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 .
Application Status
This is the first non-final action on the merits. Claims 1-20 as originally filed on February 2, 2024 are currently pending and considered below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 13, 2024 is being considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
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. an abstract idea) without significantly more.
Claims 1-10 recite a method for generating an electronic clinical report configured to visually display the determined metrics in a manner that depicts the status of the user in accordance with the body part, which is within the statutory category of a process. Claims 11-15 recite a system for generating an electronic clinical report configured to visually display the determined metrics in a manner that depicts the status of the user in accordance with the body part, which is within the statutory category of a machine. Claims 16-20 recite a non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a processor, which is within the statutory category of an article of manufacture.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they "recite" a judicial exception or in other words whether a judicial exception is "set forth" or "described" in the claims. An "abstract idea" judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Representative independent claim 1 includes limitations that recite at least one abstract idea.
Specifically, independent claim 1 recites: A method comprising:
identifying, by a device, a set of locations corresponding to a body part of a user, each of the locations of the set of location having a physically associated sensor array;
executing, by the device, a collection instruction according to a predetermined time period, the executed collection instruction causing each sensor array to commence collecting data related to movements and motions of the user in relation to a respective location within the set of locations of the body part of the user, the collection of data being performed for a duration of the predetermined time period;
analyzing, by the device executing an artificial intelligence (AI) model, the collected data and determining, based on the AI-based analysis, metrics corresponding to a current status of the user; and
generating, by the device, an electronic clinical report based on the determined metrics, the electronic clinical report configured to visually display the determined metrics in a manner that depicts the status of the user in accordance with the body part based on the collected data related to the movements and motions.
The underlined limitations constitute methods of organizing human activity and concepts performed in the human mind. The claim recites the step of collecting data related to movements and motions of the user in relation to a respective location within the set of locations of the body part of the user and generating a clinical report based on the determined metrics, which encompasses an abstract idea that falls under the methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people (e.g. creating a report of the user). If the claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a). Additionally, the claim encompasses a mental process of identifying a set of locations corresponding to a body part, analyzing the collected data and determining metrics corresponding to a current status of the user. The identified abstract idea, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind except for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind except for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The abstract idea for Claims 11 and 16 are identical as the abstract idea for Claim 1, because the only difference between Claim 1 and 11 is that Claim 1 recites a method, whereas Claim 11 recites a system, and because the only difference between Claims 1 and 16 is that Claim 1 recites a method, whereas Claim 11 recites a non-transitory computer-readable storage medium. Any limitation not identified above as part of methods of organizing human activity, are deemed “additional elements” and will be discussed further in detail below. Accordingly, claims 1, 11 and 16 recite at least one abstract idea.
Similarly, dependent claims 2-17 and 19-20 recite at least one abstract idea. Claims 2, 3, 12 and 17 recite limitations that constitute an abstract idea that falls under the mathematical concepts grouping because executing a pose estimation algorithm, under its broadest reasonable interpretation, represents mathematical calculations and relationships (see MPEP 2106.04(a)(2)). Claims 4-6, 13-15 and 18-20 describe the collected information, determining the metrics and/or the determining a type of report. Claims 2, 3, 5, 12, 14, 17 and 19 partially narrow the abstract idea as described above, and also introduce additional element(s) which will be discussed in Step 2A Prong 2 and Step 2B. These limitations only serve to further limit the abstract idea and hence, are directed toward fundamentally the same abstract ideas as independent claims 1 and 18, even when considered individually and as an ordered combination.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, claims 1-20 as a whole do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. The additional elements or combination of additional elements, beyond the above-noted at least one abstract idea will be described as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the “abstract idea(s)”).
Specifically, independent claim 1 recites: A method comprising:
identifying, by a device, a set of locations corresponding to a body part of a user, each of the locations of the set of location having a physically associated sensor array;
executing, by the device, a collection instruction according to a predetermined time period, the executed collection instruction causing each sensor array to commence collecting data related to movements and motions of the user in relation to a respective location within the set of locations of the body part of the user, the collection of data being performed for a duration of the predetermined time period;
analyzing, by the device executing an artificial intelligence (AI) model, the collected data and determining, based on the AI-based analysis, metrics corresponding to a current status of the user; and
generating, by the device, an electronic clinical report based on the determined metrics, the electronic clinical report configured to visually display the determined metrics in a manner that depicts the status of the user in accordance with the body part based on the collected data related to the movements and motions.
The independent claims recite the additional elements of a device, non-transitory computer-readable storage medium, processor, sensor array, collection instruction, artificial intelligence model and electronic report that implement the identified abstract idea. The device, non-transitory computer-readable storage medium, processor, sensor array, collection instruction, artificial intelligence model and electronic report are not described by the applicant and are recited at a high-level of generality such that they amounts to no more than mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea; see MPEP 2106.05(f)).
The dependent claims 2, 3, 5, 12, 14, 17 and 19 recite additional element(s) beyond those already recited in the independent claims that implement the identified abstract idea. Claims 2, 3, 12 and 17 recite calibrating the sensor array. Claims 5, 14 and 19 recite a large language model. However, these additional elements do not integrate the abstract idea into a practical application because, as stated above, they represent mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations).
Accordingly, the claims as a whole do not integrate the abstract idea into a practical application as they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
Regarding Step 2B, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
When viewed as a whole, claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite processes that are abstract and simply implements the process on a computer(s) is not enough to qualify as "significantly more."
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a device, non-transitory computer-readable storage medium, processor, sensor array, collection instruction, artificial intelligence model and electronic report to perform the noted steps amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception cannot provide an inventive concept (“significantly more”).
The dependent claims 2, 3, 5, 12, 14, 17 and 19 recite additional element(s) beyond those already recited in the independent claims that implement the identified abstract idea. Claims 2, 3, 12 and 17 recite calibrating the sensor array. Claims 5, 14 and 19 recite a large language model. However, these functions are not deemed significantly more than the abstract idea because, as stated above, they represent mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations).
Therefore, claims 1-20 are rejected under 35 USC §101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraph of 35 U.S.C. 102 that forms the basis for the rejections under this section set forth in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 7-11 and 16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shah (US 2020/0346072 A1).
Regarding claim 1, Shah teaches: A method comprising:
identifying, by a device, a set of locations corresponding to a body part of a user, each of the locations of the set of location having a physically associated sensor array; (“The first wearable device 120 is positioned on a first location 104 of an arm 102 of the first subject 100 near the subject's wrist. The second wearable device 130 is positioned on the arm 102 at second location 106 near a shoulder 107.”, e.g. see [0040]; “In another example, the system information may include locations on a subject's body where the wearable devices are to be attached, applied, or otherwise mounted on the subject's body.”, e.g. see [0085]; “Each wearable device can include a plurality of sensors, including but not limited to a gyroscope, an accelerometer, and a magnetometer.”, e.g. see [0006])
executing, by the device, a collection instruction according to a predetermined time period, the executed collection instruction causing each sensor array to commence collecting data related to movements and motions of the user in relation to a respective location within the set of locations of the body part of the user, the collection of data being performed for a duration of the predetermined time period; (“At stage 610, a tracking/notification agent ("tracking agent") on a first wearable device receives a notification, or otherwise determines, that a movement session has been initiated…issues initialization instructions to the movement sensors of both devices.”, e.g. see [0159]-[0160]; “feedback in the form of audio, light, or touch-based signals may be specified to que a subject to start and stop a given assessment movement”, e.g. see [0080]; “the wearable devices detect a subject's movement using the configuration and settings for its internal movement sensors initialized”, e.g. see [0088]; “the subject may walk back and forth over a period as directed by the clinician. As the subject performs this assessment movement, data is transmitted real-time”, e.g. see [0067]; “The wearable devices can be configured to execute through a single cycle of stages 510 to 570 in less than a predetermined amount of time, such as 3000 ms, 2000 ms, or 1500 ms”, e.g. see [0153])
analyzing, by the device executing an artificial intelligence (AI) model, the collected data and determining, based on the AI-based analysis, metrics corresponding to a current status of the user; and (“the comparative modeling techniques can include one, or a combination of probability-based, supervised machine learning, semi-supervised machine learning, and unsupervised machine learning methods of analysis. Through these techniques, the wearable devices can identify what movement is being performed and how it compares to a movement model.”, e.g. see [0011]; “comparative modeling technique(s) selected in stage 550 are implemented, and deviations between a movement model and a subject's movement profile for the physical movement the subject is currently performing are determined real-time”, e.g. see [0138]; “the backend, management agent, and/or the coordination service can generate a progress results report for the subject that details the subject's historical performance with respect to a plan parameter metric for each designated movement…determine an overall progress score for the subject…determining the subject's level of compliance for each designated movement.” (i.e. determining metrics corresponding to a current status of the user), e.g. see [0188])
generating, by the device, an electronic clinical report based on the determined metrics, the electronic clinical report configured to visually display the determined metrics in a manner that depicts the status of the user in accordance with the body part based on the collected data related to the movements and motions. (“sequence diagram of an example method for generating a compliance report for a session of designated movements”, e.g. see [0028]; “Primary selection options for the assessment report sub-menu 460 can include options to view movement data for specific parts of a subject's body…option for a knee report 462 and/or an option for an ankle/foot 470…Each subject results module 465 can include a results graph 466 that charts a measurable aspect of an ankle/foot or a knee's movement…Values for one or more metrics…can be determined and included in a result summary section…first metric 468a for the knee is an absolute range of flexion”, e.g. see [0116]-[0117]; “Expansion of one the entries 869 can cause a detailed progress report 880 to be displayed”, e.g. see [0206]).
Regarding claim 2, Shah teaches the method of claim 1 as described above.
Shah further teaches:
calibrating the sensor array, the calibration comprising compiling the collection instruction according to an initial iteration prior to an iteration of the collection instruction (“The calibration of the wearable devices 120, 130 may have been accomplished through an assessment of the subject's normal movement” which can be “prior to the assessment or a first training session by the subject”, e.g. see [0043], [0138]; “Calibrating may further include configuring the processing component to recognize when one or more sensors… detect a movement”, e.g. see [0043]).
Regarding claim 7, Shah teaches the method of claim 1 as described above.
Shah further teaches:
wherein the status of the patent, based on the determined metrics, correspond to at least one of posture, pain, movement, motion, treatment progress, mobility, flexibility and posture (metrics corresponding to “muscle strength, flexibility, mobility-range of motion”, e.g. see [0122]).
Regarding claim 8, Shah teaches the method of claim 1 as described above.
Shah further teaches:
enabling, based on the identification of the set of locations, placement of each sensor array (specifying “locations on a subject's body where the wearable devices are to be attached, applied, or otherwise mounted” prior to data collection, e.g. see [0085]).
Regarding claim 9, Shah teaches the method of claim 1 as described above.
Shah further teaches:
wherein the set of locations corresponds to a plurality of body parts, wherein the clinical report is based on collected data related to the plurality of body parts (tracking and generating reports for a plurality of parts, such as the “the wrist 103, elbow 105, and shoulder 107” simultaneously, e.g. see [0058]; generating reports showing multiple parts, e.g. see [0116]).
Regarding claim 10, Shah teaches the method of claim 1 as described above.
Shah further teaches:
wherein the body part is selected from at least one body part of a human, wherein the body part comprises at least one of a spine and legs of the user, wherein the sensor arrays correspond to a type of the body part (selecting body parts including “spinal discs” and “leg(s)”, e.g. see [0112], [0077]; tuning the sensors corresponding to the specific body part being tracked, e.g. see [0110]).
Regarding claim 11, Shah teaches: A system comprising:
a processor configured to: identify a set of locations corresponding to a body part of a user, each of the locations of the set of location having a physically associated sensor array; (“a processor, a memory storage, and a non-transitory computer-readable medium containing instructions”, e.g. see [0046]; “The first wearable device 120 is positioned on a first location 104 of an arm 102 of the first subject 100 near the subject's wrist. The second wearable device 130 is positioned on the arm 102 at second location 106 near a shoulder 107.”, e.g. see [0040]; “In another example, the system information may include locations on a subject's body where the wearable devices are to be attached, applied, or otherwise mounted on the subject's body.”, e.g. see [0085]; “Each wearable device can include a plurality of sensors, including but not limited to a gyroscope, an accelerometer, and a magnetometer.”, e.g. see [0006])
execute, a collection instruction according to a predetermined time period, the executed collection instruction causing each sensor array to commence collecting data related to movements and motions of the user in relation to a respective location within the set of locations of the body part of the user, the collection of data being performed for a duration of the predetermined time period; (“At stage 610, a tracking/notification agent ("tracking agent") on a first wearable device receives a notification, or otherwise determines, that a movement session has been initiated…issues initialization instructions to the movement sensors of both devices.”, e.g. see [0159]-[0160]; “feedback in the form of audio, light, or touch-based signals may be specified to que a subject to start and stop a given assessment movement”, e.g. see [0080]; “the wearable devices detect a subject's movement using the configuration and settings for its internal movement sensors initialized”, e.g. see [0088]; “the subject may walk back and forth over a period as directed by the clinician. As the subject performs this assessment movement, data is transmitted real-time”, e.g. see [0067]; “The wearable devices can be configured to execute through a single cycle of stages 510 to 570 in less than a predetermined amount of time, such as 3000 ms, 2000 ms, or 1500 ms”, e.g. see [0153])
analyze, via execution of an artificial intelligence (AI) model, the collected data and determine, based on the AI-based analysis, metrics corresponding to a current status of the user; and (“the comparative modeling techniques can include one, or a combination of probability-based, supervised machine learning, semi-supervised machine learning, and unsupervised machine learning methods of analysis. Through these techniques, the wearable devices can identify what movement is being performed and how it compares to a movement model.”, e.g. see [0011]; “comparative modeling technique(s) selected in stage 550 are implemented, and deviations between a movement model and a subject's movement profile for the physical movement the subject is currently performing are determined real-time”, e.g. see [0138]; “the backend, management agent, and/or the coordination service can generate a progress results report for the subject that details the subject's historical performance with respect to a plan parameter metric for each designated movement…determine an overall progress score for the subject…determining the subject's level of compliance for each designated movement.” (i.e. determining metrics corresponding to a current status of the user), e.g. see [0188])
generate an electronic clinical report based on the determined metrics, the electronic clinical report configured to visually display the determined metrics in a manner that depicts the status of the user in accordance with the body part based on the collected data related to the movements and motions. (“sequence diagram of an example method for generating a compliance report for a session of designated movements”, e.g. see [0028]; “Primary selection options for the assessment report sub-menu 460 can include options to view movement data for specific parts of a subject's body…option for a knee report 462 and/or an option for an ankle/foot 470…Each subject results module 465 can include a results graph 466 that charts a measurable aspect of an ankle/foot or a knee's movement…Values for one or more metrics…can be determined and included in a result summary section…first metric 468a for the knee is an absolute range of flexion”, e.g. see [0116]-[0117]; “Expansion of one the entries 869 can cause a detailed progress report 880 to be displayed”, e.g. see [0206]).
Regarding claim 16, Shah teaches: A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a processor (e.g. see [0014]), performs a method comprising:
identifying, by the processor, a set of locations corresponding to a body part of a user, each of the locations of the set of location having a physically associated sensor array; (“a non-transitory, computer-readable medium having instructions that, when executed by a processor associated with a computing device, causes the processor to perform the stages described”, e.g. see [0014]; “The first wearable device 120 is positioned on a first location 104 of an arm 102 of the first subject 100 near the subject's wrist. The second wearable device 130 is positioned on the arm 102 at second location 106 near a shoulder 107.”, e.g. see [0040]; “In another example, the system information may include locations on a subject's body where the wearable devices are to be attached, applied, or otherwise mounted on the subject's body.”, e.g. see [0085]; “Each wearable device can include a plurality of sensors, including but not limited to a gyroscope, an accelerometer, and a magnetometer.”, e.g. see [0006])
executing, by the device, a collection instruction according to a predetermined time period, the executed collection instruction causing each sensor array to commence collecting data related to movements and motions of the user in relation to a respective location within the set of locations of the body part of the user, the collection of data being performed for a duration of the predetermined time period; (“At stage 610, a tracking/notification agent ("tracking agent") on a first wearable device receives a notification, or otherwise determines, that a movement session has been initiated…issues initialization instructions to the movement sensors of both devices.”, e.g. see [0159]-[0160]; “feedback in the form of audio, light, or touch-based signals may be specified to que a subject to start and stop a given assessment movement”, e.g. see [0080]; “the wearable devices detect a subject's movement using the configuration and settings for its internal movement sensors initialized”, e.g. see [0088]; “the subject may walk back and forth over a period as directed by the clinician. As the subject performs this assessment movement, data is transmitted real-time”, e.g. see [0067]; “The wearable devices can be configured to execute through a single cycle of stages 510 to 570 in less than a predetermined amount of time, such as 3000 ms, 2000 ms, or 1500 ms”, e.g. see [0153])
analyzing, by the device executing an artificial intelligence (AI) model, the collected data and determining, based on the AI-based analysis, metrics corresponding to a current status of the user; and (“the comparative modeling techniques can include one, or a combination of probability-based, supervised machine learning, semi-supervised machine learning, and unsupervised machine learning methods of analysis. Through these techniques, the wearable devices can identify what movement is being performed and how it compares to a movement model.”, e.g. see [0011]; “comparative modeling technique(s) selected in stage 550 are implemented, and deviations between a movement model and a subject's movement profile for the physical movement the subject is currently performing are determined real-time”, e.g. see [0138]; “the backend, management agent, and/or the coordination service can generate a progress results report for the subject that details the subject's historical performance with respect to a plan parameter metric for each designated movement…determine an overall progress score for the subject…determining the subject's level of compliance for each designated movement.” (i.e. determining metrics corresponding to a current status of the user), e.g. see [0188])
generating, by the device, an electronic clinical report based on the determined metrics, the electronic clinical report configured to visually display the determined metrics in a manner that depicts the status of the user in accordance with the body part based on the collected data related to the movements and motions. (“sequence diagram of an example method for generating a compliance report for a session of designated movements”, e.g. see [0028]; “Primary selection options for the assessment report sub-menu 460 can include options to view movement data for specific parts of a subject's body…option for a knee report 462 and/or an option for an ankle/foot 470…Each subject results module 465 can include a results graph 466 that charts a measurable aspect of an ankle/foot or a knee's movement…Values for one or more metrics…can be determined and included in a result summary section…first metric 468a for the knee is an absolute range of flexion”, e.g. see [0116]-[0117]; “Expansion of one the entries 869 can cause a detailed progress report 880 to be displayed”, e.g. see [0206]).
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.
Claims 3, 12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Shah in further view of Utz (US 2025/0134413 A1).
Regarding claim 3, Shah teaches the method of claim 2 as described above.
Shah further teaches:
[…] wherein the calibration is based on information related to the user corresponding to at least one of height, leg length, joint angles or positions, spinal segment angles, vertebral body angles, and bone lengths or angles (calibrating the sensors based on the “subject's physical measurements (e.g., height, weight, torso length, inseam, knee diameter, elbow diameter, degree of curvature of a back, hip alignment, leg(s) length, arm(s) length, etc.)”, e.g. see [0077])
Shah does not teach:
wherein the calibration is performed based on the execution of a pose estimation algorithm
However, Utz in the analogous art of tracking biomechanical kinematics using wearable sensors (e.g. see [0005], [0011]) teaches:
wherein the calibration is performed based on the execution of a pose estimation algorithm (“A corresponding algorithm, which is executed by the data processing unit, may, for example, take into account at least one of the characteristics of the patient”, e.g. see [0035]; “the data processing unit is configured and programmed to determine the axes of the bones and an intersection of the axes on the basis of information of the sensor elements and to relate them to the static model data set in such a way that the axes of the bones contained therein are superimposed…Based on this, for example, a pose of the axes of the bones, an intersection of the axes and a length of the bones, e.g. see [0054]; storing “information about axes defined by the bones, dimensions of the bones, in particular their lengths…and/or an angle between the bones”, e.g. see [0039])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shah to include the calibration being performed based on the execution of a pose estimation algorithm as taught by Utz, for the purposes of detecting and simulating a better “dynamic model of the joint” (Utz [0018]).
Regarding claim 12, Shah teaches the system of claim 11 as described above.
Shah further teaches:
calibrate the sensor array, the calibration comprising compiling the collection instruction according to an initial iteration prior to an iteration of the collection instruction (“The calibration of the wearable devices 120, 130 may have been accomplished through an assessment of the subject's normal movement” which can be “prior to the assessment or a first training session by the subject”, e.g. see [0043], [0138]; “Calibrating may further include configuring the processing component to recognize when one or more sensors… detect a movement”, e.g. see [0043])
[…] wherein the calibration is based on information related to the user corresponding to at least one of height, leg length, joint angles or positions, spinal segment angles, vertebral body angles, and bone lengths or angles (calibrating the sensors based on the “subject's physical measurements (e.g., height, weight, torso length, inseam, knee diameter, elbow diameter, degree of curvature of a back, hip alignment, leg(s) length, arm(s) length, etc.)”, e.g. see [0077])
Shah does not teach:
wherein the calibration is performed based on the execution of a pose estimation algorithm
However, Utz in the analogous art teaches:
wherein the calibration is performed based on the execution of a pose estimation algorithm (“A corresponding algorithm, which is executed by the data processing unit, may, for example, take into account at least one of the characteristics of the patient”, e.g. see [0035]; “the data processing unit is configured and programmed to determine the axes of the bones and an intersection of the axes on the basis of information of the sensor elements and to relate them to the static model data set in such a way that the axes of the bones contained therein are superimposed…Based on this, for example, a pose of the axes of the bones, an intersection of the axes and a length of the bones, e.g. see [0054]; storing “information about axes defined by the bones, dimensions of the bones, in particular their lengths…and/or an angle between the bones”, e.g. see [0039])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shah to include the calibration being performed based on the execution of a pose estimation algorithm as taught by Utz, for the purposes of detecting and simulating a better “dynamic model of the joint” (Utz [0018]).
Claim 17 recites substantially similar limitations as those already addressed in claim 12, and, as such is rejected for similar reasons as given above.
Claims 4-6, 13-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shah in further view of Amarasingham (US 2023/0104655 A1).
Regarding claim 4, Shah teaches the method of claim 1 as described above.
Shah further teaches:
collecting information related to the user […]; and (“a preference of a subject for auditory, visual, or haptic feedback…a number of times a subject has performed a designated movement; and a number of times that the subject has exhibited the same deviation with respect to the designated movement”, e.g. see [0044]; “This analysis may be based on the subject's particular condition, past history (if available), and in some cases, the subject's most recent performances of a designated movement.”, e.g. see [0054]; “Subject information can include a condition of the subject, past conditions, any goals the subject has for a movement plan”, e.g. see [0107])
analyzing the collected data […] (“A selection of one comparative modeling technique in stage 550…can be based on: values for the raw sensory or formatted data; a body segment involved in a physical movement being performed; and a condition of a subject” (modifying the AI analysis based on the collected data), e.g. see [0137])
Shah does not teach:
the user information corresponding to demographic and behavior information of the user; and analyzing based in part on the collected user information, wherein the determination of metrics is based further on the user information
However, Amarasingham in the analogous art of clinical artificial intelligence and patient monitoring (e.g. see [0002]) teaches:
the user information corresponding to demographic and behavior information of the user; and analyzing based in part on the collected user information, wherein the determination of metrics is based further on the user information (“For example, hospital, ambulatory, or patient-based medical records of patients and patient profile information may be identified and automatically processed to identify content of various inputs, such as a person, age, sex, reason for visiting the medical facility” (i.e. demographic information), e.g. see [0020]; “29-year-old male” (using the demographic data in the AI generated output), e.g. see [0110], [0108]; “In another example, a predictive model 204D may receive the input…and determine whether the patient will excessively utilize the health care system...An example training dataset for predictive model 204D may include…past health care system utilization, and the like.”, e.g. see [0062]; “The NLP model 202D may receive data input 102 that is associated with a patient and generate an output that indicates potential social determinants that may affect the patient's health…housing instability, food insecurity, transportation needs, child care needs, etc.”, e.g. see [0047])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shah to include the user information corresponding to demographic and behavior information of the user and analyzing based in part on the collected user information, wherein the determination of metrics is based further on the user information as taught by Amarasingham for the purposes of preventing “adverse health outcomes” (Amarasingham [0004]).
Regarding claim 5, Shah teaches the method of claim 4 as described above.
Shah does not teach:
analyzing the user information via an executed large language model (LLM); and
determining additional information related to at least one of a medical diagnosis, initial symptoms and initial observations related to the user, wherein the additional information is further utilized to determine the metrics of the user
However, Amarasingham in the analogous art teaches:
analyzing the user information via an executed large language model (LLM); and (executing large language models, “Bidirectional Encoder Representations from Transformers (BERT) models or their variants, and/or Generative Pretrained Transformer (GPT) models, GPT-2 models, GPT-3 models”, to process patient data, e.g. see [0038]-[0039])
determining additional information related to at least one of a medical diagnosis, initial symptoms and initial observations related to the user, (using the LLM to detect “medical problems, diagnoses, symptoms” and to output “active clinical issues of a patient”, e.g. see [0045], [0050])
wherein the additional information is further utilized to determine the metrics of the user (“In another example, predictive model 204C may receive the input of one or more NLP models 202A-Z and determine whether the patient's condition will deteriorate in the future…During a training phase, the predictive model 204C may be trained on the historical outputs of one or more NLP models 202A-Z…An example training dataset for predictive model 204C may include vital signs, lab results, diagnoses, medical comorbidities, and the like.”, e.g. see [0061]; Predictive models 204 may generate risk scores from the structured data…Predictive model 204C may generate a score indicative of clinical risk of deterioration”, e.g. see [0103]; “Prioritization module 222 may receive active clinical conditions identified using NLP models 202, treatments identified using KG module 212, risk scores identified by predictive models 204…Prioritization module 222 may prioritize treatments, pending diagnostic orders, and active treatment orders into a ranked list.”, e.g. see [0065])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shah to include analyzing the user information via an executed large language model and determining additional information related to at least one of a medical diagnosis, initial symptoms and initial observations related to the user, wherein the additional information is further utilized to determine the metrics of the user as taught by Amarasingham for the purposes of reducing “human error” and ensuring critical patient context is not overlooked during care transitions (Amarasingham [0004], [0006]).
Regarding claim 6, Shah teaches the method of claim 1 as described above.
Shah further teaches:
determining, based on the determined metrics, a health score; (determining an “overall progress score” and “plan compliance score”, e.g. see [0188], [0199])
determining a type of clinic report to provide based on […] a type of the body part, wherein the generation of the electronic clinical report is based on the determine type (selecting different report types for different body parts, e.g. a “knee report 462 and/or an option for an ankle/foot”, e.g. see [0116])
Shah does not teach:
determining a type of clinic report to provide based on the determined health score
However, Amarasingham in the analogous art teaches:
determining a type of clinic report to provide based on the determined health score (dynamically selecting the format and language of the clinical report/summary based on the score and the intended audience via a “smoothing module”, e.g. see [0070])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shah to include determining a type of clinic report to provide based on the determined health as taught by Amarasingham for the purposes of including “medical terms associated with the patient's condition, diagnosis, and/or treatment” (Amarasingham [0070]).
Claims 13 and 18 recite substantially similar limitations as those already addressed in claim 4, and, as such are rejected for similar reasons as given above.
Claims 14 and 19 recite substantially similar limitations as those already addressed in claim 5, and, as such are rejected for similar reasons as given above.
Claims 15 and 20 recite substantially similar limitations as those already addressed in claim 6, and, as such are rejected for similar reasons as given above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference Goodall (US 2017/0156662 A1) discloses monitoring body movement or a condition according to a motion regimen. Reference Petterson (US 2017/0296129 A1) motion tracking, assessment and monitoring. Reference Rutowski (US 2024/0087752 A1) discloses multi-language adaptive mental health risk assessment from spoken and written language.
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/A.A./
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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