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 .
Information Disclosure Statement
In the IDS submitted on 14 MAY 2024, Non-Patent Literature Document Reference 13 by Hobart et. al., was not considered, as it was not successfully submitted to the record. There is a blank document included that is potentially intended to be this document. It has been lined-out in the IDS accordingly.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
The use of the term “BLUETOOTH” on [Page 41, line 14] and [Page 56, Line 28], which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code in [Page 37, line 28]. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claims 9 (line 8) and 10 (line 8) are objected to because of the following informalities: “a second set of receive inputs” appears to have a typographical error such that the “d” is missing. It appears that the term is intended to be “a second set of received inputs.” Appropriate correction is required.
Claim 10 is objected to because of the following informalities: “opposite form the first direction” in line 7. It appears that the term is intended to be “opposite from the first direction.” Appropriate correction is required.
Claim 11 is objected to because there appears to be an “or” missing after the term “(EDSS) value”. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“first processing unit” to ”extract”, “cause” a touchscreen display to “display”, and “calculate” in claims 17 and 18
The claim limitation is interpreted according to [Page 21, Embodiment 75] “the first processing unit and the second processing unit are the same processing unit”, [Page 21, Embodiment 76] “the first processing unit is separate from the second processing unit”, [Page 21, Embodiment 77] “the processing unit is the first processing unit or the second processing unit”, [Page 21, Embodiment 99] “a mobile device having a touchscreen display, a user input interface, and a first processing unit; and a second processing unit,” and [Page 43, Lines 38 – 42] “The processing unit 112 may be configured for machine learning. The processing unit 112 may comprise a Central Processing Unit (CPU) and/or one or more Graphics Processing Units (GPUs) and/or one or more Application Specific Integrated Circuits (ASICs) and/or one or more Tensor Processing Units (TPUs) and/or one or more field-programmable gate arrays (FPGAs) or the like.” The “first processing unit” is not particularly depicted in a figure, but “processing unit 112” is shown as a generic block in Figure 1.
“second processing unit” to ”extract”, “cause” a touchscreen display to “display”, and “calculate” in claims 17 and 18
Similarly to “first processing unit” the claim limitation is interpreted according to [Page 21, Embodiment 75], [Page 21, Embodiment 76], [Page 21, Embodiment 77], [Page 21, Embodiment 99], and [Page 43, Lines 38 – 42] “The processing unit 112…Central Processing Unit (CPU)…or the like.” The “second processing unit” is not particularly depicted in a figure, but “processing unit 112” is shown as a generic block in Figure 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 6 – 10, 11, and 13 – 17 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.
Claim 6 (line 6), Claims 17 (line 13 – 14), and Claim 18 (line 13 - 14) recite the limitation “the display of the mobile device”. There is insufficient antecedent basis for this limitation in the claim. It is unclear if this is intended to be the same or different display than the “touchscreen display of the mobile device” previously recited in Claim 1, from which claim 6 depends, and in lines 9 – 10 of claims 17 and 18. For the purposes of examination, the term “the display of the mobile device” is deemed to claim “the touchscreen display of the mobile device”. Claims 7 - 10 are similarly rejected due to their dependence on Claim 6.
Claim 6 recites the limitation “a deviation between the test start point and the reference start point” in line 13. There is insufficient antecedent basis for this limitation in the claim. It is unclear if this is intended to be the same or different deviation than the “deviation between the test start point and the reference start point” previously recited in Claim 1, from which claim 6 depends. For the purposes of examination, the term “a deviation between the test start point and the reference start point” is deemed to claim “a deviation between the test start point and the reference start point for the respective received input.” Claims 7 - 10 are similarly rejected due to their dependence on Claim 6.
Claim 6 recites the limitations “a test start point”, “a test end point”, and “a test path” in lines 6 – 7). There is insufficient antecedent basis for this limitation in the claim. It is unclear if this is intended to be the same or different start point, end point, or test path previously recited in Claim 1, from which claim 6 depends. For the purposes of examination, the terms “a test start point”, “a test end point”, and “a test path” are deemed to claim “a respective test start point”, “a respective test end point”, and “a respective test path”. Accordingly, the terms “the test start point” in lines 7, 10, 12, and 13 is interpreted as “the respective test start point”, and the term “the test end point” in lines 7, 11, and 14 is interpreted as “the respective test end point”. Claims 7 - 10 are similarly rejected due to their dependence on Claim 6.
Claim 11 recites the limitations “the disease whose status is to be predicted” in lines 3, 5 and 7. There is insufficient antecedent basis for this limitation in the claim. It is unclear if this is intended to be the same or different disease that is recited in the preamble of claim 1, from which Claim 11 depends. For the purposes of examination, the term “the disease whose status is to be predicted” is deemed to claim “a disease whose status is to be predicted”, and also that the sets of lines 2 – 3, lines 4 – 5, and lines 6 – 7 are intended to be in the alternative, such that there is an “or” after “(EDSS) value (as described in the claim objection above).
Claim 13 is rejected for the term “The computer-implemented method of claim 13”, as a claim cannot depend from itself. It is unclear from which claim this claim was intended to depend. There is a previously-recited “analysis model” in Claim 12. For the purposes of examination, the term “The computer-implemented method of claim 13”, is deemed to claim “The computer-implemented method of claim 12”.
Claim 14 is rejected for the term “The computer-implemented method of claim 14”, as a claim cannot depend from itself. It is unclear from which claim this claim was intended to depend. There is a previously-recited “analysis model” in Claim 12 and a previously-recited “trained machine learning model” in Claim 13. For the purposes of examination, the term “The computer-implemented method of claim 14”, is deemed to claim “The computer-implemented method of claim 13”. Claim 15 is similarly rejected due to its dependence on Claim 14.
Claim 15 is rejected for the term, “The computer-implemented method of claim 14”, as the limitations appear to be in the alternative to those of Claim 14, particularly “the analysis model is a classification model” (Claim 15) with “the analysis model is a regression model” (Claim 14). It is unclear if the analysis model is intended to be both a classification model and a regression model. For the purposes of examination, the term “The computer implemented method of claim 14.” Is deemed to claim “The computer implement method of claim 13.”
Claim 13 (line 2), Claim 14 (line 2), and Claim 15 (line 2) recite the limitation “the analysis model”. There is insufficient antecedent basis for this limitation in the claim. It is unclear if this is intended to be the same of different than the “at least one analysis model” previously recited in Claim 12 from which these claims depend. For the purposes of examination, the term “the analysis model” is deemed to claim “the at least one analysis model.”
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 - 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding Claim 1, the claim recites "an act or step, or series of acts or steps" and is therefore a process, which is a statutory category of invention (Step 1). The claim is then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong 1).
Regarding Claims 17 and 18, the claims recite an apparatus, which is one of the statutory categories of invention (Step 1). The claims are then analyzed to determine whether they are directed to any judicial exception (Step 2A, Prong 1).
Each of Claims 1 - 18 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1 – 18 recites at least one step or instruction for observations, evaluations, judgments, and opinions, which are grouped as a mental process under the 2019 PEG and certain methods of organizing human activity. The claimed invention involves making observations, evaluations, judgments, and opinions, and certain methods of organizing human activity (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which are concepts performed in the human mind under the 2019 PEG.
Accordingly, each of Claims 1 - 18 recites an abstract idea.
Specifically, Independent Claims 1, 17, and 18 recite (underlined are observations, judgements, evaluations, or opinions, which are grouped as a mental process and certain methods of organizing human activity under the 2019 PEG) (additional elements bolded, see Step 2A, prong 2);
Claim 1
(Original) A computer-implemented method for quantitatively determining a clinical parameter indicative of a status or progression of a disease, the computer-implemented method comprising:
providing a distal motor test to a user of a mobile device, the mobile device having a touchscreen display, wherein providing the distal motor test to the user of the mobile device comprises:
causing the touchscreen display of the mobile device to display an image comprising: a reference start point, a reference end point, and indication of a reference path to be traced between the start point and the end point;
receiving an input from the touchscreen display of the mobile device, the input indicative of a test path traced by a user attempting to trace the reference path on the display of the mobile device, the test path comprising: a test start point, a test end point, and a test path traced between the test start point and the test end point; and
extracting digital biomarker feature data from the received input, the digital biomarker feature data comprising:
a deviation between the test end point and the reference end point;
a deviation between the test start point and the reference start point; and/or
a deviation between the test start point and the reference end point; and wherein:
the extracted digital biomarker feature data is the clinical parameter; or
the method further comprises calculating the clinical parameter from the extracted biomarker feature data.
Claim 17
(Original) A system for quantitatively determining a clinical parameter indicative of a status or progression of a disease, the system including:
a mobile device having a touchscreen display, a user input interface, and a first processing unit; and
a second processing unit;
wherein:
the mobile device is configured to provide a distal motor test to a user thereof, wherein providing the distal motor test comprises:
the first processing unit causing the touchscreen display of the mobile device to display an image comprising: a reference start point, a reference end point, and indication of a reference path to be traced between the start point and the end point;
the user input interface is configured to receive from the touchscreen display, an input indicative of a test path traced by a user attempting to trace the reference path on the display of the mobile device, the test path comprising: a test start point, a test end point, and a test path traced between the test start point and the test end point; and
the first processing unit or the second processing unit is configured to extract digital biomarker feature data from the received input, the digital biomarker feature data comprising:
a deviation between the test end point and the reference end point; and/or
a deviation between the test start point and the test end point; and wherein:
the extracted digital biomarker feature data is the clinical parameter; or
the first processing unit or the second processing unit is further configured to calculate the clinical parameter from the extract digital biomarker feature data.
Claim 18
(Original) A system for determining a status or progression of a disease, the system comprising;
a mobile device having a touchscreen display, a user input interface, and a first processing unit; and
a second processing unit;
wherein:
the mobile device is configured to provide a distal motor test to a user thereof, wherein providing the distal motor test comprises:
the first processing unit causing the touchscreen display of the mobile device to display an image comprising: a reference start point, a reference end point, and indication of a reference path to be traced between the start point and the end point;
the user input interface is configured to receive from the touchscreen display, an input indicative of a test path traced by a user attempting to trace the reference path on the display of the mobile device, the test path comprising: a test start point, a test end point, and a test path traced between the test start point and the test end point; and
the first processing unit or the second processing unit is configured to extract digital biomarker feature data from the received input, the digital biomarker feature data comprising:
a deviation between the test end point and the reference end point; and/or
a deviation between the test start point and the test end point; and wherein:
the extracted digital biomarker feature data is the clinical parameter; or
the first processing unit or the second processing unit is further configured to calculate the clinical parameter from the extract digital biomarker feature data; and
the first processing unit or the second processing unit is configured to determine the status or progression of the disease based on the determined clinical parameter.
(observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
These underlined limitations describe a mathematical calculation and/or a mental process, as a skilled practitioner is capable of performing the recited limitations and making a mental assessment thereafter. Examiner notes that nothing from the claims suggests that the limitations cannot be practically performed by a human with the aid of a pen and paper, or by using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner additionally notes that nothing from the claims suggests and 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, in Independent Claims 1, 17, and 18, these limitations include:
Providing (as in communicating, or delivering) a distal motor test to a user of a mobile device
which is grouped as a mental process under the 2019 PEG and certain methods of organizing human activity
Observation and judgement of an input from the touchscreen display of the mobile device, the input indicative of a test path traced by a user attempting to trace the reference path on the display of the mobile device, the test path comprising: a test start point, a test end point, and a test path traced between the test start point and the test end point; and
Observation and judgement to extract digital biomarker feature data from the received input, the digital biomarker feature data comprising: a deviation between the test end point and the reference end point; a deviation between the test start point and the reference start point; and/or a deviation between the test start point and the reference end point; and wherein:
the extracted digital biomarker feature data is the clinical parameter
calculating the clinical parameter from the extracted biomarker feature data.
all of which are grouped as mental processes or mathematical algorithms under the 2019 PEG.
Similarly, Dependent Claims 2 – 16 include the following abstract limitations, in addition the aforementioned limitations in Independent Claims 1, 17, and 18 (underlined observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG):
receiving a plurality of inputs from the touchscreen display, each of the plurality of inputs indicative of a respective test path traced by a user attempting to trace the reference path on the display of the mobile device, the test path comprising: a test start point, a test end point, and a test path traced between the test start point and the test end point;
observation and judgment of a plurality of inputs from the touchscreen display, each of the plurality of inputs indicative of a respective test path traced by a user attempting to trace the reference path on the display of the mobile device, the test path comprising: a test start point, a test end point, and a test path traced between the test start point and the test end point;
extracting digital biomarker feature data from each of the plurality of received inputs, thereby generating a respective plurality of pieces of digital biomarker features data, each piece of digital biomarker feature data comprising: a deviation between the test end point and the reference end point for the respective received input; a deviation between the test start point and the reference start point; and/or a deviation between the test start point and the test end point for the respective input.
Observation and judgement to extract digital biomarker feature data from each of the plurality of received inputs, thereby generating a respective plurality of pieces of digital biomarker features data, each piece of digital biomarker feature data comprising: a deviation between the test end point and the reference end point for the respective received input; a deviation between the test start point and the reference start point; and/or a deviation between the test start point and the test end point for the respective input.
deriving a statistical parameter from the plurality of pieces of digital biomarker feature data.
Evaluation to derive a statistical parameter from the plurality of pieces of digital biomarker feature data.
deriving a first statistical parameter corresponding to the first subset of extracted pieces of digital biomarker feature data;
Evaluation to derive 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;
Evaluation to derive a second statistical parameter corresponding to the second subset of extracted pieces of digital biomarker feature data;
calculating a handedness 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 or the second statistical parameter.
Evaluating a handedness parameter by calculating the difference between the first statistical parameter and the second statistical parameter, and optionally further evaluating by dividing the difference by the first statistical parameter or the second statistical parameter.
calculating a directionality 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 or the second statistical parameter.
Evaluating a directionality parameter by calculating the difference between the first statistical parameter and the second statistical parameter, and optionally further evaluating by dividing the difference by the first statistical parameter or the second statistical parameter.
applying at least one analysis model to the digital biomarker feature data or a statistical parameter derived from the digital biomarker feature data
Observation and judgment to apply at least one analysis model to the digital biomarker feature data or a statistical parameter derived from the digital biomarker feature data
predicting a value of the at least one clinical parameter based on the output of the at least one analysis model.
Observation and judgment to predict a value of the at least one clinical parameter based on the output of the at least one analysis model.
executing the computer-implemented method of claim 1;
Observation and judgment to execute the computer-implemented method of claim 1;
determining the status or progression of the disease based on the determined clinical parameter.
Observation and judgment of the status or progression of the disease based on the determined clinical parameter.
all of which are grouped as mental processes or mathematical algorithms under the 2019 PEG.
Accordingly, as indicated above, each of the above-identified claims recite an abstract idea.
Step 2A, Prong 2
The above-identified abstract ideas in each of Independent Claims 1, 17, and 18 (and their respective Dependent Claims) are not integrated into a practical application under 2019 PEG because the additional elements (identified in Claims 1 - 18), either alone or in combination, generally link the use of the above-identified abstract ideas to a particular technological environment or field of use. More specifically, the additional elements of:
“computer”
“mobile device”
“touchscreen display”; “touchscreen display of the mobile device”
“user input interface”
“first processing unit”
“second processing unit”
Additional elements recited include a “computer”, “mobile device”, “touchscreen display”, “touchscreen display of the mobile device”, “user input interface”, “first processing unit” and “second processing unit” in Independent Claims 1, 17, and 18 (and their respective Dependent Claims). These components are recited at a high level of generality, i.e., as a generic touchscreen display performing a generic function of displaying an image (the displaying) and processing units performing a generic function of processing data (the extracting, determining, and calculating). These generic hardware component limitations for “computer”, “mobile device”, “touchscreen display”, “touchscreen display of the mobile device” , “user input interface”, “first processing unit” and “second processing unit” are no more than mere instructions to apply the exception using generic computer and hardware components. As such, these additional elements do not impose any meaningful limits on practicing the abstract idea.
Further additional elements from Independent Claims 1, 17, and 18 includes pre-solution activity limitations, such as:
the mobile device having a touchscreen display
causing the touchscreen display of the mobile device to display an image comprising: a reference start point, a reference end point, and indication of a reference path to be traced between the start point and the end point;
a mobile device having a touchscreen display, a user input interface, and a first processing unit
In addition the aforementioned extra-solution activity limitations in Independent Claims 1, 17, and 18, additional extra-solution activity limitations recited in Dependent Claims 2 – 16 include:
the reference start point is the same as the reference end point, and the reference path is a closed path.
the closed path is a square, a circle or a figure-of-eight.
the reference start point is different from the reference end point, and the reference path is an open path; and
the digital biomarker feature data is the deviation between the test end point and the reference end point.
the open path is a straight line, or a spiral.
the statistical parameter comprises one or more of: a mean; a standard deviation; a percentile; a kurtosis; and a median.
the plurality of received inputs includes: a first subset of received inputs, each indicative of a respective test path traced by a user attempting to trace the reference path on the touchscreen display of the mobile device in a first direction or using their dominant hand, the first subset of received inputs having a respective first subset of extracted pieces of digital biomarker data; and
a second subset of received inputs, each indicative of a respective test path traced by a user attempting to trace the reference path on the touchscreen display of the mobile device in a second direction, opposite from the first direction, or using their non-dominant hand, the second subset of received inputs having a respective second subset of extracted pieces of digital biomarker data;
the disease whose status is to be predicted is multiple sclerosis and the clinical parameter comprises an expanded disability status scale (EDSS) value,
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.
the analysis model comprises a trained machine learning model.
the 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 last-squares (PLS); random forest (RF); and extremely randomized trees (XT).
the analysis model is 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); naive Bayes (NB);random forest (RF); and extremely randomized trees (XT).
These pre-solution measurement elements are insignificant extra-solution activity, setting up the parameters of the system, and serve as data-gathering for the subsequent steps.
The “computer”, “mobile device”, “touchscreen display”, “touchscreen display of the mobile device”, “user input interface”, “first processing unit” and “second processing unit” as recited in Independent Claims 1, 17, and 18 (and their respective Dependent Claims) are generically recited computer and hardware elements which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract ideas identified above in Independent Claims 1, 17, and 18 (and their dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer processor as claimed. In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in Independent Claims 1, 17, and 18 (and their dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, Independent Claims 1, 17, and 18 (and their dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B –
None of Claims 1 - 18 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: “computer”, “mobile device”, “touchscreen display”, “touchscreen display of the mobile device”, “user input interface”, “first processing unit” and “second processing unit” as recited in Independent Claims 1, 7, and 18 (and their dependent claims).
The additional elements of the “computer”, “mobile device”, “touchscreen display”, “touchscreen display of the mobile device”, “user input interface”, “first processing unit” and “second processing unit” in Independent Claims 1, 17, and 18 (and their dependent claims), as discussed with respect to Step 2A Prong Two, amounts to no more than mere instructions to apply the exception using generic computer and hardware components. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification, the “computer” is described generically on [Page 39, Lines 39 – 40] “The mobile device may also refer to a tablet computer or any other type of portable computer.” The “computer” is shown as “mobile device 102” in Figure 5A.
Per Applicant’s specification, the “mobile device” is described generically on [Page 39, Lines 34 – 42] – [Page 40, Lines 1 – 15] including “The term "mobile device" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning”, “a cell phone or smartphone”, “tablet computer or any other type of portable computer” among the examples. The “mobile device” is shown as “mobile device 102” in Figure 5A.
Per Applicant’s specification, the “touchscreen display” and “touchscreen display of the mobile device” is described generically in [Page 81, Bottom] – [Page 82, Line 1] “The mobile device 102 includes a touchscreen display 108”. The touchscreen display is presented as “touchscreen display 108” in Figure 5A.
Per Applicant’s specification, the “user input interface”, is described generically in [Page 81, Bottom] – [Page 82, Line 1] “The mobile device 102 includes a…user input interface module 110”. The user input interface is presented as “user input interface module 110” in Figure 5A.
Per Applicant’s specification, the “first processing unit” and “second processing unit” are described generically in [Page 21, Embodiment 75] “the first processing unit and the second processing unit are the same processing unit”, [Page 21, Embodiment 76] “the first processing unit is separate from the second processing unit”, [Page 21, Embodiment 77] “the processing unit is the first processing unit or the second processing unit”, [Page 21, Embodiment 99] “a mobile device having a touchscreen display, a user input interface, and a first processing unit; and a second processing unit,” and [Page 43, Lines 38 – 42] “The processing unit 112 may be configured for machine learning. The processing unit 112 may comprise a Central Processing Unit (CPU) and/or one or more Graphics Processing Units (GPUs) and/or one or more Application Specific Integrated Circuits (ASICs) and/or one or more Tensor Processing Units (TPUs) and/or one or more field-programmable gate arrays (FPGAs) or the like.” The “first processing unit” and “second processing unit” are not particularly depicted in a figure, but “processing unit 112” is shown as a generic block in Figure 1.
Accordingly, in light of Applicant’s specification, the claimed terms “computer”, “mobile device”, “touchscreen display”, “touchscreen display of the mobile device”, “user input interface”, “first processing unit” and “second processing unit” are reasonably construed as a generic computing and hardware devices. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the “computer”, “mobile device”, “touchscreen display”, “touchscreen display of the mobile device”, “user input interface”, “first processing unit” and “second processing unit”. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Independent Claims 1, 17, and 18 (and their dependent claims) amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the apparatuses and method of Claims 1 - 18 are directed to applying an abstract idea as identified above on a general-purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1 - 18 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements for Step 2A Prong 2 in Independent Claims 1, 17, and 18 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1 - 18 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1 - 18 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1 - 18 are not patent eligible and rejected under 35 U.S.C. 101.
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 – 8, 11 – 13, and 16 – 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Baker, el. al., (United States Patent Application Publication US 2019/0214140 A1), hereinafter Baker.
Regarding Claim 1, Baker discloses A computer-implemented method for quantitatively determining a clinical parameter indicative of a status or progression of a disease ([Abstract]), the computer-implemented method comprising:
providing a distal motor test ([0048] ”…performing fine motoric activities…the Draw a Shape and/or Squeeze a Shape tests”) to a user of a mobile device ([0048] ” acquired by the mobile device from a subject”), the mobile device having a touchscreen display ([0180] “touchscreen-based application tests”; Figs 3A – 3E), wherein providing the distal motor test to the user of the mobile device ([0048]; [0180]; [0181]; Figs 3A – 3E) comprises:
causing the touchscreen display of the mobile device to display an image (Fig 3A – 3E; [0180] [0263] “display for electronically simulating and activity test on the mobile device”) comprising: a reference start point (Figs 3A – 3E at the tail of the arrow; [0181] “connect indicated start and end points…”), a reference end point (Figs 3A – 3E at the tail of the arrow; [0181] “connect indicated start and end points…), and indication of a reference path to be traced between the start point and the end point (Figs 3A – 3E at the tail of the arrow; [0181] “passing through all indicated check points and keeping within the boundaries of the writing path as much as possible.”);
receiving an input from the touchscreen display of the mobile device ([0181] “draw on a touchscreen of the mobile device 6 pre-written alternating shapes of increasing complexity”; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), the input indicative of a test path traced by a user attempting to trace the reference path ([0376] “Circles 120 along the dashed line indicate waypoints that subjects have to pass through.”; Fig 3A; Fig 13A, Figs 11A and 11B, Figs 9A and 9B; [0181]) on the display of the mobile device ([0181] “draw on a touchscreen of the mobile device 6 pre-written alternating shapes of increasing complexity”; As an example, Fig 9A and 9B circle for the reference path circle in Fig 3A; [0376] “FIGS. 9A and 9B show examples of touch traces for circle shape from two subjects.”), the test path comprising: a test start point (Fig 10A, Waypoint #1 dot on graph; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), a test end point (Fig 10A, Waypoint #14 dot on graph; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), and a test path traced between the test start point and the test end point (Fig 9A and 9B; Fig 10A, dots on graph from Waypoint #1 to #14; Fig 13A, Figs 11A and 11B); and
extracting digital biomarker feature data from the received input (Figs 9A and 9B, Figs 10A – 10C, [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”), the digital biomarker feature data comprising:
a deviation between the test end point and the reference end point (Fig 9A and 9B, Figure 10A, Waypoint #14; [0377] “FIGS. 10A, 10B and 10C show tracing performance…”; [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”)
a deviation between the test start point and the reference start point (Fig 9A and 9B, Figure 10A, Waypoint #1; [0377] “FIGS. 10A, 10B and 10C show tracing performance…”; [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”); and/or
a deviation between the test start point and the reference end point (Fig 9A and 9B, Figure 10A and Figure 10B, Waypoint #1 and #14, 4th Segment of Shape; [0377] “FIGS. 10A, 10B and 10C show tracing performance…”; [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”) and wherein:
the extracted digital biomarker feature data is the clinical parameter ([0182] – [0192] “performance parameters of interest”, “shape completion scores”; [0193] – [0203] “Segment Completion and celerity performance scores/measures”; [0204] – [0210] “Drawing precision performance scores/measures”; [0270] “by determining the degree of difference between a determined performance parameter and a reference, a quantitative assessment of progressing MS in a subject shall be possible….”) or
the method further comprises calculating the clinical parameter from the extracted biomarker feature data (Fig 9A; [0376] “FIG. 9A depicts a subject with poor 9HPT.”; [0010] “…clinical investigations…involve testing of…”; [0376] “Fig 9A depicts a subject with poor 9HPT”; [0179] “Normative data for the 9HPT…SHPT represents the key component of functional upper limb assessment from the Multiple Sclerosis Functional Composite (MSFC) scale”; ([0182] – [0192] “performance parameters of interest”, “shape completion scores”; [0193] – [0203] “Segment Completion and celerity performance scores/measures”; [0204] – [0210] “Drawing precision performance scores/measures”);
Regarding Claims 17 and 18, Baker discloses
For Claim 17:
A system for quantitatively determining a clinical parameter indicative of a status or progression of a disease ([Abstract], [0022]), the system including:
For Claim 18:
A system for determining a status or progression of a disease ([Abstract], [0022]), the system comprising;
and the first processing unit ([0262] “require a data processor…for electronically simulating an activity test on the mobile device”) or the second processing unit is configured to determine the status or progression of the disease based on the determined clinical parameter ([0276] “determined at least one performance parameter… compared to a reference by…data processor of the mobile device or by the evaluating device, e.g., the computer….assessment…progressing MS, or not”)
For both Claims 17 and 18, Baker discloses:
a mobile device having a touchscreen display ([0048] “…the mobile device from a subject”; [0180] “touchscreen-based application tests”; Figs 3A – 3E), a user input interface ([0336] “mobile device…user interface such as screen or other equipment for data acquisition.”), and a first processing unit ([0262] “require a data processor…for electronically simulating an activity test on the mobile device”); and
a second processing unit ([0276] “the evaluating device, e.g., the computer”);
wherein:
the mobile device is configured to provide a distal motor test to a user thereof ([0048] “…performing fine motoric activities…the Draw a Shape and/or Squeeze a Shape tests”; “acquired by the mobile device from a subject…”), wherein providing the distal motor test comprises:
the first processing unit ([0262] “require a data processor…for electronically simulating an activity test on the mobile device”) causing the touchscreen display of the mobile device to display an image (Fig 3A – 3E; [0180] [0263] “display for electronically simulating and activity test on the mobile device”) comprising: a reference start point (Figs 3A – 3E at the tail of the arrow; [0181] “connect indicated start and end points…”), a reference end point (Figs 3A – 3E at the tail of the arrow; [0181] “connect indicated start and end points…), and indication of a reference path to be traced between the start point and the end point (Figs 3A – 3E at the tail of the arrow; [0181] “passing through all indicated check points and keeping within the boundaries of the writing path as much as possible.”);
the user input interface ([0336] “mobile device…user interface such as screen or other equipment for data acquisition.”) is configured to receive from the touchscreen display ([0181] “draw on a touchscreen of the mobile device 6 pre-written alternating shapes of increasing complexity”; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), an input indicative of a test path traced by a user attempting to trace the reference path ([0376] “Circles 120 along the dashed line indicate waypoints that subjects have to pass through.”; Fig 3A; Fig 13A, Figs 11A and 11B, Figs 9A and 9B; [0181]) on the display of the mobile device ([0181] “draw on a touchscreen of the mobile device 6 pre-written alternating shapes of increasing complexity”; As an example, Fig 9A and 9B circle for the reference path circle in Fig 3A; [0376] “FIGS. 9A and 9B show examples of touch traces for circle shape from two subjects.”), the test path comprising: a test start point (Fig 10A, Waypoint #1 dot on graph; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), a test end point (Fig 10A, Waypoint #14 dot on graph; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), and a test path traced between the test start point and the test end point (Fig 9A and 9B; Fig 10A, dots on graph from Waypoint #1 to #14; Fig 13A, Figs 11A and 11B); and
the first processing unit ([0262] “require a data processor…for electronically simulating an activity test on the mobile device”) or the second processing unit ([0276] “…the evaluating device, e.g., the computer”); is configured to extract digital biomarker feature data from the received input (Figs 9A and 9B, Figs 10A – 10C, [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”; [0262]; [0276]), the digital biomarker feature data comprising:
a deviation between the test end point and the reference end point (Fig 9A and 9B, Figure 10A, Waypoint #14; [0377] “FIGS. 10A, 10B and 10C show tracing performance…”; [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”); and/or
a deviation between the test start point and the reference end point (Fig 9A and 9B, Figure 10A and Figure 10B, Waypoint #1 and #14, 4th Segment of Shape; [0377] “FIGS. 10A, 10B and 10C show tracing performance…”; [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”); and wherein:
the extracted digital biomarker feature data is the clinical parameter ([0182] – [0192] “performance parameters of interest”, “shape completion scores”; [0193] – [0203] “Segment Completion and celerity performance scores/measures”; [0204] – [0210] “Drawing precision performance scores/measures”; [0270] “by determining the degree of difference between a determined performance parameter and a reference, a quantitative assessment of progressing MS in a subject shall be possible….”); or
the first processing unit or the second processing unit is further configured to calculate the clinical parameter from the extract digital biomarker feature data Fig 9A; [0376] “FIG. 9A depicts a subject with poor 9HPT.”; [0010] “…clinical investigations…involve testing of…”; [0376] “Fig 9A depicts a subject with poor 9HPT”; [0179] “Normative data for the 9HPT…SHPT represents the key component of functional upper limb assessment from the Multiple Sclerosis Functional Composite (MSFC) scale”; ([0182] – [0192] “performance parameters of interest”, “shape completion scores”; [0193] – [0203] “Segment Completion and celerity performance scores/measures”; [0204] – [0210] “Drawing precision performance scores/measures”; [0262]; [0276]);
Regarding Claim 2, Baker discloses as described above, The computer-implemented method of claim 1. For the remainder of Claim 2, Baker discloses wherein:
the reference start point is the same as the reference end point (Fig 3A, circle and square, starting at the tail of the arrow and closing the shape; Fig 9A and 9B), and the reference path is a closed path (Fig 3A and 3E, circle, square, and figure 8 starting at the tail of the arrow and closing the shape; Fig 9A and 9B).
Regarding Claim 3, Baker discloses as described above, The computer-implemented method of claim 2. For the remainder of Claim 3, Baker discloses wherein:
the closed path is a square (Figure 3A), a circle (Figure 3A) or a figure-of-eight (Figure 3E).
Regarding Claim 4, Baker discloses as described above, The computer-implemented method of claim 1. For the remainder of Claim 4, Baker discloses wherein:
the reference start point is different from the reference end point (Fig 3A line, spiral start at tail of arrow and finish an open shape; Fig 11A and 11B) , and the reference path is an open path (Fig 3A, 3C, and 3D, line, spiral, start at tail of arrow and finish an open shape; Fig 11A and 11B); and
the digital biomarker feature data is the deviation ([0205] “Deviation (Dev) calculated…) between the test end point and the reference end point ([0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints….(from starting to ending checkpoints”; Figure 9A and 9B; Figure 12A, Final Waypoint; Table 2).
Regarding Claim 5, Baker discloses as described above, The computer-implemented method of claim 4. For the remainder of Claim 5, Baker discloses wherein:
the open path is a straight line (Figure 3A, Fig 3D), or a spiral (Figure 3A, Fig. 3C).
Regarding Claim 6, Baker discloses as described above, The computer-implemented method of claim 1. For the remainder of Claim 6, Baker discloses the method comprises:
receiving a plurality of inputs from the touchscreen display ([0181] ”Draw a Shape Test”…6 pre-written alternating shapes of increasing complexity…maximum two attempts to successfully complete each of the 6 shapes…”; Fig 3A, Figs 3B – 3E; [0180])(Examiner notes that there are at maximum 2 inputs for each of the 6 shapes, so 6 – 12 inputs per test.; [0180] “performed daily but could alternatively be performed at lower (e.g., weekly or bi-weekly) frequency”), each of the plurality of inputs indicative of a respective test path ([0181] ”Draw a Shape Test”…6 pre-written alternating shapes of increasing complexity (linear, rectangular, circular, sinusoidal, and spiral……maximum two attempts to successfully complete each of the 6 shapes…”)(Examiner notes that there are at maximum 2 input test paths drawn per user for each of the 6 shapes, so 6 – 12 test paths per test.); traced by a user attempting to trace the reference path on the display of the mobile device ([0181] “draw on a touchscreen of the mobile device…maximum two attempts to successfully complete each of the 6 shapes”; Figs 3A – 3E; Figs 9A and 9B, Figs 10A – 10C; [0180] “) the test path comprising: a test start point (Fig 10A, Waypoint #1 dot on graph; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), a test end point (Fig 10A, Waypoint #14 dot on graph; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), and a test path traced between the test start point and the test end point (Fig 9A and 9B; Fig 10A, dots on graph from Waypoint #1 to #14; Fig 13A, Figs 11A and 11B)(Examiner notes that repeating the test drawing a figure would be the same instruction as previously performed, tracing to try and match the end points and path.);
extracting digital biomarker feature data from each of the plurality of received inputs ([0181] “…checkpoints to connect…segments”; [0183] “Based on shape complexity…weighting factor…”; [0184] – [0192] “performance scores…shape completion scores”; [0204] – [0210] “Drawing precision performance scores/measures”), thereby generating a respective plurality of pieces of digital biomarker features data ([0181], [0183], [0184] – [0192]; [0332] “the performance parameter(s) determined from the activity measurements of the first time point are compared to the performance parameters of subsequent time points….determine a worsening, improvement or unchanged disease condition…”) each piece of digital biomarker feature data comprising:
a deviation between the test end point and the reference end point for the respective received input (Fig 9A and 9B, Figure 10A, Final Waypoint; [0377] “FIGS. 10A, 10B and 10C show tracing performance…”; [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”; [0180]; [0181], [0183], [0184] – [0192]; [0332])(Examiner notes that repeating the test drawing a figure would be the same instruction as previously performed, tracing to try and match the end points and path.);
a deviation between the test start point and the reference start point (Fig 9A and 9B, Figure 10A, Waypoint #1; [0377] “FIGS. 10A, 10B and 10C show tracing performance…”; [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”); (Examiner notes that the drawing test repeated would be the same instruction as previously performed, tracing to try and match the end points and path.); and/or
a deviation between the test start point and the test end point for the respective point (Fig 9A and 9B, Figure 10A and Figure 10B, Waypoint #1 and #14, 4th Segment of Shape; [0377] “FIGS. 10A, 10B and 10C show tracing performance…”; [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”; [0180]; [0332]; [0181], [0183], [0184] – [0192])(Examiner notes that the drawing test repeated would be the same instruction as previously performed, tracing to try and match the end points and path.).
Regarding Claim 7, Baker discloses as described above, The computer-implemented method of claim 6. For the remainder of Claim 7, Baker discloses the method comprises:
deriving a statistical parameter from the plurality of pieces of digital biomarker feature data (Table 1, “Mean Error, Std. Error”; Table 2, “Mean Error, Std. Error”; [0332]; [0180] “performed daily but could alternatively be performed at lower (e.g., weekly or bi-weekly) frequency.”[0181], [0183], [0184] – [0192]).
Regarding Claim 8, Baker discloses as described above, The computer-implemented method of claim 7. For the remainder of Claim 8, Baker discloses wherein:
the statistical parameter comprises one or more of:
a mean (Table 1, “Mean Error”; Table 2, “Mean Error”);
a standard deviation (Table 1, “Std. Error”; Table 2, “Std. Error”);
a percentile; a kurtosis; and a median ([0379] “range of error distances per subject, including median and IQR.”).
Regarding Claim 11, Baker discloses as described above, The computer-implemented method of claim 1. For the remainder of Claim 11, Baker discloses wherein:
the disease whose status is to be predicted is multiple sclerosis ([0324] “…performance parameter…identifying progressing multiple sclerosis (MS) in a subject”; [0036] – [0039] “risk prediction models”) and the clinical parameter comprises an expanded disability status scale (EDSS) value ([0028] “the method of the present disclose can be applied…”; [[0037] “risk prediction models estimating…Expanded Disability Status Scale neurostatus (EDSS)”; or
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.
Regarding Claim 12, Baker discloses as described above, The computer-implemented method of claim 1. For the remainder of Claim 12, Baker discloses further comprising:
applying at least one analysis model to the digital biomarker feature data or a statistical parameter derived from the digital biomarker feature data ([0028] “The method of the present disclosure can be applied…in the context of:”; [0037] “risk prediction models estimating probabilities of disability progression…(MS)”; Table 1, “Mean Error”; Table 2, “Mean Error”); [0042]); and
predicting a value of the at least one clinical parameter based on the output of the at least one analysis model ([0028] “The method of the present disclosure can be applied…in the context of:”; [0037] “risk prediction models estimating probabilities of disability progression …(MS)…Expanded Disability Status Scale neurostatus (EDSS), the Multiple Sclerosis Functional Composite (MSFC)…”; [0042]);.
Regarding Claim 13, Baker discloses as described above, The computer-implemented method of claim 12 (See 112b rejection and interpretation above). For the remainder of Claim 13, Baker discloses wherein:
the analysis model comprises a trained machine learning model ([0042] “developing algorithmic solutions…machine-learning and pattern recognition techniques to estimate…).
Regarding Claim 16, Baker discloses A computer-implemented method of determining a status or progression of a disease ([Abstract]), the computer-implemented method comprising the steps of:
executing the computer-implemented method of claim 1 (See citations in Claim 1); and
determining the status or progression of the disease based on the determined clinical parameter ([0024] “The term "identifying" as used herein refers to assessing whether a subject suffers from progressing MS, or not…means for evaluating a dataset of activity measurements.”; [0306]; [0308] “Typically, in said embodiment a worsening between the determined at least one performance parameter and the reference is indicative of a subject with progressing MS.”; [0280] – [0284]; [0270] “…disease stage, worsening, improvement, or unchanged disease”).
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.
Claims 9 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Baker in view of Creagh et. al., “Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape Test”, hereinafter Creagh.
Regarding Claim 9, Baker discloses as described above, The computer-implemented method of claim 6. For the remainder of Claim 9, Baker discloses wherein:
the plurality of received inputs (Fig 3A, Figs 3B – 3E; [0180] “draw on a touchscreen …6…alternating shapes…daily alteration…”; [0180] “weekly or biweekly”; [0181]) includes:
a first subset of received inputs ([0181] “draw on a touchscreen of the mobile device 6 pre-written alternating shapes of increasing complexity”; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), each indicative of a respective test path traced by a user attempting to trace the reference path on the touchscreen display of the mobile device ([0376] “Circles 120 along the dashed line indicate waypoints that subjects have to pass through.”; Fig 3A; Fig 13A, Figs 11A and 11B, Figs 9A and 9B; [0181]) using their dominant hand (Fig 3b “Use your dominant hand” 3rd bullet instruction; Figs 3C – 3E; [0181] “performed with right and left hand”), the first subset of received inputs having a respective first subset of extracted pieces of digital biomarker data (Figs 9A and 9B, Figs 10A – 10C, [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”),; and
a second subset of receive inputs ([0180] “The test is, typically, performed daily but could alternatively be performed at lower (e.g., weekly or bi-weekly) frequency.”; ([0181] “draw on a touchscreen of the mobile device 6 pre-written alternating shapes of increasing complexity”; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), each indicative of a respective test path traced by a user attempting to trace the reference path on the touchscreen display of the mobile device using their non-dominant hand ([0181] “The test will be alternatingly performed with right and left hand…”; [0376] “Circles 120 along the dashed line indicate waypoints that subjects have to pass through.”; Fig 3A; Fig 13A, Figs 11A and 11B, Figs 9A and 9B; [0181])(Examiner notes that alternatingly performing the test with each hand would allow for both the dominant and non-dominant hands to be tested.), the second subset of received inputs having a respective second subset of extracted pieces of digital biomarker data (Figs 9A and 9B, Figs 10A – 10C, [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”; [0270] “actually determined performance parameter to an earlier determined on used as a reference…difference in the value of the said performance parameter…”)(Examiner notes that pieces of digital biomarker data are extracted each time the test is performed so that they can be compared.);
the method further comprises:
deriving a first statistical parameter corresponding to the first subset of extracted pieces of digital biomarker feature data (Table 1, “Mean Error, Std. Error”; Table 2, “Mean Error, Std. Error”; [0332]; [0180] “performed daily but could alternatively be performed at lower (e.g., weekly or bi-weekly) frequency.”[0181], [0183], [0184] – [0192]);
deriving a second statistical parameter corresponding to the second subset of extracted pieces of digital biomarker feature data (Table 1, “Mean Error, Std. Error”; Table 2, “Mean Error, Std. Error”; [0332]; [0180] “performed daily but could alternatively be performed at lower (e.g., weekly or bi-weekly) frequency.”[0181], [0183], [0184] – [0192]; [0270]; [0180]) (Examiner notes that statistical parameters are derived each time the test is performed so that they can be compared.); and
calculating a parameter by calculating the difference between the first statistical parameter and the second statistical parameter ([0270] “..an improvement, worsening or unchanged overall disease condition or of symptoms thereof can be determined by comparing an actually determined performance parameter to an earlier determined one used as a reference…”), and optionally dividing the difference by the first statistical parameter or the second statistical parameter.
Baker does not specifically disclose calculating a handedness parameter.
Creagh teaches smartphone-based touchscreen drawing shape tests for assessing upper extremity function for multiple sclerosis, incorporating machine learning to predict performance in the 9-Hole Peg Test. Specifically for Claim 10, Creagh teaches calculating a handedness parameter ([Page 5, “2.3. Regression Model to 9HPT, 2.3.1. Data Selection” Section] “…prediction of clinical 9HPT times using features relating to UE function computed from the DaS test…Eq (3)…Response variable, Y…average 9HPT time…for each respective dominant and non-dominant handed 9HPT separately.”)(Examiner notes that there is a parameter for each of the handedness options, “dominant” and “non-dominant”.)
Baker discloses at [0040] and [0042] including “..the method can be applied in the context of: developing algorithmic solutions using for instance machine-learning and pattern recognition techniques to estimate probabilities of DMT response of failure as evaluated by the risk of ongoing disability progression…as measured for instance…by the…9-Hole peg test.” This indicates that it can be combined with Creagh, which teaches an algorithmic technique to predict clinical 9HPT times using features relating to a Draw a Shape dataset. Creagh also provides a motivation to combine at [Page 5, “2.3. Regression Model to 9HPT, 2.3.1. Data Selection” Section, Under Eq 3] with “We assume that drawing performance will generally vary depending on dominance (references give), therefore independent models were evaluated based on dominant and non-dominant hands used.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that investigating drawing data relative to dominant and non-dominant handedness would be critical to ensuring that differences between drawing abilities with each hand are accounted for. Creagh teaches that there is a difference in the performance of the hands in the drawing task, and Baker discloses that it investigates differences in performance by subtracting performance data for trials over time (Baker: [0270]), which is uses to show disease progression. It would be predictable to use the same performance parameter subtraction process to confirm the “difference” in dominant and non-dominant drawing performance, as taught by Creagh.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the Draw a Shape test and determining the differences between performance parameters of trials over time disclosed in Baker with Prana’s teaches to separate data by dominant and non-dominant hand, and that drawing performance varies by dominance. This would create a single smartphone-based drawing test system with capabilities to analyze motor function results to correlate drawing test performance to multiple sclerosis progression, while accounting for differing hand dominance.
Regarding Claim 14, Baker discloses as described above, The computer-implemented method of claim 13 (See 112b rejection and interpretation above). For the remainder of Claim 14, Baker does not specifically disclose the 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 last-squares (PLS); random forest (RF); and extremely randomized trees (XT). Baker broadly discloses at [0040] and [0042] including “..the method can be applied in the context of: developing algorithmic solutions using for instance machine-learning and pattern recognition techniques to estimate probabilities of DMT response of failure as evaluated by the risk of ongoing disability progression…as measured for instance…by the…9-Hole peg test.”
Creagh teaches the analysis model is a regression model [Page 5, “2.3. Regression Model to 9HPT” Section, Paragraph 1] “…a simple linear regression model”, Equation (3); [Page 6, “2.3.4. Model Evaluation” Section, Paragraph 4] “…Support vector regression (SVR)…to perform non-linear regression…”), and the trained machine learning model ([Page 5, “2.3.3. Model Generalisability” Section] “One set was denoted the training set…”; [Page 6, Bottom] “Regressor were built on raw features and trained…”; [Page 6, “2.3.4. Model Evaluation” Section, Paragraph 4] “…Support vector regression (SVR)”)(Examiner notes that that SVR is a type of machine learning.) comprises one or more of the following algorithms: a deep learning algorithm; k nearest neighbours (kNN);
linear regression ([Page 5, “2.3. Regression Model to 9HPT” Section, Paragraph 1] “…a simple linear regression model”, Equation (3));
partial last-squares (PLS);
random forest (RF) ([Page 6, “2.3.4. Model Evaluation” Section, Paragraph 4] “…non-linear random forest regression (RFR) was also investigated”; Table 4); and extremely randomized trees (XT).
Baker and Creagh both disclose and teach smartphone-based shape-drawing distal motor tests for investigating multiple sclerosis progression. Baker provides a motivation to combine at [0040] and [0042] including “..the method can be applied in the context of: developing algorithmic solutions using for instance machine-learning and pattern recognition techniques to estimate probabilities of DMT response of failure as evaluated by the risk of ongoing disability progression…as measured for instance…by the…9-Hole peg test.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that applying machine learning models to the drawing tests and scoring disclosed in Baker would be useful for generating predictions for risk factors associated with multiple sclerosis with larger datasets than a single patient.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the smartphone-based drawing tests and scoring disclosed by Baker with the trained regression models taught by Creagh, creating a single drawing test system with machine learning regression model capabilities to analyze motor function results to correlate drawing test performance to multiple sclerosis progression.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Baker in view of Dounskaia et. al., “Biased Wrist and Finger Coordination in Parkinsonian Patients during Performance of Graphical Tasks”, hereinafter Dounskaia.
Regarding Claim 10, Baker discloses as described above, The computer-implemented method of claim 6. For the remainder of Claim 10, Baker discloses wherein:
the plurality of received inputs (Fig 3A, Figs 3B – 3E; [0180] “draw on a touchscreen …6…alternating shapes…daily alteration…”; [0180] “weekly or biweekly”; [0181]) includes:
a first subset of received inputs ([0181] “draw on a touchscreen of the mobile device 6 pre-written alternating shapes of increasing complexity”; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), each indicative of a respective test path traced by a user attempting to trace the reference path on the touchscreen display of the mobile device in a first direction (Fig 3A, 1st shape is line, instruction to draw in direction from bottom left to top right), the first subset of received inputs having a respective first subset of extracted pieces of digital biomarker data (Figs 9A and 9B, Figs 10A – 10C, [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”),; and
a second subset of receive inputs ([0180] “The test is, typically, performed daily but could alternatively be performed at lower (e.g., weekly or bi-weekly) frequency.”; ([0181] “draw on a touchscreen of the mobile device 6 pre-written alternating shapes of increasing complexity”; Fig 13A, Figs 11A and 11B, Figs 9A and 9B), each indicative of a respective test path traced by a user attempting to trace the reference path on the touchscreen display of the mobile device in a second direction (Fig 3A, 2nd shape is same line, instruction to draw in direction from top right to bottom left), the second subset of received inputs having a respective second subset of extracted pieces of digital biomarker data (Figs 9A and 9B, Figs 10A – 10C, [0205] “…deviations between the drawn trajectory and the target drawing path from starting to ending checkpoints…”; [0270] “actually determined performance parameter to an earlier determined on used as a reference…difference in the value of the said performance parameter…”)(Examiner notes that pieces of digital biomarker data are extracted each time the test is performed so that they can be compared.);
the method further comprises:
deriving a first statistical parameter corresponding to the first subset of extracted pieces of digital biomarker feature data (Table 1, “Mean Error, Std. Error”; Table 2, “Mean Error, Std. Error”; [0332]; [0180] “performed daily but could alternatively be performed at lower (e.g., weekly or bi-weekly) frequency.”[0181], [0183], [0184] – [0192]);
deriving a second statistical parameter corresponding to the second subset of extracted pieces of digital biomarker feature data (Table 1, “Mean Error, Std. Error”; Table 2, “Mean Error, Std. Error”; [0332]; [0180] “performed daily but could alternatively be performed at lower (e.g., weekly or bi-weekly) frequency.”[0181], [0183], [0184] – [0192]; [0270]; [0180]) (Examiner notes that statistical parameters are derived each time the test is performed so that they can be compared.); and
calculating a parameter by calculating the difference between the first statistical parameter and the second statistical parameter ([0270] “..an improvement, worsening or unchanged overall disease condition or of symptoms thereof can be determined by comparing an actually determined performance parameter to an earlier determined one used as a reference…”), and optionally dividing the difference by the first statistical parameter or the second statistical parameter.
Baker does not specifically disclose calculating a directionality parameter. Baker does broadly disclose calculating shape-specific deviations for each shape drawing separately at [0209] “e. Shape-specific deviation…calculated…from each of the 6 distinct shape testing results separately”, and shapes 1 and 2 from Fig 3A are lines drawn in different directions. At minimum, each of the parameters calculated for lines shapes 1 and 2 would incorporate directionality.
Dounskaia teaches that there are differences in wrist and finger coordination in Parkinsonian Patients that can be discerned through instruction to draw different-direction lines. Specifically for Claim 10, Dounskaia teaches calculating a directionality parameter ([Page 5, “Data Analysis” Section”, Paragraph 2] “The data obtained for the four lines were used to calculate…Additionally, stability of wrist and finger coordination was compared between the equivalent and nonequivalent lines by calculating two characteristics, line variability and standard deviation (SD) of line orientation…”; Table 3: Mean (and SD) of line orientation and length; Figure 1—drawing line direction with “wrist only” is x’ direction, opposite to drawing line direction with “Fingers only” as y’ direction).
Dounskaia provides a motivation to combine at [Page 10, 3rd Full Paragraph] with “results of line drawing suggest that although PD causes elevated difficulties in the production of both coordination patterns, these difficulties were markedly higher during the nonequivalent pattern compared with the equivalent pattern.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that accounting for the directionality of a person’s ability to draw would be important for discerning wrist and finger coordination in an intended drawing task, as marked difficulties drawing in one direction versus another could be an indicator of a motor function disorder affecting the upper extremity. Dounskaia teaches that there is a difference in the performance of the hands and wrist depending on the direction in the drawing task, and Baker discloses that it investigates differences in performance by subtracting performance data for trials over time (Baker: [0270]), which is uses to show disease progression. It would be predictable to use the same performance parameter subtraction process for individual different-direction shape drawing trials disclosed by Baker to confirm the “difference” in directional drawing performance, as taught by Dounskaia.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the smartphone-based drawing tests with diagonal lines and individual shape testing results scoring disclosed by Baker with Dounskaia’s teaching that there are differences in finger and wrist coordination for different direction drawn lines in the presence of motor dysfunction, creating a single drawing test system with capabilities to analyze motor function results to correlate drawing test performance in different directions to motor dysfunction disease progression.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Baker in view of Kotsavasiloglou et. al., “Machine learning-based classification of simple drawing movements in Parkinson’s disease”, hereinafter Kotsavasiloglou.
Regarding Claim 15, Baker discloses as described above, The computer-implemented method of claim 13 (See 112b rejection above and interpretation). For the remainder of Claim 15, Baker does not specifically disclose wherein: the analysis model is 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); naive Bayes (NB);random forest (RF); and extremely randomized trees (XT). Baker broadly discloses at [0040] – [0044] that the methods therein can be incorporated with machine learning models.
Kotsavasiloglou teaches machine learning classification modeling applied to performance in a line drawing task to assess motor function in Parkinson’s disease. Specifically for Claim 15, Kotsavasiloglou teaches wherein: the analysis model is a classification model ([Page 178, Left Column, “3.3. Feature selection for a machine learning model” section] “final step in our analysis was to build a classification model that would be trained…”), and the trained machine learning model ([Page 178, Right Column, “3.4 The classification model” Section]) comprises one or more of the following algorithms: a deep learning algorithm; k nearest neighbours (kNN);
support vector machines (SVM) ([Page 178, Right Column, “3.4 The classification model” Section, Paragraph 1] “SVM methodologies”; [Page 179] Table IV, Table V, Table VII);
linear discriminant analysis; quadratic discriminant analysis (QDA);
naive Bayes (NB);random forest (RF) ([Page 178, Right Column, “3.4 The classification model” Section, Paragraph 1] “The best performing classifier was Naïve Bayes…using a selection of features from the feature subset defined by a wrapper of the same learning scheme”; and
extremely randomized trees (XT).
Baker and Kotsavasiloglou both disclose and teach drawing lines with the upper extremity to determine metrics associated with motor function and neurodegenerative disease state: Baker: smart-phone-based drawing by hand onto the screen for multiple sclerosis motor function assessment, and Kotsavasiloglou: hand holding a pen to draw on a tablet for Parkinson’s disease motor function assessment. Baker provides a motivation to combine at [0040] and [0042] including “..the method can be applied in the context of: developing algorithmic solutions using for instance machine-learning and pattern recognition techniques to estimate probabilities of DMT response or failure....” Kotsavasiloglou provides an additional motivation to combine at [Page 179, bottom] – [Page 180, Left column, top] “The achieved accuracy of the model at the higher end of all other similar studies known to the authors, having to do with predictive classification of PD volunteers through handwriting markers, while using more complex and cumbersome tasks and setups.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that applying machine learning models to drawing tests and scoring that would be useful for generating predictions for risk factors associated with motor function with greater confidence, informed by larger datasets than a single patient.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the smartphone-based drawing tests and scoring disclosed by Baker with the trained machine learning classifier models taught by Kotsavasiloglou, creating a single drawing test system with machine learning classifier capabilities to analyze motor function results to correlate drawing test performance to multiple sclerosis progression.
Conclusion
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/MELISSA JO MONTGOMERY/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791
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