DETAILED ACTION
This Office action is responsive to communications filed on 02/13/2026. Claims 12-20 are withdrawn. Claims 21-25 are canceled. Presently, Claims 1-20 remain pending, Claims 1-11 are rejected and are hereinafter examined on the merits.
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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-11 in the reply filed on 02/13/2026is acknowledged. Claims 12-20 are withdrawn. Claims 21-25 are canceled. Claims 1-20 remain pending. Claims 1-11 are rejected.
Examiners Notes
Applicant is reminded of manner of making amendment in application according to 37 C.F.C. 1.121.(c). The current status of all the claims in the application, including any previously canceled or withdrawn claims, must be given. Status is indicated in a parenthetical expression following the claim number by one of the following status identifiers that includes (withdrawn). See MPEP 714,II,C,(A),
The status of claims 12-20 should have been corrected to (Withdrawn) for the reason that these claims share similar features to the non-elected and therefore are also considered withdrawn.
Drawings
The drawings are objected to because several are of poor image quality. Specifically, 17C-D, 18C-D are not legible. Increase the text and drawing size such that the image(s) when scanned becomes legible. The Examiner further suggest that the FIGURES are presented on different pages. In addition, its also unclear how these images are intended to represent kinetic energy sensors. Clarity is requested. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The use of the term, “Bluetooth” “Wifi” etc. at ¶0079 a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Additionally, the lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification
Claim Objections
The following claims are objected to because of the following informalities and should recite:
Claims 2-11, “[[C]]claim”.
Appropriate correction is needed.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
PART I:
Claim 1:
“using the software to tabulate the respective composite index by calculating a probability that the respective shape of motion factor, the respective motion symmetry factor, and the respective motion speed factor correspond to one of the sets of functional movement scores.”
Claim 2:
“wherein calculating the probability comprises assigning weights to the predictors and running the predictors through a regression analysis using machine learning software.”
An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340 (Fed. Cir. 2008) (internal citation omitted). This can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015), see MPEP § 2161(I).
These limitations are computer/processor-implemented functional claim limitation as it is directed to a processor-controlled algorithm configured to calculate a probability. Yet the specification does not disclose the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed functions, i.e., “using the software to tabulate the respective composite index by calculating a probability that the respective shape of motion factor, the respective motion symmetry factor, and the respective motion speed factor correspond to one of the sets of functional movement scores.”: i.e., “wherein calculating the probability comprises assigning weights to the predictors and running the predictors through a regression analysis using machine learning software” in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. It is not enough to disclose that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015). As the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention, these claims are rejected for lack of written description. For more information regarding the written description requirement, see MPEP §§ 2161, 2162-2163.07(b).
The claim is rejected under 35 USC § 112(a) for a lack of written description. Proper written description cannot be identified in the specification, claims, and drawings directed to the computer implemented steps of how the calculated probability can be achieved by machine learning software. The machine learning described in the specification is amounts to generalization tantamount to a black box of inputs and outputs. Specifically, the specification does not provide and lacks detailed a step-by-step description, any algorithmic or flowchart-based disclosure, specific functions, and/or weights for the machine learning techniques used for calculation of the probability.
Instead of the specification detailing the internal workings of the machine learning software (i.e., the algorithm) the specification discusses the following regarding the inputs:
The system feeds to the machine learning software individual predictors that were extracted during motion assessment, which includes respective shape of motion factor, a respective motion symmetry factor, and a respective motion speed factor, ¶0011, ¶0062, ¶0122, ¶0127. In some embodiments, ultrasound muscle scores as an additional predictor are inputs, ¶0011, ¶0131.
Regarding the output, it’s the complex index tabulated by calculating a probability that combines the predictors to one set of the functional movement scores.
Furthermore, the Examiner acknowledges that the specification discusses regression analysis and Hazard modules, but it never discusses or details how the regression analysis is mathematically formulated, nor does the specification provide weights to these predictors.
Indeed, the specification is directed to a mere example of generalized machine learning models tantamount to a black box, rather than showing procession of a particular implementation. Without the level of detail regarding the weights used, parameters used, and operations of the segmentation using machine learning models, the written description requirement is not satisfied. The mere use of stating the probability is achieved by machine learning techniques is not sufficient. One of ordinary skill in the art would not be able to implement the described process without disclosure of said weights, parameters, and/or operations of the calculated probability achieved by machine learning techniques in a step-by-step manner. In addition, an assertion that could be derived using simulations or test (i.e., prophetic examples) does not demonstrate that the inventors actual did so or had possession of the specific functional relationships and constraints to obviate the lack of written description requirement.
Consequently, one of ordinary skill in the art would not deem the instant specification having sufficient detail so that they could understand how the inventor intended to achieve the aforementioned step. Since the instant specification fails to provide a finite sequence of steps for performing step, the aforementioned claim fails to meet the written description requirement under 35 U.S.C. 112(a).
PART II:
Claim 1:
“a kinetic energy sensor positioned proximately to a test patient's anatomy to gather test kinetic energy data;
a respective control kinetic energy sensor positioned proximately to a control patient's anatomy to gather control kinetic energy data;”
Claim 10:
“kinetic energy sensor and the respective control kinetic energy sensor each comprise a manometer measuring forearm pronation and bicep curl motion.”
The claims are rejected under 35 U.S.C. 112(a) for lack of written description. A kinetic energy sensor when read in the light of the specification is interpreted as encompassing this manometer. Regarding the manometer, a manometer is a pressure-measuring device and does not sense kinetic energy (i.e., manometers measure pressure (fluid/gas pressure) and cannot track or measure rotational motion of a forearm or the spatial trajectory of a bicep curl). Specifically, proper written description cannot be identified in the specification, claims, and/or drawings directed to how a manometer measures forearm pronation and bicep curl motion in sufficient detail. The specification appears to assume that manometers can be considered kinetic energy sensors, but does not adequately provide sufficient description of mechanisms or techniques by which a manometer can measure forearm pronation and bicep curl motion. The specification does not further provide proper written description to even correlate manometer data to actual kinetic energy data. There are no mathematical formulas, physical equations, structural explanation to convert the pressure measured by the manometer into a true kinetic energy to measure forearm pronation and bicep curl motion. Indeed, the system is said to gather data from the manometer/dynamometer and use the software to calculate the respective motion speed factor. But there is zero, algorithmic approaches that properly correlate pressure data to kinetic energy data for measuring forearm pronation and bicep curl motion. In addition, an assertion that could be derived using simulations or test (i.e., prophetic examples) does not demonstrate that the inventors actual did so or had possession of the specific functional relationships and constraints to obviate the lack of written description requirement.
Consequently, one of ordinary skill in the art would not deem the instant specification having sufficient detail so that they could understand how the inventor intended to achieve said aforementioned claimed feature. Since the instant specification fails to provide written description for the phrase above in claim 10, the aforementioned claim 10 fails to meet the written description requirement under 35 U.S.C. 112(a).
The dependent claims of the above rejected claims are rejected due to their dependency.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention.
Claim 1:
lines 4-11, “a wearable sensor positioned proximately to a test patient's anatomy to gather test motion shape data and test motion symmetry data; a respective wearable sensor positioned proximately to a control patient's anatomy to gather control motion shape data and control motion symmetry data; a kinetic energy sensor positioned proximately to a test patient's anatomy to gather test kinetic energy data; a respective control kinetic energy sensor positioned proximately to a control patient's anatomy to gather control kinetic energy data;”, renders the claim indefinite. It is unclear if the “a test patient’s anatomy” of the wearable sensor and kinetic energy sensor refers to the same test patient. Similarly, the same applies to the “control patient’s anatomy”. For examination purposes, the Examiner assumes the second recitation of “test patient’s anatomy” & “control patient’s anatomy” refers to “the test patent’s anatomy” and “the control patient’s anatomy”. Consistent claim language is required when referring to the same term. Appropriate correction is required.
The dependent claims of the above rejected claims are rejected due to their dependency.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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 & 7-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rovini et al (A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson's Disease Diagnosis. Sensors (Basel). 2020 May 5).
Claim 1: Rovini discloses, A computerized system for calculating a respective composite index for respective sets of functional movement scores that quantify motor function abilities for patients diagnosed with a disease, (¶Abstract, [Introduction / pg. 3], Fig. 2, [2.4.3. Feature Extraction / pg. 10-11], [2.5.2 Classification / pg. 16-17], [Discussion / pg. 23 last para], The computerized system is designed to objectively assess the motor evaluation for Parkinson’s (PD) diagnosis. Kinematic parameters quantify functional movement scores (i.e., movement frequency, maximum movement amplitude, velocity, and frequency variability). In order to aggregate these functional scores, supervised learning classifies (i.e., support vector machines, random forest, and naïve bayes) to interpret the data and characterize the motor performance. Rovini discloses, [Discussion / pg. 23 last para]-‘we propose to move towards a continuous scale that, merging the information derived from the extracted parameters by using machine learning techniques, could identify a status point for each patient and its evolution over the time.’, which constitutes as a composite index of their moto function abilities for patients diagnosed with a disease)
a wearable sensor positioned proximately to a test patient's anatomy to gather test motion shape data and test motion symmetry data; (¶Abstract, [2.1. Participants / pg. 3-4], [2.2. Instruments / pg. 4-5], Rovini teaches under the broadest reasonable interpretation a wearable system comprising inertial measurement units (IMUs), specifically the two SensHand and two SensFoot (four inertial devices) place “proximately” on the patient’s anatomy including the wrist, fingers, and dorsum of the foot (i.e., the SensHand and SensFoot constitute as a wearable sensor). These sensors were tested on a group of 40 ‘test patients” (i.e. 40 PD Patients) diagnosed with mild to mild-stage PD disease. [2.4. Signal Processing / pg. 6], [2.4.3. Feature Extraction / pg. 10-11], The device uses accelerometers and gyroscopes to record motion data, which is used to track the shape and movements such as “angular excursion” or the maximum angular distance between a finger and thumb during tapping exercises. [2.3. Experimental Protocol / pg. 5], [2.5. Data Analysis / pg. 15-16], The subjects perform each exercise on each both their right and left sides, and the system extract parameters for the right and left sides separately to analyze the unilaterality and compares these parameters, thus constituting as gathering motion symmetry data.)
a respective wearable sensor positioned proximately to a control patient's anatomy to gather control motion shape data and control motion symmetry data; (¶Abstract, [2.1. Participants / pg. 3-4], [2.3. Experimental Protocol / pg. 5], [TABLE 3 pg. 18 & Table 4 pg. 19], Rovini teaches under the broadest reasonable interpretation, the same SensHand and SensFoot sensors and assessment was applied to a control group of 40 healthy control subjects (HC) which were matches in age to the test patients (i.e., a respective wearable sensor). The HC subjects constitute as control patient’s. Just as with the test patients, the sensors recorded motion shape data and preformed separate right and left side assessments (i.e., symmetry data) for these HC subject to establish a baseline for performance.)
a kinetic energy sensor positioned proximately to a test patient's anatomy to gather test kinetic energy data; ([2.2 Instruments / pg. 4-5], [2.4.3. Feature Extraction / pg. 11 & 14-15], [TABLE 3 pg. 18 & Table 4 pg. 19], Rovini teaches under the broadest reasonable interpretation, the sensors are equipped with MEMS sensors that include a 3-axis gyroscopes and 6-axis geomagnetic modules featuring accelerometers. Under the broadest reasonable interpretation, the accelerometer of Rovini is a kinetic energy sensor which measures raw accelerations of bodily movement that is directly proportional to the forces and energy of the body (i.e., the integral of the magnitude of the total acceleration vector (IAV)). The IAV represents kinetic energy data.)
a respective control kinetic energy sensor positioned proximately to a control patient's anatomy to gather control kinetic energy data; ([2.1. Participants / pg. 3-4], [TABLE 3 pg. 18 & Table 4 pg. 19], as established above, the same sensors are applied to the 40 HC group. The system of Rovini thus computes the same IAV representing the kinetic energy data for the 40 HC group, allowing for the statistical comparison of energy output between the HC and the PD patients.)
a computer comprising software implemented by a computer processor in communication with computer memory, wherein the computer has access to the test motion shape data, the test motion symmetry data, the control motion shape data, the control motion symmetry data, the test kinetic energy data, the control kinetic energy data, and (Both sets of the wearable devices including IMUs, (i.e., SensFoot & SensHand) are equipped with Bluetooth modules that wirelessly transmit all the data to a single remote personal computer (PC), [2.2. Instruments / pg. 4-5]. This PC stores the data into memory during the physical acquisitions, [2.4. Signal Processing / pg. 6], [2.2. Instruments / pg. 4-5]-‘Both the SensFoot devices and the coordinators of SensHand are included in a plastic cover realized using a 3D printing technique. Data are collected on a PC through a custom-made interface developed in Visual Studio, C# language.’. [2.4. Signal Processing / pg. 6], [2.5. Data Analysis / pg. 15-16], in the analysis, the software implemented by the PC (i.e., Matlab) accesses this stored data to perform offline signal processing, apply filters, calculated the integrated movement amplitude (i.e., shape data), evaluate side-by-side performance (i.e., symmetry data), and calculate the IAV energy (i.e., kinetic energy) for both the 40 Test PD patients, and the 40 HC subjects. The computer utilizes machine learning software packages to build models that distinguish between the two groups using this accessed pool of data, [2.5.2 Classification / pg. 16—17].)
wherein the software executes computerized steps with the computer processor to calculate and store the composite index from predictors of the functional movement scores, ([2.2. Instruments / pg. 4-5]-‘Both the SensFoot devices and the coordinators of SensHand are included in a plastic cover realized using a 3D printing technique. Data are collected on a PC through a custom-made interface developed in Visual Studio, C# language.’, [2.4. Signal Processing / pg. 6], “The motor data recorded with SensFoot and SensHand were stored on a PC during the acquisitions and offline processed by using Matlab®R2019b (The MathWorks, Inc., Natick, MA, USA). Accelerometers and gyroscopes provided triaxial accelerations and triaxial angular rates, respectively, that were processed to measure kinematic parameters.’, [Discussion / pg 23], The software implemented by the PC (i.e., Matlab) execute the steps to process the data and calculate the kinematic parameters. These parameters serve as the “predictors” of the functional movement scores. Using machine learning to merge these extracted parameters into a single continuous scale, the identified status point for each patient and its evolution are determined overtime. This status point constitutes that tabulated composite index characterizing the subject’s motor capabilities.) the predictors comprising a respective shape of motion factor, a respective motion symmetry factor, and a respective motion speed factor for each of the sets of functional movement scores, and wherein the computerized steps comprise: ([2.4.3. Feature Extraction / pg. 10-14], [2.5. Data Analysis / pg. 15-16], [4. Discussion / pg. 22, ¶1], The software calculates the maximum movement amplitude, maximum angular excursion, and total swing distance for various task (i.e., shape of the motion factors), the software calculates features for the right and left side to evaluate the unilaterally and asymmetric onset charactering the pathology (i.e., motion symmetry factors), the software computes variables (mean frequency, opening velocity, & closing velocity), (i.e., motion speed factors).
calculating the respective shape of motion factor for each of the test motion shape data and the control motion shape data; ([2.4.3. Feature Extraction / pg. 10-11, Table ], [TABLE 3 pg. 18 & Table 4 pg. 19], the software integrates the angular rates collected during the task to map the shape of movements, applying drift corrections to calculate the angular excursion. From this shape data, the software calculates the mean of the maximum movement amplitude. These shape of motion factors are extracted for both the 40 PD (test) subjects and the 40 HC subjects.)
calculating the respective motion symmetry factor for each of the test motion symmetry data and the control motion symmetry data; ([2.3. Experimental Protocol / pg. 5], [4. Discussion / pg. 22, ¶1], [2.5. Data Analysis / pg. 15-16], Parkinson’s disease has symmetric onset, to measure this, the experimental protocol requires test and control subjects to perform task on both their right and left sides. The software segments and accesses the parameters for the right and left side separately. By calculating parameter for both the left and right side independently, the software establishes motion symmetry factors for both the 40 test PD subjects and the 40 HC subjects.)
calculating the respective motion speed factor for each of the test kinetic energy data and the control kinetic energy data; and ([2.2. Instruments / pg. 4-5], [2.4. Signal Processing / pg. 6], [2.4.3. Feature Extraction / pg. 10-14], [TABLE 3 pg. 18 & Table 4 pg. 19], the kinetic energy, as previously discussed is the raw 3-axis acceleration data recorded by the IMU accelerometers which relate to the physical forces and energy exerted by the patient. Under the broadest reasonable interpretation, the software calculates motion speed factors directly from the acceleration data in to ways. First, the Matlab software applies a fast Fourier transform to the accelerometer signals to compute the fundamental frequency. This frequency is the measurement of the rate of speed of the tremor oscillating, meaning the software derives speed factor from the acceleration data (i.e., kinetic energy). Second, the software calculates the IAV for both the 40 PD test subjects and the 40 HC subjects. Note; calculating the integral of acceleration is synonymous with calculation a motion speed factor from the kinetic energy data.)
using the software to tabulate the respective composite index by calculating a probability that the respective shape of motion factor, the respective motion symmetry factor, and the respective motion speed factor correspond to one of the sets of functional movement scores. (¶Abstract, [2.5. Data Analysis / pg. 15-16], [2.5.2 Classification / pg. 16—17 (specifically NB)], & [3.2. Classification results / pg. 20-21], in order to classify the subjects and calculate their status points (i.e., their respective composite index), the system trains and test several supervised machine learning classifiers on the extracted predictors. The NB software (Naïve Bayes) particularly, is a probabilistic learning algorithm based on Bayes’ Theorem the calculates the probability of each category for a given sample and then outputs the category with the highest probability. By feeding the shape and symmetry and speed predictors into this NB classifier, the software then evaluates the probability that those parameters correspond to Parkinson’s Disease versus Healthy Control subjects. The category with the highest calculated probability is then outputted to classify the patient’s functional movement status, [4. Discussion / pg. 22].)
Claim 7: Rovini discloses all the elements above in claim 1, Rovini discloses, wherein the wearable sensor and the respective wearable sensor each comprise a multimodal accelerometer. ([2.2. Instruments / pg. 4-5] & [2.4. Signal Processing / pg. 6])
Claim 8: Rovini discloses all the elements above in claim 7, Rovini discloses, wherein the wearable sensor and the respective wearable sensor each further comprise a gyroscope. ([2.2. Instruments / pg. 4-5] & [2.4. Signal Processing / pg. 6])
Claim 9: Rovini discloses all the elements above in claim 1, Rovini discloses, the wearable sensor and the respective wearable sensor each comprise a wireless sensor in electronic communication with the computer. ([2.2. Instruments / pg. 4-5] & [2.4. Signal Processing / pg. 6])
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The 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 2 is rejected under 35 U.S.C. 103 as being unpatentable over Rovini et al (A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson's Disease Diagnosis. Sensors (Basel). 2020 May 5), as applied to claim 1, in further view of Thomas et al ("A Treatment-Response Index From Wearable Sensors for Quantifying Parkinson's Disease Motor States," in IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1341-1349, Sept. 2018).
Claim 2: Rovini discloses all the elements above in claim 1, Rovini fails to disclose, wherein calculating the probability comprises assigning weights to the predictors and running the predictors through a regression analysis using machine learning software.
However, Thomas in the context of determine treatment response index from wearable sensors for quantifying Parkinson’s Disease Motor States, discloses, wherein calculating the probability comprises assigning weights to the predictors and running the predictors through a regression analysis using machine learning software. (¶Abstract, [Introduction / pg. 1342 last paragraph], [2) feature extraction / pg. 1343-1344], [3) Principle Component Analysis / pg. 1344], [4) Predictive Modelling – Computerized Assessment of Motor States / pg. 1344-1345], [Table V -classification accuracy of the of the machine learning algorithms])
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the probability calculation of Rovini to incorporate the teachings of Thomas. The motivation to do this yield predictable results such as to reduce high dimensional data into meaningful predictors, mathematically weight them, and accurately map them to a clinical rating scale, as suggested by Thomas, [Introduction / pg. 1341-1342].
Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Rovini et al (A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson's Disease Diagnosis. Sensors (Basel). 2020 May 5), as applied to claim 1, in further view of di Biase et al (Gait Analysis in Parkinson's Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring. Sensors (Basel). 2020 Jun 22) in view of Giuliani et al (The Influence of Age and Obesity-Altered Muscle Tissue Composition on Muscular Dimensional Changes: Impact on Strength and Function, The Journals of Gerontology: Series A, Volume 75, Issue 12, December 2020, Pages 2286–2294).
Claim 3: Rovini discloses all the elements above in claim 1, Rovini fails to disclose,
wherein the software uses the corresponding muscle scores as an additional predictor in tabulating the composite index.
However, di Biase in the context of gait analysis in Parkinson’s disease discloses: wherein the software uses the corresponding muscle scores as an additional predictor in tabulating the composite index. ([1.5. Gait Analysis Technologies / pg. 5-7], [1.6. Machine Learning Algorithms Application for Gait Analysis / pg. 7-8], Table 5], [3. Results / pg. 14])
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the software of Rovini to incorporate the teachings of di Biase. The motivation to do this yield predictable results such as improving PD management and recognition of PD motor symptoms, [Introduction].
further comprising:
an imaging device producing images of muscles of the test patient during motion assessment exercises; and
a dynamometer gathering measurements of force exerted by the muscles during the motion assessment exercise, wherein the computer receives the images and the dynamometer measurements and classifies corresponding muscle scores for the muscles; and
However, Giuliani in the context of determining muscular dimensional changes impacted by strength and function discloses: an imaging device producing images of muscles of the test patient during motion assessment exercises; and (Giulana uses a portable B-mode ultrasound imaging device to scan the femoris (RF) muscle during maximal and submaximal isometric leg extension strength assessments, ¶Abstract, [Ultrasound / pg. 2287-2288].)
a dynamometer gathering measurements of force exerted by the muscles during the motion assessment exercise, (Giuliani utilizes a calibrated HUMAC Norm Dynamometer to assess leg extension peak torque during the isometric maximal voluntary contractions, [Isometric Strength Testing / pg. 2288-2289]) wherein the computer receives the images and the dynamometer measurements and classifies corresponding muscle scores for the muscles; (Giuliani disclose that the torque signals form the dynamometer are stored on a PC and processed using LabVIEW software, , [Signal Processing / pg. 2289]. The ultrasound images were analyzed using ImageJ software to obtain specific muscle scores, such as muscle size (CSA) and echo intensity (EI), [Image Analysis / pg. 2288].)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the system of modified Rovini to incorporate the teachings of Giuliani for the advantage of providing an improved system being able to determine and model altered muscle composition compared to functional performance, as suggested by Giuliani, [Discussion / pg. 2290-2292.
Claim 4: Modified Rovini discloses all the elements above in claim 3, Rovini fails to disclose, wherein the imaging device comprises a linear array ultrasound probe that images a transverse plane and a longitudinal plane of the muscles.
However, Giuliani is relied upon above discloses: wherein the imaging device comprises a linear array ultrasound probe that images a transverse plane and a longitudinal plane of the muscles. ([Ultrasound / pg. 2287-2288], the ultrasound probe is a multifrequency linear array probe that cross-sectionally scans perpendicularly to a longitudinal axis of the thigh positioned along the traverse plane, the imaging would be at least a long a longitudinal plane of the muscles.)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the imaging device of modified Rovini to incorporate the teachings of Giuliani for the advantage of providing an improved system being able to determine and model altered muscle composition compared to functional performance, as suggested by Giuliani, [Discussion / pg. 2290-2292.
Claim 5: Modified Rovini discloses all the elements above in claim 3, Rovini fails to disclose, wherein the images comprise muscle measurements comprising anatomical cross sectional area (ACSA), muscle thickness, and tissue echogenicity.
However, Giuliani is relied upon above discloses: wherein the images comprise muscle measurements comprising anatomical cross sectional area (ACSA), muscle thickness, and tissue echogenicity. ([Image Analysis / pg. 2288], The cross-sectional area, the muscle depth (i.e., muscle thickness), and the echo intensity (EI-echogenicity)) are extracted from the images.)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the imaging analysis of modified Rovini to incorporate the teachings of Giuliani for the advantage of providing an improved system being able to determine and model altered muscle composition compared to functional performance, as suggested by Giuliani, [Discussion / pg. 2290-2292.
Claim 6: Modified Rovini discloses all the elements above in claim 3, Rovini fails to disclose, wherein the computer classifies corresponding muscle scores by calculating an anatomical cross sectional area (ACSA) of the muscles and dividing the ACSA by an average echogenicity of the muscle. ([Image Analysis / pg. 2288], The cross-sectional area, the muscle depth (i.e., muscle thickness), and the echo intensity (EI-echogenicity)) are extracted from the images. The ImageJ software determines the total CSA and the EI by calculating an average of the 3 superficial muscles. To provide an index of the muscle size CSA was normalized to EI (CSA/EI), which equates to dividing the cross-sectional area by the average echogenicity.)
However, Giuliani is relied upon above discloses: wherein the computer classifies corresponding muscle scores by calculating an anatomical cross sectional area (ACSA) of the muscles and dividing the ACSA by an average echogenicity of the muscle.
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the imaging device of modified Rovini to incorporate the teachings of Giuliani for the advantage of providing an improved system being able to determine and model altered muscle composition compared to functional performance, as suggested by Giuliani, [Discussion / pg. 2290-2292.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Rovini et al (A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson's Disease Diagnosis. Sensors (Basel). 2020 May 5), as applied to claim 1, in further view of Mirelman et al (US 20210161430 A1)
Claim 11: Rovini discloses all the elements above in claim 1, Rovini fails to disclose, further comprising a video camera recording video and audio data of the test patient during motion assessment exercises.
However, Mirelman in the context of virtual reality for movement disorder diagnosis and/or treatment discloses: further comprising a video camera recording video and audio data of the test patient during motion assessment exercises. (¶0162, ¶0227, ¶0287, ¶0289)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the system of Rovini to incorporate the teachings of Mirelman. The motivation to do this yield predictable results such as to improve and identification of at risk of fall for patients and provide interventions before their first fall, as suggested by Mirelman, ¶0012.
Conclusion
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/N.A.R./Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798
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