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 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 16-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 16 and 31 recite a computer-implemented method and a system with instructions for performing operations of the device comprising:
analyzing the biometric data to identify patterns related to falling of the person;
and calculating risk of falling of the person based on the identified patterns.
To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is
evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.05. The instant claims are evaluated according to such analysis.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claim 16 is directed towards a computer-implemented method and claim 31 is directed towards
a system and thus meet the requirements for step 1.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
Claims 16 and 31 recite a computer-implemented method and a system with instructions for performing operations of the device comprising:
analyzing the biometric data to identify patterns related to falling of the person;
and calculating risk of falling of the person based on the identified patterns.
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” grouping of abstract ideas. Therefore, claims 16 and 31 recite an abstract idea of a mental process.
Claims 16 and 31 recite the abstract idea of a mental process. The limitations as drafted in the
claims, under its broadest reasonable interpretation, covers performance of the claimed steps in the mind, but for the recitation of a generic processor. Other than reciting a generic processing system and memory, nothing in the elements of the claims precludes the step from practically being performed in the mind or manually by a clinician. For example:
“Analyzing the biometric data to identify patterns related to falling of the person.” A physician may take in data and use equations and observations to find a pattern that helps predict a fall.
“And calculating risk of falling of the person based on the identified patterns.” A physician may use the patterns identified to diagnosis a likelihood of when someone might fall. A physician is also capable of observing a patient walking to make this diagnosis.
Furthermore claim 29 recite additional steps that can be manually performed by the
Clinician.
Identifying follow-up patterns and presenting follow-up feedback is the same as repeating the analyzing and calculation steps as disclosed above.
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial
exception into a practical application?
Claims 16 and 31 recite the additional elements of a “wearable sensor unit”, “wearable communication unit “, “processing unit”, “mobile device”, and “user interface” which are being interpreted as a processor of a data gathering device.
“Obtaining, by a wearable communication unit, at least one sensor signal comprising a temporal sequence of sensor data from at least one wearable sensor unit arranged to measure locomotion of a person and processing, by a signal processing unit of the wearable communication unit, the sensor signal to extract biometric data relating to locomotion of the person.” The additional elements present pre-solution activity to the step of data gathering.
“transmitting the biometric data to a mobile device.” This is computer implementation to the diagnosis step, which provides feedback to the patient.
However, these elements are recited at a high level of generality performing the function of generic data processing such that they amount to no more than mere instructions to simply implement the abstract idea using generic computer components. See MPEP 2106.05(b) and (f).
Accordingly, the additional elements do not integrate the abstract idea into a practical
application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
The additional elements when considered individually and in combination are not enough to
qualify as significantly more than the abstract idea.
“Obtaining, by a wearable communication unit, at least one sensor signal comprising a temporal sequence of sensor data from at least one wearable sensor unit arranged to measure locomotion of a person and processing, by a signal processing unit of the wearable communication unit, the sensor signal to extract biometric data relating to locomotion of the person.” The additional elements present pre-solution activity to the step of data gathering.
“transmitting the biometric data to a mobile device.” This is computer implementation to the diagnosis step, which provides feedback to the patient.
As discussed above with respect to integration of the abstract idea into a practical application, “wearable sensor unit”, “wearable communication unit “, “processing unit”, “mobile device”, and “user interface” which are being interpreted as a processor of a data gathering device which are being interpreted as a processor of a data gathering device as recited to perform the steps of:
analyzing the biometric data to identify patterns related to falling of the person;
and calculating risk of falling of the person based on the identified patterns.
amount to no more than mere instructions to apply the exception using generic computer
components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. These additional elements are well‐understood, routine (For example Chang et al. US Pub.: US 20180177436 A1, hereinafter Chang) teaches a data gathering device with a processor and memory, and conventional limitations that amount to mere instructions or elements to implement the abstract idea. In addition, the end result of the system/method, the essence of the whole, is a patent-ineligible concept. Therefore, the claims are not patent eligible.
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 16-35 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by Chang et al. US Pub.: US 20180177436 A1, hereinafter Chang.
Regarding claim 16, Chang teaches a computer-implemented method for dynamic, non-
obtrusive monitoring of locomotion of a person, the method comprising (fig. 1; paragraph 29; 150 is the computer device for monitoring biomechanical data, which includes locomotion):
obtaining, by a wearable communication unit, at least one sensor signal comprising a temporal sequence of sensor data from at least one wearable sensor unit arranged to measure locomotion of a person (fig. 1; paragraph 30, 39, and 68-70); Inertial measurement unit 112 can be mounted to different body locations to measure locomotion.
processing, by a signal processing unit of the wearable communication unit, the sensor signal to extract biometric data relating to locomotion of the person (fig. 1; paragraph 35 and 39); A processing system 114 comprise a plurality of modules. A first set of biomechanical processing modules 120 measure properties of gait locomotion (e.g., walking, running and the like).
transmitting the biometric data to a mobile device (fig. 1; paragraph 34-36); The processing can take place on the biomechanical sensing device 110 or be wirelessly transmitted to a smartphone. The communication module 116 functions to relay data between the biomechanical sensing device 110 and at least one other system.
analyzing the biometric data, using a machine learning-based risk prediction algorithm executed on a processor of the mobile device or called by the processor of the mobile device from a remote server, to identify patterns related to falling of the person (fig. 1; paragraph 49 and 123-124); Risk analysis model 130 can additionally or alternatively integrate machine learning models that use statistical or data driven modeling to the measuring and classification of fall risk.
and calculating risk of falling of the person based on the identified patterns (fig. 1; paragraph 29-30, 49 and 123-124).
Regarding claim 17, Chang teaches wherein the method further comprises transmitting a
warning based on the calculated risk of falling to a predefined second person, together with location data of the person obtained from the mobile device (fig. 1; paragraph 20). Method can preferably proactively alert or warn a user or caretaker of falling risk prior to the occurrence of a fall.
Regarding claim 18, Chang teaches wherein the at least one wearable sensor unit comprises at
least one motion sensor, and the sensor signal comprises temporal sequences of motion data (fig. 1; paragraph 32). The inertial measurement unit 112 may include an accelerometer, gyroscope, and magnetometer.
Regarding claim 19, Chang teaches wherein the at least one wearable sensor unit comprises two
motion sensors, the two motions sensor being configured to be attached to a foot of a user, and an inter-foot distance measurement system configured to measure inter-foot distance based on the position of the two motion sensors, wherein the sensor signal further comprises temporal sequences of inter-foot distance data (fig. 1-2; paragraph 32, 40, 83, 92-97, and 153). The sensor unit 112 may be attached to the foot. The inertial measurement unit 112 preferably includes a set of sensors aligned for detection of kinematic properties along three perpendicular axes. For step length asymmetries, detecting segments of the sensor data with asymmetric gait dynamics comprises detecting right and left step lengths and comparing the right step length(s) and left step length(s).
Regarding claim 20, Chang teaches wherein the two motion sensors comprise an inertial
measurement unit, and the sensor signal comprises 3-axis linear acceleration, 3-axis angular velocity, and 3-axis orientation data (fig. 1; paragraph 32). The inertial measurement unit 112 may include 3-axis orientation accelerometer, gyroscope, and magnetometer.
Regarding claim 21, Chang teaches wherein processing the sensor signal comprises aggregating
the sensor signal into time slots of equal size by the signal processing unit before transmitting to the mobile device (paragraph 59-61).
Regarding claim 22, Chang teaches wherein the extracted biometric data comprises inter-foot
distance based on measurements from an inter-foot distance measurement system (fig. 1-2; paragraph 83). For step length asymmetries, detecting segments of the sensor data with asymmetric gait dynamics comprises detecting right and left step lengths and comparing the right step length(s) and left step length(s).
Regarding claim 23, Chang teaches wherein the extracted biometric data comprises stride length
and frequency measured by motion sensors attached to the feet of the person (fig. 1-2; paragraph 83 and 92-97). Different conditions based on stride asymmetry can be used to determine when to deliver feedback or initiate another response.
Regarding claim 24, Chang teaches wherein the extracted biometric data comprises single
contact time and double contact time measured by motion sensors or local pressure sensors attached to or arranged at the feet of the person (fig. 1-2; paragraph 83 and 92-97). Double stance time is preferably detected and collected by detecting ground contact time for both feet and counting simultaneous foot contact time for the two feet.
Regarding claim 25, Chang teaches wherein the extracted biometric data comprises center of
body displacement measured by motion sensors attached to the body of the person (fig. 1-2; paragraph 83 and 92-97). Detecting segments of sensor data indicative of shuffling gait patterns can include detecting vertical step displacements of the right and/or left steps and classifying the gait as shuffling when vertical step displacements satisfy a shuffle condition.
Regarding claim 26, Chang teaches wherein identifying patterns related to falling of the person
comprises analyzing a combination of the biometric data and at least one type of sensor data extracted from the sensor signal (fig. 1; paragraph 29-30, 49 and 123-124). The high resolution biomechanical data generated by the biomechanical sensing device along with location data, time, weather, temperature and other data sets can also be analyzed with machine learning models to help identify specific conditions, behaviors or patterns that predict the risk profiles of individuals that may be at high risk to falling.
Regarding claim 27, Chang teaches wherein the machine learning-based risk prediction
algorithm comprises a neural network pre-trained using data collected from test persons wearing at least one wearable sensor unit while performing gait cycles (fig. 1; paragraph 29-30, 49, 92-97, and 123-124). Risk analysis model 130 can additionally or alternatively integrate machine learning models that use statistical or biomechanical data driven modeling to the measuring and classification of fall risk. Detecting segments of sensor data indicative of shuffling gait patterns can include detecting vertical step displacements of the right and/or left steps and classifying the gait as shuffling when vertical step displacements satisfy a shuffle condition.
Regarding claim 28, Chang teaches wherein the method further comprises:
determining a feedback, using an artificial intelligence algorithm executed on a processor of the mobile device or called by the processor of the mobile device from a remote server, based on the calculated risk of falling and a personalized training plan comprising a set of actions with assigned execution dates (fig. 1-2; paragraph 49 and 127); The risk analysis model 130 can additionally or alternatively integrate machine learning models that use statistical or data driven modeling to the measuring and classification of fall risk.
and presenting the feedback to the person on a user interface of the mobile device, wherein presenting the feedback comprises presenting at least one action assigned to a date of determining the feedback (fig. 1-2; paragraph 28-29, 50 and 120). The rest recommendation is preferably provided to the user through a suitable feedback interface (e.g., a displayed graphic, a notification, an audio alert, etc.).
Regarding claim 29, Chang teaches wherein the method further comprises:
identifying follow-up patterns in the biometric data by comparing follow-up biometric data extracted from sensor signals after presenting a feedback to the person to expected biometric data determined based on the personalized training plan (fig. 1-2; paragraph 28-29, 50 and 120). The risk analysis model 130 can additionally or alternatively integrate machine learning models that use statistical or data driven modeling to the measuring and classification of fall risk. The rest recommendation is preferably provided to the user through a suitable feedback interface (e.g., a displayed graphic, a notification, an audio alert, etc.).
and determining, using a reinforcement learning based algorithm, a follow-up feedback to be presented to the person (fig. 1-2; paragraph 28-29, 50 and 120). Multiple recommended feedbacks may be given as follow-up.
Regarding claim 30, Chang teaches wherein the method further comprises detecting a
behavioral change pattern of the person based on comparing biometric data extracted from sensor signals obtained real-time to existing records of biometric data of the same person (fig. 1-2; paragraph 16-17, 105, and 130-132); The processing of the mobility metrics preferably performs real-time and historical analysis on mobility metrics to determine when the mobility of a user transitions to a different level of risk.
and automatically adjusting the personalized training plan based on the behavioral change pattern of the person (fig. 1-2; paragraph 16-17, 105, and 130-132). Processing is done in real-time and is automatically adjusting.
Regarding claim 31, Chang teaches a system for dynamic, non-obtrusive monitoring of
locomotion of a person, the system comprising (fig. 1; paragraph 29; 150 is the computer device for monitoring biomechanical data, which includes locomotion):
at least one wearable sensor unit arranged to measure locomotion of a person and to generate a sensor signal comprising a temporal sequence of sensor data (fig. 1; paragraph 30, 39, and 68-70); Inertial measurement unit 112 can be mounted to different body locations to measure locomotion.
a wearable communication unit configured to obtain a sensor signal, to process the sensor signal using a signal processing unit to extract biometric data relating to locomotion of the person, and to transmit the biometric data to a mobile device (fig. 1; paragraph 34-36); The processing can take place on the biomechanical sensing device 110 or be wirelessly transmitted to a smartphone. The communication module 116 functions to relay data between the biomechanical sensing device 110 and at least one other system.
and a mobile device comprising (fig. 1; paragraph 34-36; The processing can take place on the biomechanical sensing device 110 or be wirelessly transmitted to a smartphone):
a processor configured to analyze, using a machine learning-based risk prediction algorithm executed on the processor or called by the processor from a remote server, the biometric data to identify patterns related to falling and to calculate risk of falling of the person based on the identified patterns (fig. 1; paragraph 35 and 39); A processing system 114 comprise a plurality of modules. A first set of biomechanical processing modules 120 measure properties of gait locomotion (e.g., walking, running and the like).
and a user interface configured to present feedback to the person based on the calculated risk of falling (fig. 1; paragraph 29-30, 49 and 123-124).
Regarding claim 32, Chang teaches wherein the at least one wearable sensor unit comprises two
motion sensors, the two motion sensors configured to be attached to a foot of a user, and an inter-foot distance measurement system configured to measure inter-foot distance based on the position of the two motion sensors, wherein the sensor signal further comprises temporal sequences of inter-foot distance data (fig. 1-2; paragraph 32, 40, 83, 92-97, and 153). The sensor unit 112 may be attached to the foot. The inertial measurement unit 112 preferably includes a set of sensors aligned for detection of kinematic properties along three perpendicular axes. For step length asymmetries, detecting segments of the sensor data with asymmetric gait dynamics comprises detecting right and left step lengths and comparing the right step length(s) and left step length(s).
Regarding claim 33, Chang teaches wherein the extracted biometric data comprises inter-foot
distance based on measurements from an inter-foot distance measurement system (fig. 1-2; paragraph 83). For step length asymmetries, detecting segments of the sensor data with asymmetric gait dynamics comprises detecting right and left step lengths and comparing the right step length(s) and left step length(s).
Regarding claim 34, Chang teaches, wherein the machine learning-based risk prediction
algorithm comprises a neural network pre-trained using data collected from test persons wearing at least one wearable sensor unit while performing gait cycles (fig. 1; paragraph 29-30, 49, 92-97, and 123-124). Risk analysis model 130 can additionally or alternatively integrate machine learning models that use statistical or biomechanical data driven modeling to the measuring and classification of fall risk. Detecting segments of sensor data indicative of shuffling gait patterns can include detecting vertical step displacements of the right and/or left steps and classifying the gait as shuffling when vertical step displacements satisfy a shuffle condition.
Regarding claim 35, Chang teaches computer program product encoded on a non-transitory
computer-readable storage device, configured to cause a processor to perform operations according to the method of claim 16 (paragraph 34-35 and 160).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THIEN J TRAN whose telephone number is (571)272-0486. The examiner can normally be reached M-F. 8:30 am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at 571-270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.J.T./Examiner, Art Unit 3792
/MALLIKA D FAIRCHILD/Primary Examiner, Art Unit 3792