from the disease risk estimation model (See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Groteke which already discloses the use of a camera or other capturing device for monitoring various parts of walking/gait to further include sensors, e.g. biosensors, such as for sensing certain health metrics directly from the user, as disclosed by Coffey, because this allows for said sensors to be implemented into one or more devices to be used by the user that can generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time (See Coffey Par [0072]-[0073]).
Claim 5 –
Regarding Claim 5, Groteke and Coffey disclose the disease risk estimation device according to claim 2 in its entirety. Coffey and Asthana further disclose a device, wherein:
the processor is configured to execute the instructions to:
execute the instructions to determine a disease risk for each disease according to a change tendency of the disease risk score (a “change tendency” under BRI is understood to include any tendency such as increasing, decreasing, positive, negative, etc., therefore see Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; see Coffey Par [0087] & [0095] which discloses the fall inference being generated as a fall inference score and a classification of said score as a high risk, medium risk, or low risk based on longitudinal data over time; See Coffey Par [0100] which discloses the control system being able to further analyze data to determine whether the amount of time for the resident to complete one or more of the aforementioned activities has increased (e.g., indicating the resident is more likely to fall) or decreased (e.g., indicating the resident is improving and less likely to fall) over time)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure to modify the combined disclosure of Groteke and Coffey, which already discloses calculating a risk index score to track the risk of musculoskeletal injury and/or early detection signs of MSK disease to further include determine that a disease risk is high with respect to a disease for which the disease risk score, i.e. fall risk which is indicative of disease risk, is on an increasing tendency and determining that the disease risk is low with respect to a disease on any one of a decreasing tendency, as further disclosed by Coffey, because this allows for longitudinal analysis of disease risk, such as over a period of time, and determining whether disease risks have increased or decreased for said patient/user based on said longitudinal data/analysis (See Coffey Par [0100]).
Claim 6 –
Regarding Claim 6, Groteke and Coffey disclose the disease risk estimation device according to claim 5 in its entirety. Groteke and Coffey further disclose a device, wherein:
the processor is configured to execute the instructions to:
determine that a disease risk is high with respect to a disease for which the disease risk score is on an increasing tendency (“for which the disease risk is on an increasing tendency” is understood to be a mere optimization within prior art conditions and does not necessarily hold patentable weight, see MPEP 2144.05, therefore see Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; see Coffey Par [0087] & [0095] which discloses the fall inference being generated as a fall inference score and a classification of said score as a high risk, medium risk, or low risk based on longitudinal data over time; See Coffey Par [0100] which discloses the control system being able to further analyze data to determine whether the amount of time for the resident to complete one or more of the aforementioned activities has increased (e.g., indicating the resident is more likely to fall) or decreased (e.g., indicating the resident is improving and less likely to fall) over time); and
determine that the disease risk is low with respect to a disease on any one of a decreasing tendency and a stagnation tendency of the disease risk score (“for which the disease risk is on an increasing tendency” is understood to be a mere optimization within prior art conditions and does not necessarily hold patentable weight, see MPEP 2144.05, therefore see Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; see Coffey Par [0087] & [0095] which discloses the fall inference being generated as a fall inference score and a classification of said score as a high risk, medium risk, or low risk based on longitudinal data over time; See Coffey Par [0100] which discloses the control system being able to further analyze data to determine whether the amount of time for the resident to complete one or more of the aforementioned activities has increased (e.g., indicating the resident is more likely to fall) or decreased (e.g., indicating the resident is improving and less likely to fall) over time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure to modify the combined disclosure of Groteke and Coffey, which already discloses calculating a risk index score to track the risk of musculoskeletal injury and/or early detection signs of MSK disease to further include determine that a disease risk is high with respect to a disease for which the disease risk score, i.e. fall risk which is indicative of disease risk, is on an increasing tendency and determining that the disease risk is low with respect to a disease on any one of a decreasing tendency, as further disclosed by Coffey, because this allows for longitudinal analysis of disease risk, such as over a period of time, and determining whether disease risks have increased or decreased for said patient/user based on said longitudinal data/analysis (See Coffey Par [0100]).
Claim 7 –
Regarding Claim 7, Groteke and Coffey disclose the disease risk estimation device according to claim 5 in its entirety. Groteke and Coffey further discloses a device, wherein:
the processor is configured to execute the instructions to:
determine that a disease risk is high with respect to a disease for which the disease risk score exceeds a threshold (See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; See Coffey Par [0134] which discloses the control system can consider and process data related to a risk stratification model (e.g., a traffic light based risk stratification model) of multiple residents across a facility, such that a gait analysis and/or fall inference can be determined for multiple residents, and a risk stratification level for each resident can include comparing scores associated with each resident's gait analysis and/or fall inference to a set of threshold levels to assign each resident to one of the levels of the risk stratification model, such that in traffic light model, the model would include at least two thresholds, such that those with a risk score above the first (e.g., highest) threshold would be considered “high risk,” those with a risk score above the second threshold and up to the first threshold would be considered “medium risk,” and those with a risk score at or below the second threshold would be considered “low risk); and
determine that a disease risk is low with respect to a disease for which the disease risk score is below the threshold (See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; See Coffey Par [0134] which discloses the control system can consider and process data related to a risk stratification model (e.g., a traffic light based risk stratification model) of multiple residents across a facility, such that a gait analysis and/or fall inference can be determined for multiple residents, and a risk stratification level for each resident can include comparing scores associated with each resident's gait analysis and/or fall inference to a set of threshold levels to assign each resident to one of the levels of the risk stratification model, such that in traffic light model, the model would include at least two thresholds, such that those with a risk score above the first (e.g., highest) threshold would be considered “high risk,” those with a risk score above the second threshold and up to the first threshold would be considered “medium risk,” and those with a risk score at or below the second threshold would be considered “low risk).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure to modify the combined disclosure of Groteke and Coffey, which already discloses calculating a risk index score to track the risk of musculoskeletal injury and/or early detection signs of MSK disease to further include determining that a disease risk is high with respect to a disease for which the disease risk score exceeds a threshold and determining that a disease risk is low with respect to a disease for which the disease risk score is below the threshold, as disclosed by Coffey, because this allows for development of a risk stratification model and thereby comparative analysis of multiple users for determinations of risk tiers that one or more users falls in, such that the model can be dynamically adjusted according to how many users fall into each risk tier (See Coffey Par [0134]).
Claim 8 –
Regarding Claim 8, Groteke and Coffey disclose the disease risk estimation device according to claim 2 in its entirety. Groteke further discloses a device, wherein:
the processor is configured to execute the instructions to generate proposal information for an insurance-related institution according to the disease risk reflecting a risk for each disease (while “according to the disease risk reflecting a risk for each disease” is not unclear, it is substantially broad, and therefore any generated proposal for insurance that has an associated disease risk is understood to read on this claim, therefore see Groteke Par [0079] which discloses the MMH prospective outcome modeling tool may be a provider or patient-led tool configured to create realistic goals for a patient's growth/recovery and can be further utilized by insurance companies and self-funded employers in populations of covered members to model risk and potential healthcare expense reductions, as well as by payors in value-based reimbursement and compensation models).
Claim 9 –
Regarding Claim 9, Groteke and Coffey disclose the disease risk estimation device according to claim 8 in its entirety. Groteke and Coffey further discloses a device, wherein:
the insurance-related institution is a health insurance union (See Groteke Par [0079] which discloses the MMH prospective outcome modeling tool may be a provider or patient-led tool configured to create realistic goals for a patient's growth/recovery and can be further utilized by insurance companies, e.g. an insurance union, and self-funded employers in populations of covered members to model risk and potential healthcare expense reductions, as well as by payors in value-based reimbursement and compensation models);
acquire the sensor data measured according to a gait of an insured person of the health insurance union (See Groteke Par [0079] which discloses the MMH prospective outcome modeling tool may be a provider or patient-led tool configured to create realistic goals for a patient's growth/recovery and can be further utilized by insurance companies and self-funded employers in populations of covered members to model risk and potential healthcare expense reductions, as well as by payors, i.e. an insured person, in value-based reimbursement and compensation models),
execute to estimate the disease risk reflecting a risk for each disease using the acquired sensor data (See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; See Coffey Par [0068] & [0072] which discloses a control system analyzing data from one or more sensors to determine a gait for one or more users; See Coffey Par [0072]-[0073] discloses that the one or more sensors generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time, and said sensors can be implemented into one or more devices to be used by the user),
execute to generate the proposal information including a timing of notifying the insured person of a specific health guidance according to the disease risk reflecting the estimated risk for each disease (See Groteke Par [0079] which discloses the MMH prospective outcome modeling tool may be a provider or patient-led tool configured to create realistic goals for a patient's growth/recovery and can be further utilized by insurance companies and self-funded employers in populations of covered members to model risk and potential healthcare expense reductions, as well as by payors, i.e. an insured person, in value-based reimbursement and compensation models, and further discloses allow clinical users and other providers to model individual factors based on their correlation to outcomes to create objective, quantifiable goals and targets that can be measured over time to create tangible, attainable goals for a patient during their recovery or general health improvements may help to keep them on track and ensure the proper follow-up therapies and procedures are utilized), and
transmit, to a terminal device used in the health insurance union, the proposal information including a timing at which the specific health guidance is notified (See Groteke Par [0079] which discloses the MMH prospective outcome modeling tool may be a provider or patient-led tool configured to create realistic goals for a patient's growth/recovery and can be further utilized by insurance companies and self-funded employers in populations of covered members to model risk and potential healthcare expense reductions, as well as by payors, i.e. an insured person, in value-based reimbursement and compensation models, and further discloses allow clinical users and other providers to model individual factors based on their correlation to outcomes to create objective, quantifiable goals and targets that can be measured over time to create tangible, attainable goals for a patient during their recovery or general health improvements may help to keep them on track and ensure the proper follow-up therapies and procedures are utilized, i.e. the insurance company would thereby be notified at each goal according to said progression of the goals and targets that can be measured over time since they are tracked).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Groteke which already discloses the use of a camera or other capturing device for monitoring various parts of walking/gait to further include sensors, e.g. biosensors, such as for sensing certain health metrics directly from the user, as disclosed by Coffey, because this allows for said sensors to be implemented into one or more devices to be used by the user that can generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time (See Coffey Par [0072]-[0073]).
Claim 12 –
Regarding Claim 12, Groteke and Coffey disclose the disease risk estimation device according to claim 1 in its entirety. Groteke and Coffey further discloses a device, wherein:
a measurement device that is installed on footwear of the subject who is an estimation target of the disease risk information (See Coffey Par [0017] & [0032] & [0068] which discloses a footwear garment for determining gait of a user and for estimating future fall risk), wherein
the measurement device includes a sensor that measures spatial acceleration and spatial angular velocity (See Coffey Par [0033] & [0099]-[0100] which discloses the sensor measuring range as well as angle and velocity and said measurements occurring over time, which would be indicative of acceleration, i.e. velocity over time),
a controller that generates the sensor data using the measured spatial acceleration and spatial angular velocity (See Coffey Par [0033] & [0099]-[0100] which discloses the sensor measuring range as well as angle and velocity, i.e. angular velocity, and said measurements occurring over time, which would be indicative of acceleration, i.e. velocity over time; See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; See Coffey Par [0068] & [0072] which discloses a control system analyzing data from one or more sensors to determine a gait for one or more users; See Coffey Par [0072]-[0073] discloses that the one or more sensors generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time, and said sensors can be implemented into one or more devices to be used by the user), and
a transmitter that transmits the generated sensor data to the disease risk estimation device via wireless communication (See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; See Groteke Par [0144] which discloses tracking velocities of left and right ankle and distances therebetween; See Coffey Par [0068] & [0072] which discloses a control system analyzing data from one or more sensors to determine a gait for one or more users; See Coffey Par [0072]-[0073] discloses that the one or more sensors generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time, and said sensors can be implemented into one or more devices to be used by the user; see Groteke Par [0060] which discloses “providing” a quantitative value called a “injury risk index score”, i.e. a risk value, that is highly correlated to the risk of musculoskeletal injury, a value which takes into account both known, reported issues as well as asymptomatic, hidden medical conditions; See Groteke Par [0067] which discloses the injury risk index score may be reported as a diagnostic figure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Groteke which already discloses the use of a camera or other capturing device for monitoring various parts of walking/gait to further include sensors, e.g. biosensors, such as for sensing certain health metrics directly from the user, as disclosed by Coffey, because this allows for said sensors to be implemented into one or more devices to be used by the user that can generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time (See Coffey Par [0072]-[0073]). It would have been additionally
obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Groteke which already discloses the use of a camera or other capturing device for monitoring various parts of walking/gait to further include generates the sensor data using the measured spatial acceleration and spatial angular velocity via footwear, as disclosed by Coffey, because tracking velocity/speed of a user’s foot while the resident is standing, while the resident is walking, while the resident is running, or a combination thereof allows for predicting an impending fall and/or to determine a risk of fall for the resident (See Coffey Par [0099]-[0100]).
Claim 13 –
Regarding Claim 13, Groteke and Coffey disclose the disease risk estimation device according to claim 1 in its entirety. Groteke further discloses a device, 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 a user (“optimized for the subject on a screen of a terminal device browsable by a user” is understood to be a whereby clause that simply expresses the intended result of a process step positively recited and does not necessarily impart patentable weight, see MPEP 2111.04; therefore, see Groteke Par [0045] & [0178] which discloses displaying detailed medical reports including results of the assessments, including risk factors and risk levels determined therein).
Claim 14 –
Regarding Claim 14, Groteke discloses a disease risk estimation method for causing a computer to execute:
acquiring sensor data measured according to a movement of a foot of a subject who is an estimation target of a disease risk (See Groteke Par [0188]-[0194] which discloses gait analysis being performed, including monitoring various parts of walking/gait such as various heel strikes, foot raises, etc.; See Groteke Par [0220] which discloses various devices that can be used to measure and record their range of motion using computer, tablet, etc., or other device having a two-dimensional video camera (e.g. a camera configured to capture standard, two dimensional video footage), and while not explicitly mentioned as a “sensor” per se, an additional reference will be applied hereinafter for advancing prosecution; See Groteke Par [0326]-[0328] & [0334]-[0344] which discloses measuring angles, movement, and/or rotation of feet);
estimating a disease risk reflecting a risk for each disease using the acquired sensor data (See Groteke Par [0008] which discloses providing a risk value for quickly estimating and assessing a patient’s risk of experiencing musculoskeletal (MSK) injury for specific body regions or conditions; See Groteke Par [0146] which discloses an AI generated risk analysis care plan and evidence-based guidelines, including tracking of improvements against MSK risk factors; See Groteke Par [0111] which discloses containing a record of a patient’s MSK health over time, such as by an individual’s MSK tracking including a patient’s personal range of motion; See Groteke Par [0130] & [0136] which discloses extracting join angles from midstance frames gait phases including feet movement; See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions); and
outputting disease risk information relevant to the estimated disease risk (Without specifying “output”, “outputting” could simply include any means of generating said information, therefore see Groteke Par [0060] which discloses “providing” a quantitative value called a “injury risk index score”, i.e. a risk value, that is highly correlated to the risk of musculoskeletal injury, a value which takes into account both known, reported issues as well as asymptomatic, hidden medical conditions; See Groteke Par [0067] which discloses the injury risk index score may be reported as a diagnostic figure).
Coffey discloses sensing certain health metrics directly from the user (See Coffey Par [0068] & [0072] which discloses a control system analyzing data from one or more sensors to determine a gait for one or more users; See Coffey Par [0072]-[0073] discloses that the one or more sensors generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time, and said sensors can be implemented into one or more devices to be used by the user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Groteke which already discloses the use of a camera or other capturing device for monitoring various parts of walking/gait to further include sensors, e.g. biosensors, such as for sensing certain health metrics directly from the user, as disclosed by Coffey, because this allows for said sensors to be implemented into one or more devices to be used by the user that can generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time (See Coffey Par [0072]-[0073]).
Claim 15 –
Regarding Claim 15, Groteke and Coffey disclose the disease risk estimation method of claim 14. Groteke further discloses a method, wherein:
the computer executes:
calculating a gait index using the sensor data (See Groteke Par [0080] which discloses the AI utilizing angle-based and key-point detection data capture techniques, such as described in the gait analysis section, i.e. Par [0151]-[0157] which discloses gait analysis being performed, including monitoring various parts of walking/gait such as various heel strikes, foot raises, etc.);
inputting 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 a disease risk related to the disease according to an input of the data including the gait index (See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning, i.e. a model, to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; See Coffey Par [0068] & [0072] which discloses a control system analyzing data from one or more sensors to determine a gait for one or more users; See Coffey Par [0072]-[0073] discloses that the one or more sensors generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time, and said sensors can be implemented into one or more devices to be used by the user), and estimates disease risk information relevant to the disease risk score output from the disease risk estimation model (See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions); and
estimating disease risk information according to the disease risk score output from the disease risk estimation model (See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Groteke which already discloses the use of a camera or other capturing device for monitoring various parts of walking/gait to further include sensors, e.g. biosensors, such as for sensing certain health metrics directly from the user, as disclosed by Coffey, because this allows for said sensors to be implemented into one or more devices to be used by the user that can generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time (See Coffey Par [0072]-[0073]).
Claim 18 –
Regarding Claim 18, Groteke and Coffey disclose the disease risk estimation method of claim 15. Groteke and Coffey further disclose a method, wherein:
the computer executes:
determining a disease risk for each disease according to a change tendency of the disease risk score (a “change tendency” under BRI is understood to include any tendency such as increasing, decreasing, positive, negative, etc., therefore see Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; see Coffey Par [0087] & [0095] which discloses the fall inference being generated as a fall inference score and a classification of said score as a high risk, medium risk, or low risk based on longitudinal data over time; See Coffey Par [0100] which discloses the control system being able to further analyze data to determine whether the amount of time for the resident to complete one or more of the aforementioned activities has increased (e.g., indicating the resident is more likely to fall) or decreased (e.g., indicating the resident is improving and less likely to fall) over time)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure to modify the combined disclosure of Groteke and Coffey, which already discloses calculating a risk index score to track the risk of musculoskeletal injury and/or early detection signs of MSK disease to further include determine that a disease risk is high with respect to a disease for which the disease risk score, i.e. fall risk which is indicative of disease risk, is on an increasing tendency and determining that the disease risk is low with respect to a disease on any one of a decreasing tendency, as further disclosed by Coffey, because this allows for longitudinal analysis of disease risk, such as over a period of time, and determining whether disease risks have increased or decreased for said patient/user based on said longitudinal data/analysis (See Coffey Par [0100]).
Claim 19 –
Regarding Claim 19, Groteke and Coffey disclose the disease risk estimation method of claim 15. Groteke further discloses a method, wherein:
generating proposal information for an insurance-related institution according to the disease risk reflecting a risk for each disease (while “according to the disease risk reflecting a risk for each disease” is not unclear, it is substantially broad, and therefore any generated proposal for insurance that has an associated disease risk is understood to read on this claim, therefore see Groteke Par [0079] which discloses the MMH prospective outcome modeling tool may be a provider or patient-led tool configured to create realistic goals for a patient's growth/recovery and can be further utilized by insurance companies and self-funded employers in populations of covered members to model risk and potential healthcare expense reductions, as well as by payors in value-based reimbursement and compensation models).
Claim 20 –
Regarding Claim 20, Groteke and Coffey disclose a computer-readable non-transitory recording medium having recorded therein a program for causing a computer to execute:
a process 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 (See Groteke Par [0188]-[0194] which discloses gait analysis being performed, including monitoring various parts of walking/gait such as various heel strikes, foot raises, etc.; See Groteke Par [0220] which discloses various devices that can be used to measure and record their range of motion using computer, tablet, etc., or other device having a two-dimensional video camera (e.g. a camera configured to capture standard, two dimensional video footage), and while not explicitly mentioned as a “sensor” per se, an additional reference will be applied hereinafter for advancing prosecution; See Groteke Par [0326]-[0328] & [0334]-[0344] which discloses measuring angles, movement, and/or rotation of feet);
a process of estimating a disease risk reflecting a risk for each disease using the acquired sensor data (See Groteke Par [0008] which discloses providing a risk value for quickly estimating and assessing a patient’s risk of experiencing musculoskeletal (MSK) injury for specific body regions or conditions; See Groteke Par [0146] which discloses an AI generated risk analysis care plan and evidence-based guidelines, including tracking of improvements against MSK risk factors; See Groteke Par [0111] which discloses containing a record of a patient’s MSK health over time, such as by an individual’s MSK tracking including a patient’s personal range of motion; See Groteke Par [0130] & [0136] which discloses extracting join angles from midstance frames gait phases including feet movement; See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions); and
a process of outputting disease risk information relevant to the estimated disease risk (Without specifying “output”, “outputting” could simply include any means of generating said information, therefore see Groteke Par [0060] which discloses “providing” a quantitative value called a “injury risk index score”, i.e. a risk value, that is highly correlated to the risk of musculoskeletal injury, a value which takes into account both known, reported issues as well as asymptomatic, hidden medical conditions; See Groteke Par [0067] which discloses the injury risk index score may be reported as a diagnostic figure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Groteke which already discloses the use of a camera or other capturing device for monitoring various parts of walking/gait to further include sensors, e.g. biosensors, such as for sensing certain health metrics directly from the user, as disclosed by Coffey, because this allows for said sensors to be implemented into one or more devices to be used by the user that can generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time (See Coffey Par [0072]-[0073]).
Claims 3-4 & 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Groteke in view of Coffey, further in view of Asthana et al. (U.S. Patent Publication No. 2019/0371463), hereinafter “Asthana”.
Claim 3 –
Regarding Claim 3, Groteke and Coffey disclose the disease risk estimation device according to claim 2 in its entirety. Groteke further discloses a device, wherein:
the processor is configured to execute the instructions to:
calculate the disease risk score reflecting a risk for each disease by multiplying the disease risk score by a weight for each disease according to a rank indicating a risk of a disease (See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions).
While Groteke generally discloses calculating a risk index score to track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions, Groteke does not explicitly disclose multiplying the disease risk score by a weight for each disease according to a rank indicating a risk of a disease.
However, Asthana discloses multiplying the disease risk score by a weight for each disease according to a rank indicating a risk of a disease (It is understood that “according to a rank indicating a risk of a disease” could be any arbitrarily applied rank or weight since diseases and associated ranks/weights are not claimed, therefore see Asthana Par [0134] which discloses determining one or more weights associated with each of the one or more health metrics, e.g. using the classification model, such that assignment of weights may extend monitoring to new metrics over time, e.g. as a health condition progresses and/or secondary health conditions become relevant). The disclosure of Asthana is directly applicable to the disclosure of Groteke and Coffey, because the disclosures share limitations and capabilities, such as being directed towards calculating health risk metrics and potential medical conditions for one or more users.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure to modify the combined disclosure of Groteke and Coffey, which already discloses calculating a risk index score to track the risk of musculoskeletal injury and/or early detection signs of MSK disease to further include multiplying the disease risk score by a weight for each disease, as disclosed by Asthana, because this allows for assigning relative importance of a given metric in predicting a future health status of the patient, and assigning weights for new metrics over time or for attenuating metrics that are no longer relevant, such as ceasing to be monitored (See Asthana Par [0134]).
Claim 4 –
Regarding Claim 4, Groteke and Coffey disclose the disease risk estimation device according to claim 2 in its entirety. Groteke and Asthana further disclose a device, wherein:
the processor is configured to execute the instructions to:
calculate the disease risk score reflecting a risk for each combination of diseases by multiplying the disease risk score by a weight for the each combination of diseases (It is understood that “according to a rank indicating a risk of a disease” could be any arbitrarily applied rank or weight since diseases and associated ranks/weights are not claimed, therefore see Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; See Asthana Par [0134] which discloses determining one or more weights associated with each of the one or more health metrics, e.g. using the classification model, such that assignment of weights may extend monitoring to new metrics over time, e.g. as a health condition progresses and/or secondary health conditions become relevant, i.e. combination of diseases, or assignment of weights may also or alternatively cause a particular metric to cease being monitored, e.g. if no longer relevant to a particular health condition for a given patient).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure to modify the combined disclosure of Groteke and Coffey, which already discloses calculating a risk index score to track the risk of musculoskeletal injury and/or early detection signs of MSK disease to further include multiplying the disease risk score by a weight for each disease, as disclosed by Asthana, because this allows for assigning relative importance of a given metric in predicting a future health status of the patient, and assigning weights for new metrics over time or for attenuating metrics that are no longer relevant, such as ceasing to be monitored (See Asthana Par [0134]).
Claim 16 –
Regarding Claim 16, Groteke and Coffey disclose the disease risk estimation method of claim 15. Groteke and Asthana further disclose a method, wherein:
calculating the disease risk score reflecting a risk for each disease by multiplying the disease risk score by a weight for each disease according to a rank indicating a risk of a disease (See Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; See Asthana Par [0134] which discloses determining one or more weights associated with each of the one or more health metrics, e.g. using the classification model, such that assignment of weights may extend monitoring to new metrics over time, e.g. as a health condition progresses and/or secondary health conditions become relevant). The disclosure of Asthana is directly applicable to the disclosure of Groteke and Coffey, because the disclosures share limitations and capabilities, such as being directed towards calculating health risk metrics and potential medical conditions for one or more users.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure to modify the combined disclosure of Groteke and Coffey, which already discloses calculating a risk index score to track the risk of musculoskeletal injury and/or early detection signs of MSK disease to further include multiplying the disease risk score by a weight for each disease, as disclosed by Asthana, because this allows for assigning relative importance of a given metric in predicting a future health status of the patient, and assigning weights for new metrics over time or for attenuating metrics that are no longer relevant, such as ceasing to be monitored (See Asthana Par [0134]).
Claim 17 –
Regarding Claim 17, Groteke and Coffey discloses the disease risk estimation method of claim 15. Groteke and Asthana further disclose a method, wherein:
calculating the disease risk score reflecting a risk for each combination of diseases by multiplying the disease risk score by a weight for the each combination of diseases (It is understood that “according to a rank indicating a risk of a disease” could be any arbitrarily applied rank or weight since diseases and associated ranks/weights are not claimed, therefore see Groteke Par [0063] which discloses an AI-enabled virtual care platform that uses artificial intelligence and machine learning to create an objective measure called an injury risk index score that is a value configured to stratify and track the risk of musculoskeletal injury and/or early detection signs of MSK disease and identifying those who are at imminent risk for more complex interventions; See Asthana Par [0134] which discloses determining one or more weights associated with each of the one or more health metrics, e.g. using the classification model, such that assignment of weights may extend monitoring to new metrics over time, e.g. as a health condition progresses and/or secondary health conditions become relevant, i.e. combination of diseases, or assignment of weights may also or alternatively cause a particular metric to cease being monitored, e.g. if no longer relevant to a particular health condition for a given patient).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure to modify the combined disclosure of Groteke and Coffey, which already discloses calculating a risk index score to track the risk of musculoskeletal injury and/or early detection signs of MSK disease to further include multiplying the disease risk score by a weight for each disease, as disclosed by Asthana, because this allows for assigning relative importance of a given metric in predicting a future health status of the patient, and assigning weights for new metrics over time or for attenuating metrics that are no longer relevant, such as ceasing to be monitored (See Asthana Par [0134]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Groteke in view of Coffey, further in view of Apfeld et al. (U.S. Patent Publication No. 2006/0147947), hereinafter “Apfeld”.
Claim 10 –
Regarding Claim 10, Groteke and Coffey disclose the disease risk estimation device according to claim 8 in its entirety. Groteke and Coffey further disclose a device, wherein:
the insurance-related institution is a life insurance company (See Groteke Par [0079] which discloses the MMH prospective outcome modeling tool may be a provider or patient-led tool configured to create realistic goals for a patient's growth/recovery and can be further utilized by insurance companies and self-funded employers in populations of covered members to model risk and potential healthcare expense reductions, as well as by payors, i.e. an insured person, in value-based reimbursement and compensation models, and further discloses allow clinical users and other providers to model individual factors based on their correlation to outcomes to create objective, quantifiable goals and targets that can be measured over time to create tangible, attainable goals for a patient during their recovery or general health improvements may help to keep them on track and ensure the proper follow-up therapies and procedures are utilized, albeit not recited for a life insurance company in particular; See Apfeld Par [0292] which discloses using the outcome to determine whether to continue, discontinue, enroll an individual in an insurance plan or program, e.g. a health insurance or life insurance plan or program, i.e. company), the processor is configured to execute the instructions to:
acquire the sensor data measured according to a gait of an insurance contractor of the life insurance company (See Groteke Par [0079] which discloses the MMH prospective outcome modeling tool may be a provider or patient-led tool configured to create realistic goals for a patient's growth/recovery and can be further utilized by insurance companies and self-funded employers in populations of covered members to model risk and potential healthcare expense reductions, as well as by payors, i.e. an insured person, in value-based reimbursement and compensation models, and further discloses allow clinical users and other providers to model individual factors based on their correlation to outcomes to create objective, quantifiable goals and targets that can be measured over time to create tangible, attainable goals for a patient during their recovery or general health improvements may help to keep them on track and ensure the proper follow-up therapies and procedures are utilized, albeit not recited for a life insurance company in particular; See Coffey Par [0068] & [0072] which discloses a control system analyzing data from one or more sensors to determine a gait for one or more users; See Coffey Par [0072]-[0073] discloses that the one or more sensors generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time, and said sensors can be implemented into one or more devices to be used by the user),
estimate the disease risk reflecting a risk for each disease using the acquired sensor data (ee Groteke Par [0079] which discloses the MMH prospective outcome modeling tool may be a provider or patient-led tool configured to create realistic goals for a patient's growth/recovery and can be further utilized by insurance companies and self-funded employers in populations of covered members to model risk and potential healthcare expense reductions, as well as by payors, i.e. an insured person, in value-based reimbursement and compensation models, and further discloses allow clinical users and other providers to model individual factors based on their correlation to outcomes to create objective, quantifiable goals and targets that can be measured over time to create tangible, attainable goals for a patient during their recovery or general health improvements may help to keep them on track and ensure the proper follow-up therapies and procedures are utilized, i.e. the insurance company would thereby be notified at each goal according to said progression of the goals and targets that can be measured over time since they are tracked; See Coffey Par [0068] & [0072] which discloses a control system analyzing data from one or more sensors to determine a gait for one or more users; See Coffey Par [0072]-[0073] discloses that the one or more sensors generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time, and said sensors can be implemented into one or more devices to be used by the user;),
generate the proposal information including a timing of granting an incentive to the insurance contractor according to the disease risk reflecting the estimated risk for each disease (See Groteke Par [0079] which discloses the MMH prospective outcome modeling tool may be a provider or patient-led tool configured to create realistic goals for a patient's growth/recovery and can be further utilized by insurance companies and self-funded employers in populations of covered members to model risk and potential healthcare expense reductions, as well as by payors, i.e. an insured person, in value-based reimbursement and compensation models, and further discloses allow clinical users and other providers to model individual factors based on their correlation to outcomes to create objective, quantifiable goals and targets that can be measured over time to create tangible, attainable goals for a patient during their recovery or general health improvements may help to keep them on track and ensure the proper follow-up therapies and procedures are utilized), and
transmit the proposal information including the timing of granting the incentive to a terminal device used in the life insurance company (See Groteke Par [0079] which discloses the MMH prospective outcome modeling tool may be a provider or patient-led tool configured to create realistic goals for a patient's growth/recovery and can be further utilized by insurance companies and self-funded employers in populations of covered members to model risk and potential healthcare expense reductions, as well as by payors, i.e. an insured person, in value-based reimbursement and compensation models, and further discloses allow clinical users and other providers to model individual factors based on their correlation to outcomes to create objective, quantifiable goals and targets that can be measured over time to create tangible, attainable goals for a patient during their recovery or general health improvements may help to keep them on track and ensure the proper follow-up therapies and procedures are utilized, i.e. the insurance company would thereby be notified at each goal according to said progression of the goals and targets that can be measured over time since they are tracked).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Groteke which already discloses the use of a camera or other capturing device for monitoring various parts of walking/gait to further include sensors, e.g. biosensors, such as for sensing certain health metrics directly from the user, as disclosed by Coffey, because this allows for said sensors to be implemented into one or more devices to be used by the user that can generate data that can be processed by the control system to determine whether the resident has fallen and/or to predict that the user is about to fall with a certain amount of time (See Coffey Par [0072]-[0073]).
While Groteke and Coffey generally disclose the above aspects with relation to a generalized health insurance company, Groteke and Coffey do not specifically disclose said embodiments for a life insurance company, per se.
However, Apfeld discloses the insurance-related institution is a life insurance company (See Apfeld Par [0292] which discloses an entity, e.g., a hospital, care giver, government entity, or an insurance company or other entity which pays for, or reimburses medical expenses, can use the outcome of a method described herein to determine whether a party, e.g., a party other than the subject patient, will pay for services or treatment provided to the patient, such that an insurance company, e.g. a health insurance or life insurance company, can use the outcome of a method described herein to determine whether to provide financial payment to, or on behalf of, a patient, e.g., whether to reimburse a third party, e.g., a vendor of goods or services, a hospital, physician, or other care-giver, for a service or treatment provided to a patient). The disclosure of Apfeld is directly applicable to the combined disclosure of Groteke and Coffey, because the disclosures share limitations and capabilities, such as being directed towards monitoring of one or more diseases over time, especially for reporting said progression of one or more disease to one or more entities.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Groteke and Coffey, which already discloses the above aspects with relation to a generalized health insurance company, to further specify said insurance-related institution being a life insurance company, as disclosed by Apfeld, because this allows for said life insurance company to use the outcome to determine whether to continue, discontinue, enroll an individual in said life insurance plan or program (See Apfeld Par [0292]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Groteke in view of Coffey, further in view of Nazábal et al. (Reference U: “Handling incomplete heterogeneous data using VAEs” – NPL – June 2020), hereinafter “Nazabal”.
Claim 11 –
Regarding Claim 11, Groteke and Coffey disclose the disease risk estimation device according to claim 2 in its entirety. Groteke and Coffey further disclose a device, wherein:
the disease risk estimation model is a model learned using a machine learning method, and includes an incomplete heterogeneous variational autoencoder (See Groteke Par [0061] which discloses utilizing machine learning to standardize the collection and diagnosis of musculoskeletal deterioration and identify conditional criteria that are precursors to more complex medical and surgical issues; See Coffey Par [0172] which discloses the use of neural networks, including an auto encoder, however).
Therefore, while Coffey discloses the use of neural networks and an autoencoder, Coffey does not necessarily disclose said autoencoder being an incomplete heterogeneous variational autoencoder, in particular.
However, Nazabal discloses the use of an incomplete heterogeneous variational autoencoder (See Nazabal Abstract and Box 1, which discloses the use of Variational autoencoders, including a heterogeneous, incomplete variational autoencoder, such that the learning generative models can accurately capture the distribution, and therefore the underlying latent structure, of such incomplete and heterogeneous datasets to better understand the data, estimate missing or corrupted values, detect outliers, and make predictions (e.g., on patients’ diagnosis) on unseen data; See Nazabal Boxes 2-4 which specifically details a simple VAE architecture that handles incomplete and heterogeneous data). The disclosure of Nazabal is directly applicable to the disclosure of Groteke and Coffey, such as being directed towards analyzing data, especially for purposes of patient’s diagnosis.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Groteke and Coffey which already discloses the use of neural networks and an autoencoder, to specifically include an incomplete heterogeneous variational autoencoder, because this allows for accurately capturing the underlying latent structure of incomplete and heterogeneous datasets to better understand the data, estimate missing or corrupted values, detect outliers, and make predictions (e.g., on patients’ diagnosis) on unseen data (See Nazabal Box 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Kumar et al. (U.S. patent Publication No. 2024/0057892) discloses a system for quantifying gait and posture at high precision and sensitivity to determine proper function of numerous neural and muscular systems for determining psychiatric, neurodegenerative, and neuromuscular illnesses;
Naveh et al. (U.S. Patent Publication No. 2020/0289027) discloses a system for monitoring gait of an end-user bearing a wearable device to determine a gait analysis process which yields at least one parameter characterizing the end-user's gait;
Pathak et al. (U.S. Patent Publication No. 2017/0287146) discloses a system for tracking and analyzing target person movements, captured while the target person is performing ordinary tasks outside of a medical context, for medical diagnosis and treatment review.
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/H.R./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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