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 .
Election/Restrictions
Applicant’s election of claims 1-12 and 14-19 without traverse in the reply filed on 12/23/2025 is acknowledged.
Claims 20-21 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim.
Claims 20-21 have been canceled.
Claims 1-12 and 14-19 are hereby under examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/05/2024 and 12/23/2025 have been considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
Paragraphs [0168, 0347, 0449, 0539, 0645, 0660, 0701, 0759, and 0766] of the published specification recites “partial last-squares (PLS)”. Each instance should read “partial least-squares (PLS)”.
Paragraph [0002] of the published written specification recites “After the onset of the disease, theses disease...”. This should read “After the onset of the disease, these diseases...”
Appropriate correction is required.
Claim Objections
Claim 15 is objected to because of the following informalities:
Claim 15 recites “partial last-squares” in line 7. This should read “partial least-squares”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-4, 7, 10-12, and 14-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 2-4, claim 2 recites the limitation "the digital biomarker feature data" in line 6. There is insufficient antecedent basis for this limitation in the claim. Similar recitations are present in claim 3 (line 7) and claim 4 (lines 9 and 12).
For the purposes of examination, the phrases in each claim are interpreted as “the extracted biomarker feature data”, as recited in claim 1 from which claims 2-4 depend.
Regarding claim 7, the claim recites “the mean of the plurality…”, “the standard deviation of the plurality…”, “the kurtosis of the plurality…” and “the median of the plurality…” in lines 7-11. There is insufficient antecedent basis for these limitations in the claim.
For the purposes of examination, the phrases in the claim are interpreted as “a mean of the plurality…”, “a standard deviation of the plurality…”, “a kurtosis of the plurality…” and “a median of the plurality…”.
Regarding claim 10, the claim recites “the mean; the standard deviation; the median; the kurtosis” in lines 12-15. There is insufficient antecedent basis for these limitations in the claim.
For the purposes of examination, the phrases in the claim are interpreted as: “a mean; a standard deviation; a median; a kurtosis”
Regarding claim 11, the claim recites “a magnitude of the z-component of the acceleration…”, “the ratio of the z-component…”, “determining the mean of the determined ratio…”, “determining the standard deviation of the determined ratio…” in lines 8, 10, 12, and 22. There is insufficient antecedent basis for these limitations in the claim.
For the purposes of examination, the phrases in the claim are interpreted as: “a magnitude of a z-component of the acceleration…”, “a ratio of the z-component…”, “determining a mean of the determined ratio…”, and “determining a standard deviation of the determined ratio…”
Regarding claim 12, the claim recites “the disease whose status is to be predicted” in lines 2, 4, and 6. This phrase renders the claim indefinite as the previously recited disease is not claimed to be predicted, therefore it is unclear whether this is intended to be the same disease as recited in line 2 of claim 1 or if these recitations are indicative of a different disease. Clarification is requested.
For the purposes of examination, the phrase “the disease whose status is to be predicted” is interpreted as “the disease whose status is to be indicated by the clinical parameter”.
All claims not explicitly addressed above are rejected under 35 U.S.C. 112(b) are rejected by virtue of their dependency on a rejected base claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 and 14-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Step 1
Regarding claim 1, the claim recites a series of steps or acts, including extracting digital biomarker feature data from a received input from a touchscreen display of a mobile device. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
Step 2A, Prong One
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of including extracting digital biomarker feature data from a received input from a touchscreen display of a mobile device wherein the extracted digital biomarker feature data is the clinical parameter sets forth a judicial exception. A received input from a mobile device may comprise data, and a human is capable of extracting a digital biomarker from this received data, and determining that the extracted digital biomarker feature data is the clinical parameter. Therefore, this step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
The claim further recites the step of calculating the clinical parameter from the extracted digital biomarker feature data. This step describe the use of mathematical relationships, mathematical formulas or equations, and mathematical calculations, to determine the clinical parameter. Thus, the claim is also drawn to a Mathematical Concept, which is an Abstract Idea.
Step 2A, Prong Two
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 does not recite any additional steps that apply, or rely, or use the Abstract Idea. The extraction of the digital biomarker feature data does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the extracted of the digital biomarker feature data, nor does the method use a particular machine to perform the Abstract Idea.
Step 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of causing the touchscreen device to display a test image and receiving an input from the touchscreen display of the mobile device. Obtaining data (receiving data from the mobile device) after causing the device to display an image is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the causing and receiving steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the receiving steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
The same rationale applies to claims 17-18.
Regarding claim 18, the device recited in the claim is a generic device comprising generic components configured to perform the Abstract Idea. The recited mobile device is a generic computer device configured to perform pre-solutional data gathering activity and to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to data gathering and the display of data. The extracting and calculating steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1, 5-6, 12, 14, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Patent Publication 2019/0214140 by Baker et al. – cited by Applicant.
Regarding claim 1, Baker teaches a computer-implemented method for quantitatively determining a clinical parameter which is indicative of the status or progression of a disease ([0050-0051]; The mobile device is adapted for performing or acquiring data from an electronic Symbol Digit Modalities Test (eSDMT)), the computer-implemented method comprising: providing a distal motor test to a user of a mobile device, the mobile device having a touchscreen display ([0175]; “A computer-implemented test evaluating fine motoric capabilities (fine motoric assessments), in particular, hand motor functions and, in particular, the touchscreen-based “Draw a Shape” and “Squeeze a Shape” tests.”), wherein providing the distal motor test to the user of the mobile device comprises: causing the touchscreen display of the mobile device to display a test image (Figs. 4 and 8, [0371]; test image displayed on the mobile device for performing the Squeeze A Shape are shown); receiving an input from the touchscreen display of the mobile device, the input indicative of an attempt by a user to place a first finger on a first point in the test image and a second finger on a second point in the test image, and to pinch the first finger and the second finger together, thereby bringing the first point and the second point together (Fig. 8, [0375]; The attempts from a user comprise placing their fingers on the screen and pinching together); and extracting digital biomarker feature data from the received input wherein, either: (i) the extracted digital biomarker feature data is the clinical parameter ([0212-0224]; Typical Squeeze a Shape test performance parameters of interest are based on pinching motions), or (ii) the method further comprises calculating the clinical parameter from the extracted digital biomarker feature data ([0035-0037]; The present disclosure can be applied to estimating progression in patients as measured by the Expanded Disability Status Scale neurostatus (EDSS)).
Regarding claim 5, Baker teaches the computer-implemented method of claim 1, wherein: the method comprises: receiving a plurality of inputs from the touchscreen display of the mobile device, each of the plurality of inputs indicative of a respective attempt by a user to place a first finger on a first point in the test image and a second finger on a second point in the test image, and to pinch the first finger and the second finger together, thereby bringing the first point and the second point together (Fig. 8, [0211]; Distal motor control is evaluated based on repeated attempts to pinch the images); and extracting a respective piece of digital biomarker feature data from each of the plurality of received inputs, thereby generating a respective plurality of pieces of digital biomarker feature data ([0212-0224]; Each of the typical Squeeze a Shape test performance parameters of interest is based on the plurality of inputs from the user).
Regarding claim 6, Baker teaches the computer-implemented method of claim 1, wherein: the method comprises: receiving a plurality of inputs from the touchscreen display of the mobile device, each of the plurality of inputs indicative of a respective attempt by a user to place a first finger on a first point in the test image and a second finger on a second point in the test image, and to pinch the first finger and the second finger together, thereby bringing the first point and the second point together (Fig. 8, [0211]; Distal motor control is evaluated based on repeated attempts to pinch the images); determining a subset of the plurality of received inputs which correspond to successful attempts; and extracting a respective piece of digital biomarker feature data from each of the determined subset of plurality of received inputs, thereby generating a respective plurality of pieces of digital biomarker feature data ([0213-0224]; The number of successful attempts is recorded and parameters corresponding to successful attempts are determined, such as Pinching finger asynchrony, Pinching finger velocity, etc.).
Regarding claim 12, Baker teaches the computer-implemented method of claim 1, wherein: the disease whose status is to be predicted is multiple sclerosis and the clinical parameter comprises an expanded disability status scale (EDSS) value ([0035-0037]; The method is suitable for risk assessments in MS patients and for estimating probabilities of disability progression as measured by the Expanded Disability Status Scale neurostatus (EDSS)), the disease whose status is to be predicted is spinal muscular atrophy and the clinical parameter comprises a forced vital capacity (FVC) value, or wherein the disease whose status is to be predicted is Huntington's disease and the clinical parameter comprises a total motor score (TMS) value; and wherein the method further comprises the steps of: applying at least one analysis model to the digital biomarker feature data; and determining the clinical parameter based on the output of the at least one analysis model ([0035-0037]; The method is used by using the outputs of the eSDMT for risk prediction models (the models can be considered an analysis model) to estimate disability progression as measured by EDSS).
Regarding claim 14, Baker teaches the computer-implemented method of claim 12, wherein: the at least one analysis model comprises a trained machine learning model ([0040-0042]; the estimation of the probabilities of disability progression as determined by the EDSS can be a machine-learning estimation).
Regarding claim 17, Baker teaches a computer-implemented method of determining a status or progression of a disease, the computer-implemented method comprising: executing the computer-implemented method of claim 1; and determining the status or progression of the disease based on the determined clinical parameter ([0037, 0042]; risk prediction models and machine-learning algorithms can be used to determine the progression of the disease based on the EDSS).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Baker.
Regarding claim 2, Baker teaches the computer-implemented method of claim 1, wherein: the received input includes: data indicative of the time when the first finger leaves the touchscreen display (Fig. 8B, the graph shows the time of the first finger touching and leaving the display); data indicative of the time when the second finger leaves the touchscreen display (Fig. 8B, the graph shows the time of the second finger touching and leaving the display). Baker does not teach the digital biomarker feature data includes the difference between the time when the first finger leaves the touchscreen display and the time when the second finger leaves the touchscreen display.
However, Baker teaches digital biomarker features such as double touching asynchrony (DTA). The double touching asynchrony comprises the lag time (difference in time) between when the first finger touches the screen and when the second finger touches the screen. This measure establishes that the asynchrony between contact of the finger on the screen is a performance measure that describes the quality of the task. As shown in Fig. 8B, there are differences between when the first and second fingers touch the screen and differences of when they leave the screen ([0218]). Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Baker such that the digital biomarker feature data includes the difference between the time when the first finger leaves the touchscreen display and the time when the second finger leaves the touchscreen display, as asynchrony between the fingers contacting the screen has been established as a performance measure.
Regarding claim 18, Baker teaches a system for quantitatively determining a clinical parameter which is indicative of a the status or progression of a disease according to the method of claim 1, the system including: a mobile device having a touchscreen display, a user input interface, and a first processing unit (Fig. 4, [0175, 0263]; The method of Baker is carried out on a mobile device, the mobile device comprises a touchscreen, which acts as a user input interface, and a processor.); wherein: the mobile device is configured to provide the distal motor test to a user thereof, wherein: the first processing unit is configured to cause the touchscreen display of the mobile device to display the test image; the user input interface is configured to receive from the touchscreen display the input indicative of an attempt by a user to place a first finger on a first point in the test image and a second finger on a second point in the test image, and to pinch the first finger and the second finger together, thereby bringing the first point and the second point together (See the rejection of claim 1); and the first processing unit is configured to extract the digital biomarker feature data from the received input ([0263]; the mobile device is configured to carry out the Squeeze-A-Shape test).
Baker does not teach a second processing unit.
However, according to MPEP § 2144.04-VI-B, the courts have held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced. It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the system of Baker to include a second processing unit. No unexpected results would occur from the duplication of the processing units.
Regarding claim 19, Baker teaches a system for determining a status or progression of a disease comprising the system of claim 18, wherein the first processing unit or the second processing unit is further configured to determine the status or progression of the disease based on the extracted digital biomarker feature data ([0037, 0042]; risk prediction models and machine-learning algorithms can be used to determine the progression of the disease based on the EDSS).
Claims 7-8, 10, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Baker in view of WIPO Patent Publication 2021/048514 by Hu et al., hereinafter “Hu”.
Regarding claim 7, Baker teaches the computer-implemented method of claim 5, but does not teach wherein: the method further comprises deriving a statistical parameter from either: the plurality of pieces of digital biomarker feature data, or the determined subset of the respective pieces of digital biomarker feature data which correspond to successful attempts; and the statistical parameter includes: the mean of the plurality of pieces of digital biomarker feature data; and/or the standard deviation of the plurality of pieces of digital biomarker feature data; and/or the kurtosis of the plurality of pieces of digital biomarker feature data; the median of the plurality of pieces of digital biomarker feature data; a percentile of the plurality of pieces of digital biomarker feature data.
Hu teaches a method of determining the statistical features from data indicative of a neurological disease that recorded by a smartphone (Abstract, Page 8, lines 20-34). Tables 1B-C describes a set of features that can be extracted from an activity in which the user interacts multiple times with the smartphone. Among these features are the statistical characteristics of the feature data, such as mean, median, standard deviation, and kurtosis.
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Baker, such that the method further comprises deriving a statistical parameter from either: the plurality of pieces of digital biomarker feature data, or the determined subset of the respective pieces of digital biomarker feature data which correspond to successful attempts; and the statistical parameter includes: the mean of the plurality of pieces of digital biomarker feature data; and/or the standard deviation of the plurality of pieces of digital biomarker feature data; and/or the kurtosis of the plurality of pieces of digital biomarker feature data; the median of the plurality of pieces of digital biomarker feature data; a percentile of the plurality of pieces of digital biomarker feature data, as taught by Hu. The statistical measures of Hu comprise alternate measures to determine motor function, and therefore calculating and using the statistical measures in lieu of the features comprises a simple substitution of one known prior art element for another. See MPEP 2143-I-B. It is noted that Baker teaches determining features from the determined subset of data corresponding to successful attempts ([0212-0224]).
Regarding claim 8, Baker teaches the computer-implemented method of claim 5, but does not teach wherein: the plurality of received inputs are received in a total time consisting of a first time period followed by a second time period; the plurality of received inputs includes: a first subset of received inputs received during the first time period, the first subset of received inputs having a respective first subset of extracted pieces of digital biomarker feature data; and a second subset of inputs received during the second time period, the second subset of received inputs having a respective second subset of extracted pieces of digital biomarker feature data; the method further comprises: deriving a first statistical parameter corresponding to the first subset of extracted pieces of digital biomarker feature data; deriving a second statistical parameter corresponding to the second subset of extracted pieces of digital biomarker feature data; and calculating a fatigue parameter by calculating the difference between the first statistical parameter and the second statistical parameter, and optionally dividing the difference by the first statistical parameter. Baker teaches that performance parameters are parameters that are indicative of a subject to perform a certain physical or cognitive activity, including fatigue ([0047]).
Hu teaches a method of determining the statistical features from data indicative of a neurological disease that recorded by a smartphone (Abstract, Page 8, lines 20-34). Hu teaches that the data indicative of a neurological disease can be divided into subsets (calculated as percentages) at the beginning and ends of a task in order to determine the fatigue as the task progresses. The fatigue is determined as the difference in the average parameter value (e.g., a statistical parameter) (Table 1B-1C; Fatigue).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Baker to such that the plurality of received inputs are received in a total time consisting of a first time period followed by a second time period; the plurality of received inputs includes: a first subset of received inputs received during the first time period, the first subset of received inputs having a respective first subset of extracted pieces of digital biomarker feature data; and a second subset of inputs received during the second time period, the second subset of received inputs having a respective second subset of extracted pieces of digital biomarker feature data; the method further comprises: deriving a first statistical parameter corresponding to the first subset of extracted pieces of digital biomarker feature data; deriving a second statistical parameter corresponding to the second subset of extracted pieces of digital biomarker feature data; and calculating a fatigue parameter by calculating the difference between the first statistical parameter and the second statistical parameter, and optionally dividing the difference by the first statistical parameter, as taught by Hu. It is noted that Baker teaches that fatigue is a performance parameter that would be indicative of the subject’s motor skills, and therefore one of ordinary skill in the art would be motivated to determine performance parameters related to fatigue.
Regarding claim 10, Baker teaches the computer-implemented method of claim 1, but does not teach wherein: the method further comprises obtaining acceleration data including one or more of the following: (a) a statistical parameter derived from the magnitude of the acceleration throughout the duration of the whole test; (b) a statistical parameter derived from the magnitude of the acceleration only during periods where the first finger, the second finger, or both fingers are in contact with the touchscreen display; and (c) a statistical parameter of the magnitude of the acceleration only during periods where no finger is in contact with the touchscreen display; and the statistical parameter includes one or more of the following: the mean; the standard deviation; the median; the kurtosis; and a percentile.
However, Baker teaches that additional movement parameters may be useful in determining the performance parameters associated with the progression of the neurological disease, such as parameters related to velocity (Fig. 8D, [0380]). It is noted that many performance parameters (such as velocity) as taught by Baker are determined while two fingers are in contact with the display ([0212-0224]). Baker further teaches that a subject’s temporal performance for mobile device based tests can be analyzed by using the acceleration as well as velocity ([0380]). Therefore, it would have been prima facie obvious to one of ordinary skill in the art to have modified the method such that the method further comprises obtaining acceleration data including a statistical parameter derived from the magnitude of the acceleration only during periods where the first finger, the second finger, or both fingers are in contact with the touchscreen display.
This combination of Baker does not teach the statistical parameter includes one or more of the following: the mean; the standard deviation; the median; the kurtosis; and a percentile.
Hu teaches a method of determining the statistical features from data indicative of a neurological disease that recorded by a smartphone (Abstract, Page 8, lines 20-34). Tables 1B-C describes a set of features that can be extracted from an activity in which the user interacts multiple times with the smartphone. Among these features are the statistical characteristics of the feature data, such as mean, median, standard deviation, and kurtosis.
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of modified Baker, such that the method further comprises deriving a statistical parameter from the acceleration data, the statistical parameter including one or more of the following: the mean; the standard deviation; the median; the kurtosis; and a percentile.
Regarding claim 15, Baker teaches the computer-implemented method of claim 14, but does not teach wherein: the at least one analysis model is a regression model, and the trained machine learning model comprises one or more of the following algorithms: a deep learning algorithm; k nearest neighbours (kNN); linear regression; partial least-squares (PLS); random forest (RF); and extremely randomized trees (XT).
Hu teaches a method of using a machine learning model comprising a machine learning algorithm to use performance measures of an individual with a neurological disease, collected from an electronic device, to predict a clinical outcome (Abstract). Hu teaches that the machine learning algorithm may be a random forest algorithm that may be used for classification and regression applications. Random forests are advantageous in this application because they are relatively robust to outliers and noisy features, which the sensor data are susceptible to (Page 15, lines 23-28).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Baker such that the at least one analysis model is a regression model, and the trained machine learning model comprises one or more of the following algorithms: a deep learning algorithm; k nearest neighbours (kNN); linear regression; partial least-squares (PLS); random forest (RF); and extremely randomized trees (XT), because random forests are advantageous in this application because they are relatively robust to outliers and noisy features, which the sensor data are susceptible to, as taught by Hu (Page 15, lines 23-28).
Regarding claim 16, Baker in view of Hu teaches the computer implemented method of claim 15, wherein: the at least one analysis model is a classification model (Hu, the random forest algorithm can be applied as a classification model), and the trained machine learning model comprises one or more of the following algorithms: a deep learning algorithm; k nearest neighbours (kNN); support vector machines (SVM); linear discriminant analysis; quadratic discriminant analysis (QDA); naïve Bayes (NB); random forest (RF) (The model of Hu uses a random forest algorithm); and extremely randomized trees (XT).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Baker in view of US Patent Publication 2021/0093240 by Douglas et al., hereinafter “Douglas”.
Baker teaches the computer-implemented method of claim 1, but does not teach wherein: the method further comprises obtaining acceleration data; wherein the acceleration data includes either: a horizontality parameter, wherein determining the horizontality parameter includes: for each of a plurality of points in time, determining: a magnitude of the acceleration; and a magnitude of the z-component of the acceleration, wherein the z-direction is defined as the direction which is perpendicular to a plane of the touchscreen display; the ratio of the z-component of the acceleration and the magnitude of the acceleration; determining the mean of the determined ratio over the plurality of points in time, or an orientation stability parameter, wherein determining the orientation stability parameter includes: for each of a plurality of points in time, determining: a magnitude of the acceleration; and a magnitude of the z-component of the acceleration, wherein the z-direction is defined as the direction which is perpendicular to a plane of the touchscreen display; the ratio of the z-component of the acceleration and the magnitude of the acceleration value; determining the standard deviation of the determined ratio over the plurality of points in time.
Douglas teaches a method of monitoring motor skills in an individual by measuring user activities while the user responds to prompts on an electronic device (Abstract). Douglas teaches that the electronic device may be a smartphone comprising an accelerometer, and 3 dimensional accelerometer data (which includes z-component data) may be determined in order to generate a motion profile of the user, wherein the motion profile may be normalized based on the magnitude of the test profile data ([0042]). The acceleration data may be averaged in order to increase the accuracy of the acceleration data ([0062]). Calculating the acceleration while the user responds to the prompt on the electronic device provides a measure of jerky or shaky movements, thereby quantifying a motor skill metric ([0005]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Baker such that the method further comprises obtaining acceleration data; wherein the acceleration data includes a horizontality parameter, wherein determining the horizontality parameter includes: for each of a plurality of points in time, determining: a magnitude of the acceleration; and a magnitude of the z-component of the acceleration, wherein the z-direction is defined as the direction which is perpendicular to a plane of the touchscreen display; the ratio of the z-component of the acceleration and the magnitude of the acceleration; determining the mean of the determined ratio over the plurality of points in time, in order to further quantify motor skill while interacting with the electronic device, as taught by Douglas ([0005]).
Examiner’s Note
Claims 3-4 and 9 are rejected under 35 U.S.C. 112(b). The following is a reason for the lack of prior art rejections for claims 2-4 and 11.
Regarding claim 3, Baker teaches the computer-implemented method of claim 1, wherein: the received input includes: data indicative of the location of the first finger when it leaves the touchscreen display (Fig. 8E, the graph shows the location of the first finger touching and leaving the display); and data indicative of the location of the second finger when it leaves the touchscreen display (Fig. 8E, the graph shows the location of the first finger touching and leaving the display). Baker does not teach that the digital biomarker feature data includes the distance between the location of the first finger when it leaves the touchscreen display and the location of the second finger when it leaves the touchscreen display.
The limitations of the digital biomarker feature data including the distance between the location of the first finger when it leaves the touchscreen display and the location of the second finger when it leaves the touchscreen display are not taught by Baker and are patentably distinct over the prior art cited in this Office action and any other prior art.
Regarding claim 4, Baker teaches the computer-implemented method of claim 1, wherein: the received input includes: data indicative of the first path traced by the first finger from the time when it initially touches the first point to the time when it leaves the touchscreen (Fig. 8C and 8E the data points include the distance traveled by the fingers from when they touch the screen to when they leave), the data including a first start point (Fig. 8E, the point furthest from the image 9), a first end point (Fig. 8E, the point furthest from the image 9), and a first path length ([0220]; the distances slid by each finger is determined when determining the pinching finger movement asymmetry); and data indicative of the second path traced by the second finger from the time when it initially touches the second point to the time when it leaves the touchscreen, the data including a second start point, a second end point, and a second path length (All measures calculated for the first finger are also calculated for the second finger). Baker does not teach wherein the digital biomarker feature data includes a first smoothness parameter, the first smoothness parameter being the ratio of the first path length and the distance between the first start point and the first end point; and the digital biomarker feature data includes a second smoothness parameter, the second smoothness parameter being the ratio of the second path length and the distance between the second start point and the second end point. Baker does teach that the performance parameters related to the Squeeze a Shape test are related to the movement of the finger.
US Patent Publication 2016/0262685 by Wagner et al., hereinafter “Wagner” teaches a method of extracting metrics related to the quality of movement of an individual with a neurological disease ([0010]). An example of a metric that can describe the quality of movement of an individual with a neurological disease is movement smoothness ([0101]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Baker to include a first smoothness parameter and a second movement parameter, as these parameters would provide additional information about the quality of the movement of the fingers, as taught by Wagner ([0101]).
The limitations of the first smoothness parameter being the ratio of the first path length and the distance between the first start point and the first end point or the second smoothness parameter being the ratio of the second path length and the distance between the second start point and the second end point are not taught by Baker in view of Wagner and are patentably distinct over the prior art cited in this Office action and any other prior art.
Regarding claim 9, Baker teaches the computer-implemented method of claim 5, wherein: the method further comprises: determining a first subset of the plurality of received inputs corresponding to user attempts in which only the first finger and the second finger contact the touchscreen display ([0213-0217]; The total number of successful pinches are considered the first subset). Baker does not teach determining a second subset of the plurality of received inputs corresponding to user attempts in which either only one finger, or three or more fingers contact the touchscreen display; and the digital biomarker feature data comprises: the number of received inputs in the first subset of received inputs; and/or the proportion of the total number of received inputs which are in the first subset of received inputs.
Baker teaches determining a second subset of user attempts in which the shape is not successfully squeezed on the first attempt, however, this does not teach determining if the attempt comprises only one finger, or three or more fingers contacting the touchscreen display. In contrast, Baker teaches that the total number of attempts are determined by double screen contacts, and therefore unsuccessful attempts comprise double finger contacts.
The limitations of determining a second subset of the plurality of received inputs corresponding to user attempts in which either only one finger, or three or more fingers contact the touchscreen display; and the digital biomarker feature data comprises: the number of received inputs in the first subset of received inputs; and/or the proportion of the total number of received inputs which are in the first subset of received inputs are not taught by Baker and are patentably distinct over the prior art cited in this Office action and any other prior art.
Conclusion
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/NELSON ALEXANDER GLOVER/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791