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 .
Priority
Acknowledgment is made of applicant’s claim for priority. The current application claims benefit of provisional application 63/464,707, filed on May 8th, 2023. Examiner acknowledges the applicant’s claim for priority.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1
The claims recite subject matter within a statutory category as a process, machine, and/or article of manufacture. However, it will be shown in the following steps, that claims 1-19 are nonetheless unpatentable under 35 U.S.C. 101.
Step 2A Prong One
Claim 1 states:
A method of progression assessment of a neurological disease of a patient, the method comprising:
receiving, from a device that is wearable by the patient, three-dimensional (3D) acceleration data and at least one physiological signal;
determining, by a server, with a deep learning algorithm a progression of a neurological disease of the patient based on the received 3D acceleration data and the at least one physiological signal, wherein determination of the progression of the neurological disease comprises:
determining a first metric for patient’s activity;
determining a second metric based on a measured gait speed; and
aggregating the first metric and the second metric for approximation of progression of the neurological disease based on inference of the deep learning algorithm.
The broadest reasonable interpretation of these steps includes mental processes and/or organizing human activity because each bolded component can practically be performed by the human mind or with pen and paper. It is noted that “a device that is wearable by the patient” is not providing any of the steps recited in claim 1. While claim 1 only requires “a server” to carry out the “determining” step, claim 1 does not limit the receiving step to be performed by any device. Other than reciting generic computer terms like a device and a server, nothing in the claims precludes the bold-font portions from practically being performed in the mind. For example, but for the “a server with a deep learning algorithm a progression” language, “determining… of a neurological disease of the patient based on the received 3D acceleration data and the at least one physiological signal, wherein determination of the progression of the neurological disease comprises” in the context of this claim encompasses a mental process of a healthcare professional making a differential diagnosis based on recorded diagnosis data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
These steps of:
A method of progression assessment of a neurological disease of a patient, the method comprising:
receiving, from a device that is wearable by the patient, three-dimensional (3D) acceleration data and at least one physiological signal;
determining, by a server, … a progression of a neurological disease of the patient based on the received 3D acceleration data and the at least one physiological signal, wherein determination of the progression of the neurological disease comprises:
determining a first metric for patient’s activity;
determining a second metric based on a measured gait speed; and
aggregating the first metric and the second metric for approximation of progression of the neurological disease
, as drafted, under the broadest reasonable interpretation, includes multiple abstract ideas that will be identified as a single abstract idea moving forward.
Independent claim 18 cover similar steps of receiving physiological signals, determining a progression of a neurological disease by determining the first metric for a patient’s activity, determining a second metric based on the measured gait speed, and aggregating the first and second metric for approximation of progression of the neurological disease. These claims fall under the same category of an abstract idea and follows the same rationale as claim 1.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 14, reciting particular aspects of how “determining, by the server, a walking stage by the patient as an indication that the patient is no longer lying in bed, wherein the walking stage is determined by combining the received 3D acceleration data and the at least one physiological signal.” may be performed in the mind but for recitation of generic computer components).
Dependent claims 12 and 13 add additional elements to their parent claims which will be further inspected in the following steps for a practical application to their abstract idea.
Step 2A Prong Two
This judicial exception of “Mental Processes” or “Organizing Human Activity” is not integrated into a practical application. Independent claim 1 and 18’s method and system recite additional elements such as a server, a wearable device, and machine learning algorithm. The server and machine learning algorithm will be treated as generic computer components. The wearable device will be considered for conventionality in later steps. In particular, these additional elements do not integrate the abstract idea into a practical application because the additional elements:
amount to mere instructions to apply an exception (such as recitation of “by a server, with a deep learning algorithm” and “based amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification [0042], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of “a wearable device, to monitor three-dimensional (3D) acceleration data and at least one physiological signal” in independent claim 18 amounts to mere data gathering, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. For instance, dependent claims 13 add additional elements of pressure sensor, a mat and insole to their parent claims. Additionally, claim 12 “receiving, by the server, gait speed data from at least one sensor, and wherein the 3D acceleration data is acquired during the gait speed measurement by the at least one sensor; and determining, by the server, correlation between the patient’s gait speed and the received 3D acceleration data.” and claim 13 “wherein the at least one sensor comprises a pressure sensor that is embedded in at least one of: a pressure mat and an insole, and wherein the pressure sensor measures pressure that is caused by the patient stepping on the pressure sensor.” , add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering, see MPEP 2106.05(g)). Looking at the limitations as an ordered combination 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 or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The remaining dependent claims 2-11, 14-17 and 19 do not recite additional elements or activity but further narrow or define the abstract idea embodied in the claims and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As previously noted, the claim recites an additional element of a pressure sensor in a wearable device. Plancon et al. (US 20040233788) demonstrates in para [0095] “The actual pressure sensor may be any kind of conventional pressure sensor, well-known in the art.” that a pressure sensor was conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
To elaborate:
“receiving, from a device that is wearable by the patient, three-dimensional (3D) acceleration data and at least one physiological signal”, is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. These additional limitations amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As previously noted, the claim recites an additional element of a mat and insole. Park (KR 20030034999) demonstrates in their abstract “The present invention relates to conventional mat and bamboo shoe pad windows” that a mat and insole were conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
To elaborate:
claim 12 “receiving, by the server, gait speed data from at least one sensor, and wherein the 3D acceleration data is acquired during the gait speed measurement by the at least one sensor; and determining, by the server, correlation between the patient’s gait speed and the received 3D acceleration data.”, is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
claim 13 “wherein the at least one sensor comprises a pressure sensor that is embedded in at least one of: a pressure mat and an insole, and wherein the pressure sensor measures pressure that is caused by the patient stepping on the pressure sensor.”, is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
Looking at the limitations as an ordered combination 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 or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 102
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, 2, 5-12 and 18-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Howard et al. (US20170258390).
Regarding claim 1, Howard teaches a method of progression assessment of a neurological disease of a patient, the method comprising: ([Abstract] “a method of detecting neurodegenerative disease may comprise measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject”) receiving, from a device that is wearable by the patient, three-dimensional (3D) acceleration data and at least one physiological signal; ([0008] “gathering movement data with the wearable body sensor system, wherein measuring functioning of the cognitive function may comprise gathering cognitive function data” see also [0257] “triaxial gyroscopes were used to measure the angular orientation of a body segment”) determining, by a server, ([0887] “server”) with a deep learning algorithm [i.e., multi-modal data analysis] a progression of a neurological disease of the patient based on the received 3D acceleration data and the at least one physiological signal, ([0368] “data collection and analysis used for the initial development and validation of a more general approach to early detection of PD… spread across three foundational domains: the motor system, cognitive function and brain activity. The approach is predicated on multi-modal data analysis across three domains to capture the heterogeneity of the PD spectrum” where multimodal data analysis comprises deep learning algorithms) wherein determination of the progression of the neurological disease comprises: ([0146] “Features from each domain can be measured quantitatively to link cognitive and behavioral indicators of disease”) determining a first metric for patient’s activity; ([0146-147] “FIG. 5 illustrates an example of an approach is to measure emerging properties from parameters of 3 domains: the motor system, cognitive function, and brain activity”) determining a second metric based on a measured gait speed; (see [0147] and [0377] “Upper limb movement irregularities, such as linear speed and corrective movements have been exhibited in PD patients and could potentially be used as a measure of early detection”) and aggregating the first metric and the second metric for approximation of progression of the neurological disease based on inference of the deep learning algorithm. ([0368] above)
Regarding claim 2, Howard teaches all of the limitations of claim 1. Howard also teaches wherein the 3D acceleration data is sampled with a frequency of at least 25Hz. ([0392] “Data for both the Codamotion and the IMUs was acquired at 100 Hz”)
Regarding claim 5, Howard teaches all of the limitations of claim 1. Howard also teaches comprising receiving, by the server, statistical data associated with at least one characteristic of the patient. ([0633] “data analysis uses statistical tests to correlate UPDRS scores to examine relationships between speech and selected movement symptoms.”)
Regarding claim 6, Howard teaches all of the limitations of claim 1. Howard also teaches receiving, by the server, data from the wearable device via wireless communication, wherein communication between the server and the wearable device is carried out via a proxy gateway. ([0483] “wearable sensor network” and [0866] “The network may comprise… wireless transmission… gateway computers, and/or edge servers”)
Regarding claim 7, Howard teaches all of the limitations of claim 1. Howard also teaches training the deep learning algorithm with self-supervision learning. ([0332] “Support Vector Machine (SVM)” is self-supervised learning used with these algorithms)
Regarding claim 8, Howard teaches all of the limitations of claim 1. Howard also teaches wherein the first metric comprises at least one of: walk duration, activity intensity level. ([0553] “a walking trial and associated power/frequency”)
Regarding claim 9, Howard teaches all of the limitations of claim 1. Howard also teaches wherein the second metric comprises at least one of: a gait score, an activity patterns score [i.e., velocity profile], an overall performance score, and a cognitive approximation score. ([0157] “Measuring the acceleration time/deceleration time (DT/AT) ratio allowed the authors to develop velocity profiles for different patient”)
Regarding claim 10, Howard teaches all of the limitations of claim 1. Howard also teaches wherein the combined approximation is normalized to reflect a specific neurologic disorder, and wherein the neurologic disorder is at least one of: Multiple Sclerosis (MS), Parkinson Disease (PD), and Dementia. ([0265] The Unified PD Rating Scale (UPDRS) is a commonly used scale to measure symptom severity in PD patients.” And [0284] “we performed a number of variable pairing regressions to examine the properties of UPDRS data of values from two (instead of just one) categories of symptoms”)
Regarding claim 11, Howard teaches all of the limitations of claim 1. Howard also teaches comprising monitoring the patient's performance over time to identify events comprising at least one of: fall events, pain, fatigue, and a spasm. ([0355] “classify with highest accuracy possible, the sensitivity and specificity of pain vs. no-pain and high vs. low pain intensity in order to objectively provide a pain diagnosis”)
Regarding claim 12, Howard teaches all of the limitations of claim 1. Howard also teaches receiving, by the server, gait speed data from at least one sensor, and wherein the 3D acceleration data is acquired during the gait speed measurement by the at least one sensor; and determining, by the server, correlation between the patient’s gait speed and the received 3D acceleration data. ([0153] “gait segments were devised to classify quantitative metrics: stopping and moving, stance and swing phase, and positive peaks in acceleration. As a result, the authors devised a “general gait detection algorithm” using 3 dimensional acceleration measurements in the ankles of PD and non-PD patients. From the output of the general gait detection algorithm, “kinetic data” (speed, balance etc.) can later be extracted and analyzed” occurs via a server)
Regarding claim 18, Howard teaches a system for progression assessment of a neurological disease of a patient, the system comprising: ([Abstract] “a method of detecting neurodegenerative disease may comprise measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject”) a wearable device, to monitor three-dimensional (3D) acceleration data and at least one physiological signal; ([0008] “gathering movement data with the wearable body sensor system, wherein measuring functioning of the cognitive function may comprise gathering cognitive function data” see also [0257] “triaxial gyroscopes were used to measure the angular orientation of a body segment”) and a server, ([0887] “server”) in communication with the wearable device, wherein the server is configured to: ([0483] “a wearable sensor network”) receive the 3D acceleration data and the at least one physiological signal; ([0008] and [0257] above) and determination of a first metric for patient’s activity; ([0146-147] “FIG. 5 illustrates an example of an approach is to measure emerging properties from parameters of 3 domains: the motor system, cognitive function, and brain activity”) determination of a second metric based on a measured gait speed; (see [0147] and [0377] “Upper limb movement irregularities, such as linear speed and corrective movements have been exhibited in PD patients and could potentially be used as a measure of early detection”) and determine using a deep learning algorithm a progression of a neurological disease of the patient based on the received 3D acceleration data and the at least one physiological signal, ([0146] “Features from each domain can be measured quantitatively to link cognitive and behavioral indicators of disease”) wherein determination of the progression of the neurological disease by the server comprises: aggregation of the first metric and the second metric for approximation of progression of the neurological disease based on inference of the deep learning algorithm. ([0368] “data collection and analysis used for the initial development and validation of a more general approach to early detection of PD… spread across three foundational domains: the motor system, cognitive function and brain activity. The approach is predicated on multi-modal data analysis across three domains to capture the heterogeneity of the PD spectrum” where multi-modal data analysis comprises a deep learning algorithm)
Regarding claim 19, Howard teaches all of the limitations of claim 18. Howard also teaches wherein the combined approximation is normalized to reflect a specific neurologic disorder, and wherein the neurologic disorder is at least one of: Multiple Sclerosis (MS), Parkinson Disease (PD), and Dementia. ([0265] The Unified PD Rating Scale (UPDRS) is a commonly used scale to measure symptom severity in PD patients.” And [0284] “we performed a number of variable pairing regressions to examine the properties of UPDRS data of values from two (instead of just one) categories of symptoms”)
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 3, 4 and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Howard et al. (US20170258390) in view of Zaphrir et al. (WO2021/161314).
Similar to Howard, Zaphrir “relates to motion determination and analysis” [Abstract]. Specifically, Zaphrir teaches that existing devices “to detect the walking and walking counting of the elderly individuals or psychiatric patients is highly inexact, This is primarily due to a lower signal-to-noise ratio (SNR) when the elderly user has a smaller and/or slower movement, and a high variability of the signal generated by using various walking aid tools.”. For these teahcings, Zaphrir’s teaching would have been a primary source of the state of the art for Howard while researching improvements to improve signal feedback for gait analysis.
Regarding claim 3, Howard teaches all of the limitations of claim 1. Howard does not explicitly teach, as taught by Zaphrir wherein the at least one physiological signal comprises information on heart activity with at least one of: heart rate data, heart rate variability data and a 1 lead electrocardiogram (ECG). ([020]”wearable sensor is configured to detect… heart rate, heart rate variability… electrocardiogram”) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Howard with the teachings of Zaphrir, with a reasonable expectation of success, by incorporating EKG data into the physiological parameters of the gait analysis for disease prediction. This would have provided a more accurate representation of the gait analysis through seamless integration of biomechanical motion capture using foot pressure alongside acceleration of the subject.
Regarding claim 4, Howard teaches all of the limitations of claim 1. Howard does not explicitly teach, as taught by Zaphrir wherein the at least one physiological signal comprises oxygen saturation with SPO2 measurements. ([020]”wearable sensor is configured to detect… SPO2”) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Howard with the teachings of Zaphrir, with a reasonable expectation of success, by incorporating SPO2 data into the physiological parameters of the gait analysis for disease prediction. This would have provided a more accurate representation of the gait analysis through seamless integration of biomechanical motion capture using foot pressure alongside acceleration of the subject.
Regarding claim 13, Howard teaches all of the limitations of claim 1. Howard does not explicitly teach, as taught by Zaphrir: wherein the at least one sensor comprises a pressure sensor that is embedded in at least one of: a pressure mat and an insole, and wherein the pressure sensor measures pressure that is caused by the patient stepping on the pressure sensor. ([046] “In some embodiments, at least one wearable sensor 204 may include at least one of the following sensors: a pressure sensor (e.g., in the insole sensor)” where an insole sensor is engaged with a step from a patient) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Howard with the teachings of Zaphrir, with a reasonable expectation of success, by incorporating pressure sensor insoles into the inputs of the gait analysis for disease prediction. This would have provided a more accurate representation of the gait analysis through seamless integration of biomechanical motion capture using foot pressure alongside acceleration of the subject.
Regarding claim 14, Howard teaches all of the limitations of claim 1. Howard does not explicitly teach, as taught by Zaphrir comprising: determining, by the server, a walking stage by the patient as an indication that the patient is no longer lying in bed, wherein the walking stage is determined by combining the received 3D acceleration data and the at least one physiological signal. ([057] “clustering of gait… can thus be used to gather the time sequence in which the walk is observed and/or separate the time sequence in which the walk is observed with other activities (e.g., sleep)” where observing the separate time sequences of walking and sleeping comprise determining the walking stage by an indication that a patient is no longer in bed; see also “wearable sensor 204 can … detect the corresponding response of the heart rate walking” where determining the walking stage comprises integrating the sensor data from multiple the accelerometer and the heart rate sensors.) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Howard with the teachings of Zaphrir, with a reasonable expectation of success, by incorporating pressure sensor insoles into the inputs of the gait analysis for disease prediction. This would have provided a more accurate representation of the gait analysis through seamless integration of biomechanical motion capture using foot pressure alongside acceleration of the subject.
Regarding claim 15, Howard-Zaphrir as a combination teaches all of the limitations of claim 14. Zaphrir also teaches wherein the at least one physiological signal comprises information on heart activity with at least one of: heart rate data, heart rate variability data and a lead electrocardiogram (ECG). ([020]”wearable sensor is configured to detect… heart rate, heart rate variability… electrocardiogram”) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Howard with the teachings of Zaphrir, with a reasonable expectation of success, by incorporating EKG data into the physiological parameters of the gait analysis for disease prediction. This would have provided a more accurate representation of the gait analysis through seamless integration of biomechanical motion capture using foot pressure alongside acceleration of the subject.
Regarding claim 16, Howard-Zaphrir as a combination teaches all of the limitations of claim 14. Zaphrir also teaches wherein the at least one physiological signal comprises oxygen saturation with SPO2 measurements. ([020]”wearable sensor is configured to detect… SPO2”) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Howard with the teachings of Zaphrir, with a reasonable expectation of success, by incorporating SPO2data into the physiological parameters of the gait analysis for disease prediction. This would have provided a more accurate representation of the gait analysis through seamless integration of biomechanical motion capture using foot pressure alongside acceleration of the subject.
Regarding claim 17, Howard- Zaphrir as a combination teaches all of the limitations of claim 14. Howard also teaches wherein the 3D acceleration data is sampled with a frequency of at least 25Hz. ([0082] “Step 22B can further comprise a resampling of the signals to a predetermined sampling frequency. A suitable sampling frequency can be 50 Hz.”)
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ganzetti (US20240153632) discloses a system for determining clinical parameters indicative of a disease progression. The system uses machine learning models to diagnose multiple sclerosis based on motor tests on patients. This system incorporates sensor based data collection using accelerometers, supervised learning, and more.
Stevens et al. (US20230189918) discloses a system that provides insole and pad sensors for measuring the movement of a subject. The system provides gait analysis, and can sense sleeping patterns of a user.
Baker et al. (US20200258631) discloses a system for using biomarkers for diagnosis movement related disorders. This system uses accelerometers, pedometers, heart rate detectors, touch sensors, light sensors, pressure sensors, time recorders, more to measure daily activities of a user from a mobile phone. This system incorporates supervised neural networks to diagnose the severity of a neurological disease state.
Zanon et al. (US20240268709) discloses a gait analysis system comprising 3D accelerators, various physical sensors, and a system for determining abnormal movements directed towards neurological disorders.
Xu et al. (CN107174255) discloses a system for 3D gait information and acquisition and analysis.
Molero Leon et al. (US20230377747) provides gait analysis using integrated accelerometers across a network of devices for a user to help determine disease progression
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT ANTHONY SKROBARCZYK whose telephone number is (571)272-3301. The examiner can normally be reached Monday thru Friday 7:30AM -5PM CST.
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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.
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/R.A.S/Examiner, Art Unit 3792
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792