DETAILED ACTION
This action is in reference to the communication filed on 20 MARCH 2025.
Claims 1-20 are present and have been examined.
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.
Claim 7, 17, ( and by dependency, claim 8) 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. Claims 7, 17 recite the limitations including “…assessment for producing a third subindex score; assigning an additional weighting factor to the third subindex scores….” There is a discrepancy in the plural form of the word score, as it is unclear if the intention is to weight one or more scores, and as such the scope of the claim is indefinite.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 4 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 4 broadens the definition of the data in claim 1 by stating the data may be “objective and/or subjective.” Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-20 the independent claim(s) 1,12, 20 recite(s) a system or method, both of which are a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-20 the independent claim(s) (claims 1, 12, 20) is/are directed, in part, to:
A system for generating a consolidated health index score based on physical performance by an individual, the system comprising:
initiate a first
obtain a first performance dataset of the individual from the
initiate a second
obtain a second performance dataset of the individual from the
assign a weighting factor to each of the first and second subindex scores; and
generate the consolidated health index score for presentation
These claim elements (excluding strikethroughs) are considered to be abstract ideas because they are directed to a method of mental processes, i.e. concepts performed in the human mind (including observation, evaluation, judgement, and opinion. Generating a consolidated score based on observed results of two separate assessments involves at least the tasks of observation, evaluation, and judgement. The claims also recite certain methods of human activities, including managing personal behavior and/or relationships between people (including social activities, teaching, and following rules or instructions). Directing a first and second assessment includes such concepts, particularly in the types of assessments and the subsequent results.
If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the mind, then it falls within the “mental processes” grouping of abstract ideas. If a claim limitation under its broadest reasonable interpretation covers managing personal behaviors/relationships, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional elements: claim 1 recites a computing unit with a processor which executes the claim limitations and directs the assessments, a tracking device connected therein, an “assessment application” operating thereon, and a display on which the score is displayed. Claim 12 recites similar elements, including a computer, display and tracking device connected/in communication, as does claim 20. The computer/processor elements, as well as the “application” common to all of the claims are recited at a high level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). Similarly, the tracking device and the display itself are not found to recite anything additional in this context. There is no improvement to the functioning of the computer or any other technology or technical field in the above identified elements as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e). Examiner notes that the display used to display information, as well as the general interconnectivity of the computing elements/tracking device are found to be analogous to adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g).
Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as: claim 1 recites a computing unit with a processor which executes the claim limitations and directs the assessments, a tracking device connected therein, an “assessment application” operating thereon, and a display on which the score is displayed. Claim 12 recites similar elements, including a computer, display and tracking device connected/in communication, as does claim 20.
When considered individually, the above identified claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in:
[0040] The term ‘computer’ or ‘computing unit’ may include any device that comprises at least one processor and that electronically executes one or more programs, such as a user interface program or software program, and may include personal computers, laptop computers, servers, portable media players, handheld devices, cellular phones, microprocessor-based programmable consumer electronic or appliances, and other similar electronic devices that include circuitry for wirelessly sending or receiving information.
[0047] The term ‘processor’ or ‘processing unit’ refers to one or more devices, circuits, or processing cores configured to process data, such as computer program instructions, and includes personal computers, desktop computers, laptop computers, message processors, handheld devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, and the like. Unless otherwise stated, references to a first processor may also apply to a second processor and vice versa.
[0079] The computing unit 102 includes a memory 108, operating system 110, assessment application 112, one or more processor(s) 114, storage 116, input-output interface 118, graphical user interface 120, and display 122. The computing unit 102 may represent any suitable type of computer, computing system, server, disk array, or programmable devices such as a handheld device, a networked device, or an embedded device, etc. The computing unit 102 may communicate with one or more networked computers via one or more networks 124, such as a cluster or other distributed computing system, through the I/O interface 118. The I/O interface 118 is configured to transmit data between the computing unit 102 and the tracking device 104, e.g., via wired or wireless connection.
[0081] The processor 114 may operate under the control of an operating system 110 that resides in memory 108. The operating system 110 may manage computer resources so that computer program code embodied as one or more computer programs, such as the assessment application 112 communicatively connected to memory 108 may have instructions executed by the processor 114. In an alternative embodiment, the processor 114 may execute the assessment application 112 directly, in which case the operating system 110 may be omitted.
[0052] The term ‘tracking device’ refers to an instrument for tracking quantifiable data pertaining to health and function of an individual. As further explained below, the tracking device can include any sensor, camera, or other device capable of tracking movement and location and providing data acquisition that would be understood and available to one having ordinary skill in the art. Non-limiting examples of the tracking device include a headset, camera, IMU, or LiDAR for tracking movement, a force plate for balance, a dynamometer for strength, an eye tracker for eye movement or attention, an electromyography (EMG) or mechanomyogram (MMG) for muscle activity/contraction, an electroencephalogram (EEG) for brain activity or cognition, an electrocardiogram (ECG) for heart activity, a voice recognition unit for cognition, or a wearable device that is communicatively connected to a computing unit of the system.
[0065] In an embodiment, the tracking device 104 is a distinct hardware component of the system 100. The tracking device 104 may be a wearable device attached to the individual. For example, tracking device 104 comprises a sensor unit or sensor 105, with one or more inertial measurement units (IMUs) (or similar) attached to an individual. In an embodiment, the tracking device 104 is an adjustable headgear that measures head movement via a Bluetooth-enabled, custom-built sensor 105 that syncs with a computing unit 102 or application 112. The tracking device 104 advantageously features a minimalist design that allows a patient to easily progress through physical therapy without excess weight or resistance aggravating their impairment. In an embodiment, the tracking device 104 weighs less than 55 g, preferably less than 100 g.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2-11, 13-19:
Dependent claims 2-4, 6-11, 13-14, 16-19 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as the assessments themselves, the elements measured/compared, the scores themselves, and the source of indices. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
Dependent claims 5, 15 are not directed to any additional abstract ideas, however, they do recite non-abstract elements regarding the tracking device itself. Examiner makes reference to the above discussion with regard to the tracking device as noted in the independent claims. Examiner notes that as claimed, the description of the tracking device (including use of camera, LIDAR and/or IMU) is not found to recite a practical application as the claims appear to just “apply” the existing technology to implement the abstract idea. Neither the identified technology nor a technical aspect of the invention is found to be improved. Similarly, when considering if significantly more than the abstract idea is present, Examiner makes reference to the above identified paragraphs indicating clearly that the claims rely on the existing technology for monitoring/tracking a user. As such, these claims are also found to recite an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Orr et al (US 20200401214 A1, hereinafter Orr) in view of An et al (US 20170231568 A1), hereinafter An.
In reference to claim 1, 12:
Orr teaches A system for generating a consolidated health index score based on physical performance by an individual, the system comprising:
a first computing unit having an assessment application, a processor, one or more hardware storage devices, and a display, wherein the assessment application includes one or more computer-directed assessments (at least [fig 2, 3, and related text] gaming system provides tasks, computer architecture in figure 3);
a tracking device communicatively connected to the first computing unit and configured to observe physical assessment performance of the individual during the one or more computer-directed assessments (at least [fig 1, 2 and related text] “In various embodiments, system 100 is used to collected data from motion sensors including hand sensors (not pictured), sensors included in headset 101, and additional sensors such as torso sensors or a stereo camera. In some embodiments, data from these sensors is collected at a rate of up to about 150 Hz. As pictured, data may be collected in six degrees of freedom: X—left/right; Y—up/down/height; Z—foreword/backward; P—pitch; R—roll; Y—yaw.”…” Referring to FIG. 2, an exemplary system according to embodiments of the present disclosure is illustrated. The collected data from the sensors can be stored on a database 304 for medical analysis in the exemplary architecture illustrated in FIG. 2. Data is gathered from user 101 by wearable 102. “);
wherein the one or more hardware storage devices store instructions that are executable by the system to:
initiate a first computer-directed assessment on the assessment application arranged to be completed using the tracking device (at least [fig 11 and related text] “FIG. 11 is a flow diagram illustrating an exemplary process 1100 for neck proprioception rehabilitation. At 1102, a clinician controls the location of a target in space and the number of repetitions for a patient…” at [0107] “. In various embodiments, the clinician may instruct the patient to perform a first activity, e.g., look at (or move a body part towards) a first location and then recreate the same motion with the visual field restricted or blacked out (in part or in total).”);
obtain a first performance dataset of the individual from the tracking device and based on the first computer-directed assessment for producing a first subindex score (at least [fig 11 and related text] “…At 1104, the patient sees the center point and the target in a clear environment with no objects to assist the patient. At 1106, the patient is guided to point at the target using a VR/AR sensor. At 1108, the patient is guided to point back at the center point (The actual center of his field of view)… At 1114, the patient and the clinician receive results on each repetition, in addition to statistics, such as, e.g., mean and standard deviation. In various embodiments, the process may repeat back to 1102 for any suitable number of repetitions. “ at [0107] “ In various embodiments, the positional information of the patient while performing the activity may be compared against a predetermined set of positional information representing an ideal path to the target. In various embodiments, the systems of the present disclosure may determine a compliance metric as described in more detail above based on, for example, how closely a patient recreates the initial motion while having their visual field restricted or blacked out. In various embodiments, the compliance metric may be a score.”);
initiate a second computer-directed assessment on the assessment application arranged to be completed using the tracking device (at least [fig 11 and related text] “At 1110, both the target and the center point disappears. At 1112, the patient is guided to point back to the target estimated point for a number of repetitions controlled by the clinician. “ at [107] “Based on the patient's performance of completing this activity, the clinician may instruct the patient to perform a second activity, e.g., look at (or move a body part towards) a second location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, a compliance metric may also be determined for the second activity.”);
obtain a second performance dataset of the individual from the tracking device and based on the second computer-directed assessment for producing a second subindex score (at least [fig 11 and related text] “At 1114, the patient and the clinician receive results on each repetition, in addition to statistics, such as, e.g., mean and standard deviation. In various embodiments, the process may repeat back to 1102 for any suitable number of repetitions.” At [0107] “Based on the patient's performance of completing this activity, the clinician may instruct the patient to perform a second activity, e.g., look at (or move a body part towards) a second location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, a compliance metric may also be determined for the second activity.”);
generate the consolidated health index score for presentation on the display based on the combined first and second subindex scores (at least [fig 10, 5 and related text] “In some embodiments, the center of mass is represented by a 3-dimensional position calculated from the head mounted display and two hand sensors. This point, C, may be calculated as a weighted average of the three sensors according to Equation 1, where X, Y, Z are the coordinates of a given sensor, a, b, c are constants, rhs identified the left hand sensor, lhs identifies the right hand sensor, and hmd identifies the head-mounted display.” – i.e. an aggregate value regarding the health or effectiveness of the therapy is generated based on the weighted averages of the values from the first/second/…n repetitions of the assessment; see also [0109] “The average between the results according to the number of repetitions and the locations in space that were chosen is calculated and presented at the end of the procedure, presenting an “Asterix” that enables to see progression over time.”).
While Orr as cited teaches all the limitations above, and further teaches the use of a “weighted average” of various values to arrive at a score (see 0090), it does not specifically teach weighting factor applied to each separate score. An however does teach:
assign a weighting factor to each of the first and second subindex scores (at least [077-078] “n an example, the combination may be a linear weighted combination, such as show in Equation (3) as follows: Similar to the discussion with reference to the blending circuit 234 in FIG. 2, the weight functions w.sub.i, w.sub.j, and w.sub.k may each be determined based on signal use or signal characteristics of the corresponding signal metric during a particular patient monitoring mode…In an example, the blending circuit 440 may compute a combination of the cDS.sub.C, the cDS.sub.R, and the cDS.sub.P, such as a linear weighted combination: where the weight factors a.sub.1 through a.sub.3 may each be specified or adjusted by the user based on the patient health condition or target disease. For example, if a patient is hospitalized for pulmonary edema, then a larger weight a.sub.3 may be applied to the pulmonary function-indicated disposition score DS is hospitalized for worsening pulmonary edema, then a larger weight a.sub.3 may be applied to the pulmonary function-indicated disposition score DS.sub.P because an indication of pulmonary function recovery may play a decisive role in assessing the patient's readiness to be discharged from the hospital.” – based on the disposition score’s (i.e. index scores) importance, the weight assigned to each example category is altered, and these weighting factors may be different or equally assigned based on the category of values they are applied to); and
generate the consolidated health index score for presentation on the display based on weighting factors and combined first and second subindex scores (at least [077-079, as discussed above, see also fig 2-4 and related text] “0078] The physiological function-indicated composite disposition scores, such as the cDS.sub.C, the cDS.sub.R, and the cDS.sub.P, may be presented on the display of the user interface 240, and stored in the memory 250.” See also [093-094] for additional discussion of a total score determined by the weights applied to different health scores of the user being displayed as “human perceptible” on display 240). Orr and An are analogous, as both references disclose the importance of numerical quantification of patient progress. One of ordinary skill would have found the use of weighting factor, as taught by An to be obvious to consider in the composite score(s) common to both, as An teaches that a weighting factor allows a clinician to make more informed decisions based on the perceived importance of one or more metrics affecting the patient (see 0078). As Orr/An both teach that different assessments of a patient may be seeking different data points in order to determine the overall wellness of a patient, it would follow that a clinician would find it necessary to value or weigh these metrics differently when making such an important conclusion. Examiner also notes that as cited, Orr teaches that a weighted average of multiple assessments may be used, and as such, the use of an individual weight as taught by An would have also been an obvious substation in determining a composite value.
In reference to claim 2:
Orr further teaches: wherein the first computer-directed assessment generates and evaluates a first metric or set of metrics quantifying at least one of range of motion, proprioception, balance, sensorimotor control, neuromuscular control, strength, oculomotor control, coordination, vestibular function, reaction time, endurance, or cognition (at least [081-096, generally] discussion of proprioception, at [042-6] balance, range of motion, neuromuscular control, etc.).
In reference to claim 3, 13:
Orr further teaches: wherein the second computer-directed assessment generates and evaluates a second metric or set of metrics different from the first metric (at least [0107] “ In various embodiments, the clinician may instruct the patient to perform a first activity, e.g., look at (or move a body part towards) a first location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, positional information of the user may be recorded during this process… Based on the patient's performance of completing this activity, the clinician may instruct the patient to perform a second activity, e.g., look at (or move a body part towards) a second location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, a compliance metric may also be determined for the second activity.” – i.e. second compliance metric)
In reference to claim 4:
Orr further teaches: wherein the first subindex score is based on subjective and/or objective data (at least [0107] “In various embodiments, the positional information of the patient while performing the activity may be compared against a predetermined set of positional information representing an ideal path to the target. In various embodiments, the systems of the present disclosure may determine a compliance metric as described in more detail above based on, for example, how closely a patient recreates the initial motion while having their visual field restricted or blacked out. In various embodiments, the compliance metric may be a score. “ – i.e. objective data recording, as compared to subjective metric); at [076-077] compliance metric is measured, i.e. objective data, [086, 097] “Quantify proprioception—providing patients and clinicians with tools that can quantify proprioception abilities easily provides clinician and patient an “Asterix,” which is an objective value that can give clinical information about patient's performance and reflect whether the treatment is beneficial.”
In reference to claim 5:
Orr further teaches: wherein the tracking device comprises at least one of:
a camera (at least fig 1 and related text] “In various embodiments, system 100 is used to collected data from motion sensors including hand sensors (not pictured), sensors included in headset 101, and additional sensors such as torso sensors or a stereo camera. In some embodiments, data from these sensors is collected at a rate of up to about 150 Hz. As pictured, data may be collected in six degrees of freedom: X—left/right; Y—up/down/height; Z—foreword/backward; P—pitch; R—roll; Y—yaw…In some embodiments, camera 106 observes user 105. Video is provided to computing node 107, which in turn sends the video data via a network.”),
a lidar sensor, and
an inertial measurement unit (IMU).
In reference to claim 6:
Orr further teaches: wherein the assessment application is configured to produce a collective index scoring report including the first and second subindex scores and the consolidated health index score (at least [fig 11 and related text] “At 1112, the patient is guided to point back to the target estimated point for a number of repetitions controlled by the clinician. At 1114, the patient and the clinician receive results on each repetition, in addition to statistics, such as, e.g., mean and standard deviation. In various embodiments, the process may repeat back to 1102 for any suitable number of repetitions….” At [0107] “In various embodiments, the clinician may instruct the patient to perform a first activity, e.g., look at (or move a body part towards) a first location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, positional information of the user may be recorded during this process. In various embodiments, the positional information of the patient while performing the activity may be compared against a predetermined set of positional information representing an ideal path to the target. In various embodiments, the systems of the present disclosure may determine a compliance metric as described in more detail above based on, for example, how closely a patient recreates the initial motion while having their visual field restricted or blacked out. In various embodiments, the compliance metric may be a score. In various embodiments, the compliance metric may be recorded in an electronic health record. In various embodiments, the compliance metric may be presented to the user (e.g., visually, audibly, etc.). Based on the patient's performance of completing this activity, the clinician may instruct the patient to perform a second activity, e.g., look at (or move a body part towards) a second location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, a compliance metric may also be determined for the second activity…” at [0109] “The average between the results according to the number of repetitions and the locations in space that were chosen is calculated and presented at the end of the procedure, presenting an “Asterix” that enables to see progression over time.”) – i.e. the compliance metric from each activity may be used to determine an overall score for the activities, per repetition of a single activity or two or more activities).
In reference to claim 7, 17:
Orr further teaches: wherein the instructions stored by the one or more hardware storage devices are further executable by the system to:
initiate a third computer-directed assessment on the assessment application (at least [004, claim 3] “In various embodiments, the event marker includes a visual object displayed within the virtual or augmented reality environment. In various embodiments, the method further includes adjusting the position of the event marker to a third location based on the applied first adjustment.” – i.e. a third position/assessment after the first/second assessment, based on the information collected therein; at [0107] “Clinicians can perform an adjustment according to the patient's needs, and control the number of repetitions for this procedure, and the locations of the required point is space the patient is supposed to recreate. In various embodiments, the clinician may instruct the patient to perform a first activity, e.g., look at (or move a body part towards) a first location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, positional information of the user may be recorded during this process. In various embodiments, the positional information of the patient while performing the activity may be compared against a predetermined set of positional information representing an ideal path to the target. “ – i.e. repetitions 1…n);
obtain a third performance dataset of the individual based on the third computer- directed assessment for producing a third subindex score (at least [004, claim 3] “In various embodiments, the event marker includes a visual object displayed within the virtual or augmented reality environment. In various embodiments, the method further includes adjusting the position of the event marker to a third location based on the applied first adjustment.” - i.e. a third position/assessment after the first/second assessment, based on the information collected therein; at [fig 11 and related text] “At 1114, the patient and the clinician receive results on each repetition, in addition to statistics, such as, e.g., mean and standard deviation. In various embodiments, the process may repeat back to 1102 for any suitable number of repetitions.” At [0107] “Based on the patient's performance of completing this activity, the clinician may instruct the patient to perform a second activity, e.g., look at (or move a body part towards) a second location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, a compliance metric may also be determined for the second activity.” – each repetition, the activity is tracked and presented)
generate the consolidated health index score for presentation on the display based combined first, second, and third subindex scores (at least [fig 10, 5 and related text] “In some embodiments, the center of mass is represented by a 3-dimensional position calculated from the head mounted display and two hand sensors. This point, C, may be calculated as a weighted average of the three sensors according to Equation 1, where X, Y, Z are the coordinates of a given sensor, a, b, c are constants, rhs identified the left hand sensor, lhs identifies the right hand sensor, and hmd identifies the head-mounted display.” – i.e. an aggregate value regarding the health or effectiveness of the therapy is generated based on the weighted averages of the values from the first/second/…n repetitions of the assessment; see also [0109] “The average between the results according to the number of repetitions and the locations in space that were chosen is calculated and presented at the end of the procedure, presenting an “Asterix” that enables to see progression over time.”).
While Orr as cited teaches all the limitations above, and further teaches the use of a “weighted average” of various values to arrive at a score (see 0090), it does not specifically teach weighting factor applied to each separate score. An however does teach:
assign an additional weighting factor to the third subindex scores (at least [077-078] “n an example, the combination may be a linear weighted combination, such as show in Equation (3) as follows: Similar to the discussion with reference to the blending circuit 234 in FIG. 2, the weight functions w.sub.i, w.sub.j, and w.sub.k may each be determined based on signal use or signal characteristics of the corresponding signal metric during a particular patient monitoring mode…In an example, the blending circuit 440 may compute a combination of the cDS.sub.C, the cDS.sub.R, and the cDS.sub.P, such as a linear weighted combination: where the weight factors a.sub.1 through a.sub.3 may each be specified or adjusted by the user based on the patient health condition or target disease. For example, if a patient is hospitalized for pulmonary edema, then a larger weight a.sub.3 may be applied to the pulmonary function-indicated disposition score DS is hospitalized for worsening pulmonary edema, then a larger weight a.sub.3 may be applied to the pulmonary function-indicated disposition score DS.sub.P because an indication of pulmonary function recovery may play a decisive role in assessing the patient's readiness to be discharged from the hospital.” – based on the disposition score’s (i.e. index scores) importance, the weight assigned to each example category is altered, and these weighting factors may be different or equally assigned based on the category of values they are applied to); and
generate the consolidated health index score for presentation on the display based on weighting factors and combined first, second and third subindex scores (at least [077-079, as discussed above, see also fig 2-4 and related text] “0078] The physiological function-indicated composite disposition scores, such as the cDS.sub.C, the cDS.sub.R, and the cDS.sub.P, may be presented on the display of the user interface 240, and stored in the memory 250.” See also [093-094] for additional discussion of a total score determined by the weights applied to different health scores of the user being displayed as “human perceptible” on display 240). Orr and An are analogous, as both references disclose the importance of numerical quantification of patient progress. One of ordinary skill would have found the use of weighting factor, as taught by An to be obvious to consider in the composite score(s) common to both, as An teaches that a weighting factor allows a clinician to make more informed decisions based on the perceived importance of one or more metrics affecting the patient (see 0078). As Orr/An both teach that different assessments of a patient may be seeking different data points in order to determine the overall wellness of a patient, it would follow that a clinician would find it necessary to value or weigh these metrics differently when making such an important conclusion. Examiner also notes that as cited, Orr teaches that a weighted average of multiple assessments may be used, and as such, the use of an individual weight as taught by An would have also been an obvious substation in determining a composite value.
In reference to claim 8:
Orr further teaches: wherein the third computer-directed assessment evaluates a third metric or set of metrics different from the first and second metrics (at least [004, claim 3] “In various embodiments, the event marker includes a visual object displayed within the virtual or augmented reality environment. In various embodiments, the method further includes adjusting the position of the event marker to a third location based on the applied first adjustment.” – i.e. a third position/assessment after the first/second assessment, based on the information collected therein; at [0107] “ In various embodiments, the clinician may instruct the patient to perform a first activity, e.g., look at (or move a body part towards) a first location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, positional information of the user may be recorded during this process… Based on the patient's performance of completing this activity, the clinician may instruct the patient to perform a second activity, e.g., look at (or move a body part towards) a second location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, a compliance metric may also be determined for the second activity.” – i.e. second compliance metric).
In reference to claim 9, 18:
Orr further teaches: wherein the first and second subindex scores are produced using historical performance datasets stored in a collective database and factored into a dynamic calculation with the first and second performance datasets of the individual, the collective database being communicatively connected to the first computing unit (at least [047] “In various embodiments, the systems of the present disclosure may present a predetermined rehabilitation protocol to one or more users. In various embodiments, the system may determine compliance with the predetermined rehabilitation protocol, e.g., by comparing recorded positional information from the one or more users to a set of positional data representing an ideal and/or standard procedure. In various embodiments, the compliance metric may be determined at the remote server. In various embodiments, the compliance metric may be determined as a measurement of how accurately and/or completely a user is performing a prescribed set of motions for the predetermined protocol. In various embodiments, the positional data of the user may be compared to positional data representative of the correct motions in the protocol. In various embodiments, the compliance metric may include a range of acceptable values. In various embodiments, the compliance metric may include a biometric measurement.” at [0107] “ In various embodiments, positional information of the user may be recorded during this process. In various embodiments, the positional information of the patient while performing the activity may be compared against a predetermined set of positional information representing an ideal path to the target. In various embodiments, the systems of the present disclosure may determine a compliance metric as described in more detail above based on, for example, how closely a patient recreates the initial motion while having their visual field restricted or blacked out. In various embodiments, the compliance metric may be a score. In various embodiments, the compliance metric may be recorded in an electronic health record. “ – further in 107 a compliance score is determined for each of a first and second task/assessment).
In reference to claim 10:
Orr further teaches: wherein the assessment application includes a machine learning algorithm to automatically recommend one or more rehabilitation or exercise strategies based on metric and index analysis (at least [0133] “[0133] The VR/AR technology according to various embodiments provides a fully immersive environment that enables a user to be immersed in an automated close circuit system. Within this environment, a virtual clinician (an avatar) utilizing machine learning and artificial intelligence (AI) can communicate with, assess, and monitor the patient and create an automated close circuit decision to identify the right treatment protocol for the user. The VR/AR technology also allows the environment to be manipulated, e.g., multiple layers can be added to the environment to create different tasks and situations for the users. This enables determining a more precise evaluation, training, or treatment regimen, while monitoring the user constantly and providing immediate feedback. “)
In reference to claim 11:
Orr further teaches: wherein the assessment application is arranged to dynamically update the one or more rehabilitation or exercise strategies based on the performance of the individual during and or after completion of the first and second computer-directed assessments (at least [0133-0137] “At 1501, a virtual environment is provided to the user via a virtual or augmented reality system. The virtual environment includes an avatar using machine learning or artificial intelligence to communicate with the user. At 1502, screening data is collected from the user's interaction with the avatar in the virtual environment. At 1503, a customized evaluation, training, or treatment protocol is determined for the user based at least in part on the screening data. At 1504, the user is guided to perform a task in the evaluation, training, or treatment protocol via the virtual or augmented reality system. Data is collected from a plurality of sensors relating to the user's performance of the task. At 1505, the collected data is analyzed and a report is generated based on the user's performance of the task…During the treatment/workout session, the avatar constantly monitors and provides feedback for the user and continues to adjust the VR environment constantly. At the end of the session the Avatar can perform additional screening, provide feedback to the user, and recommend the next step that is most suitable for the user. After each session the user will be able to access all his or her data and performance evaluations.”).
In reference to claim 14:
Orr further teaches: wherein the step of obtaining a first performance dataset of the individual includes monitoring physical performance of the individual using a tracking device attached to or associated with the individual, the tracking device being communicatively connected to the first computing unit (at least [fig 1, 2 and related text] “In various embodiments, system 100 is used to collected data from motion sensors including hand sensors (not pictured), sensors included in headset 101, and additional sensors such as torso sensors or a stereo camera. In some embodiments, data from these sensors is collected at a rate of up to about 150 Hz. As pictured, data may be collected in six degrees of freedom: X—left/right; Y—up/down/height; Z—foreword/backward; P—pitch; R—roll; Y—yaw.”…” Referring to FIG. 2, an exemplary system according to embodiments of the present disclosure is illustrated. The collected data from the sensors can be stored on a database 304 for medical analysis in the exemplary architecture illustrated in FIG. 2. Data is gathered from user 101 by wearable 102. “).
In reference to claim 15:
Orr further teaches: wherein the step of obtaining a first performance dataset of the individual includes monitoring physical performance of the individual using a camera communicatively connected to the first computing unit (at least fig 1 and related text] “In various embodiments, system 100 is used to collected data from motion sensors including hand sensors (not pictured), sensors included in headset 101, and additional sensors such as torso sensors or a stereo camera. In some embodiments, data from these sensors is collected at a rate of up to about 150 Hz. As pictured, data may be collected in six degrees of freedom: X—left/right; Y—up/down/height; Z—foreword/backward; P—pitch; R—roll; Y—yaw…In some embodiments, camera 106 observes user 105. Video is provided to computing node 107, which in turn sends the video data via a network.”).
In reference to claim 16:
Orr further teaches: further comprising the step of producing a collective index scoring report comprising: the first and second subindex scores; the consolidated health index score (at least (at least [fig 11 and related text] “At 1112, the patient is guided to point back to the target estimated point for a number of repetitions controlled by the clinician. At 1114, the patient and the clinician receive results on each repetition, in addition to statistics, such as, e.g., mean and standard deviation. In various embodiments, the process may repeat back to 1102 for any suitable number of repetitions….” At [0107] “In various embodiments, the clinician may instruct the patient to perform a first activity, e.g., look at (or move a body part towards) a first location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, positional information of the user may be recorded during this process. In various embodiments, the positional information of the patient while performing the activity may be compared against a predetermined set of positional information representing an ideal path to the target. In various embodiments, the systems of the present disclosure may determine a compliance metric as described in more detail above based on, for example, how closely a patient recreates the initial motion while having their visual field restricted or blacked out. In various embodiments, the compliance metric may be a score. In various embodiments, the compliance metric may be recorded in an electronic health record. In various embodiments, the compliance metric may be presented to the user (e.g., visually, audibly, etc.). Based on the patient's performance of completing this activity, the clinician may instruct the patient to perform a second activity, e.g., look at (or move a body part towards) a second location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, a compliance metric may also be determined for the second activity…” at [0109] “The average between the results according to the number of repetitions and the locations in space that were chosen is calculated and presented at the end of the procedure, presenting an “Asterix” that enables to see progression over time.”) – i.e. the compliance metric from each activity may be used to determine an overall score for the activities, per repetition of a single activity or two or more activities);
wherein the assessment application is arranged to dynamically adjust based on the physical performance of the individual during and or after completion of the first and second computer-directed assessments (at least [0133-0137] “At 1501, a virtual environment is provided to the user via a virtual or augmented reality system. The virtual environment includes an avatar using machine learning or artificial intelligence to communicate with the user. At 1502, screening data is collected from the user's interaction with the avatar in the virtual environment. At 1503, a customized evaluation, training, or treatment protocol is determined for the user based at least in part on the screening data. At 1504, the user is guided to perform a task in the evaluation, training, or treatment protocol via the virtual or augmented reality system. Data is collected from a plurality of sensors relating to the user's performance of the task. At 1505, the collected data is analyzed and a report is generated based on the user's performance of the task…During the treatment/workout session, the avatar constantly monitors and provides feedback for the user and continues to adjust the VR environment constantly. At the end of the session the Avatar can perform additional screening, provide feedback to the user, and recommend the next step that is most suitable for the user. After each session the user will be able to access all his or her data and performance evaluations.”).
In reference to claim 19:
Orr further teaches: wherein the subindex is calculated based on a first metric or set of metrics, at least one of the first and second computer-directed assessments, patient characteristics, or a comparison of population used (at least [0107] “In various embodiments, positional information of the user may be recorded during this process. In various embodiments, the positional information of the patient while performing the activity may be compared against a predetermined set of positional information representing an ideal path to the target. In various embodiments, the systems of the present disclosure may determine a compliance metric as described in more detail above based on, for example, how closely a patient recreates the initial motion while having their visual field restricted or blacked out…Based on the patient's performance of completing this activity, the clinician may instruct the patient to perform a second activity, e.g., look at (or move a body part towards) a second location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, a compliance metric may also be determined for the second activity.“)
In reference to claim 20:
Orr teaches: A method of enhancing patient education based on physical performance by a patient using a tracking device communicatively connected to a computing unit having an assessment application, a processor, one or more hardware storage devices, and a display (at least [fig 1, 2 and related text] “In various embodiments, system 100 is used to collected data from motion sensors including hand sensors (not pictured), sensors included in headset 101, and additional sensors such as torso sensors or a stereo camera. In some embodiments, data from these sensors is collected at a rate of up to about 150 Hz. As pictured, data may be collected in six degrees of freedom: X—left/right; Y—up/down/height; Z—foreword/backward; P—pitch; R—roll; Y—yaw.”…” Referring to FIG. 2, an exemplary system according to embodiments of the present disclosure is illustrated. The collected data from the sensors can be stored on a database 304 for medical analysis in the exemplary architecture illustrated in FIG. 2. Data is gathered from user 101 by wearable 102. “), the method comprising:
initiating a first computer-directed assessment on the assessment application and arranged to be completed with the tracking device (at least [fig 11 and related text] “FIG. 11 is a flow diagram illustrating an exemplary process 1100 for neck proprioception rehabilitation. At 1102, a clinician controls the location of a target in space and the number of repetitions for a patient…” at [0107] “. In various embodiments, the clinician may instruct the patient to perform a first activity, e.g., look at (or move a body part towards) a first location and then recreate the same motion with the visual field restricted or blacked out (in part or in total).”);
obtaining a first performance dataset of the patient from the tracking device based on the first computer-directed assessment for producing a first subindex score (at least [fig 11 and related text] “…At 1104, the patient sees the center point and the target in a clear environment with no objects to assist the patient. At 1106, the patient is guided to point at the target using a VR/AR sensor. At 1108, the patient is guided to point back at the center point (The actual center of his field of view)… At 1114, the patient and the clinician receive results on each repetition, in addition to statistics, such as, e.g., mean and standard deviation. In various embodiments, the process may repeat back to 1102 for any suitable number of repetitions. “ at [0107] “ In various embodiments, the positional information of the patient while performing the activity may be compared against a predetermined set of positional information representing an ideal path to the target. In various embodiments, the systems of the present disclosure may determine a compliance metric as described in more detail above based on, for example, how closely a patient recreates the initial motion while having their visual field restricted or blacked out. In various embodiments, the compliance metric may be a score.”);
initiating a second computer-directed assessment on the assessment application and arranged to be completed with the tracking device (at least [fig 11 and related text] “At 1110, both the target and the center point disappears. At 1112, the patient is guided to point back to the target estimated point for a number of repetitions controlled by the clinician. “ at [107] “Based on the patient's performance of completing this activity, the clinician may instruct the patient to perform a second activity, e.g., look at (or move a body part towards) a second location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, a compliance metric may also be determined for the second activity.”);
obtaining a second performance dataset of the patient from the tracking device and based on the second computer-directed assessment for producing a second subindex score (at least [fig 11 and related text] “At 1114, the patient and the clinician receive results on each repetition, in addition to statistics, such as, e.g., mean and standard deviation. In various embodiments, the process may repeat back to 1102 for any suitable number of repetitions.” At [0107] “Based on the patient's performance of completing this activity, the clinician may instruct the patient to perform a second activity, e.g., look at (or move a body part towards) a second location and then recreate the same motion with the visual field restricted or blacked out (in part or in total). In various embodiments, a compliance metric may also be determined for the second activity.”);
generating a consolidated health index score based on the combined first and second subindex scores (at least [fig 10, 5 and related text] “In some embodiments, the center of mass is represented by a 3-dimensional position calculated from the head mounted display and two hand sensors. This point, C, may be calculated as a weighted average of the three sensors according to Equation 1, where X, Y, Z are the coordinates of a given sensor, a, b, c are constants, rhs identified the left hand sensor, lhs identifies the right hand sensor, and hmd identifies the head-mounted display.” – i.e. an aggregate value regarding the health or effectiveness of the therapy is generated based on the weighted averages of the values from the first/second/…n repetitions of the assessment; see also [0109] “The average between the results according to the number of repetitions and the locations in space that were chosen is calculated and presented at the end of the procedure, presenting an “Asterix” that enables to see progression over time.”);
displaying the consolidated health index score or subindex scores represented on one or more bounded scales to the patient using the display (at least [fig 11 and related text] “At 1110, both the target and the center point disappears. At 1112, the patient is guided to point back to the target estimated point for a number of repetitions controlled by the clinician. At 1114, the patient and the clinician receive results on each repetition, in addition to statistics, such as, e.g., mean and standard deviation. In various embodiments, the process may repeat back to 1102 for any suitable number of repetitions.”; at [fig 14 and related text] “At 1401, a user is guided to perform a task involving movement of a given body part of the user via a virtual or augmented reality display. At 1402, data is collected from a plurality of sensors relating to the user's performance of the task. At 1403, the data is analyzed and a report is generated reflecting the proprioception abilities of the user based on the performance of the task.” – i.e. for each iteration of the task in figure 11, a report is generated with the first/second compliance scores for the user);
wherein the steps of producing the first subindex score and the second subindex score includes generating a metric or set of metrics that quantifies at least one of range of motion, proprioception, balance, sensorimotor control, neuromuscular control, strength, oculomotor control, coordination, vestibular function, reaction time, endurance, and cognition (at least [081-096, generally] discussion of proprioception, at [042-6] balance, range of motion, neuromuscular control, etc.).
While Orr as cited teaches all the limitations above, and further teaches the use of a “weighted average” of various values to arrive at a score (see 0090), it does not specifically teach weighting factor applied to each separate score. An however does teach:
assigning a weighting factor to each of the first and second subindex scores (at least [077-078] “n an example, the combination may be a linear weighted combination, such as show in Equation (3) as follows: Similar to the discussion with reference to the blending circuit 234 in FIG. 2, the weight functions w.sub.i, w.sub.j, and w.sub.k may each be determined based on signal use or signal characteristics of the corresponding signal metric during a particular patient monitoring mode…In an example, the blending circuit 440 may compute a combination of the cDS.sub.C, the cDS.sub.R, and the cDS.sub.P, such as a linear weighted combination: where the weight factors a.sub.1 through a.sub.3 may each be specified or adjusted by the user based on the patient health condition or target disease. For example, if a patient is hospitalized for pulmonary edema, then a larger weight a.sub.3 may be applied to the pulmonary function-indicated disposition score DS is hospitalized for worsening pulmonary edema, then a larger weight a.sub.3 may be applied to the pulmonary function-indicated disposition score DS.sub.P because an indication of pulmonary function recovery may play a decisive role in assessing the patient's readiness to be discharged from the hospital.” – based on the disposition score’s (i.e. index scores) importance, the weight assigned to each example category is altered, and these weighting factors may be different or equally assigned based on the category of values they are applied to); and
generating a consolidated health index score based on weighting factors and combined first and second subindex scores (at least [077-079, as discussed above, see also fig 2-4 and related text] “0078] The physiological function-indicated composite disposition scores, such as the cDS.sub.C, the cDS.sub.R, and the cDS.sub.P, may be presented on the display of the user interface 240, and stored in the memory 250.” See also [093-094] for additional discussion of a total score determined by the weights applied to different health scores of the user being displayed as “human perceptible” on display 240). Orr and An are analogous, as both references disclose the importance of numerical quantification of patient progress. One of ordinary skill would have found the use of weighting factor, as taught by An to be obvious to consider in the composite score(s) common to both, as An teaches that a weighting factor allows a clinician to make more informed decisions based on the perceived importance of one or more metrics affecting the patient (see 0078). As Orr/An both teach that different assessments of a patient may be seeking different data points in order to determine the overall wellness of a patient, it would follow that a clinician would find it necessary to value or weigh these metrics differently when making such an important conclusion. Examiner also notes that as cited, Orr teaches that a weighted average of multiple assessments may be used, and as such, the use of an individual weight as taught by An would have also been an obvious substation in determining a composite value.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20140156043, to Blackadar, discloses a sensor based measurement system for directed exercises
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687
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