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 .
Claim Objections
Claims 4-6, 8, 12-13, and 22 are objected to because of the following informalities:
Regarding claims 4, 12 and 22, the term “regression model” in line 2 is missing a prerequisite “a” such that the term reads “a regression model”.
Regarding claim 5 and 13, the term “comprises one of feature-based…” in line 1 is missing a prerequisite “a” such that the term reads “comprises one of a feature-based…”.
Regarding claim 8, the terms “postural sway” and “fractal dimension” in line 2, “QT-interval” in line 3, and “power spectral density (PSD)” in line 4 are missing a prerequisite “a” such that the terms read “a postural sway”, “a fractal dimension”, “a QT-interval”, and “a power spectral density (PSD)”, respectively. Additionally, in line 3, the list is missing a requisite “or” such that the claim should read “…comprising the elliptical area sway, entropy, fractal dimension, fractal dynamics, a Lyapunov exponent, raw ECG signals, heart rate variability (HRV), QT-interval, or power spectral density (PSD)”.
Claim 19 is objected to under 37 CFR 1.75 as being a substantial duplicate of claim 17. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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.
Claim 8 is 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 8 recites the limitation "the elliptical area sway" in line 2. There is insufficient antecedent basis for this limitation in the claim. For the purposes of Examination, Examiner interprets “the elliptical area sway” as “an elliptical area sway”.
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Eligibility Step 1 – The Four Categories of Statutory Subject Matter
Claims 1-23 fall within one of the four categories of statutory subject matter. Claims 1-8 are drawn to “a method” (i.e., a process), claims 9-15 are drawn to “a system” (i.e., a machine), and claims 16-23 are drawn to “a tangible, non-transitory computer readable medium” (i.e., a machine), and thus fall within one of the four statutory categories.
Eligibility Step 2A, Prong One
Claims 1-23 recite an abstract idea:
Regarding independent claims 1, 9, and 16, the limitations of “inputting the ECG data and IMU data to a first computational model and a second computational model; and inferring a presence of the ND based on the first computational model, or a change in the ND based on the second computational model” are directed to an abstract idea.
The limitations, as drafted, describe a process that, under its broadest reasonable interpretation, includes performance of the limitation in the mind except for the recitations of “a memory” in independent claim 9, “a processor” in independent claims 9 and 16, and “A tangible, non-transitory computer readable medium that stores instructions” in independent claim 16.
Additionally, other than reciting “a memory”, “a processor”, and “a tangible, non-transitory computer readable medium that stores instructions” are performing these tasks, nothing in the claim precludes the steps from practically being performed in the human mind or being considered as methods of organizing human activity since these steps are performed by computational models.
This claim language is identified as an abstract idea. MPEP 2106.04(a)(2)(II) states that the sub-grouping "managing personal behavior or relationships or interactions between people" include social activities, teaching, and following rules or instructions and MPEP 2106.04(a)(2)(III) states that the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea.
In the instant case, this determination does not require any structure to perform the step, where a person or medical professional may determine presence or progression of such a disease by analyzing relevant patient data, such that the claims are directed to organizing human activity. Furthermore, the claim encompasses aiding the medical professional in computing and evaluating parameters associated with ECG and IMU data (mental process) to determine and communicate the condition of a patient (organizing human activity). The claims do not require the use of a computer beyond the recitation of a general-purpose processor to gather information about a subject, therefore they are not self-evidently patent eligible.
Eligibility Step 2A, Prong Two
Claims 1-23 do not recite additional elements that integrate the judicial exception into a practical application:
Regarding independent claims 1 and 9, the limitation of “an electrocardiogram (ECG) sensor and an inertial measurement unit (IMU) sensor” generally link the use of the mental process to a particular field and are merely insignificant, extra-solution activity used for data gathering.
Regarding independent claims 1, 9, and 16, the limitation of “gathering ECG data and IMU data for the subject” is merely insignificant, extra-solution activity that generally links the use of the mental process to a particular field of use or device.
Regarding independent claims 9 and 16, the limitation of “a processor” generally links the use of the mental process to a particular field and merely uses a computer as a tool to perform the mental process.
Regarding independent claim 9, the limitation of “a memory that stores instructions” generally links the use of the mental process to a particular field and merely uses a computer as a tool to perform the mental process.
Additionally, the claims recite “a memory”, “a processor, and “a tangible, non-transitory computer readable medium that stores instructions” to perform the abstract steps. These components read on a computer implemented system and method and are recited at a high level of generality, i.e., as a generic processor, performing a generic computer function of processing data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional limitation does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Eligibility Step 2B
Claims 1, 9, and 16 do not amount to significantly more than the abstract ideas recited therein. As discussed with respect to Step 2A, Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial except into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. Applicant’s specification in ¶[0036] and ¶[0033] does not provide any indication that the computer is anything other than a generic, off-the-shelf computer component. Court decisions cited in MPEP 2106.05(d)(II) indicate that computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim, as a whole, amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking). Accordingly, a conclusion that the generic computer functions merely being used to implement an abstract idea is well-understood, routine, conventional activity is supported under Berkheimer Option 2.
Furthermore, regarding independent claims 1 and 9, the limitations of “an electrocardiogram (ECG) sensor” and “an inertial measurement unit (IMU) sensor” generally link the use of the mental process to a particular field and are merely insignificant, extra-solution activity used for data gathering. For example, Al-Ali (U.S. Pub. No. 2017/0055851 A1) teaches that an electrocardiogram sensor and an inertial measurement unit sensor is well-known within the art (¶[0091], where “Accelerometers 210 are well known to those skilled in the art”, ¶[0119], where “A skilled artisan will appreciate that many structures, forms, and formats of ECG electrodes are well known in the art”).
Regarding dependent claims 2-8, 10-15, and 17-23, the limitations of these claims further define the limitations already indicated as being directed to the abstract idea as recited in claims 1, 9, and 16.
Therefore, these additional elements do not amount to significantly more than the judicial exception and the claimed subject matter appears to be ineligible under §101.
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.
Claims 1-23 are rejected under 35 U.S.C. 103 as being unpatentable over Pascual-Leone et al. (hereinafter “Pascual”) (U.S. Pub. No. 2023/0255564 A1) in view of Sobol et al. (hereinafter “Sobol”) (U.S. Pub. No. 2019/0209022 A1).
Regarding claim 1, Pascual teaches a method of analyzing a neurodegenerative and/or a neurological disorder (ND) (Abstract, where “Systems, methods, and computer program products are provided for determining one or more biomarker and/or health condition of a target patient … a method is provided where a plurality of health data of the target patient and/or a plurality of first order features determined from the plurality of health data of the target patient are received as input to a pre-trained artificial neural network ... The pre-trained learning system is trained to receive as input the plurality of latent variables and output one or more biomarker and/or health condition of the target patient,” ¶[0032], where “the present disclosure provides systems, methods, and computer program products for machine-learning-assisted determination of patient biomarkers and/or health conditions, and generation of a latent representation of patient cognitive health. In various embodiments, a system can administer a series of cognitive assessments to an individual to capture raw health data regarding the patient (e.g., speech, gait and balance, eye motion, drawing, sleep, facial expressions, gestures) from various different modalities, generate first-order features from the raw health data derived from these data, and relate those to specific brain health domains, clinical diagnoses, and/or treatment plans”), the method comprising:
gathering ECG data and IMU data for the subject (¶[0033], where “In various embodiments, the present disclosure may integrate data received from health tasks across multiple modalities captured using smartphone, tablet, or other sensors to generate aggregate measures of brain function for different cognitive biomarkers and/or diagnoses. In various embodiments, health data from the various modalities may be provided to a machine learning system to thereby generate associations between the data,” ¶[0105], where “As shown in FIG. 11, raw data and first order and/or second order features feed into a predictive model to produce a particular output,” ¶[0106], where “the system can leverage various tasks and/or assessments of patients delivered on mobile devices to predict values for other modalities that may be too difficult to directly measure in patients ... In one example, a hospital may only have a 2-lead electrocardiogram (ECG/EKG), but a particular machine learning model may require 6 or 12 lead values as input. Machine learning models as described herein may be used to generate synthesized 6 and/or 12 lead data based on the raw 2 lead data, as well as other recorded health data for that particular patient, and first order and/or second order features determined from that data, to generate the synthesized 6 and/or 12 lead data,” ¶[0114], where “In various embodiments, the tasks and/or assessments may include one or more ball balancing tasks to assess motor control and coordination. In various embodiments, a test taker may hold a device parallel to the ground and tilts the screen as needed to keep a virtual ball within a target area. In various embodiments, inertial measurement unit (IMU) sensors may be used to measure reaction time, fine motor control, movement characteristics, tremor, and/or dyskinesia.” Examiner interprets that ECG and IMU data are gathered since they are input into the predictive model, where the relevant data must be gathered in order to be inputted into the predictive model.);
inputting the ECG data and IMU data to a first computational model (¶[0007], where “a method is provided of training a system to determine one or more biomarker and/or health condition of a target patient where a plurality of health data and/or a plurality of first order features determined from the plurality of health data is received as input to a first artificial neural network”) and a second computational model (¶[0007], where “A second artificial neural network is trained to output one or more biomarker and/or health condition based on the plurality of latent variables”); and
inferring a presence of the ND based on the first computational model (¶[0088], where “FIG. 13 illustrates an exemplary model leveraging first and second order features to predict the onset of Alzheimer's disease … In various embodiments, features can be targeted for prediction or detection within a shorter period of time more for immediate diagnostic purposes. In various embodiments, the patient data model may include first order features (shown in FIGS. 12 and 13)”), or a change in the ND based on the second computational model.
Although Pascual teaches the utilization of an electrocardiogram (ECG) sensor (¶[0106], where “a hospital may only have a 2-lead electrocardiogram (ECG/EKG), but a particular machine learning model may require 6 or 12 lead values as input. Machine learning models as described herein may be used to generate synthesized 6 and/or 12 lead data based on the raw 2 lead data, as well as other recorded health data for that particular patient, and first order and/or second order features determined from that data, to generate the synthesized 6 and/or 12 lead data”) and an inertial measurement unit (IMU) sensor (¶[0114], where “a test taker may hold a device parallel to the ground and tilts the screen as needed to keep a virtual ball within a target area. In various embodiments, inertial measurement unit (IMU) sensors may be used to measure reaction time, fine motor control, movement characteristics, tremor, and/or dyskinesia”) to gather related data, Pascual does not explicitly teach applying an electrocardiogram (ECG) sensor and an inertial measurement unit (IMU) sensor to a subject.
Sobol teaches a wearable electronic device, a system and methods of monitoring with a wearable electronic device with a predictive health care protocol that uses a machine learning model (Abstract), and further teaches applying an electrocardiogram (ECG) sensor (¶[0015], where “at least one of the sensors that are configured to detect physiological data includes at least one sensor selected from the group consisting of … an electrocardiogram sensor”) and an inertial measurement unit (IMU) sensor (¶[0016], where “at least one of the sensors that are configured to detect activity data comprises at least one sensor selected from the group consisting of … an inertial measurement unit”) to a subject (¶[0153], where “the wearable electronic device 100 includes … The housing 110 includes a central body, as well as two opposing lateral extensions 111, 112. As will be discussed in more detail below, these lateral extensions 111, 112—in addition to providing a mounting location for a strap (such as the one shown in FIG. 2H as a conventional NATO-style band 190) such as that used with a wristwatch—may provide a trough or cavity-like recess into which the sensors 121 … may be placed,” ¶[0167], where “the sensors 121 may be placed into three major groups for the acquisition of the other components of the LEAP data. In one form, sensors representative of these other three major groups include environmental sensors 121A, activity sensors 121B and physiological sensors 121C … activity sensors 121B that are used to collect activity data may include accelerometers, gyroscopes, magnetometers or the like … and the physiological sensors 121C used to collect physiological data may include those configured to acquire heart rate, breathing rate, glucose, blood pressure, cardiac activity, temperature, oxygen saturation, smells (such as total volatile organic compounds (TVOC)) or the like”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Sobol, which teaches applying an electrocardiogram (ECG) sensor and an inertial measurement unit (IMU) sensor to a subject, with the invention of Pascual in order to collect respective data and to contribute to a fusion of the acquired data in order to improve the accuracy of the inferred event (Sobol ¶[0167]).
Regarding claim 2, Pascual in combination with Sobol teaches all limitations of claim 1 as described in the rejection above.
Pascual teaches that, when inferring the presence of the ND, the first computational model comprises a classification model (¶[0042], where “the formation of clusters can then be used to assign discrete classification scores to data, thus changing the second order features from multiple real valued components to single discrete classes,” ¶[0073], where “the patient models (using the model of FIGS. 5A-5B) for each patient may be analyzed for the importance of the features for each patient … The Kernel SHAP algorithm provides model-agnostic (black box), human interpretable explanations suitable for regression and classification models applied to tabular data”).
Regarding claim 3, Pascual in combination with Sobol teaches all limitations of claim 2 as described in the rejection above.
Pascual teaches that the classification model comprises one of a feature-based machine learning (ML) algorithm or an end-to-end deep learning (DL) model (¶[0043], where “machine learning models such as linear regression, deep learning, random forests, and gradient boosters may be used to produce a prediction model for that clinical label, taking in either the raw data, or the computed second-order metrics which may allow faster processing and improved interpretability”).
Regarding claim 4, Pascual in combination with Sobol teaches all limitations of claim 1 as described in the rejection above.
Pascual teaches that, when inferring the change of the ND, the second computational model comprises regression model (¶[0054], where “FIGS. 5A-5B illustrate an exemplary neural network for predicting a MOCA score from multimodal data. In particular, FIGS. 5A-5B illustrate an example application of a Long Short Term Memory version of an RNN to train on predicting a target variable of MOCA score. In various embodiments, the activation function may be selected to train a regression model,” ¶[0073], where “the patient models (using the model of FIGS. 5A-5B) for each patient may be analyzed for the importance of the features for each patient … The Kernel SHAP algorithm provides model-agnostic (black box), human interpretable explanations suitable for regression and classification models applied to tabular data”).
Regarding claim 5, Pascual in combination with Sobol teaches all limitations of claim 4 as described in the rejection above.
Pascual teaches that the regression model comprises one of feature-based machine learning (ML) algorithm or an end-to-end deep learning (DL) model (¶[0043], where “machine learning models such as linear regression, deep learning, random forests, and gradient boosters may be used to produce a prediction model for that clinical label, taking in either the raw data, or the computed second-order metrics which may allow faster processing and improved interpretability”).
Regarding claim 6, Pascual in combination with Sobol teaches all limitations of claim 1 as described in the rejection above.
Pascual teaches that, after the ECG and IMU data are gathered, compiling ECG ground truth data (GTD) and IMU GTD from the subject (¶[0007], where “a method is provided of training a system to determine one or more biomarker and/or health condition of a target patient where a plurality of health data and/or a plurality of first order features determined from the plurality of health data is received as input to a first artificial neural network,” ¶[0105], where “As shown in FIG. 11, raw data and first order and/or second order features feed into a predictive model to produce a particular output. In various embodiments, the model may be interrogated to understand the feature importance and contribution to this particular output,” ¶[0106], where “Machine learning models as described herein may be used to generate synthesized 6 and/or 12 lead data based on the raw 2 lead data, as well as other recorded health data for that particular patient, and first order and/or second order features determined from that data, to generate the synthesized 6 and/or 12 lead data,” ¶[0114], where “In various embodiments, the tasks and/or assessments may include one or more ball balancing tasks to assess motor control and coordination ... inertial measurement unit (IMU) sensors may be used to measure reaction time, fine motor control, movement characteristics, tremor, and/or dyskinesia.” Examiner interprets that the ECG GTD and IMU GTD from the subject are compiled since the relevant data is inputted and synthesized within the machine learning model, where ECG and IMU data are inputted. Furthermore, based on Examiner’s understanding of the claim, Examiner interprets a plurality of first order features as GTD since Applicant’s specification states that “Training of feature-based ML computational models includes the use of so-called ground truth data (GTD), as mentioned above, which comprises data related to certain parameters (sometimes referred to as “features”) germane to the particular goal of the ML” in ¶[0069] of Applicant’s specification.); and
training and testing the first computational model to predict the presence of the ND (¶[0007], where “a method is provided of training a system to determine one or more biomarker and/or health condition of a target patient where a plurality of health data and/or a plurality of first order features determined from the plurality of health data is received as input to a first artificial neural network,” ¶[0102], where “In various embodiments, different modeling techniques can be used and the AUC of each model compared to identify whether different algorithms would be advised. In various embodiments, supervised learning models capable of handling the number and type of dimensions generated may be used. In various embodiments, model training and evaluation may have the data and hyperparameters versioned such that the models can ultimately be compared against all other updated model versions prior to marking which achieves optimal performance and accuracy as define by the metrics indicated. In various embodiments, models may not be immediately rushed to production but run through a thorough development operations testing processes to ensure that new models not only meet criteria of machine learning metrics, but also do not adversely impact the operational system. In various embodiments, manual checks and audits can also be instituted prior to deployment of updated models. In various embodiments, due to regulation by government bodies, any modeling changes may need to be filed and reviewed prior to deployment.”).
Regarding claim 7, Pascual in combination with Sobol teaches all limitations of claim 1 as described in the rejection above.
Pascual teaches that, after the ECG and IMU data are gathered, compiling ground truth ECG and IMU data from the subject (¶[0007], where “A second artificial neural network is trained to output one or more biomarker and/or health condition based on the plurality of latent variables,” ¶[0105], where “As shown in FIG. 11, raw data and first order and/or second order features feed into a predictive model to produce a particular output. In various embodiments, the model may be interrogated to understand the feature importance and contribution to this particular output,” ¶[0106], where “Machine learning models as described herein may be used to generate synthesized 6 and/or 12 lead data based on the raw 2 lead data, as well as other recorded health data for that particular patient, and first order and/or second order features determined from that data, to generate the synthesized 6 and/or 12 lead data,” ¶[0114], where “In various embodiments, the tasks and/or assessments may include one or more ball balancing tasks to assess motor control and coordination ... inertial measurement unit (IMU) sensors may be used to measure reaction time, fine motor control, movement characteristics, tremor, and/or dyskinesia.” Examiner interprets that the ECG GTD and IMU GTD from the subject are compiled since the relevant data is inputted and synthesized within the machine learning model, where ECG and IMU data are inputted. Furthermore, based on Examiner’s understanding of the claim, Examiner interprets a plurality of first order features as GTD since Applicant’s specification states that “Training of feature-based ML computational models includes the use of so-called ground truth data (GTD), as mentioned above, which comprises data related to certain parameters (sometimes referred to as “features”) germane to the particular goal of the ML” in ¶[0069] of Applicant’s specification.); and
training the second computational model to predict the change in the ND (¶[0007], where “A second artificial neural network is trained to output one or more biomarker and/or health condition based on the plurality of latent variables,” ¶[0102], where “In various embodiments, different modeling techniques can be used and the AUC of each model compared to identify whether different algorithms would be advised. In various embodiments, supervised learning models capable of handling the number and type of dimensions generated may be used. In various embodiments, model training and evaluation may have the data and hyperparameters versioned such that the models can ultimately be compared against all other updated model versions prior to marking which achieves optimal performance and accuracy as define by the metrics indicated. In various embodiments, models may not be immediately rushed to production but run through a thorough development operations testing processes to ensure that new models not only meet criteria of machine learning metrics, but also do not adversely impact the operational system. In various embodiments, manual checks and audits can also be instituted prior to deployment of updated models. In various embodiments, due to regulation by government bodies, any modeling changes may need to be filed and reviewed prior to deployment.”).
Regarding claim 8, Pascual in combination with Sobol teaches all limitations of claim 7 as described in the rejection above.
Sobol teaches that IMU and ECG GTD comprises one or more features related to postural sway, comprising the elliptical area sway, entropy, fractal dimension, fractal dynamics, a Lyapunov exponent, raw ECG signals, heart rate variability (HRV), QT-interval, power spectral density (PSD) (¶[0341], where “one or more features may be extracted from the acquired LEAP data using linear (for example, short-time Fourier transform) or non-linear (such as fractal dimension) functions for subsequent use by a suitably-trained classification, regression or reinforcement model”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Sobol, which teaches that that IMU and ECG GTD comprises one or more features related to postural sway, comprising the elliptical area sway, entropy, fractal dimension, fractal dynamics, a Lyapunov exponent, raw ECG signals, heart rate variability (HRV), QT-interval, power spectral density (PSD), with the invention of Pascual in order to mathematically describe a patient's cognition in sufficient detail in order to correlate a representation of one or more psychiatric or neuropsychiatric conditions to the symptoms being observed through the data (Sobol ¶[0341]).
Regarding claim 9, the claim is directed to a system comprising substantially the same subject matter of claim 1 and is rejected under substantially the same sections of Pascual in combination with Sobol. However, claim 9 adds “A system for analyzing a neurodegenerative and/or a neurological disorder (ND)”, “an electrocardiogram (ECG) sensor and an inertial measurement unit (IMU) sensor configured to collect ECG data and IMU data for a subject, respectively”, “a processor”, and “a memory that stores instructions, executed by the processor”.
Pascual teaches a system for analyzing a neurodegenerative and/or a neurological disorder (ND) (Abstract, where “Systems, methods, and computer program products are provided for determining one or more biomarker and/or health condition of a target patient … a method is provided where a plurality of health data of the target patient and/or a plurality of first order features determined from the plurality of health data of the target patient are received as input to a pre-trained artificial neural network ... The pre-trained learning system is trained to receive as input the plurality of latent variables and output one or more biomarker and/or health condition of the target patient,” ¶[0032], where “the present disclosure provides systems, methods, and computer program products for machine-learning-assisted determination of patient biomarkers and/or health conditions, and generation of a latent representation of patient cognitive health. In various embodiments, a system can administer a series of cognitive assessments to an individual to capture raw health data regarding the patient (e.g., speech, gait and balance, eye motion, drawing, sleep, facial expressions, gestures) from various different modalities, generate first-order features from the raw health data derived from these data, and relate those to specific brain health domains, clinical diagnoses, and/or treatment plans”), comprising:
a processor (¶[0009], where “a system for determining one or more biomarker and/or health condition of a target patient is provided. The system includes … a processor of the computing node”); and
a memory that stores instructions, executed by the processor (¶[0009], where “a system for determining one or more biomarker and/or health condition of a target patient is provided. The system includes a computing node with a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor of the computing node to cause the processor to perform a method where a plurality of health data of the target patient and/or a plurality of first order features determined from the plurality of health data of the target patient are received as input to a pre-trained artificial neural network. The plurality of health data is derived from a plurality of modalities”).
Although Pascual teaches the utilization of an electrocardiogram (ECG) sensor (¶[0106], where “a hospital may only have a 2-lead electrocardiogram (ECG/EKG), but a particular machine learning model may require 6 or 12 lead values as input. Machine learning models as described herein may be used to generate synthesized 6 and/or 12 lead data based on the raw 2 lead data, as well as other recorded health data for that particular patient, and first order and/or second order features determined from that data, to generate the synthesized 6 and/or 12 lead data”) and an inertial measurement unit (IMU) sensor (¶[0114], where “a test taker may hold a device parallel to the ground and tilts the screen as needed to keep a virtual ball within a target area. In various embodiments, inertial measurement unit (IMU) sensors may be used to measure reaction time, fine motor control, movement characteristics, tremor, and/or dyskinesia”) to gather related data, Pascual does not explicitly teach an electrocardiogram (ECG) sensor and an inertial measurement unit (IMU) sensor configured to collect ECG data and IMU data for a subject, respectively.
Sobol teaches an electrocardiogram (ECG) sensor (¶[0015], where “at least one of the sensors that are configured to detect physiological data includes at least one sensor selected from the group consisting of … an electrocardiogram sensor”) and an inertial measurement unit (IMU) sensor (¶[0016], where “at least one of the sensors that are configured to detect activity data comprises at least one sensor selected from the group consisting of … an inertial measurement unit”) configured to collect ECG data and IMU data for a subject, respectively (¶[0153], where “the wearable electronic device 100 includes … The housing 110 includes a central body, as well as two opposing lateral extensions 111, 112. As will be discussed in more detail below, these lateral extensions 111, 112—in addition to providing a mounting location for a strap (such as the one shown in FIG. 2H as a conventional NATO-style band 190) such as that used with a wristwatch—may provide a trough or cavity-like recess into which the sensors 121 … may be placed,” ¶[0167], where “the sensors 121 may be placed into three major groups for the acquisition of the other components of the LEAP data. In one form, sensors representative of these other three major groups include environmental sensors 121A, activity sensors 121B and physiological sensors 121C … activity sensors 121B that are used to collect activity data may include accelerometers, gyroscopes, magnetometers or the like … and the physiological sensors 121C used to collect physiological data may include those configured to acquire heart rate, breathing rate, glucose, blood pressure, cardiac activity, temperature, oxygen saturation, smells (such as total volatile organic compounds (TVOC)) or the like”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Sobol, which teaches an electrocardiogram (ECG) sensor and an inertial measurement unit (IMU) sensor configured to collect ECG data and IMU data for a subject, respectively, with the invention of Pascual in order to collect respective data and to contribute to a fusion of the acquired data in order to improve the accuracy of the inferred event (Sobol ¶[0167]).
Regarding claims 10-15, the claims are directed to a system comprising substantially the same subject matter of claims 2-7, respectively, and are rejected under substantially the same sections of Pascual in combination with Sobol.
Regarding claim 16, the claim is directed to a tangible, non-transitory computer readable medium comprising substantially the same subject matter of claims 1 and 9 and is rejected under substantially the same sections of Pascual in combination with Sobol. However, claim 16 adds “A tangible, non-transitory computer readable medium that stores instructions, executed by a processor”.
Pascual teaches a tangible, non-transitory computer readable medium that stores instructions, executed by a processor (¶[0009], which teaches “a computing node with a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor of the computing node to cause the processor to perform a method where a plurality of health data of the target patient and/or a plurality of first order features determined from the plurality of health data of the target patient are received as input to a pre-trained artificial neural network. The plurality of health data is derived from a plurality of modalities,” ¶[0142], where “The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device”).
Regarding claims 17 and 19, the claims are directed to a tangible, non-transitory computer readable medium comprising substantially the same subject matter of claim 6 and are rejected under substantially the same sections of Pascual in combination with Sobol.
Regarding claims 18 and 20-23, the claims are directed to a tangible, non-transitory computer readable medium comprising substantially the same subject matter of claims 7 and 2-5, respectively, and are rejected under substantially the same sections of Pascual in combination with Sobol.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Neumann (U.S. Pub. No. 2020/0315527 A1), which describes a diagnostic engine operating on at least a server and configured to receive a first training set including a plurality of first data entries and a second training set including a plurality of second data entries, where an advisory module is configured to generate at least an advisory output as a function of the diagnostic output and transmit the at last an advisory output to at least an advisor client device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEFRA D. MANOS whose telephone number is (703)756-5937. The examiner can normally be reached M-F: 7:00 AM - 3:30 PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Unsu Jung can be reached at (571) 272-8506. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SEFRA D. MANOS/ Examiner, Art Unit 3792
/AMANDA L STEINBERG/ Examiner, Art Unit 3792
Prosecution Insights