DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see page 11, filed 06/29/2026, with respect to the objection to the specification have been fully considered and are persuasive. The objection to the specification has been withdrawn.
Applicant’s arguments, see page 11, filed 06/29/2026, with respect to the rejection of Claims 1-16, 18, and 20 under 35 U.S.C. § 112(a) have been fully considered and are persuasive. The rejection of Claims 1-16, 18, and 20 under 35 U.S.C. § 112(a) has been withdrawn as passive activities such as sitting and standing ([0025], [0028]) are construed as corresponding to postural performance as argued by the applicant.
Applicant's other arguments filed 06/29/2026 have been fully considered but they are not persuasive.
35 U.S.C. § 101-Step 2A, Prong One
The applicant has argued the most recent Office Action 1) improperly isolates portions of the claim from the rest and 2) incorrectly interprets these portions as an abstract idea(s).
Regarding Argument 1), all 101 analysis must necessarily consider the claim as a whole (Diamond v. Diehr, 450 U.S. 175, 188 (1981)) but also analyze different claim limitations at different steps, depending on whether the claim limitation recites a judicial exception or an additional element to the judicial exception. Even when read in context of the claim as a whole (i.e., as an ordered combination), the limitations analyzed under this step still qualify as abstract ideas.
Regarding Argument 2, the applicant asserts “When considered as an ordered combination, the claims recite a particular technological process that transforms raw sensor measurements into clinically meaningful information through a sequence of specifically recited computational operations” (page 14); “The Office Action focuses almost exclusively on the machine learning limitations while ignoring the remaining claim limitations that define how the sensor data is acquired, transformed, organized, analyzed, and ultimately converted into a stability score...Here, the claimed change-of basis transformation, activity-specific grouping, generation of data matrices, and machine learning classification collectively define a particular technological implementation for processing inertial sensor data.” (page 14). However, each of these recited steps and computational operations are themselves abstract ideas and thus unable to implement the claim into a practical application, even in a specific field of use. Furthermore, the claim limitations that define how the sensor data is acquired are clearly addressed at Step 2A, Prong Two.
The applicant has also argued “the Office Action improperly conflates the judicial exception for mathematical concepts with the distinct judicial exception for mental processes. The 2019 Revised Patent Subject Matter Eligibility Guidance recognizes these as separate categories of abstract ideas requiring separate analyses. The Office Action merely labels the claimed operations as "mathematical calculations" and concludes that they therefore constitute mental processes, but provides no explanation as to how the claimed operations could practically be performed in the human mind (and certainly none regarding how these aspects can be accomplished with pen and paper). In particular, the Office Action does not explain how a person could mentally obtain continuous inertial sensor measurements from a wearable device, perform a change-of-basis transformation on grouped sensor signals to generate data matrices, process those matrices using a previously trained convolutional neural network, classify the resulting matrices into predefined stability categories indicative of postural performance and gait performance, and generate weighted stability scores”. However, in the 101 rejection, these limitations were clearly assessed in separate bullet points as either a mathematical calculation or mental process. In addition, the rejection has been updated to show more clearly how limitations which are considered mathematical calculations can be practically performed by a human using a generic processor and/or pen and paper.
35 U.S.C. § 101- Step 2A, Prong Two and Step 2B
The applicant argues “The claimed change-of-basis transformation is not performed as an abstract mathematical exercise, but instead converts raw inertial sensor signals into a representation suitable for machine learning analysis. Likewise, the trained convolutional neural network is not invoked to classify abstract information, but instead classifies transformed sensor data into predefined stability categories that expressly indicate the wearer's postural performance and gait performance. The resulting stability score is generated from physical sensor measurements collected during real-world movement and provides a technological assessment of the wearer's stability while performing specific physical activities.” (page 16). However, qualitative or quantitative information reflecting a biological reality obtained via a sensor transducing a physical property into raw sensor signals has routinely been rejected under 35 U.S.C. § 101 by the Office, when no additional elements to integrate the claims into a practical application or amount to significantly more are found. As merely an example, see Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012). A stability score (an abstract number reflecting the invention’s assessment of the user’s real-world stability) produced from a sequence of computational operations on raw sensor signals requires additional elements to be considered significantly more than an abstract idea.
The applicant has also argued their specification “demonstrate[s] that the claims are directed to improving the processing of wearable sensor data to generate more accurate and timely stability assessments, rather than merely automating a human observation or organizing information. Accordingly, the pending claims integrate any alleged abstract idea into a practical technological application and therefore satisfy Step 2A, Prong Two of the Alice/Mayo framework.” However, improving the accuracy of a data processing step does not qualify as an improvement in technology. Rather, it is an improvement of an abstract idea, as “accuracy” is a statistical measure of a value relative to its true value. This makes an improvement in accuracy an improvement in mathematics, (e.g., statistics and other mathematical calculations).
Regarding the Examiner’s reliance on Example 47, the Applicant has argued those claim limitations “operates on generic data to produce anomaly information and is not directed to improving any physical sensing technology or wearable device”. This is a distinction without a meaningful difference for Step 2A, Prong Two or Step 2B analysis. The exact location and field of use of the sensor which acquires the measured data (provided the sensor is generic and a mere data-gathering step) does not make an abstract idea integrated into a practical application. Rather, what the Applicant is asserting as “a specific technological pipeline for evaluating human stability from physical sensor measurements rather than a generalized mathematical analysis of arbitrary data” and “a specific sequence of operations that begins with acquiring physical measurements from a wearable inertial sensor, transforms those measurements in a manner tailored to wearable sensing, and generates an activity-specific evaluation of a real- world physical condition” amount to merely indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate a judicial exception into a practical application.
Finally, the applicant argued “The Office Action notes that the specification describes the stability score as categorical or numerical and utilizes this statement as support for finding the pending claims unpatentable. This is improper at least because the form of the output does not determine whether the claims are directed to an abstract idea. Numerous eligible inventions generate numerical outputs, including image-processing systems, navigation systems, medical imaging systems, and industrial control systems. Here, the claimed stability score is not merely a mathematical result. Rather, as explained in additional detail below and in the context of the pending claims themselves, it represents the physical stability of a wearer performing an identified physical activity and is generated from measurements obtained by a wearable inertial sensor through the claimed sequence of technological processing operations. The numerical or categorical representation merely communicates the result of evaluating a real- world physical condition”. However, it is other, additional elements in eligible inventions generating numerical outputs that overcome 35 U.S.C. § 101, not the specific field of use or numerical output.
For these reasons, the rejection of the claims under 35 U.S.C. § 101 is maintained.
35 U.S.C. § 103
The Applicant argues Mazumder does not teach pre-defined stability categories, wherein the first category and the second category indicate postural performance because 1) Mazumder does not qualify as analogous art and 2) the postural stability index in Mazumder is different than the claimed pre-defined postural performance categories in the instant invention.
Regarding Argument 1), the applicant argues “Mazumder, in contrast, is directed to an entirely different technical problem, namely, evaluating a user's postural stability during a prescribed Single Limb Stance (SLS) clinical assessment by measuring parameters such as Single Limb Stance duration, body joint vibration, and body sway area to generate a postural stability index.” However, the Applicant is artificially narrowing the scope of what qualifies as a relevant invention. Determining posture continuously in the background of daily life versus a dedicated test are both pertinent to the problem of determining a person’s postural stability.
Regarding Argument 2), the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). The applicant’s arguments about the literal details of combining the systems and methods of Mazumder and Minici in view of Cheng, as opposed to what the combined teachings would have suggested to one of ordinary skill in the art, are therefore not relevant.
This response also applies to the Applicant’s arguments regarding Asada.
For these reasons, the rejections of the claims under 35 U.S.C. § 103 are maintained.
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 1-16, 18, and 20-21 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Regarding Claim 1, the claim recites a computer-implemented method for determining a user stability score. Thus, the claim is directed to a process, which is one of the statutory categories of invention (Step 1).
The claim is then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong One). The following limitations set forth a judicial exception:
obtaining…a data sample from an inertial sensor device, …wherein the data sample is comprised of data signals and a time vector, wherein the data sample is obtained by the inertial sensor device based on the inertial sensor device monitoring physical activities of a wearer during a time period represented by the time vector
segmenting…the data sample into segments, wherein each segment comprises data from a time slice of the time period represented by the time vector
determining…a physical activity of the physical activities of the wearer performed during each time slice
grouping…into at least one group, times slices in which the wearer performed a common physical activity of the physical activities
applying…a change of basis transformation on data signals comprising each group of the at least one group, to generate one or more data matrices
classifying… based on utilizing a trained machine learning algorithm, the one or more data matrices generated from each group of the at least one group into one or more pre-defined stability categories, wherein the pre-defined stability categories comprise a first category and a second category, wherein the first category indicates stability and the second category indicates instability, wherein the first category and the second category indicate postural performance and gait performance
wherein training the machine learning algorithm comprises: training…with training data, a convolutional neural network process comprising a machine learning algorithm to classify segments into a finite number of groups, wherein each group represents a distinct physical activity of the physical activities
utilizing… the previously trained convolutional neural network process, to classify the one or more data matrices into the one or more pre-defined stability categories
generating…a stability score representing stability of the wearer when performing the common physical activity for each group of the at least one group, based on analyzing the one or more data matrices and weighting the classifications into the one or more pre-defined stability categories
These limitations describe a mathematical calculation and/or a mental process as the skilled artisan is capable of performing the recited limitations and making a mental assessment thereafter. Examiner also notes that nothing from the claims suggest that the limitations cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner also notes that nothing from the claims suggests an undue level of complexity that the mathematical calculations and/or the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps.
For example:
A human is capable of manually/mentally obtaining, by one or more processors, a data sample from an inertial sensor device, e.g. by visually reading a display.
Segmenting the data sample into segments, wherein each segment comprises data from a time slice of the time period represented by the time vector is a mathematical relationship (e.g., organizing information and manipulating information through mathematical correlations) that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations steps in real time.
Determining a physical activity of the physical activities of the wearer performed during each time slice is a mental processes that can be performed in the human mind by observations, evaluations, judgments, and opinions, or by a human with the aid of a pen and paper, or using a generic computer as a tool to perform these mental process steps in real time.
Grouping into at least one group, times slices in which the wearer performed a common physical activity of the physical activities is a mathematical relationship (e.g., organizing information and manipulating information through mathematical correlations) that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations steps in real time.
Applying a change of basis transformation on data signals comprising each group of the at least one group, to generate one or more data matrices is a mathematical calculation that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations in real time.
Classifying based on utilizing a trained machine learning algorithm, the one or more data matrices generated from each group of the at least one group into one or more pre-defined stability categories, wherein the pre-defined stability categories comprise a first category and a second category, wherein the first category indicates stability and the second category indicates instability, and wherein the first category and the second category indicate postural performance and gait performance is a mental processes that can be performed in the human mind by observations, evaluations, judgments, and opinions, or by a human with the aid of a pen and paper, or using a generic computer as a tool to perform these mental process steps in real time.
Training, by the one or more processors, with training data, a convolutional neural network process comprising a machine learning algorithm to classify segments into a finite number of groups, wherein each group represents a distinct physical activity of the physical activities is a mathematical calculation that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations in real time.
Utilizing the previously trained convolutional neural network process, to classify the one or more data matrices into the one or more pre-defined stability categories is a mathematical calculation that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations steps in real time.
Generating a stability score representing stability of the wearer when performing the common physical activity for each group of the at least one group, based on analyzing the one or more data matrices and weighting the classifications into the one or more pre-defined stability categories is a mathematical calculation that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations steps in real time.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, integrates the identified judicial exception into a practical application (Step 2A, Prong Two).
The following limitations amount to insignificant extra-solution activity to the judicial exception, e.g. mere data gathering. See MPEP 2106.05(g).
wherein a wearable device comprises the inertial sensor device
wherein the data sample is obtained by the inertial sensor device based on the inertial sensor device monitoring physical activities of a wearer during a time period represented by the time vector
The following limitations amount to a recitation of the words "apply it" (or an equivalent)and/or nothing more than mere instructions to implement the abstract idea on a generic computer. See MPEP 2106.05(f).
obtaining, by one or more processors…
determining, by the one or more processors…
determining, by the one or more processors…
grouping, by the one or more processors…
applying, by the one or more processors…
classifying, by the one or more processors…
training, by the one or more processors…
utilizing, by the one or more processors…
generating, by the one or more processors…
Therefore, these additional limitations do not integrate the judicial exception into a practical application.
The following limitations amount to merely indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h).
wherein a wearable device comprises the inertial sensor device
Therefore, these additional limitations do not integrate the judicial exception into a practical application.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, amounts to significantly more than the identified judicial exception (Step 2B):
The following limitations do not amount to significantly more than the abstract idea for substantially similar reasons applied in Step 2A, Prong Two.
wherein a wearable device comprises the inertial sensor device
wherein the data sample is obtained by the inertial sensor device based on the inertial sensor device monitoring physical activities of a wearer during a time period represented by the time vector
wherein a wearable device comprises the inertial sensor device
obtaining, by one or more processors…
determining, by the one or more processors…
determining, by the one or more processors…
grouping, by the one or more processors…
applying, by the one or more processors…
classifying, by the one or more processors…
training, by the one or more processors…
utilizing, by the one or more processors…
generating, by the one or more processors…
The following limitations is/are considered to be well-understood, routine, and conventional (WURC).
The inertial sensor device inside a wearable device is considered to be well-understood, routine, and conventional based on statement from the applicant' s specification filed 10/19/2021 (“The inertial sensor device 200 can be a hardware element that comprises one or more sensor elements configured to obtain data, including one or more data signals 10, from the user 32. In some examples, the sensor is an accelerometer, gyroscope, magnetometer, equivalent technologies that are known by a person of ordinary skill in the art, or combinations thereof”, [0069]; see Fig. 3; the wearable device 300 is a conventional belt).
Independent Claim 20 also not patent eligible for substantially similar reasons as it recites the same abstract idea(s) and additional element(s) as Claim 1 but as an apparatus-type claim.
Independent Claim 21 also not patent eligible for substantially similar reasons as it recites the same abstract idea(s) and additional element(s) as Claim 1 but as an apparatus-type claim. The additional limitations “executing, by the one or more processors, coordinate axis-shifting, the coordinate axis-shifting comprising: extracting, by the one or more processors, a direction vector from the data sample; determining, by the one or more processors, if the direction vector and a predetermined coordinate vector have a different referral system, based on comparing the direction vector to the predetermined coordinate vector; based on determining, by the one or more processors, that the direction vector and the predetermined coordinate vector have the different referral system, generating a modified direction vector to replace the direction vector; and augmenting, by the one or more processors, the data sample to include the modified direction vector in place of the direction vector” represent a mathematical calculation that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations in real time.
Dependent Claims 2-7, 9-16 and 18 also fail to add subject matter qualifying as significantly more to the abstract independent claims as they merely further limit the abstract idea.
Dependent Claims 8-11, and 15 also fail to add subject qualifying as significantly more to the abstract independent claims as they recite limitations that do not integrate the claims into a practical application for substantially similar reasons as set forth above.
Dependent Claims 8-11, and 15 also fail to add subject matter integrating the judicial exception or qualifying as significantly more to the abstract independent claims as they do not recite significantly more than the identified abstract idea for substantially similar reasons as set forth above.
Therefore, Claims 1-16, 18, and 20-21 are not patent eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 6-8, 12-13, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the Non-Patent Literature (NPL) to Minici et al (“Wavelet-based analysis of gait for automated frailty assessment with a wrist-worn device”, hereinafter Minici) in view of Cheng et al (US 20200218974 Al, hereinafter Cheng) and Mazumder et al (US 20190008417 A1, hereinafter Mazumder).
Regarding Claim 1, Minici discloses a computer-implemented method for determining a user stability score (“Wavelet-based analysis of gait for automated frailty assessment with a wrist-worn device”, Title), the method comprising:
obtaining, by one or more processors, a data sample from an inertial sensor device, wherein a wearable device comprises the inertial sensor device (“Shimmer 3 is a wearable device embedding a tri-axial accelerometer (STMicroLSM303DLHC) [15], which was used to collect acceleration samples at 102:4 Hz”, Section III.A; “Filtered acceleration components are then provided to the gait segment detection module”, Section II.A), wherein the data sample is comprised of data signals and a time vector (“Acceleration is sampled at 102:4 Hz”, Section II.A; this means acceleration data signals are sampled with a certain number of samples per unit time, i.e. a time vector accompanies the data), wherein the data sample is obtained by the inertial sensor device based on the inertial sensor device monitoring physical activities of a wearer during a time period represented by the time vector (“participants were asked to walk 60 meters at their preferred pace”, Section III.A);
segmenting, by the one or more processors, the data sample into segments (“a gait segment detection technique identifies gait segments on the whole signal trace”, Section II), wherein each segment comprises data from a time slice of the time period represented by the time vector (“A gait cycle is the sequence of events that occur during the walking process between two consecutive heel strikes of the same foot. Henceforth, we will use the term gait segment to indicate four consecutive gait cycles”, Section II.B; each gait cycle would be accompanied by the time vector data corresponding to it);
determining, by the one or more processors, a physical activity of the physical activities of the wearer performed during each time slice (“a gait segment detection technique identifies gait segments on the whole signal trace”, Section II);
grouping, by the one or more processors, into at least one group, times slices in which the wearer performed a common physical activity of the physical activities (“Henceforth, we will use the term gait segment to indicate four consecutive gait cycles”, Section II.B; “Wavelet analysis is applied to gait segments detected in the previous step”, Section II.C);
applying, by the one or more processors, a change of basis transformation on data signals comprising each group of the at least one group (“we use the Continuous Wavelet Transform (CWT) on the acceleration magnitude signal to obtain a representation of the gait segment into the time-frequency domain,”, Section II.C), to generate one or more data matrices (“The phase discussed above produces as output a matrix MCWT of coefficients cij for each gait segment, where i denotes the frequency value, obtained from the scale factor, and j denotes the instant in time ”, Section II.D);
classifying, by the one or more processors, the one or more data matrices generated from each group of the at least one group into one or more pre-defined stability categories, wherein the pre-defined stability categories comprise a first category and a second category, wherein the first category indicates stability and the second category indicates instability (“Finally, band-based feature extraction is responsible for gathering information from the CWT output, which is used to determine which Frequency Bands (FBs) contain the most valuable information for frailty status assessment”, Section II; “the subject was classified according to a majority voting scheme: if more than 40% of the band-based gait instances were classified as non-robust, the subject was classified as non-robust”, Section III.B), wherein the first category and the second category indicate gait performance (“if more than 40% of the band-based gait instances were classified as non-robust, the subject was classified as non-robust”, Section III.B; therefore, a <40% or >40% non-robust classification are a first and second category that indicate stability and instability, respectively, and also indicate gait performance), and
generating, by the one or more processors, a stability score representing stability of the wearer when performing the common physical activity for each group of the at least one group, based on analyzing the one or more data matrices and weighting the classifications into the one or more pre- defined stability categories (“the subject was classified according to a majority voting scheme: if more than 40% of the band-based gait instances were classified as non-robust, the subject was classified as non-robust”, Section III.B; therefore, a <40% or >40% non-robust classification is a stability score representing stability of the wearer).
Minici discloses the claimed invention except for expressly disclosing classifying, by the one or more processors, based on utilizing a trained machine learning algorithm, the one or more data matrices generated from each group of the at least one group into one or more pre-defined stability categories, wherein the first category and the second category indicate postural performance, and wherein training the machine learning algorithm comprises:
training, by the one or more processors, with training data, a convolutional neural network process comprising a machine learning algorithm to classify segments into a finite number of groups, wherein each group represents a distinct physical activity of the physical activities; and
utilizing, by the one or more processors, the previously trained convolutional neural network process, to classify the one or more data matrices into the one or more pre-defined stability categories.
However, Cheng teaches classifying, by the one or more processors, based on utilizing a trained machine learning algorithm, the one or more data matrices generated from each group of the at least one group into one or more pre-defined activity categories (See Fig. 4), wherein training the machine learning algorithm comprises:
training, by the one or more processors, with training data (“The input data 20 may also be applied to training the CNN 30”, [0158]), a convolutional neural network process (“The NN may be a convolutional neural network (“CNN”)”, [0009]) comprising a machine learning algorithm to classify segments (“The processor 18 applies a deep learning neural network that is a convolutional neural network 30 (“CNN”) to the input data 20 for classifying the subject's activity and resulting in classified activity data 38 that may be expressed in any suitable form”, [0157]) into a finite number of groups, wherein each group represents a distinct physical activity of the physical activities (“the activity includes standing, sitting, lying, crouching, walking, running, shuffling, skipping, dancing, ascending or descending stairs”, [0063]); and
utilizing, by the one or more processors, the previously trained convolutional neural network process, to classify the one or more data matrices (See Fig. 4; after the recursive “TRAINING: YES” step, the trained CNN can be used to classify activity data in the “TRAINING: NO” step) into the one or more pre-defined activity categories (See Fig. 4; after the recursive “TRAINING: YES” step, the trained CNN can be used to classify activity data in the “TRAINING: NO” step).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer-implemented method of Minici with the classifying input data using a convolutional neural network (i.e. classifying data matrices into the pre-defined stability categories already taught by Minici), because using a convolutional neural network to classify data matrices improves classification by distinguishing between potentially small measurable differences, as taught by Cheng ([0005]).
Mazumder, which also discloses a computer-implemented method for determining a user stability score (“By processing the SLS duration, the body joint vibration, and the body sway area together, a postural stability index score for the user is determined, and based on this score, postural stability assessment for the user is performed”, Abstract) teaches pre-defined stability categories, wherein the first category and the second category indicate postural performance (“A stability index generation module of the postural stability assessment system then determines the SLS duration, the body joint vibration, and the body sway area, of the user as falling under at least one respective category, and further generates a postural stability index score for the user, based on the determined at least one category of the SLS duration, the body joint vibration, and the body sway area. A stability assessment module of the postural stability assessment system then assesses postural stability of the user, based on the postural stability index score”, [0007]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include postural performance in the first and second categories of Minici, because postural instability is one of the prominent symptoms associated with geriatric population (Mazumder, [0003]), and accounting for posture makes the computer-implemented method of Minici more accurate in its assessment of frailty.
Regarding Claim 2, modified Minici discloses the computer-implemented method of claim 1, wherein the change of basis transformation comprises a wavelet transformation (“we use the Continuous Wavelet Transform (CWT) on the acceleration magnitude signal to obtain a representation of the gait segment into the time-frequency domain,”, Section II.C).
Regarding Claim 3, modified Minici computer-implemented of claim 1, wherein each time slice comprises: a pre-defined length of time or a quantifiable number of acts completed in a given physical activity of the physical activities during the time slice (“A gait cycle is the sequence of events that occur during the walking process between two consecutive heel strikes of the same foot. Henceforth, we will use the term gait segment to indicate four consecutive gait cycles”, Section II.B; each gait cycle would be accompanied by the time vector data corresponding to it).
Regarding Claim 4, modified Minici discloses the computer-implemented method of claim 1, wherein determining the physical activity is based on one or more of: a portion of the data sample from the inertial sensor device (“a gait segment detection technique identifies gait segments on the whole signal trace”, Section II), or data entry by the wearer through an interface of the inertial sensor device.
Regarding Claim 6, modified Minici discloses the computer-implemented method of claim 1, wherein a given group of the at least one group comprises a gait group (“Henceforth, we will use the term gait segment to indicate four consecutive gait cycles”, Section II.B).
Regarding Claim 7, modified Minici discloses the computer-implemented method of claim 1, wherein the pre-defined stability categories comprise a first category and a second category, wherein the first category indicates stability and the second category indicates instability (“Specifically, we implemented seven Random Forest binary classifiers by means of the Python module Scikit-learn … in order to distinguish robust subjects from non-robust ones (i.e., pre-frail or frail)”, Section III.B).
Regarding Claim 8, modified Minici discloses the computer-implemented method of claim 1, wherein the inertial sensor device comprises one inertial measurement unit sensor (“Shimmer 3 is a wearable device embedding a tri-axial accelerometer (STMicroLSM303DLHC) [15], which was used to collect acceleration samples at 102:4 Hz”, Section III.A).
Regarding Claim 12, modified Minici discloses the computer-implemented method of claim 1, wherein the one or more data matrices comprise scalograms (“The phase discussed above produces as output a matrix MCWT of coefficients cij for each gait segment, where i denotes the frequency value, obtained from the scale factor, and j denotes the instant in time. Usually, this matrix of coefficients is plotted through the wavelet power spectrum (or scalogram).”, Section II.D).
Regarding Claim 13, Minici discloses the computer-implemented method of claim 1, wherein the data signals are selected from the group consisting of: three-axis-low noise acceleration (“Shimmer 3 is a wearable device embedding a tri-axial accelerometer (STMicroLSM303DLHC) [15], which was used to collect acceleration samples at 102:4 Hz”, Section III.A; “After a preprocessing step aimed at reducing noise…”, Section II), three-axis- wide range acceleration, and three-axis-gyroscopic angular rate.
Regarding Claim 16, modified Minici discloses the computer-implemented method of claim 1, wherein the physical activity of the physical activities of the wearer performed during each time slice comprises:
identifying, by the one or more processors, the physical activity, based on at least one parameter of data comprising each segment selected from the group consisting of: mean, variance, standard deviation, energy, leading frequencies, maximum values (“In this phase, gait segments are automatically identified by means of the walking detection algorithm described in [13], which is based on the analysis of the acceleration magnitude signal.”, Section II.B), minimum values, and correlation values.
Regarding Claim 18, modified Minici discloses the computer-implemented method of claim 1. Modified Minici discloses the claimed invention except for expressly disclosing wherein each group of the finite number of groups represents a distinct physical activity selected from the group consisting of: walking group, walking on stairs, and standing. However, Cheng teaches wherein each group of the finite number of groups represents a distinct physical activity selected from the group consisting of: walking group, walking on stairs, and standing (“the activity includes standing, sitting, lying, crouching, walking, running, shuffling, skipping, dancing, ascending or descending stairs”, [0063]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the computer-implemented method of Minci with the physical activity group of Cheng, because all of the claimed physical activities were known in the prior art before the effective filing date of the claimed invention, and one with ordinary skill in the art could have combined all the claimed physical activity groups of the references by known methods, and the result would have been obvious to one of ordinary skill in the art.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Minici in view of Cheng and Mazumder, and further in view of Zhang (US 20150269824 A1, hereinafter Zhang).
Regarding Claim 5, modified Minici discloses the computer-implemented method of claim 1. Modified Minici discloses the claimed invention except for expressly disclosing the computer-implemented method further comprising:
automatically alerting, by the one or more processors, at least one pre-configured contact, of the stability score, via a communications network.
However, Zhang, which is also directed towards monitoring a patient’s movement (“the wellness index may be calculated using a dynamically growing number of parameters passively monitored by the monitoring device, including at least one of the following: a body temperature value, a temperature trend, one or more non-purposeful movements, one or more purposeful activities patterns, a movement intensity, a movement quality…”, [0044]), teaches automatically alerting, by the one or more processors (“the memory 1340, in at least one embodiment, includes an operating system 1344 and one or more application programs, modules, or services for implementing the features disclosed herein. For example, the contents of the memory 1340 can include a wellness monitoring engine 1348 and an emergency alert engine 1350”, [0180]; “Merely by way of example, one or more processes described with respect to the method(s) discussed above might be implemented as code and/or instructions executable by the wrist-worn device 108 (and/or processing unit(s) 1303 within a wrist-worn device 108)”, [0181]), at least one pre-configured contact (“The emergency detection engine 1620 can cause the emergency notification engine 1624 to generate and transmit an alert notification. … the notification engine 1624 may cause a notification to be sent to the indicated physician/emergency contact/emergency personnel.”, [0211]), of the stability score (“The minimum wellness value can indicate a wellness index that is indicative of the lowest wellness index a patient may have without triggering an emergency notification. In said example, the emergency detection engine 1620 can determine that the current wellness index of 4 is less than the minimum wellness of 5. The emergency notification engine 1624 can transmit a request to the notification engine 1624 to generate and transmit an alert notification.”, [0218]; “the wellness index may be calculated using a dynamically growing number of parameters passively monitored by the monitoring device, including at least one of the following: …one or more non-purposeful movements, one or more purposeful activities patterns, a movement intensity, a movement quality…”, [0044]), via a communications network (“Based on the wellness index of 4 and the strength of the cellular/Wi-Fi signals, the emergency notification engine 1624 can determine that the alert notification should be sent as a text message on the cellular network to increase the chances the alert notification is successfully transmitted. The notification engine can generate the alert notification and instruct the wireless communication interface 1304 to transmit the alert notification via the cellular network”, [0219]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the automatic alerting of Zhang to the computer-implemented method of modified Minici, because this assists medical crews in locating and attending to the wearer, as taught by Zhang (“The alert notification may include information about the wearer's location and elevation, as well as any other information relevant to assisting medical crews in locating and attending to the wearer”, [0050]).
Claims 9-10, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Minici in view of Cheng and Mazumder, and further in view of Asada et al (US 20190254569 A1, hereinafter Asada).
Regarding Claim 9, modified Minici discloses the computer-implemented method of claim 1, further comprising:
prior to the segmenting, validating, by the one or more processors, the data sample (“After a preprocessing step aimed at reducing noise”, Section II).
Modified Minici discloses the claimed invention except for expressly disclosing wherein the validating comprises:
determining, by the one or more processors, if the data signals comprising the data sample indicate that the wearer was in motion during the time period; and
based on determining, by the one or more processors, that the wearer was in motion during the time period, commencing the segmenting.
However, Asada, which is also directed towards monitoring a patient’s movement and gait (Figs. 1-5), teaches determining, by the one or more processors (Element 42, Fig. 2), if the data signals comprising the data sample indicate that the wearer was in motion during the time period (Step S04, Fig. 6; “At step S04, signal processing circuit 24 determines whether subject M starts moving, based on an output signal from sensor unit 10”, [0130]); and
based on determining, by the one or more processors, that the wearer was in motion during the time period (See Fig. 6; Steps S06-S08 are based on “YES” in S04), commencing the segmenting (Step S18, Fig. 6, which is further explained in Fig. 7; mid-stance, heel contact and stepping motion times are segmented).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the validating process of Asada to the computer-implemented method of modified Minici, because this is a way to discard faulty data from the gait analysis process.
Regarding Claim 10, modified Minici discloses the computer-implemented method of claim 1, further comprising:
prior to the segmenting, validating, by the one or more processors, the data sample (“After a preprocessing step aimed at reducing noise…”, Section II).
Minici discloses the claimed invention except for expressly disclosing wherein the validating comprises:
determining, by the one or more processors, if the data signals comprising the data sample indicate that the wearer was in motion during the time period; and
based on determining, by the one or more processors, that the wearer was not in motion during the time period, obtaining, from the inertial sensor device, additional data over a new period of time, wherein based on the determining, the new period of time comprises the time period and the additional data comprises the data sample.
However, Asada teaches wherein the validating comprises:
determining, by the one or more processors (Element 42, Fig. 2), if the data signals comprising the data sample indicate that the wearer was in motion during the time period; and
based on determining, by the one or more processors, that the wearer was not in motion during the time period (Steps S02-S04, Fig. 6; “at step S02, signal processing circuit 24 determines whether subject M is standing still, based on an output signal of sensor unit 10”, [0128]; S04 “NO”, Fig. 6), obtaining, from the inertial sensor device (Element 1, Fig. 4), additional data over a new period of time (See the flowchart from S01-S07, Fig. 6, which happens after a recursive “NO” in Step S04”), wherein based on the determining, the new period of time comprises the time period and the additional data comprises the data sample (See the flowchart from S01-S07, Fig. 6, which happens after a recursive “NO” in Step S04”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the validating process of Asada to the computer-implemented method of modified Minici, because this is a way to discard faulty data from the gait analysis process.
Regarding Claim 14, modified Minici discloses the computer-implemented method of claim 8, wherein the data signals comprise acceleration data (“Shimmer 3 is a wearable device embedding a tri-axial accelerometer (STMicroLSM303DLHC) [15], which was used to collect acceleration samples at 102:4 Hz”, Section III.A). Modified Minici discloses the claimed invention except for expressly disclosing wherein determining if the data signals comprising the data sample indicate that the wearer was in motion during the time period comprises determining that the acceleration data is within a predetermined acceleration range. However, Asada teaches wherein determining if the data signals comprising the data sample indicate that the wearer was in motion during the time period comprises determining that the acceleration data is within a predetermined acceleration range (“At step S04, signal processing circuit 24 determines whether subject M starts moving, based on an output signal from sensor unit 10. If a change is observed in at least one of front-back acceleration, right-left acceleration, and up-down acceleration (for example, if the variation range of at least one acceleration is greater than a threshold), signal processing circuit 24 determines that subject M starts moving”, [0130]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the predetermined acceleration range of Asada to the computer-implemented method of modified Minici, because this is a way to discard faulty data from the gait analysis process.
Regarding Claim 20, Minici discloses obtaining, by one or more processors, a data sample from an inertial sensor device, wherein a wearable device comprises the inertial sensor device (“Shimmer 3 is a wearable device embedding a tri-axial accelerometer (STMicroLSM303DLHC) [15], which was used to collect acceleration samples at 102:4 Hz”, Section III.A; “Filtered acceleration components are then provided to the gait segment detection module”, Section II.A), wherein the data sample is comprised of data signals and a time vector (“Acceleration is sampled at 102:4 Hz”, Section II.A; this means acceleration data signals are sampled with a certain number of samples per unit time, i.e. a time vector accompanies the data), wherein the data sample is obtained by the inertial sensor device based on the inertial sensor device monitoring physical activities of a wearer during a time period represented by the time vector (“participants were asked to walk 60 meters at their preferred pace”, Section III.A);
segmenting, by the one or more processors, the data sample into segments (“a gait segment detection technique identifies gait segments on the whole signal trace”, Section II), wherein each segment comprises data from a time slice of the time period represented by the time vector (“A gait cycle is the sequence of events that occur during the walking process between two consecutive heel strikes of the same foot. Henceforth, we will use the term gait segment to indicate four consecutive gait cycles”, Section II.B; each gait cycle would be accompanied by the time vector data corresponding to it);
determining, by the one or more processors, a physical activity of the physical activities of the wearer performed during each time slice (“a gait segment detection technique identifies gait segments on the whole signal trace”, Section II);
grouping, by the one or more processors, into at least one group, times slices in which the wearer performed a common physical activity of the physical activities (“Henceforth, we will use the term gait segment to indicate four consecutive gait cycles”, Section II.B; “Wavelet analysis is applied to gait segments detected in the previous step”, Section II.C);
applying, by the one or more processors, a change of basis transformation on data signals comprising each group of the at least one group (“we use the Continuous Wavelet Transform (CWT) on the acceleration magnitude signal to obtain a representation of the gait segment into the time-frequency domain,”, Section II.C), to generate one or more data matrices (“The phase discussed above produces as output a matrix MCWT of coefficients cij for each gait segment, where i denotes the frequency value, obtained from the scale factor, and j denotes the instant in time ”, Section II.D);
classifying, by the one or more processors, the one or more data matrices generated from each group of the at least one group into one or more pre-defined stability categories, wherein the pre-defined stability categories comprise a first category and a second category, wherein the first category indicates stability and the second category indicates instability (“Finally, band-based feature extraction is responsible for gathering information from the CWT output, which is used to determine which Frequency Bands (FBs) contain the most valuable information for frailty status assessment”, Section II; “the subject was classified according to a majority voting scheme: if more than 40% of the band-based gait instances were classified as non-robust, the subject was classified as non-robust”, Section III.B), wherein the first category and the second category indicate gait performance (“if more than 40% of the band-based gait instances were classified as non-robust, the subject was classified as non-robust”, Section III.B; therefore, a <40% or >40% non-robust classification are a first and second category that indicate stability and instability, respectively, and also indicate gait performance), and
generating, by the one or more processors, a stability score representing stability of the wearer when performing the common physical activity for each group of the at least one group, based on analyzing the one or more data matrices and weighting the classifications into the one or more pre- defined stability categories (“the subject was classified according to a majority voting scheme: if more than 40% of the band-based gait instances were classified as non-robust, the subject was classified as non-robust”, Section III.B; therefore, a <40% or >40% non-robust classification is a stability score representing stability of the wearer).
Minici discloses the claimed invention except for expressly disclosing a computer readable storage medium readable by one or more processors of a computing system and storing instructions for execution by the one or more processors for performing a method comprising: the aforementioned method steps of Claim 20;
classifying, by the one or more processors, based on utilizing a trained machine learning algorithm, the one or more data matrices generated from each group of the at least one group into one or more pre-defined stability categories,
wherein training the machine learning algorithm comprises:
training, by the one or more processors, with training data, a convolutional neural network process comprising a machine learning algorithm to classify segments into a finite number of groups, wherein each group represents a distinct physical activity of the physical activities; and
utilizing, by the one or more processors, the previously trained convolutional neural network process, to classify the one or more data matrices into the one or more pre-defined stability categories.
However, Asada teaches a computer program product (Element 2, Fig. 2) comprising:
a computer readable storage medium readable by one or more processors of a computing system (“The movement ability evaluating program causes the computer to execute the steps of…”, [0072]) and storing instructions for execution by the one or more processors for performing a method (“A computer-readable storage medium such as USB (Universal Serial Bus) memory, flexible disc, CD (Compact Disc), DVD. Blu-ray Disc (registered trademark). MO (Magneto-Optical disc), SD card, memory stick (registered trademark), magnetic disc, optical disc, magneto-optical disc, semiconductor memory, and magnetic tape can be used as a storage medium to store the movement ability evaluating program”, [0074]) comprising:
a computer-method for determining a user stability score (“When the subject is moving in a correct posture, the value of integral Sr is equal to the value of integral Sl and therefore the ratio Sr/Sl is a value close to 1. On the other hand, if the body center of gravity is inclined to the left, the body center of gravity shifts in the left direction when the right heel touches the ground, and the value of integral Sr is greater, so that the ratio Sr/Sl is a value greater than 1. When the body center of gravity is inclined to the right, the body center of gravity shifts in the right direction when the left heel touches the ground, and the value of integral Sl is greater, so that the ratio Sr/Sl is a value smaller than 1. Control unit 64 gives a score to the calculated ratio Sr/Sl, where the ratio Sr/Sl=1 is an ideal value”, [0188]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the computer program product comprising the computer readable storage medium storing instructions for execution by one or more processors for performing the computer-implemented method of Minici, as suggested by Asada, because all of the claimed elements were known in the prior art before the effective filing date of the claimed invention, and one with ordinary skill in the art could have combined all the claimed elements by known methods, and the result would have been obvious to one of ordinary skill in the art.
Cheng teaches classifying, by the one or more processors, based on utilizing a trained machine learning algorithm, the one or more data matrices generated from each group of the at least one group into one or more pre-defined activity categories (See Fig. 4), wherein the first category and the second category indicate postural performance, and wherein training the machine learning algorithm comprises:
training, by the one or more processors, with training data (“The input data 20 may also be applied to training the CNN 30”, [0158]), a convolutional neural network process (“The NN may be a convolutional neural network (“CNN”)”, [0009]) comprising a machine learning algorithm to classify segments (“The processor 18 applies a deep learning neural network that is a convolutional neural network 30 (“CNN”) to the input data 20 for classifying the subject's activity and resulting in classified activity data 38 that may be expressed in any suitable form”, [0157]) into a finite number of groups, wherein each group represents a distinct physical activity of the physical activities (“the activity includes standing, sitting, lying, crouching, walking, running, shuffling, skipping, dancing, ascending or descending stairs”, [0063]); and
utilizing, by the one or more processors, the previously trained convolutional neural network process, to classify the one or more data matrices (See Fig. 4; after the recursive “TRAINING: YES” step, the trained CNN can be used to classify activity data in the “TRAINING: NO” step) into the one or more pre-defined activity categories (See Fig. 4; after the recursive “TRAINING: YES” step, the trained CNN can be used to classify activity data in the “TRAINING: NO” step).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of Minici with the classifying input data using a convolutional neural network (i.e. classifying data matrices into the pre-defined stability categories already taught by Minici), because using a convolutional neural network to classify data matrices improves classification by distinguishing between potentially small measurable differences, as taught by Cheng ([0005]).
Mazumder, which also discloses a method for determining a user stability score (“By processing the SLS duration, the body joint vibration, and the body sway area together, a postural stability index score for the user is determined, and based on this score, postural stability assessment for the user is performed”, Abstract) teaches pre-defined stability categories, wherein the first category and the second category indicate postural performance (“A stability index generation module of the postural stability assessment system then determines the SLS duration, the body joint vibration, and the body sway area, of the user as falling under at least one respective category, and further generates a postural stability index score for the user, based on the determined at least one category of the SLS duration, the body joint vibration, and the body sway area. A stability assessment module of the postural stability assessment system then assesses postural stability of the user, based on the postural stability index score”, [0007]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include postural performance in the first and second categories of Minici, because postural instability is one of the prominent symptoms associated with geriatric population (Mazumder, [0003]), and accounting for posture makes the method of Minici more accurate in its assessment of frailty.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Minici in view of Cheng, Asada and Mazumder, and further in view of Walsh et al (US 20160107309 A1, hereinafter Walsh).
Regarding Claim 11, modified Minici discloses the computer-implemented method of claim 10. Modified Minici discloses the claimed invention except for expressly disclosing the computer-implement method further comprising:
based on determining that the wearer was in not motion during the time period, alerting, by the one or more processors, the wearer that the inertial sensor device will collect additional data.
However, Walsh teaches based on determining that the wearer was in not motion during the time period (“Thus, the soft exosuit control system continuously monitors, or monitors at a high frequency, a status of the wearer's movements (or corresponding absence of movements)”, [0260]), alerting, by the one or more processors, the wearer that the inertial sensor device will collect additional data (“Hyper-alert mode”, element 1055, Fig. 25; “Thus, the soft exosuit control system continuously monitors, or monitors at a high frequency, a status of the wearer's movements (or corresponding absence of movements)”, [0260]; the exosuit being in hyper-alert mode means the wearer is also alerted that the inertial sensor device will collect additional data). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the method steps of Walsh to the computer-implemented method of modified Minici, because this enables the device to be modulated for the activity or activities in which the wearer is engaged, as taught by Walsh ([0260]).
Examiner’s Note
The Examiner notes that Claims 15 and Claim 21 are not currently rejected under prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See Andrews (US 20220406466 A1).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN EPHRAIM COOPER whose telephone number is (571)272-2860. The examiner can normally be reached Monday-Friday 7:30AM-5:30PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jacqueline Cheng can be reached at (571) 272-5596. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JONATHAN E. COOPER/Examiner, Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791
Prosecution Insights