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 .
This Office Action is responsive to the Reply to Office Action filed September 17, 2025. The Examiner acknowledges the amendments to claims 1-2, 4, 6, 11-12, 14 and 17-18, and the cancellation of claim 5. Claims 1-4 & 6-20 are currently pending.
Response to Arguments
Applicant’s arguments, see remarks , filed September 17, 2025, with respect to the previous objections made to claims 1 & 14 have been fully considered and are persuasive. The previous objections made to claims 1 & 14 have been withdrawn.
While Applicant’s amendments of September 17, 2025 have overcome some of the previous rejections under 35 U.S.C. 112(b), indefiniteness issues remain within the claims. See 35 U.S.C. 112(b) rejections below.
Applicant’s arguments regarding the rejection of the claims under 35 U.S.C. 101, filed September 17, 2025, have been fully considered but they are not persuasive. Applicant argues that the claims as amended do not fall into the category of “mental processes” and are therefore not directed to an abstract idea because the human mind cannot apply models to assess musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals. The Examiner respectfully disagrees with this argument. The abstract idea does describe a concept performed in the human mind (including an observation, evaluation, judgment, or opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea. The recitation of non-descript models (i.e., the statistical model and the mechanistic model) does not preclude the claim limitation from being drawn to a mental process because the human mind can apply basic models to make assessments and predictions. Further, the generation of a visualization of the model results for the individual, group of individuals, or an agent is not the abstract idea that can be performed in the human mind. Instead, that limitation is insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The generation of the visualization for the individual/group of individuals does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the generated visualization, nor does the method use a particular machine to perform the Abstract Idea. Furthermore, Applicant’s assertion of an improvement to the technological field without sufficient evidence is not a persuasive argument. Additionally, though Applicant’s claimed invention pertains to using an AI model trained in a specific manner (i.e., for making predictions of clinical outcomes and musculoskeletal injury risk), the claims still contain a judicial exception that is not integrated into a practical application and that does not provide significantly more than the exception (see July 2024 Subject Matter Eligibility Examples, Example 47. Anomaly Detection, claim 2). Therefore, the claims still stand rejected under 35 U.S.C. 101. See 35 U.S.C. 101 rejection below.
Applicant's arguments regarding the rejections of the claims in view of prior art, filed September 17, 2025, have been fully considered but they are not persuasive. Regarding Applicant’s arguments that Langheier focuses on methodology for “automatically generating a predictive model” and not using the model itself, the Examiner respectfully disagrees. Langheier discloses the model is used to provide healthcare-related decision support (see Langheier, par 0030, 0038). Therefore, though Langheier describes how to generate the predictive model, Langheier nonetheless teaches that the predictive model is used to provide healthcare-related decision support.
Regarding applicant's arguments that Langheier does not teach using biopsychosocial biomarkers, using motion sensors to quantify system function, predicting clinical outcomes, predicting musculoskeletal injury risk, or assessing interventions for an individual or group of individuals, the Examiner respectfully disagrees. Langheier teaches a biomarker causality identification system that extracts biomarkers from clinical literature to generate a clinical data warehouse that is used to predict clinical or medical outcomes for individuals (see Langheier, par 0030-0032, fig. 2). Furthermore, Langheier specifically states that the created models are used to predict clinical or medical outcomes (see Langheier, par 0030). Therefore, Langheier teaches using biopsychosocial biomarkers and predicting clinical outcomes using models for an individual or group of individuals.
The examiner agrees that Langheier does not teach using motion sensors to quantify system function, but the examiner did not use the Langheier reference to teach said limitation. Instead, Ren was used to modify the teachings of Langheier such that the sensors of Langheier comprise one or more IMU sensors because Ren teaches that IMU sensors permit body part motion analysis that can be used for medical diagnostics such as determining the extent of an injury or disease or measuring medical treatment efficacy, therefore providing additional diagnostic data to ascertain an individual’s clinical outcome (see Ren, par 0011 & 0028).
The examiner disagrees with Applicant’s argument that Langheier as modified by Stein, Feczko and Hada fails to teach applying a statistical model to classify the individual or group of individuals in accordance with the biopsychosocial biomarkers into subgroups that each represent a similar set of observable traits and features that are indicative of outcomes, applying a mechanistic model to estimate musculoskeletal characteristics of the individual or group of individuals that cannot be directly measured, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals in accordance with outputs of the statistical model and the mechanistic model, and further that one of ordinary skill in the art would have no motivation to combine Feczko and Hada with Langheier and Stein.
Feczko teaches subtyping heterogeneous disorders using functional random forest models, wherein a functional random forest model (FRF model) is used to make predictions about an outcome using input data (i.e., biomarkers) (see Feczko par 0007, 0058, 0063, 0101, 0103). The FRF model further employs a community detection approach to identify subgroups based upon shared and unique features among the input data (see Feczko, abstract, par 0061-0062, 0064, 0069, 0293, 0446). This therefore demonstrates the use of statistical models that identify subgroups, such as an FRF model that employs a community detection approach to identify subgroups in data, which provides improvements to clinical diagnosis and treatment (see Feczko, par 0061).
Further, Hada teaches a method of constructing a computer-based musculoskeletal model, wherein the computer-based musculoskeletal model comprises a musculoskeletal construction step for constructing the musculoskeletal model, a load estimation step of estimating a plurality of loads acting on the different segments of the body, and a stress estimation step of estimating a stress and/or a strain occurring at each segment of the human body, based upon the plurality of loads (see Hada, par 0026, 0030 & 0644-0646). This demonstrates the use of a mechanistic model that constructs musculoskeletal models for the estimation of a plurality of loads acting on different segments of the body in the musculoskeletal model, as well as for the estimation of stress and/or strain occurring at each segment of the human body based upon the plurality of loads (see Hada, par 0026, 0030 & 0644-0646).
Therefore, the combination of Langheier as modified by Stein, Feczko, and Hada teaches applying a statistical model to classify the individual or group of individuals in accordance with the biopsychosocial biomarkers into subgroups that each represent a similar set of observable traits and features that are indicative of outcomes, applying a mechanistic model to estimate musculoskeletal characteristics of the individual or group of individuals that cannot be directly measured, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals in accordance with outputs of the statistical model and the mechanistic model, and as such, the claims are still rejected with this combination of prior art. See Rejections of the claims under 35 USC 103 below.
Claim Objections
Claim 1 is objected to because of the following informalities: "generating a visualization of the the statistical model and the mechanistic model results for the individual, group of individuals, or an agent of the individual or individuals by the computing device" should read as --generating a visualization of the statistical model and the mechanistic model results for the individual, group of individuals, or an agent of the individual or individuals by the computing device--. Appropriate correction is required.
Claim 14 is objected to because of the following informalities: "generate a visualization of the the statistical model and the mechanistic model results for the individual, group of individuals, or an agent of the individual or individuals" should read as --generate a visualization of the statistical model and the mechanistic model results for the individual, group of individuals, or an agent of the individual or individuals-. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4 & 6-20 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites that data associated with an individual or a group of individuals is received by a computing device, which makes it unclear who the individual is of “the individual’s musculoskeletal system or musculoskeletal occupational exposure” as recited at lines 5-6 (i.e., it is unclear if the individual is an individual of the group of individuals or the singular individual previously recited). For examination purposes, it will be interpreted that an individual’s (that is either a singular individual or an individual of the group of individuals) data is received and processed to quantify parameters associated with the individual’s musculoskeletal system or musculoskeletal occupational exposure. Claim 14 is similarly rejected.
Moreover, claim 1 recites “classify the individual or group of individuals in accordance with the biopsychosocial biomarkers into subgroups that each represent a similar set of observable traits and features that are indicative of outcomes” at lines 7-9. If data is received for just an individual, it is unclear how the method classifies the singular individual in accordance with the biopsychosocial biomarkers into subgroups that each represent a similar set of traits and features. The Examiner respectfully requests clarification of this claim limitation. Claim 14 is similarly rejected.
Claim 2 recites “wherein receiving data comprises receiving data from one or more sensors worn by the individual or the group of individuals”. It is unclear whether the one or more sensors generate the biopsychosocial biomarkers, or whether the received data is now the data from the one or more sensors, instead of the biopsychosocial biomarkers. For examination purposes, it will be interpreted that data is derived from biopsychosocial biomarkers, and that further data is received from one or more sensors worn by the individual or the group of individuals. The Examiner suggests Applicant amends the claim to recite “receiving data further comprises” to distinguish that data is from both the biopsychosocial biomarkers and one or more sensors.
Claim 4 recites “wherein receiving data further comprises receiving medical history data for the individual” [emphasis added]. It is unclear whether medical history data is also received for the group of individuals. For examination purposes, it will be interpreted that medical history data is received for the individual or the group of individuals. Claim 17 is similarly rejected and interpreted.
Dependent claims are similarly rejected as their base claim.
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 1-4 & 6-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Regarding claim 1, the claim recites a series of steps or acts, including applying, by a computing device, a statistical model to classify the individual or group of individuals in accordance with biopsychosocial biomarkers into subgroups that each represent a similar set of observable traits and features that are indicative of outcomes, applying, by the computing device, a mechanistic model to estimate musculoskeletal characteristics of the individual or group of individuals that cannot be directly measured, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess intervention for an individual or a group of individuals in accordance with outputs of the statistical model and the mechanistic model. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The step
of applying a statistical model and a mechanistic model to assess musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess intervention for an individual or a group of individuals sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites generating a visualization of the statistical model and the mechanistic model results for the individual, the group of individuals, or an agent of the individual by the computing device, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The generation of the visualization for the individual/group of individuals does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the generated model results, nor does the method use a particular machine to perform the Abstract Idea. In fact, the method recites a generic computing device as the machine to perform the Abstract Idea.
Next, the claim as a whole is analyzed to determine whether any element, or combination of
elements, is sufficient to ensure that the claim amounts to significantly more than the exception.
Besides the Abstract Idea, the claim recites additional steps of receiving data associated with an individual or group of individuals and processing data with the computing device to quantify an individual’s musculoskeletal status or musculoskeletal exposure. The receiving and processing steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data receiving and model applying activity engaged in by medical professionals prior to Applicant's invention.
Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the receiving and processing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)). Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claim 14, the device recited in the claim is a generic device comprising generic
components configured to perform the abstract idea. The recited computing device comprising a
computer-readable medium with computer-executable instructions stored thereon is a generic
computer configured to perform pre-solutional data receiving activity, and is configured to perform
WURC displaying (i.e., providing a visualization of the model results to the individual, group of individuals, or an agent), and the computer system is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims 2-4, 6-13 & 15-20 also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to data receiving and predicting based upon the received data. The receiving, processing, applying, and providing steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
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.
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.
Claim(s) 1-2, 4, 6, 9-15 & 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication 20060173663 --as previously cited--, hereinafter referenced as "Langheier" in view of US Patent 10327697 --as previously cited--, hereinafter referenced as "Stein" in further view of US Patent Application Publication 20200219619 –as previously cited--, hereinafter referenced as “Feczko” and US Patent Application Publication 20070172797 –as previously cited--, hereinafter referenced as “Hada”.
With respect to claim 1, Langheier teaches a method comprising:
receiving data associated with an individual or group of individuals by a computing device (i.e., receiving factors possessed by an individual and potential interventions for the individual) (see Langheier, par 0029, 0032, fig. 1), the data including biopsychosocial biomarkers that are derived from a questionnaire (i.e., patient data is obtained via the web using online questionnaires) (see Langheier, par 0106) for the individual or group of individuals (see Langheier, par 0030 & 0031, 0106, fig. 2);
applying, by a computing device (see Langheier, fig. 1), a model to assess musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals by the computing device (i.e., applying a predictive model derived from clinical and molecular data to an individual’s inputted data to output results from the predictive model that indicates an individual’s risk of having one of several clinical outcomes based upon the individual’s data) (see Langheier, par 0029-0032);
and generating a visualization of the model results for the individual, group of individuals, or an agent of the individual or individuals by the computing device (i.e., displaying the model, associated risk scores and predictions, and treatment regiments to a patient) (see Langheier, par 0025-0026, 0032, 0117-123, figs. 1, 9A-9F, 10A & 10B).
Langheier fails to teach processing the data with the computing device to quantify parameters associated with the individual’s musculoskeletal system or musculoskeletal occupational exposure, applying, by the computing device, a statistical model to classify the individual or group of individuals in accordance with the biopsychosocial biomarkers into subgroups that each represent a similar set of observable traits and features that are indicative of outcomes, applying, by the computing device, a mechanistic model to estimate musculoskeletal characteristics of the individual or group of individuals that cannot be directly measured, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals in accordance with outputs of the statistical model and the mechanistic model.
Stein teaches a digital platform and method for identifying health conditions and therapeutic interventions using an artificial intelligence system wherein physiological scores are computed for a user. The physiological scores can comprise a predicted risk for musculoskeletal injury that is derived from a postural and body alignment score, an intervertebral disc generation risk prediction, an effective spinal age prediction, and patient survey data (see Stein, fig. 13, Col. 16, lines 28-38).
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 method of Langheier such that it includes processing the data with the computing device to quantify parameters associated with the individual’s musculoskeletal system or musculoskeletal occupational exposure because that would improve the system of Langheier by enabling it to predict a musculoskeletal injury risk for an individual or group of individual based upon metric such as a postural and body alignment score, an intervertebral disc generation risk prediction, and an effective spinal age prediction that characterize the musculoskeletal condition of an individual or group of individuals (see Stein, fig. 13, Col. 16, lines 28-38).
Langheier as modified by Stein fails to teach applying, by the computing device, a statistical model to classify the individual or group of individuals in accordance with the biopsychosocial biomarkers into subgroups that each represent a similar set of observable traits and features that are indicative of outcomes, applying, by the computing device, a mechanistic model to estimate musculoskeletal characteristics of the individual or group of individuals that cannot be directly measured, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals in accordance with outputs of the statistical model and the mechanistic model.
Feczko teaches subtyping heterogeneous disorders using functional random forest models, wherein a functional random forest model (FRF model) is used to make predictions about an outcome using input data (i.e., biomarkers) (see Feczko par 0007, 0058, 0063, 0101, 0103). The FRF model further employs a community detection approach to identify subgroups based upon shared and unique features among the input data (see Feczko, abstract, par 0061-0062, 0064, 0069, 0293, 0446).
It would have been obvious to one of ordinary skill in the art to modify the method of Langheier as modified by Stein such that it comprises applying, by the computing device, a statistical model to classify the individual or group of individuals in accordance with the biopsychosocial biomarkers into subgroups that each represent a similar set of observable traits and features that are indicative of outcome, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals in accordance with outputs of the statistical model because statistical models that identify subgroups, such as an FRF model that employs a community detection approach to identify subgroups in data, provide improvements to clinical diagnosis and treatment (see Feczko, par 0061).
Langheier as modified by Stein and Feczko fails to teach applying, by the computing device, a mechanistic model to estimate musculoskeletal characteristics of the individual or the group of individuals that cannot be directly measured, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals in accordance with outputs of the mechanistic model.
Hada teaches a method of constructing a computer-based musculoskeletal model, wherein the computer-based musculoskeletal model comprises a musculoskeletal construction step for constructing the musculoskeletal model, a load estimation step of estimating a plurality of loads acting on the different segments of the body, and a stress estimation step of estimating a stress and/or a strain occurring at each segment of the human body, based upon the plurality of loads (see Hada, par 0026, 0030 & 0644-0646).
It would have been obvious to one of ordinary skill in the art to modify the method of Langheier as modified by Stein and Feczko such that it comprises applying, by the computing device, a mechanistic model to estimate musculoskeletal characteristics of the individual or group of individuals that cannot be directly measured, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals in accordance with outputs of the mechanistic model because a mechanistic model enables the determination of loads and stress/strain on the musculoskeletal system of an individual (see Hada, par 0026, 0030 & 0644-0646) which once determined, can be corrected and/or treated.
With respect to claim 2, Langheier as modified by Stein, Feczko, and Hada teaches the method of claim 1. Langheier further teaches that receiving data comprises receiving data from one or more sensors worn by the individual or the group of individuals (i.e., by obtaining electrophysiology data coordinates from electrocardiogram or electroencephalography data from an individual) (see Langheier, par 0034).
With respect to claim 4, Langheier as modified by Stein, Feczko, and Hada teaches the method of claim 1. Langheier further teaches that receiving data further comprises receiving medical history data for the individual or the group of individuals (see Langheier, claim 2, par 0067-0072, fig. 2).
With respect to claim 6, Langheier as modified by Stein, Feczko, and Hada teaches the method of claim 1. Langheier further teaches that receiving data further comprises receiving data from one or more digital questionnaires completed by the individual or group of individuals, wherein the biopsychosocial biomarkers are captured from the one or more digital questionnaires (see Langheier, par 0106).
With respect to claim 9, Langheier as modified by Stein, Feczko, and Hada teaches the method of claim 1. Langheier further teaches predicting a success likelihood for a medical procedure (i.e., using models to predict clinical or medical outcomes for individuals, such as predicting outcomes relating to coronary surgery, or predicting adverse outcomes such as complications of a surgery, and therefore predicting the likelihood of the success of the surgery) (see Langheier, par 0030-0031 & 0036).
With respect to claim 10, Langheier as modified by Stein, Feczko, and Hada teaches the method of claim 1. Langheier as modified by Stein, Feczko, and Hada further teaches predicting an injury risk for the individual or group of individuals while performing a task (i.e., data such as tracking patient activity during physical therapeutic exercises prescribed for postural conditions is used to update a patient predictive/remedial health advocacy model) (see Stein, fig. 1, 13 & 27-31, Col. 5, lines 33- 45, Col. 6, lines 10- 23, Col. 16, lines 28-38, Col. 22, lines 29-57, Col. 24, lines 31-44 & lines 52-67, Col. 25, lines 4-18) .
With respect to claim 11, Langheier as modified by Stein, Feczko, and Hada teaches the method of claim 1. Langheier as modified by Stein, Feczko, and Hada further teaches predicting an injury likelihood for each individual in the group of individuals (i.e., patients of a physician) (see Stein, fig. 13, Col. 5, lines 33- 45, Col. 16, lines 28-38, Col. 24, lines 8-30).
With respect to claim 12, Langheier as modified by Stein, Feczko, and Hada teaches the method of claim 1. Langheier further teaches that the individual is a patient and/or an employee (see Langheier, par 0030-0031, figs. 1 & 2).
With respect to claim 13, Langheier as modified by Stein, Feczko, and Hada teaches the method of claim 1. Langheier further teaches that the method comprises:
receiving a historical reference database comprising a plurality of records (i.e., receiving clinical data from a plurality of different sources such as from clinical literature, a clinical data cohort, gene expression data, or various imaging data) (see Langheier, par 0030 & 0034, fig. 2);
for each record, identifying unique biomarkers associated with the record (i.e., extracting biomarkers from clinical literature) (see Langheier, par 0030);
and training the model using the plurality of records and biomarkers to identify unique phenotypes (i.e., the model is generated from the clinical data of a population of individuals and is used to identify phenotypes, such as predictive factors based on the biomarkers, that can be linked to clinical outcomes for a population and individuals) (see Langheier, par 0031).
With respect to claim 14, Langheier teaches a technology platform comprising:
at least one computing device (see Langheier, par 0015);
and a computer-readable medium with computer-executable instructions stored thereon (see Langheier, par 0015) that when executed by the at least one computing device cause the at least one computing device to:
receive data associated with an individual or a group of individuals (i.e., receiving factors possessed by an individual and potential interventions for the individual) (see Langheier, par 0029, 0032, fig. 1), the data including biopsychosocial biomarkers that are derived from a questionnaire (i.e., patient data is obtained via the web using online questionnaires) (see Langheier, par 0106) for the individual or group of individuals (see Langheier, par 0030 & 0031, fig. 2);
apply a model to assess musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals (i.e., applying a predictive model derived from clinical and molecular data to an individual’s inputted data to link predictive factors of a population to clinical outcomes for the individual) (see Langheier, par 0029-0032);
generate a visualization of the model results for the individual, group of individuals, or an agent of the individual or individuals (i.e., displaying the model, associated risk scores and predictions, and treatment regiments to a patient) (see Langheier, par 0025-0026, 0032, 0117-123, figs. 1, 9A-9F, 10A & 10B);
Langheier fails to teach that the technology platform processes the data to quantify parameters associated with the individual’s musculoskeletal system or musculoskeletal occupational exposure, applying, by the computing device, a statistical model to classify the individual or group of individuals in accordance with the biopsychosocial biomarkers into subgroups that each represent a similar set of observable traits and features that are indicative of outcomes, applying, by the computing device, a mechanistic model to estimate musculoskeletal characteristics of the individual or group of individuals that cannot be directly measured, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or the group of individuals in accordance with outputs of the statistical model and the mechanistic model.
Stein teaches a digital platform for identifying health conditions and therapeutic interventions using an artificial intelligence system wherein physiological scores are computed for a user. The physiological scores comprise a predicted risk for musculoskeletal injury that is derived from a postural and body alignment score, an intervertebral disc generation risk prediction, an effective spinal age prediction, and patient survey data (see Stein, fig. 13, Col. 16, lines 28-38).
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 method of Langheier such that it includes processing the data with the computing device to quantify parameters associated with the individual’s musculoskeletal system or musculoskeletal exposure because that would improve the system of Langheier by enabling it to predict a musculoskeletal injury risk for an individual or group of individual based upon metric such as a postural and body alignment score, an intervertebral disc generation risk prediction, and an effective spinal age prediction that characterize the musculoskeletal condition of an individual or group of individuals (see Stein, fig. 13, Col. 16, lines 28-38).
Langheier as modified by Stein fails to teach applying, by the computing device, a statistical model to classify the individual or group of individuals in accordance with the biopsychosocial biomarkers into subgroups that each represent a similar set of observable traits and features that are indicative of outcomes, applying, by the computing device, a mechanistic model to estimate musculoskeletal characteristics of the individual or group of individuals that cannot be directly measured, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals in accordance with outputs of the statistical model and the mechanistic model.
Feczko teaches subtyping heterogeneous disorders using functional random forest models, wherein a functional random forest model (FRF model) is used to make predictions about an outcome using input data (i.e., biomarkers) (see Feczko par 0007, 0058, 0063, 0101, 0103). The FRF model further employs a community detection approach to identify subgroups based upon shared and unique features among the input data (see Feczko, abstract, par 0061-0062, 0064, 0069, 0293, 0446).
It would have been obvious to one of ordinary skill in the art to modify the method of Langheier as modified by Stein such that it comprises applying, by the computing device, a statistical model to classify the individual or group of individuals in accordance with the biopsychosocial biomarkers into subgroups that each represent a similar set of observable traits and features that are indicative of outcome, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals in accordance with outputs of the statistical model because statistical models that identify subgroups, such as an FRF model that employs a community detection approach to identify subgroups in data, provide improvements to clinical diagnosis and treatment (see Feczko, par 0061).
Langheier as modified by Stein and Feczko fails to teach applying, by the computing device, a mechanistic model to estimate musculoskeletal characteristics of the individual or group of individuals that cannot be directly measured, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals in accordance with outputs of the mechanistic model.
Hada teaches a method of constructing a computer-based musculoskeletal model, wherein the computer-based musculoskeletal model comprises a musculoskeletal construction step for constructing the musculoskeletal model, a load estimation step of estimating a plurality of loads acting on the different segments of the body, and a stress estimation step of estimating a stress and/or a strain occurring at each segment of the human body, based upon the plurality of loads (see Hada, par 0026, 0030 & 0644-0646).
It would have been obvious to one of ordinary skill in the art to modify the method of Langheier as modified by Stein such that it comprises applying, by the computing device, a mechanistic model to estimate musculoskeletal characteristics of the individual or group of individuals that cannot be directly measured, and assessing musculoskeletal impairment or changes in musculoskeletal impairment, predict clinical outcomes, predict musculoskeletal injury risk, or assess interventions for the individual or a group of individuals in accordance with outputs of the mechanistic model because a mechanistic model enables the determination of loads and stress/strain on the musculoskeletal system of an individual (see Hada, par 0026, 0030 & 0644-0646) which once determined, can be corrected and/or treated.
With respect to claim 15, Langheier as modified by Stein, Feczko, and Hada teaches the technology platform of claim 14. Langheier further teaches that the computer-executable instructions stored thereon that when executed by the at least one computing device cause the at least one computing device to receive data from one or more sensors worn by the individual or the group of individuals (i.e., by obtaining electrophysiology data coordinates from electrocardiogram or electroencephalography data from an individual) (see Langheier, par 0034).
With respect to claim 17, Langheier as modified by Stein, Feczko, and Hada teaches the technology platform of claim 14. Langheier further teaches that the received data further comprises medical history data for the individual (see Langheier, claim 2, par 0067-0072, fig. 2).
With respect to claim 18, Langheier as modified by Stein, Feczko, and Hada teaches the technology platform of claim 14. Langheier further teaches that the received data comprises data from one or more digital questionnaires completed by the individual or the group of individuals, wherein the biopsychosocial biomarkers are captured from the one or more digital questionnaires (see Langheier, par 0106).
With respect to claim 19, Langheier as modified by Stein, Feczko, and Hada teaches the technology platform of claim 14. Langheier further teaches predicting a success likelihood for a medical procedure (i.e., using models to predict clinical or medical outcomes for individuals, such as predicting outcomes relating to coronary surgery, or predicting adverse outcomes such as complications of a surgery, and therefore predicting the likelihood of the success of the surgery) (see Langheier, par 0030-0031 & 0036).
With respect to claim 20, Langheier as modified by Stein, Feczko, and Hada teaches the technology platform of claim 14. Langheier as modified by Stein, Feczko, and Hada further teaches predicting an injury risk for a user (see Stein, fig. 13, Col. 16, lines 28-38).
Claim(s) 3, 7-8 & 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Langheier in view of Stein, Feczko and Hada, as applied to claims 1 & 14 above, and in further view of US Patent Application Publication 20200281508 --as previously cited--, hereinafter referenced as "Ren".
With respect to claim 3, Langheier as modified by Stein, Feczko, and Hada teaches the method of claim 2 as described above in paragraph 13. Langheier as modified by Stein, Feczko, and Hada fails to teach that the one or more sensors comprises one or more inertial measurement unit (IMU) sensors.
Ren teaches one or more sensors comprising one or more IMU sensors that can be used for body part motion analysis, wherein the body part motion analysis can be used for medical diagnostics such as determining the extent of an injury or disease or measuring medical treatment efficacy (see Ren, par 0011 & 0028).
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 sensors of Langheier as modified by Stein, Feczko, and Hada to comprise one or more IMU sensors because Ren teaches that IMU sensors permit body part motion analysis that can be used for medical diagnostics such as determining the extent of an injury or disease or measuring medical treatment efficacy, therefore providing additional diagnostic data to ascertain an individual’s clinical outcome (see Ren, par 0011).
With respect to claim 7, Langheier as modified by Stein, Feczko, and Hada teaches the method of claim 1. Langheier as modified by Stein, Feczko, and Hada fails to teach that an identified phenotype is derived from one or more biomarkers that is an indicator of dynamic low back motion function.
Ren teaches that the IMU sensors are placed on an individual’s neck or back (see Ren, par 0011). Furthermore, Ren discloses that data, such as changes in electrical information, is obtained from the sensors mounted on the body parts of the individual, where the changes in electrical information are caused by motion of the body part, and therefore provide an indication of the function of the motion of an individual’s body parts, such as their back or neck motion function (see Ren, par 0011).
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 method of Langheier as modified by Stein, Feczko, and Hada to identify phenotypes of an individual derived from one or more biomarkers that is an indicator of dynamic low back motion function because Ren teaches that data such as changes in electrical information obtained from sensors mounted on body parts of an individual are indicative of the motion function of that body part, such as the back of an individual, and can be used for medical diagnostics or measuring medical treatment efficacy, therefore providing additional diagnostic data to ascertain an individual’s clinical outcome (see Ren, par 0011).
With respect to claim 8, Langheier as modified by Stein, Feczko, and Hada teaches the method of claim 1. Langheier as modified by Stein, Feczko, and Hada fails to teach that an identified phenotype is derived from one or more biomarkers that is an indicator of dynamic neck motion function.
Ren teaches that the IMU sensors are placed on an individual’s neck or back (see Ren, par 0011). Furthermore, Ren discloses that data, such as changes in electrical information, is obtained from the sensors mounted on the body parts of the individual, where the changes in electrical information are caused by motion of the body part, and therefore provide an indication of the function of the motion of an individual’s body parts, such as their back or neck motion function (see Ren, par 0011).
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 method of Langheier as modified by Stein, Feczko, and Hada to identify phenotypes of an individual derived from one or more biomarkers that is an indicator of dynamic low back motion function because Ren teaches that data such as changes in electrical information obtained from sensors mounted on body parts of an individual are indicative of the motion function of that body part, such as the neck of an individual, and can be used for medical diagnostics or measuring medical treatment efficacy, therefore providing additional diagnostic data to ascertain an individual’s clinical outcome (see Ren, par 0011).
With respect to claim 16, Langheier as modified by Stein, Feczko, and Hada teaches the technology platform of claim 15. Langheier as modified by Stein, Feczko, and Hada does not teach that the one or more sensors comprises an inertial measurement unit (IMU) sensor.
Ren teaches one or more sensors comprising one or more IMU sensors that can be used for body part motion analysis, wherein the body part motion analysis can be used for medical diagnostics such as determining the extent of an injury or disease or measuring medical treatment efficacy (see Ren, par 0011 & 0028).
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 sensors of Langheier as modified by Stein, Feczko, and Hada to comprise one or more IMU sensors because Ren teaches that IMU sensors permit body part motion analysis that can be used for medical diagnostics such as determining the extent of an injury or disease or measuring medical treatment efficacy, therefore providing additional diagnostic data to ascertain an individual’s clinical outcome (see Ren, par 0011).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CHARLES A MARMOR II/Supervisory Patent Examiner
Art Unit 3791
/D.J.C./Examiner, Art Unit 3791