DETAILED ACTION
Claims 1-20 are hereby the present claims under consideration.
Examiner’s Note: all references to Applicant’s specification are made using the paragraph numbers assigned in the US publication of the present application US 20250204845 A1.
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 .
Claim Objections
Claims 1, 6, 9-10, and 19-20 are objected to because of the following informalities:
Claims 1, 19, and 20 it appears that “based on the first and second order features” should read “based on the first and the second order features”
Claim 6 lines 1-2 it appears that “a patient” should read “the user” since claim 1 establishes that the multimodal data is collected from a user interaction
Claim 9 line 2 it appears that “one or more stylus position” should read “one or more position of a stylus” to establish proper antecedent basis for line 6 “the stylus”
Claim 10 line 2 it appears that “acceleration” should read “the acceleration”
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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 9-15 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 9 recites “applying the machine learning model further comprises: interpolating one or more stylus position from the processed collection of multimodal data” which appears to indicate that the machine learning model is acting upon the collection of multimodal data. Claim 1 establishes that the machine learning model is applied to the patient data model and further establishes that the patient data model is created based on the first and second order features. Thus it would seem that the machine learning model of claim 1 is applied to the first and second order features rather than the processed collection of multimodal data from which the features are derived. It is unclear if the patient data model further comprises the processed collection of multimodal data in addition to the features or if the machine learning model is applied to both the patient data model and the processed collection of multimodal data. It is unclear how the processing of the processed collection of data recited in claim 9 relates to the application of the machine learning model to determine a cognitive impairment status of claim 1. For the purposes of this examination, the patient data model will be interpreted as including the processed collection of multimodal data in addition to the features derived therefrom and the process of claim 9 is interpreted as part of the determination of cognitive impairment status.
Claims 10-12 are rejected by virtue of their dependence on claim 9.
Claim 10 recites “measuring total energy associated with the velocity and acceleration” but it is unclear if the “total energy” is being calculated using a velocity and acceleration value from a single time point, a subset of the predetermined time points, or all of the predetermined time points. It is unclear if the energy value is associated with one of, a grouping of, or all of the values of the velocity and acceleration. For the purposes of this examination, the total energy values are interpreted as being measured from a collection of the values from all time points.
Claim 11 recites “a perpendicular acceleration motion and/or a parallel acceleration motion” but it is unclear to what reference the perpendicular and parallel components are being compared to. In particular, claim 9 indicates that the acceleration is determined for each predetermined time point. Thus it would seem that each time point has an associated velocity and acceleration value. Claim 10 indicates that energy values are calculated from the acceleration and velocity values and is interpreted as the energy being measured from the collection of all time points. However no reference direction has been established to compare the acceleration values to in order to generate “perpendicular” and/or “parallel” components. For the purpose of this examination, any acceleration metric may be considered perpendicular or parallel.
Claim 13 recites “outputting an individual patient cognitive status” but it is unclear if the limitation “an individual patient cognitive status” is the same as, related to, or different from “a cognitive impairment status” of claim 1. For the purposes of this examination, the limitations are interpreted as referring to the same patient cognitive status.
Claim 14 recites “outputting a group level cognitive status, wherein the group comprises the user and each member having a common attribute” but it is unclear if the limitation “a group level cognitive status” is the same as, related to, or different from “a cognitive impairment status” of claim 1. For the purposes of this examination, the limitations are interpreted as referring to the same patient cognitive status.
Claim 15 recites “designating a class designation of cognitive impairment” but it is unclear how this step relates to the rest of the claimed method. It is unclear how the designation is determined or how it relates to the cognitive impairment status output in claim 1. For the purposes of this examination, the limitation is interpreted as designating the cognitive impairment status of claim 1 as belonging to a class of cognitive impairments.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 15, and 19-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “applying a machine learning model to the patient data model to determine a cognitive impairment status”. The specification does not appear to support the claimed determination being performed using the recited inputs to produce the recited outputs using a machine learning model. MPEP 2161.01(i) recites the claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are notexplained in sufficient detail (simply restating the function recited in the claim is not necessarilysufficient). In other words, the algorithm or steps/procedure taken to perform the function mustbe described with sufficient detail so that one of ordinary skill in the art would understand howthe inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsectionIV.” In particular, the specification does not particularly describe how the input data is considered to produce the recited output. Paragraphs 0068, 0071, 0073, and 0076 are each directed towards the determination of a cognitive status. In particular these paragraphs appear to indicate that the cognitive impairment status is determined using the stylus derived metrics which are supplied to a machine learning model. The machine learning model appears to be described as a “black box” algorithm which may be applied to any form of the recited input data in order to produce the recited output of cognitive impairment status. The specification does not appear to describe how the model considers the plurality of types of input data and processed them into the recited output of a cognitive impairment status. While paragraphs 0045-0060 do describe how movement metrics of the stylus can be used to differentiate healthy patients from patient with essential tremor, such teachings are not considered sufficient to support the claimed genus as they are not considered a representative number of species for the claimed genus. Additionally the recitations of paragraphs 0068-0073 and 0076 are considered mere generic statements of functionality. The specification does not appear to describe specifically how the stylus derived metrics are being incorporated into previously known cognitive tests to produce the claimed result of a cognitive impairment status. As per MPEP 2161.01: It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. This rejection is similarly applied to the similar limitations of claims 19 and 20.
Claim 15 recites “designating a class designation of cognitive impairment” however the specification does not appear to describe how such a class designation is determined. MPEP 2161.01(i) recites the claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are notexplained in sufficient detail (simply restating the function recited in the claim is not necessarilysufficient). In other words, the algorithm or steps/procedure taken to perform the function mustbe described with sufficient detail so that one of ordinary skill in the art would understand howthe inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsectionIV. In particular, paragraph 0069 recites this limitation in purely functional language. The specification does not appear to describe how the class designation is generated. The particular algorithm or steps take to achieve the recited function are not seemingly disclosed. Thus the claim is considered to lack sufficient written description support.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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. Claims 1-20 are directed to a method of processing multimodal signals using a computational algorithm, which is an abstract idea. Claims 1-20 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019) and the 2024 Update on Subject Matter Eligibility (Federal Register, Vol 89, No. 137, page 58128, July 17, 2024).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is drawn to a process
Step 2A – Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations:
[A1] processing the collection of multimodal data according to each modality within the collection of multimodal data
[B1] deriving one or more first order features from the processed collection of multimodal data
[C1] deriving one or more second order features from the processed collection of multimodal data
[D1] creating a patient data model based on the first and second order features
[E1] determine a cognitive impairment status
These elements [A1]-[E1] of claim 1 are drawn to an abstract idea since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
[A2] receiving a collection of multimodal data of a user interaction with a computing device
[B2] applying a machine learning model to the patient data model
These elements [A2]-[B2] of claim 1 do not integrate the exception into a practical application of the exception. In particular, the element [A2] is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g). Additionally, the element [B2] is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation “receiving a collection of multimodal data of a user interaction with a computing device” does not qualify as significantly more because this limitation merely describes the nature of the received data and does not incorporate the computing device or any other data gathering device as part of the claimed invention.
Furthermore, the element [B2] is merely an instruction to implement the abstract idea onto a computer using a machine learning algorithm to replace the human clinician’s decision making capabilities. The machine learning algorithm is merely a computerized substitution for the decision making process of the clinician and does not amount to significantly mor than the abstract idea.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 2-18 depend from claim 1, and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm), with the following exceptions:
Claim 2: one or more of a touchscreen, a stylus, a webcam, and/or a microphone;
Claim 8; an artificial neural network;
Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of claim 2 are merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g). Additionally, the elements of claim 8 are merely a recitation to implement the decision making ability of a clinician into a computer.
Furthermore, the limitations of claims 6-7 merely serve to describe the nature of the received data and do not incorporate the clinical assessment or stylus component into the claimed method.
Also, each of these limitations does not recite additional elements that amount to significantly more than the judicial exception itself because they are merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, each of the recited data gathering modalities of claim 2 are routine, conventional, and/or well-known as evidenced by Applicant’s lack of a particular description regarding the structure and/or function of any of these components. In particular, the touchscreen, webcam, and microphone are all conventional components of a generic computer recited at a high level of generality. Additionally, the stylus is well-known and commercially available as evidenced by paragraph 0062 of Applicant’s specification which recites that an Apple Pencil may be a suitable instrumented stylus.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
The analysis of claims 19-20 is performed in light of the above analysis of claims 1-18 and is abridged where similar limitations have already been addressed,
The analysis of claim 19 is as follows:
Step 1: Claim 19 is drawn to a machine.
Step 2A – Prong One: Claim 19 recites an abstract idea. In particular, claim 19 recites the same abstract idea as claim 1.
The abstract idea of claim 1 involves a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 19 recites the following limitations that are beyond the judicial exception and have not already been addressed in the above rejection of claim 1:
[A2] at least one input device
[B2] a computing node comprising a computer readable storage medium and a processor
These elements [A2]-[B2] of claim 19 do not integrate the exception into a practical application of the exception. In particular, the elements [A2]-[B2] are merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
Step 2B: Claim 19 does not recite additional elements that amount to significantly more than the judicial exception itself.
The elements [A2]-[B2] do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
The analysis of claim 20 is as follows:
Step 1: Claim 20 is drawn to a machine.
Step 2A – Prong One: Claim 20 recites an abstract idea. In particular, claim 20 recites the same abstract idea as claim 1.
The abstract idea of claim 1 involves a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 20 recites the following limitations that are beyond the judicial exception and have not already been addressed in the above rejection of claims 1 and 19:
[A2] a computer readable storage medium
[B2] a processor
These elements [A2]-[B2] of claim 20 do not integrate the exception into a practical application of the exception. In particular, the elements [A2]-[B2] are merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
Step 2B: Claim 20 does not recite additional elements that amount to significantly more than the judicial exception itself.
The elements [A2]-[B2] do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
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, 13, 16, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pascual-Leone International Patent Application Publication Number WO 2022067189 A1 hereinafter Pascual.
Regarding claim 1, Pascual discloses a method (Abstract), comprising:
receiving a collection of multimodal data of a user interaction with a computing device (Paragraphs 0076-0077 and 0105: multimodal data is collected from a battery of tests; Fig. 11; Paragraphs 0117, 0119, and 0128-0130: a variety of tasks may be carried out by a user interacting with a computing device);
processing the collection of multimodal data according to each modality within the collection of multimodal data (Paragraphs 0078, 0119-0132: the various data types from the different assessments include processed data such as gaze location and gaze patterns which are determined from a full face video. Thus the various raw multimodal measurements are processed according to their respective modalities to produce the parameters used by the system);
deriving one or more first order features from the processed collection of multimodal data (Paragraphs 0105-0106: derivation of first and second order features; Fig. 11);
deriving one or more second order features from the processed collection of multimodal data (Paragraphs 0105-0106: derivation of first and second order features; Fig. 11);
creating a patient data model based on the first and second order features (Paragraphs 0080 and 0107: generation of the digital twin or patient data model; Fig. 11); and
applying a machine learning model to the patient data model to determine a cognitive impairment status (Paragraphs 0090, 0093-0094, 0096, and 0103: the patient data model may be supplied to a machine learning algorithm which may perform differential diagnosis or other processes to output a diagnosis such as a likelihood of Alzheimer’s disease which is a cognitive impairment status).
Regarding claim 2, Pascual discloses the method of claim 1. Pascual further discloses the method wherein the collection of multimodal data comprises data collected from one or more of a touchscreen, a stylus, a webcam, and/or a microphone (Paragraphs 0119-0132: the various assessments include data gathering from a tablets or smartphones which include microphones and cameras for speech and gaze tracking, digitized ballpoint pen, or stylus, for DCT clock assessments, and a tablet and stylus for drawing assessments).
Regarding claim 3, Pascual discloses the method of claim 1. Pascual further discloses the method wherein the first order features comprises one or more of a stroke-based drawing feature, a time-based drawing feature, an eye tracking feature, a sentiment feature, a stylus orientation feature, a stylus force strength feature, a speech content feature, and/or a speech aural qualities feature (Paragraphs 0119-0132: the time-stamped drawing signal, the DCTclock test records every change in pen position including every pen stroke, linguistic and phonetic measures are made from speech measurements, eye tracking features are generated from gaze tracking assessments; Paragraphs 0053, 0057-0059: first order features include any features derived from raw data inputs ).
Regarding claim 4, Pascual discloses the method of claim 3. Pascual further discloses the method wherein the stylus orientation feature comprises a measurement of one or more of azimuth, altitude, temporal dynamics, and/or an ink distance (Paragraphs 0049, 0054, and 0056: data from a digital clock drawing assessment include X coordinate, Y coordinate, Azimuth pair, Altitude, and Force; Paragraph 0047: multimodal data inputs may come from a mobile device stylus; Fig. 3: the first order metrics from the digital clock drawing).
Regarding claim 5, Pascual discloses the method of claim 3. Pascual further discloses the method wherein the stylus force strength feature comprises a measurement of pressure on the stylus (Paragraphs 0049, 0054, 0056: force is one of the received measurements; Paragraph 0047: multimodal data inputs may come from a mobile device stylus).
Regarding claim 6, Pascual discloses the method of claim 1. Pascual further discloses the method wherein the collection of multimodal data is collected from a patient during a clinical assessment (Paragraphs 0110-0132: the variety of tasks and/or assessments performed on the patient. Each of the performed assessments during which data is gathered may be considered a clinical assessment.).
Regarding claim 7, Pascual discloses the method of claim 6. Pascual further discloses the method, wherein the clinical assessment comprises a stylus component (Paragraphs 0047 and 0119-0125: the assessments may include stylus-based assessments)
Regarding claim 8, Pascual discloses the method of claim 1. Pascual further discloses the method wherein the machine learning model comprises an artificial neural network (Paragraphs 0005-0016: the algorithm may include an artificial neural network).
Regarding claim 13, Pascual discloses the method of claim 1. Pascual further discloses the method further comprising outputting an individual patient cognitive status (Paragraphs 0090, 0093-0094, 0096, and 0103: the patient data model may be supplied to a machine learning algorithm which may perform differential diagnosis or other processes to output a diagnosis such as a likelihood of Alzheimer’s disease which is a cognitive impairment status).
Regarding claim 16, Pascual discloses the method of claim 1. Pascual further discloses the method wherein the collection of multimodal data further comprises one or more questionnaire assessments or electronic health records (Paragraphs 0033 and 0132: the assessments may include questionnaires), wherein, determining the second order feature uses data embedded in the one or more questionnaire assessments or electronic health records (Paragraph 0061: second order features may include electronic health records; Paragraph 0089: second order features may include embedded data).
Regarding claim 19, pascual discloses a system (Abstract) comprising:
at least one input device (Paragraphs 0046-0048: multimodal data inputs from devices such as the Bluetooth stylus);
a computing node coupled to the at least one input device and comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor of the computing node to cause the processor to perform a method (Paragraphs 0133-0141 and Fig. 15: the computing node comprises a processor and memory for executing the algorithm and may communicate with external devices such as the devices which produce the multimodal input which is received by the system as in paragraphs 0046-0048) comprising:
receiving a collection of multimodal data of a user interaction with the at least one input device (Paragraphs 0076-0077 and 0105: multimodal data is collected from a battery of tests; Fig. 11; Paragraphs 0117, 0119, and 0128-0130: a variety of tasks may be carried out by a user interacting with a computing device);
processing the collection of multimodal data according to each modality within the collection of multimodal data (Paragraphs 0078, 0119-0132: the various data types from the different assessments include processed data such as gaze location and gaze patterns which are determined from a full face video. Thus the various raw multimodal measurements are processed according to their respective modalities to produce the parameters used by the system);
deriving one or more first order features from the processed collection of multimodal data (Paragraphs 0105-0106: derivation of first and second order features; Fig. 11);
deriving one or more second order features from the processed collection of multimodal data (Paragraphs 0105-0106: derivation of first and second order features; Fig. 11);
creating a patient data model based on the first and second order features (Paragraphs 0080 and 0107: generation of the digital twin or patient data model; Fig. 11); and
applying a machine learning model to the patient data model to determine a cognitive impairment status (Paragraphs 0090, 0093-0094, 0096, and 0103: the patient data model may be supplied to a machine learning algorithm which may perform differential diagnosis or other processes to output a diagnosis such as a likelihood of Alzheimer’s disease which is a cognitive impairment status).
Regarding claim 20, Pascual discloses a computer program product for determining a cognitive impairment status, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method (Abstract; Paragraphs 0133-0141 and Fig. 15: the computer and memory for carrying out the method) comprising:
receiving a collection of multimodal data of a user interaction with a computing device (Paragraphs 0076-0077 and 0105: multimodal data is collected from a battery of tests; Fig. 11; Paragraphs 0117, 0119, and 0128-0130: a variety of tasks may be carried out by a user interacting with a computing device);
processing the collection of multimodal data according to each modality within the collection of multimodal data (Paragraphs 0078, 0119-0132: the various data types from the different assessments include processed data such as gaze location and gaze patterns which are determined from a full face video. Thus the various raw multimodal measurements are processed according to their respective modalities to produce the parameters used by the system);
deriving one or more first order features from the processed collection of multimodal data (Paragraphs 0105-0106: derivation of first and second order features; Fig. 11);
deriving one or more second order features from the processed collection of multimodal data (Paragraphs 0105-0106: derivation of first and second order features; Fig. 11);
creating a patient data model based on the first and second order features (Paragraphs 0080 and 0107: generation of the digital twin or patient data model; Fig. 11); and
applying a machine learning model to the patient data model to determine a cognitive impairment status (Paragraphs 0090, 0093-0094, 0096, and 0103: the patient data model may be supplied to a machine learning algorithm which may perform differential diagnosis or other processes to output a diagnosis such as a likelihood of Alzheimer’s disease which is a cognitive impairment status).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The 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 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Pascual-Leone International Patent Application Publication Number WO 2022067189 A1 hereinafter Pascual as applied to claim 1 above and further in view of Davis US Patent Application Publication Number US 20220054077 A1 hereinafter Davis
Regarding claim 9, Pascual discloses the method of claim 1. Pascual fails to further disclose the method wherein applying the machine learning model further comprises: interpolating one or more stylus position from the processed collection of multimodal data at predetermined points in time; filtering the processed collection of multimodal data with both a low-pass and band-pass filter to determine an impulse response; and estimating a velocity and an acceleration of the stylus at the predetermined points in time.
Davis teaches systems and methods for detecting tremors in a subject. The method comprising receiving data from a digital device, the data comprising a plurality of digital device positions and a plurality of timestamps, each timestamp in the plurality of timestamps being associated with a digital device position in the plurality of digital device positions (Abstract). Thus, Davis is reasonably pertinent to the problem at hand.
Davis teaches which utilizes a digital pen or stylus to collect movement information (Paragraph 0049 a stylus for recording the pen strokes during a digital clock test) and a method wherein applying the machine learning model (Paragraph 0006: the method may be implemented using machine learning) further comprises: interpolating one or more stylus position from the processed collection of multimodal data at predetermined points in time (Paragraphs 0052 and 0060: interpolating the position of the pen between sampled points, or predetermined points in time; Fig. 8 reference 810); filtering the processed collection of multimodal data with both a low-pass and band-pass filter to determine an impulse response (Paragraphs 0060-0061 and Fig. 8: the low-pass and band-pass filters and their corresponding impulse response; Paragraphs 0068 and 0075-0076: the signal is passed through both filters as comparing the two provides useful insight into the patient condition); and estimating a velocity and an acceleration of the stylus at the predetermined points in time (Paragraphs 0060-0061: the estimation of the acceleration and velocity; Figs 12B-D and 13B-D).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the stylus movement processing taught by Davis into the method of Pascual because Davis teaches that the recited method of processing produce signals that readily allow a user to distinguish a patient with Essential Tremor from a healthy patient (Davis: Paragraphs 0042, 0066-0067, and 0088) which would allow the method of Pascual to detect essential tremor which is a motor disorder associated with some cognitive disorders such as Parkinson’s disease and Pascual contemplates detecting essential tremor as an input for cognitive impairment detection (Pascual: paragraph 0062) and thus incorporating the processing Davis into the method of Pascual would allow the method to detect essential tremor directly from the digital clock test which may improve the diagnosis output of Pascual.
Regarding claims 10-12, Pascual in view of Davis teaches the method of claim 9. Modified Pascual fails to further disclose the method further comprising measuring a tremor in the stylus by: measuring total energy associated with the velocity and acceleration to determine one or more energy norms; wherein the one or more energy norms include one or more of an acceleration magnitude, a perpendicular acceleration motion, and/or a parallel acceleration motion; and wherein the machine learning model is trained by comparing a tremor measured in a patient diagnosed with Essential Tremor with a tremor measured in a healthy patient.
Davis teaches a method of measuring a tremor in the stylus by: measuring total energy associated with the velocity and acceleration to determine one or more energy norms (Paragraphs 0079-0085: the strength of the velocity and acceleration signals can be used to quantify tremor. The total energy may be determined and normalized to determine “norms” or reference values); wherein the one or more energy norms include one or more of an acceleration magnitude, a perpendicular acceleration motion, and/or a parallel acceleration motion (Paragraph 0076: the perpendicular and parallel components of both velocity and acceleration; Paragraph 0079: the equations include sums for acceleration magnitude and sums for each of the perpendicular and parallel components individually); and comparing a tremor measured in a patient diagnosed with Essential Tremor with a tremor measured in a healthy patient (Paragraphs 0087-0093: Threshold are determined to distinguish healthy patients from patients with Essential Tremor). Davis further teaches that the method may be implemented on machine learning algorithms (Paragraph 0006).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to incorporate the energy calculations of Davis into the method of Pascual because Davis provides a direct method of identifying essential tremor from instrumented stylus signals and Pascual contemplates detecting essential tremor as an input for cognitive impairment detection (Pascual: paragraph 0062) and thus incorporating the processing Davis into the method of Pascual would allow the method to detect essential tremor directly from the digital clock test which may improve the diagnosis output of Pascual.
Claims 14-15 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Pascual-Leone International Patent Application Publication Number WO 2022067189 A1 hereinafter Pascual as applied to claim 1 above and further in view of Martucci US Patent Application Publication Number US 20160262680 A1 hereinafter Martucci.
Regarding claim 14, Pascual discloses the method of claim 1. Pascual fails to further disclose the method further comprising outputting a group level cognitive status, wherein the group comprises the user and each member having a common attribute.
Martucci a computer-implemented cognitive assessment tool is provided for assessing cognitive ability of an individual while multi-tasking (Abstract). Thus, Martucci falls within the same field of endeavor as Applicant’s invention.
Martucci teaches outputting a group level cognitive status, wherein the group comprises the user and each member having a common attribute (Paragraph 0113: The model is used to label the user and assign the user to a group. Thus the output is the group cognitive status wherein the group comprises the user and all other patients with the common attribute of cognitive disorder)
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the grouping of patients with the same or similar cognitive disorder and outputting the patient’s assignment to one of these groups as taught by Martucci into the method of Pascual because grouping commonly diagnosed patients may allow better tracking of treatment effect and may allow more effective treatments to be recommended based on known outcome of other patients in the group thereby improving the effectiveness of treatment recommendation.
Regarding claim 15, Pascual discloses the method of claim 1. Pascual fails to further disclose the method further comprising designating a class designation of cognitive impairment.
Martucci teaches a method further comprising designating a class designation of cognitive impairment (Paragraphs 0135-0136 and 0156: Outputting the severity of the cognitive deficit).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to combine the output of severity as taught by Martucci into the method of Pascual because outputting a severity assessment would further allow the grouping of patient’s based on the severity of their condition and may allow more appropriate treatments to be recommended based on their assigned severity class thereby improving patient outcomes.
Regarding claims 17 and 18, Pascual discloses the method of claim 1. Pascual fails to further disclose the method further comprising tracking the cognitive impairment status over time; and detecting changes in the cognitive impairment status based on said tracking.
Martucci teaches a method including tracking the cognitive impairment status over time, and detecting changes in the cognitive impairment status based on said tracking. (Paragraphs 0125-0133 and 0136: measures may be taken over time in order to allow the system to monitor cognitive deficits over time and track the progression of a disease. Sudden changes in symptoms may trigger precautions).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the testing over time in order to monitor the state of the patient as taught by Martucci into the method of Pascual because Martucci teaches that performing such monitoring allows the method to determine when patients have a sudden change in disease severity so that precautions may be taken for their health (Martucci: Paragraph 0136). Performing tracking over time allows the method to evaluate the progress of a user’s disease and adjust treatments accordingly.
Conclusion
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/MATTHEW ERIC OGLES/Examiner, Art Unit 3791
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