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 .
DETAILED ACTION
In the amendment filed 27 October 2025:
Claims 1,3,8-9,12,14-17,19,22,24,27,29-30, 33 are amended.
Claim(s) 1-35 are pending
Information Disclosure Statement
The Information Disclosure Statement(s) (lDS) submitted on 28 October 2025 is/are in compliance with the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
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 1-35 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.
For Claims 1, 8, 22, and 29 are indefinite because it is unclear which steps are being performed by the computer of the computer-implemented method or the wearable sensor system. The claim recites a “computer-implemented method,” but then recites that a majority/all of the claim functions are performed by “a wearable sensor system.” Spec. Para. 0003 describes the computer and the wearable sensor system to be different devices meaning that the computer is not actually required to perform these steps. For Claim 1, the only step required to be performed by the computer is the “storing” step; for Claims 8, 22, and 29, none of the steps are performed by the computer. Claim 15 is the only claim that requires that the steps are performed by the computer. Any step that is not performed by the computer represents non-functional, descriptive information (it is merely descriptive of the data manipulated by the computer) and is not required to meet the claim language. For the purposes of examination, the Examiner interprets the claims as being performed by the combination of the computer and wearable sensor system. The wearable sensor system is considered to be a generic computing device. The prior art references of Vane et al (US Publication No. 20200110643) at Para. 0075,0077 and Lu et al (US Publication No. 20190090756) at Para. 0014,0016 refer to the wearable devices mentioned as generic computing device. The Examiner suggests that Claims 1, 8, 22, and 29 be amended to recite that the wearable sensor system performs the method.
For Claim 16, the claim is indefinite because it is unclear how the third annotation data can comprise content indicative of performance of the clinical activity when Claim 15 claims the third annotation data as being associated with activity other than the clinical activity.
Claim 28 recites the limitation "plurality of clinical providers". There is insufficient antecedent basis for this limitation in the claim.
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 1-35 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1,8,15,22,29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a computer-implemented method. The limitations of:
Claim(s) 1,8,15,22,29 (Claim 1 being representative)
receiving, at a first time during a motor exam comprising first sensor data comprising tri-axial accelerometer data and gyroscope data, wherein the first sensor data is sampled at a predefined sampling rate and the first sensor data is indicative of a first user activity performed during the motor exam;
receiving a first annotation associated with the first sensor data, wherein the first annotation is appended to the first sensor data;
receiving, at a second time different from the first time, second sensor data indicative of a second user activity;
generating second annotation corresponding to the second sensor data at the second time based on the first sensor data, the first annotation, and the second sensor data, wherein the second annotation is different from the first annotation and the second annotation is appended to the second sensor data;
receiving in response to generating the second annotation, confirmation of the second annotation;
and storing the second sensor data with the second annotation.
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to receive and annotate sensor data in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “receiving and annotating sensor data” as indicated supra. Other than reciting generic computer components (discussed infra), i.e., a system implemented by wearable sensor system, the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claim further recites “training a machine learning model.” When given its broadest reasonable interpretation in light of the disclosure, the training of a machine learning algorithm represents the creation of mathematical interrelationships between data. As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional element of a computer and/or wearable sensor system that implements the identified abstract idea (see 112(b) rejection, supra). The computer and/or wearable sensor system is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer and/or wearable device performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim further recites the additional element of using a trained machine learning model to generate annotations. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to generate annotations merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
The claims further recite the additional elements of an input device, tri-axial accelerometer and gyroscope. The input device, tri-axial accelerometer and gyroscope merely generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
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 integration of the abstract idea into a practical application, the additional element of using a computer and/or wearable sensor system to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning model to generate annotations was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use . This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of an input device, tri-axial accelerometer and gyroscope was determined to generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Claims 2-7,9-14,26-21,23-28,30-35 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 2 merely describe(s) the first annotation, which further defines the abstract idea.
Claim(s) 3, 30 merely describe(s) the sensors, which further defines the abstract idea.
Claim(s) 3, 30 also includes the additional element of “a photoplethysmography sensor or a heart rate sensor” which is analyzed the same as the “an input device” and does not provide a practical application or significantly more for the same reasons.
Claim(s) 4 merely describe(s) confirmation of a second annotation, which further defines the abstract idea.
Claim(s) 5 merely describe(s) the second annotation, which further defines the abstract idea.
Claim(s) 6 merely describe(s) generating the second annotation, which further defines the abstract idea.
Claim(s) 7 merely describe(s) receiving input from a user device, which further defines the abstract idea.
Claim(s) 9 merely describe(s) the clinical exam activity, which further defines the abstract idea.
Claim(s) 10 merely describe(s) the clinical exam score, which further defines the abstract idea.
Claim(s) 11 merely describe(s) the annotation, which further defines the abstract idea.
Claim(s) 12 merely describe(s) the annotation, which further defines the abstract idea.
Claim(s) 13 merely describe(s) determining user activity, which further defines the abstract idea.
Claim(s) 14 merely describe(s) receiving a confirmation, which further defines the abstract idea.
Claim(s) 4, 14 also includes the additional element of “a user interface” which is analyzed the same as the “an input device” and does not provide a practical application or significantly more for the same reasons.
Claim(s) 16 merely describe(s) the first and second annotations, which further defines the abstract idea.
Claim(s) 17 merely describe(s) the first annotation, which further defines the abstract idea.
Claim(s) 18 merely describe(s) the user device, which further defines the abstract idea.
Claim(s) 19 merely describe(s) context data, which further defines the abstract idea.
Claim(s) 7,13,17-19 also includes the additional element of “a user device” which is analyzed the same as the “an input device” and does not provide a practical application or significantly more for the same reasons.
Claim(s) 20 merely describe(s) using location data, which further defines the abstract idea.
Claim(s) 21 merely describe(s) the activity, which further defines the abstract idea.
Claim(s) 23 merely describe(s) the first and second user inputs, which further defines the abstract idea.
Claim(s) 24 merely describe(s) the first signal data, which further defines the abstract idea.
Claim(s) 25 merely describe(s) generating second annotation, which further defines the abstract idea.
Claim(s) 26 merely describe(s) generating the second annotation, which further defines the abstract idea.
Claim(s) 27 merely describe(s) the first annotation, which further defines the abstract idea.
Claim(s) 28 merely describe(s) the first annotation, which further defines the abstract idea.
Claim(s) 31 merely describe(s) the third annotation, which further defines the abstract idea.
Claim(s) 32 merely describe(s) determining the activity window, which further defines the abstract idea.
Claim(s) 33 merely describe(s) selecting the activity window, which further defines the abstract idea.
Claim(s) 34 merely describe(s) the third annotation, which further defines the abstract idea.
Claim(s) 35 merely describe(s) the third annotation, which further defines the abstract idea.
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 Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 1-7,14 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Sobol et al (US Publication No. 20190209022) in view of NAKAJIMA et al (US Publication No. 20200397346) in view of Pennanen et al (US Publication No. 20170078418).
Regarding Claim 1
Sobol teaches a computer-implemented method, comprising:
receiving, at a first time during a motor exam and by a wearable sensor system comprising a tri-axial accelerometer and a gyroscope [Sobol at Para. 00175 teaches In one form, some of the electronic components of the wearable electronic device 100 include: • one or more accelerometers (such as that associated with microelectromechanical (MEMS) Digital Output Motion Sensors: Ultra-Low-Power High-Performance 3-Axis "Femto" Accelerometer, 1.71 to 3.6 V, -40 to 85 degrees C, l2-Pin LGA, RoHS, Tape and Reel, as manufactured by STMicroelectronics as part number LIS2DH12TR) (interpreted as tri-axial accelerometer); Sobol at Para. 00229 teaches SVM-based approaches are a supervised form of learning used to build a model that represents data examples as points in space that are mapped in a manner to place the examples into clearly separated categories. New examples may then be mapped to opposing sides of a gap that is used between the separated categories. In one form, SVM may be used in conjunction with the kinematic ones of the sensors 121 of the wearable electronic device 100 to detect and classify various activities. For example, accelerometers, gyroscopes and other kinematic or spatial- sensing approaches, taken in conjunction with one another can measure ambulation, body movements and body orientations for a better understanding of HAR], first sensor data comprising tri-axial accelerometer data from the tri-axial accelerometer and gyroscope data from the gyroscope, wherein the first sensor data is sampled at a predefined sampling rate and the first sensor data is indicative of a first user activity performed during the motor exam, wherein the wearable sensor system is configured to be worn by a user [Sobol at Para. 0293 teaches in one form, other assessment tools, such as the Functional Independence Measure (FIM), may be used to form a score of an individual to show a degree of independence based on motor and cognitive functions. The score may be used to assess how well an individual can be expected to meet ADL minimums. In addition, an FIM score—which may be based on a previous visit to a physician in a doctor's office under controlled conditions—may help establish useful intra-patient baseline data 1700. Depending on the nature of the health condition, other assessment tools or rating scales may be used to establish baseline data 1700. For example, if Parkinson's Disease is suspected, the Unified Parkinson's Disease Rating Scale (UPDRS) may be used to clinically assess whether an individual is at risk of developing the disease. It will be appreciated that other diseases and their scales for assessment may be correlated to some or all of the LEAP data being acquired by the wearable electronic device 100, and that the inference of all such diseases through the correlation of one or more of their criteria with such data is within the scope of the present disclosure (accelerometer interpreted as tri-axial accelerometer)];
receiving, at a second time different from the first time and by the wearable sensor system, second sensor data indicative of a second user activity [Sobol at Para. 0104 teaches FIG. 10C depicts a notional dashboard that can be displayed to a caregiver on the remote computing device of FIG. 9 to identify the daily frequency of room visits by a particular patient over the course of a week and that is based on LEAP data that is generated by the wearable electronic device and system of FIG. 1 according to one or more embodiments shown or described herein];
and storing the second sensor data with the second annotation on the wearable sensor system [Sobol at Para. 0168 teaches Moreover, the acquired data-which is received in raw form—may subsequently be labeled in order to correlate it to one of the LEAP data categories. Such labeled data may then correspond to an event that may be further grouped according to time, date, type of sensor or the like that may be stored in memory 173B].
Sobol does not teach receiving, by the wearable sensor system, a first annotation associated with the first sensor data, wherein the first annotation is appended to the first sensor data;
generating, by the wearable sensor system, second annotation corresponding to the second sensor data at the second time based on the first sensor data, the first annotation, and the second sensor data, wherein the second annotation is different from the first annotation and the second annotation is appended to the second sensor data;
receiving, by the wearable sensor system, in response to generating the second annotation, confirmation of the second annotation via the wearable sensor system;
NAKAJIMA teaches receiving, by the wearable sensor system, a first annotation associated with the first sensor data, wherein the first annotation is appended to the first sensor data [NAKAJIMA at Para. 0026 teaches for example, the annotation device according to one aspect of the present invention is an information processing device including a data acquisition unit for acquiring sensor data in which the outputs of a sensor are arranged in time series, and a label assignment unit for assigning a plurality of labels to the acquired sensor data (interpret to combine with wearable device of Sobol)];
generating, by the wearable sensor system, second annotation corresponding to the second sensor data at the second time based on the first sensor data, the first annotation, and the second sensor data, wherein the second annotation is different from the first annotation and the second annotation is appended to the second sensor data [NAKAJIMA at Fig. 10; NAKAJIMA at Para. 0057 teaches the annotation device 1 is an information processing device for acquiring sensor data in which the outputs of a sensor are arranged in time series and assigning a plurality of labels to the acquired sensor data (interpret to combine with wearable sensor system of Sobol)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine sensors of Sobol with the annotation of NAKAJIMA with the motivation to reduce the cost of annotations.
Sobol/NAKAJIMA do not teach receiving, by the wearable sensor system, in response to generating the second annotation, confirmation of the second annotation via the wearable sensor system;
Pennanen teaches receiving, by the wearable sensor system, in response to generating the second annotation, confirmation of the second annotation via the wearable sensor system [Pennanen at Para. 0009 teaches the mobile communication device then requests its user to provide a confirmation whether or not the information indicating the most likely activity types associated with a temporal zone represents a correct analysis, and then communicates the confirmation back to the computing hardware for amending parameters and/or algorithms employed in the software products, which execute analysis of the sensor signals to improve their accuracy; Pennanen at Para. 0011 teaches the mobile communication device is implemented by way of at least one of: a portable computer such as a laptop, a smartphone, a wrist-worn phone, a phablet, a mobile telephone, a tablet computer, a portable media device or any other computing device that can be worn by the user and is capable of processing and displaying data. Moreover, one or more sensors of the mobile communication device are implemented using at least one of: a gyroscopic angular sensor, an accelerometer, a GPS position sensor, a cellular position sensor (i.e. using position derived/calculated in relation to one or more radio base stations (cellular and local area), a magnetometer, a microphone, a camera, a temperature sensor];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Sobol, NAKAJIMA with the annotation of Pennanen with the motivation to improve the analysis and identification of the activities.
Regarding Claim 2
Sobol/NAKAJIMA/Pennanen teach the computer-implemented method of claim 1,
Sobol/NAKAJIMA/Pennanen further teach wherein the first annotation comprises contextual data describing an activity performed and an observation on performance of the activity [Sobol at Para. 0030 teaches The hybrid wireless communication system includes a first wireless communication sub-module that during its operation selectively receives location data in the form of a beacon signal, a second wireless communication sub-module that during its operation selectively receives location data in the form of a GNSS signal, and a third wireless communication sub-module that during its operation transmits an LPWAN-based signal that provides location indicia of the wearable electronic device based on acquired location data from at least one of the first and second wireless communication sub-modules. Numerous sensors are supported by the platform such that during operation, at least one sensor detects a respective one of environmental data, activity data and physiological data from an individual to whom the wearable electronic device is secured (location data interpreted as context data); Sobol at Para. 0052 teaches in one form, an embodiment of the fourth aspect may include one or more of the previous forms, wherein the machine code to execute at least a portion of a pneumonia analysis comprises machine code to execute at least a portion of at least one of a PSI score, CURB-65 score, SMART-COP score and an A-DROP score based on at least one of the location data and activity data in conjunction with the physiological data that comprises at least one of respiratory data and heat rate data].
Regarding Claim 3
Sobol/NAKAJIMA/Pennanen teach the computer-implemented method of claim 1,
Sobol/NAKAJIMA/Pennanen further teach wherein the wearable sensor system further comprises at least one of a photoplethysmography sensor or a heart rate sensor [Sobol at Para. 0015 teaches in one form, an embodiment of the first aspect may include one or more of the previous forms, in which at least one of the sensors that are configured to detect physiological data includes at least one sensor selected from the group consisting of a heart rate sensor, a breathing rate sensor, a temperature sensor, a respiration sensor, a pulse oximetry sensor, a respiratory rate sensor, an oxygen saturation sensor, an electrocardiogram sensor, a cardiac output index sensor, a systematic pressure sensor, a systematic systolic arterial pressure sensor].
Regarding Claim 4
Sobol/NAKAJIMA/Pennanen teach the computer-implemented method of claim 1,
Sobol/NAKAJIMA/Pennanen further teach wherein the confirmation of the second annotation is received through a user interface of the wearable sensor system at the second time [Pennanen at Para. 0009 (see Claim 1 for explanation)].
Regarding Claim 5
Sobol/NAKAJIMA/Pennanen teach the computer-implemented method of claim 1,
Sobol/NAKAJIMA/Pennanen further teach wherein the second annotation comprises a predicted score that quantifies the second user activity or a user health state [Sobol at Para. 0052 teaches in one form, an embodiment of the fourth aspect may include one or more of the previous forms, wherein the machine code to execute at least a portion of a pneumonia analysis comprises machine code to execute at least a portion of at least one of a PSI score, CURB-65 score, SMART-COP score and an A-DROP score based on at least one of the location data and activity data in conjunction with the physiological data that comprises at least one of respiratory data and heat rate data. These acronyms will be further defined in more detail later in this disclosure, where the pneumonia analysis may further include one or more factors from each of these scores].
Regarding Claim 6
Sobol/NAKAJIMA/Pennanen teach the computer-implemented method of claim 1,
Sobol/NAKAJIMA/Pennanen further teach wherein generating the second annotation based on the first sensor data, the first annotation, and the second sensor data comprises generating the second annotation using a machine learning algorithm trained prior to receiving the second sensor data and using the first sensor data and the first annotation, the machine learning algorithm having an input of the second sensor data [Sobol at Para. 0061 teaches according to a fifth aspect of the present disclosure, a method of using a machine learning model to evaluate a health condition of an individual is disclosed. The method includes acquiring, using a wearable electronic device, location data from one or both of BLE location data and GNSS location data and acquiring, with a plurality of sensors that are formed as part of the wearable electronic device, at least one of environmental data, activity data and physiological data. The method also includes wirelessly transmitting at least a portion of the LEAP data from the wearable electronic device to a wireless low power wide area network receiver using a star topology network, as well as executing a machine learning model based at least in part on at least a portion of the LEAP data].
Regarding Claim 7
Sobol/NAKAJIMA/Pennanen teach the computer-implemented method of claim 1,
Sobol/NAKAJIMA/Pennanen further teach further comprising receiving an input at a user device indicating the user has taken a medication, and wherein the second annotation comprises a comparison of performance of the first and second user activity before and after the input [Sobol at Para. 0280 teaches in particular, a quantifiable scoring system may be used, where Table 2 shows a functional decline may involve a 0 to 4 scale for an individual's self-performance of different ADLs for an activity occurring 3 or more times and that can be compared to baseline scores for the same individual where—as shown in FIG. 6—could be embodied as baseline data 1700. Guidelines are used to quantify the score as follows (baseline interpreted as activity data before input); Sobol at Para. 0282 teaches for example, directly acquiring information pertaining to taking medication, eating, ambulating and socializing may be more difficult than others, regardless of their probative value].
Regarding Claim 14
Sobol/NAKAJIMA teach the computer-implemented method of claim 8,
Sobol/NAKAJIMA do not teach further comprising receiving, by the wearable sensor system, a confirmation of the annotation via a user interface of the wearable sensor system.
Pennanen teaches further comprising receiving, by the wearable sensor system, a confirmation of the annotation via a user interface of the wearable sensor system [Pennanen at Para. 0009, 0011 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Sobol, NAKAJIMA with the confirmation of Pennanen with the motivation to improve the analysis and identification of the activities.
Claims 8-9,12,15-21 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Sobol et al (US Publication No. 20190209022) in view of NAKAJIMA et al (US Publication No. 20200397346).
Regarding Claim 8
Sobol teaches a computer-implemented method, comprising:
receiving sensor data by a wearable sensor system comprising a tri-axial accelerometer and a gyroscope during a user activity in a free-living environment [Sobol at Para. 0328 teaches for example, the location data may provide indicia that an individual has spent time in a location where the likelihood coming into contact with another who may have pneumonia is heightened. Similarly, activity, along with respiratory and other physiological data, may provide indicia of speed of movement, respiration rate, heat rate, shallowness of breathing or the like that can be correlated to the likelihood of pneumonia; Sobol at Para. 00175, 00229 (see Claim 1 for explanation)], the sensor data comprising tri-axial accelerometer data from the tri-axial accelerometer and gyroscope data from the gyroscope, the sensor data sampled at a predefined sampling rate [Sobol at Para. 0293 (see Claim 1 for explanation)];
determining, by the wearable sensor system and based on the sensor data, that the user activity corresponds with a clinical exam activity [Sobol at Para. 0052 teaches in one form, an embodiment of the fourth aspect may include one or more of the previous forms, wherein the machine code to execute at least a portion of a pneumonia analysis comprises machine code to execute at least a portion of at least one of a PSI score, CURB-65 score, SMART-COP score and an A-DROP score based on at least one of the location data and activity data in conjunction with the physiological data that comprises at least one of respiratory data and heat rate data];
and generating, using a machine learning algorithm and by the wearable sensor system, an annotation indicative of a predicted clinical exam score of the clinical exam activity, wherein:
and prior to generating the annotation, the machine learning algorithm is trained using clinical exam data and clinical exam annotations indicating a performance of the clinical exam activity by a subject [Sobol at Para. 0332 teaches As with the UTI as described above, in one form, analyzing whether an individual shows predictors of pneumonia onset versus risk for developing the illness may include applying machine learning logic to the acquired LEAP data. In particular, patterns arising from a particular combination of patient location, movement (or relative lack thereof) using the first and second wireless communication sub-modules 175A, 175B and physiological condition (using one or more of the sensors 121), possibly in conjunction with ambient environmental conditions (also using one or more of the sensors 121) allows for a data-driven predictive analytic approach to infer the likelihood of a pneumonia even absent direct clinical measurement of a patient's condition, such as through the conventional PSI, CURB-65, SMART-COP or A-DROP score approaches. In another form, the data may be used as a way to supplement such score-based symptom information].
Sobol does not teach the annotation is appended to the sensor data;
NAKAJIMA teaches the annotation is appended to the sensor data [NAKAJIMA at Para. 0026 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine sensors of Sobol with the annotation of NAKAJIMA with the motivation to reduce the cost of annotations.
Regarding Claim 9
Sobol/NAKAJIMA teach the computer-implemented method of claim 8,
Sobol/NAKAJIMA further teach wherein the clinical exam activity comprises a motor exam to evaluate progression of a disease affecting user motor control [Sobol at Para. 0057 teaches in one form, an embodiment of the fourth aspect may include one or more of the previous forms, wherein the neuropsychiatric condition includes cognitive impairment such that at least a portion of the set of machine codes that are on at least one of the non-transitory computer readable medium of the wearable electronic device and the non-transitory computer readable medium of the backhaul server and that are operated upon by the respective processor further comprises a machine code to execute a cognitive impairment analysis based on at least a portion of at least one of the location data, environmental data, activity data and physiological data; Sobol at Para. 0058 teaches in one form, an embodiment of the fourth aspect may include one or more of the previous forms, wherein the machine code to execute at least a portion of a cognitive impairment condition analysis comprises machine code to determine at least one of a stage of dementia selected from the group consisting of at least one of (a) early stage dementia, (b) moderate stage dementia, (c) late stage dementia and (d) terminal stage dementia (cognitive impairment condition analysis interpreted as motor exam); Sobol at Para. 0278 teaches the decrease in the functional status of person P as dementia progresses along the timeline may be grouped into various stages using a dementia trajectory chart where the functional status extends along the Y-axis and the timeline extends along the X-axis. In one form, dementia may be broken down into an early stage, a moderate stage, a late stage, a terminal stage and ultimately death. In the early stage (typically between one and two years after a diagnosis), relatively small anomalies can be noticed, including lack of initiation of activities, confusion about places and times (including arrival at an improper location at an improper time) and loss of love of life].
Regarding Claim 12
Sobol/NAKAJIMA teach the computer-implemented method of claim 8,
Sobol/NAKAJIMA further teach wherein the annotation is generated by the wearable sensor system at a time of receiving the sensor data [NAKAJIMA at Para. 0079 teaches the input device 15 is a device for inputting, such as a mouse, a keyboard, or the like. The output device 16 is a device for outputting, such as a display, a speaker, or the like. The labelling to the sensor data 122 may be automatically performed by the device or may be performed by operation of the user on the device. When the labelling is performed by the user, the user can assign a label to the sensor data 122 by operating the annotation device 1 using the input device 15 and the output device 16 (automatically interpreted to be at a time of receiving the sensor data)].
Regarding Claim 15
Sobol teaches a computer-implemented method, comprising:
receiving, at a first time and from a wearable sensor system comprising a tri-axial accelerometer and a gyroscope [Sobol at Para. 0129, 0175 (see Claim 1 for explanation)], first sensor data indicative of a clinical activity comprising tri-axial accelerometer data from the tri-axial accelerometer and gyroscope data from the gyroscope, the first sensor data sampled at a predefined sampling rate [Sobol at Para. 0293 (see Claim 1 for explanation)];
training a first machine learning algorithm using the first sensor data and the first annotation data [Sobol at Para. 0038 teaches in one form, an embodiment of the third aspect may include one or more of the previous forms, further including machine code to train the machine learning classification model, the machine code to train the machine learning classification model comprising (a) a machine code to cleanse at least a portion of the LEAP data, (b) a machine code to extract at least one feature vector from the cleansed data, and (c) a machine code to execute at least one algorithm based on the one or more feature vectors a machine code to execute at least one algorithm based on the at least one feature vector such that upon execution of the at least one algorithm, the machine learning classification model is configured provide a predictive analytical output of the health condition to a caregiver];
receiving, at a second time different from the first time and from the wearable sensor system, second sensor data indicative of a first user performing an activity outside of a clinical environment [Sobol at Para. 0283 teaches one way to gain insight into HAR, ADL or IADL metrics is to first establish the patient baseline data 1700 (shown in FIG. 6) that can be used to establish certain activity, health or behavioral norms. An intra-patient version of such baseline data 1700 may be created through an experimental protocol where the patient is first equipped with one or more wearable sensors (which in one form may include the sensors 121 similar to or the same as the ones that make up the wearable electronic device 100) and asked to perform routine daily functions under so-called normal conditions such as those encountered in one's home or other familiar environment];
training a second machine learning algorithm using the second annotation data and the second sensor data [Sobol at Para. 0263 teaches for example, annotated HAR data for training may include those associated with publicly available or proprietary data sets. More particularly, at least some of the inputs of the sensed data may be arranged into features within the data table such that multiple inputs form an input vector, one exemplary form of which accelerometer-based or gyroscope-based activity data];
and receiving, from the wearable sensor system, third annotation data associated with an activity other than the clinical activity generated by the wearable sensor system and using the second machine learning algorithm trained using the second annotation data and the second sensor data [Sobol at Para. 0103 teaches FIG. 10B depicts a notional dashboard that can be displayed to a caregiver on the remote computing device of FIG. 9 to identify a particular patient, along with a bar chart form of the patient's daily bathroom visits and a weekly comparison based on LEAP data that is generated by the wearable electronic device and system of FIG. 1 according to one or more embodiments shown or described herein; Sobol at Para. 0283 teaches one way to gain insight into HAR, ADL or IADL metrics is to first establish the patient baseline data 1700 (shown in FIG. 6) that can be used to establish certain activity, health or behavioral norms. An intra-patient version of such baseline data 1700 may be created through an experimental protocol where the patient is first equipped with one or more wearable sensors (which in one form may include the sensors 121 similar to or the same as the ones that make up the wearable electronic device 100) and asked to perform routine daily functions under so-called normal conditions such as those encountered in one's home or other familiar environment].
Sobol does not teach receiving, from the wearable sensor system, first annotation data associated with the first sensor data, wherein the first annotation data is appended to the first sensor data;
receiving, from the wearable sensor system, second annotation data associated with the second sensor data generated by the wearable sensor system and using the first machine learning algorithm, wherein the second annotation data is appended to the second sensor data;
NAKAJIMA teaches receiving, from the wearable sensor system, first annotation data associated with the first sensor data, wherein the first annotation data is appended to the first sensor data [NAKAJIMA at Fig. 10; NAKAJIMA at Para. 0057 (see Claim 1 for explanation)];
receiving, from the wearable sensor system, second annotation data associated with the second sensor data generated by the wearable sensor system and using the first machine learning algorithm, wherein the second annotation data is appended to the second sensor data [NAKAJIMA at Fig. 10; NAKAJIMA at Para. 0026, 0057 (see Claim 1 for explanation; interpret to combine with machine learning of Sobol)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine sensors of Sobol with the annotation of NAKAJIMA with the motivation to reduce the cost of annotations.
Regarding Claim 16
Sobol/NAKAJIMA teaches the computer-implemented method of claim 15,
Sobol/NAKAJIMA further teaches wherein the first annotation data and the second annotation data each comprise content indicative of performance of the clinical activity [Sobol at Para. 0293 teaches in one form, other assessment tools, such as the Functional Independence Measure (FIM), may be used to form a score of an individual to show a degree of independence based on motor and cognitive functions. The score may be used to assess how well an individual can be expected to meet ADL minimums].
Regarding Claim 17
Sobol/NAKAJIMA teaches the computer-implemented method of claim 15,
Sobol/NAKAJIMA further teaches wherein the first annotation data comprises information received from a second user via a user device [Sobol at Para. 0118 teaches in addition to the wearable electronic device, the present disclosure teaches a system that may track and monitor the device, log the collected data, perform analysis on the collected data and generate alerts. The data and alerts may be accessed through a user interface that can be displayed on a remote computing device or other suitable device with internet or cellular access].
Regarding Claim 18
Sobol/NAKAJIMA teaches the computer-implemented method of claim 8,
Sobol/NAKAJIMA further teaches wherein determining that the user activity corresponds with the clinical exam activity comprises receiving an input at a user device indicating that a user is beginning a virtual motor exam [Sobol at Para. 0118 (see Claim 17 for explanation)].
Regarding Claim 19
Sobol/NAKAJIMA teaches the computer-implemented method of claim 15,
Sobol/NAKAJIMA further teaches further comprising, receiving context data from a user device, the context data describing one or more contexts associated with the first user performing the activity [Sobol at Para. 0030 (see Claim 2 for explanation)].
Regarding Claim 20
Sobol/NAKAJIMA teaches the computer-implemented method of claim 19,
Sobol/NAKAJIMA further teaches wherein the one or more contexts comprise user location data [Sobol at Para. 0041 teaches according to a fourth aspect of the present disclosure, a method of monitoring an individual with a wearable electronic device is disclosed. The method includes acquiring, using the wearable electronic device, location data from at least one of BLE location data and GNSS location data, and wirelessly transmitting the location data from the wearable electronic device to a wireless LPWAN receiver using a star topology network].
Regarding Claim 21
Sobol/NAKAJIMA teaches the computer-implemented method of claim 15,
Sobol/NAKAJIMA further teaches wherein the activity other than the clinical activity is performed outside of a clinical environment [Sobol at Para. 0283 teaches one way to gain insight into HAR, ADL or IADL metrics is to first establish the patient baseline data 1700 (shown in FIG. 6) that can be used to establish certain activity, health or behavioral norms. An intra-patient version of such baseline data 1700 may be created through an experimental protocol where the patient is first equipped with one or more wearable sensors (which in one form may include the sensors 121 similar to or the same as the ones that make up the wearable electronic device 100) and asked to perform routine daily functions under so-called normal conditions such as those encountered in one's home or other familiar environment].
Claims 10-11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Sobol, NAKAJIMA as applied to claim 8 above, and further in view of MIRELMAN et al (US Publication No. 20210161430).
Regarding Claim 10
Sobol/NAKAJIMA teach the computer-implemented method of claim 8,
Sobol/NAKAJIMA do not teach wherein the predicted clinical exam score provides a quantitative score for the performance of the clinical exam activity based on the sensor data.
MIRELMAN teaches wherein the predicted clinical exam score provides a quantitative score for the performance of the clinical exam activity based on the sensor data [MIRELMAN at Para. 0204 (see Claim 9 for explanation); MIRELMAN at Para. 0211 teaches for example, freezing of gait (FOG) may be provoked using, for example, images of narrow passageways, to provoke FOG and/or near FOG conditions and/or to train a subject for them. Optionally, FOG is detected using one or more physiological sensors, for example, an accelerometer, which may also be used for other disease conditions].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Sobol, NAKAJIMA with the score of MIRELMAN with the motivation to help improve functional ability, lower the risk of falls and/or maintain health.
Regarding Claim 11
Sobol/NAKAJIMA teach the computer-implemented method of claim 8,
Sobol/NAKAJIMA do not teach wherein the annotation comprises a predicted subjective user rating during performance of the user activity.
MIRELMAN teaches wherein the annotation comprises a predicted subjective user rating during performance of the user activity [MIRELMAN at Para. 0204 teaches in an exemplary embodiment of the invention, a risk assessment includes a score, built of, for example, a weighted combination of the number of falls, number of real falls and/or deficient in cognitive performance and/or speed of walking, weighted, for example, by the level at which they occur].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Sobol, NAKAJIMA with the rating of MIRELMAN with the motivation to help improve functional ability, lower the risk of falls and/or maintain health.
Claims 13, 29-35 are rejected under 35 U.S.C. 103(a) as being unpatentable over Sobol, NAKAJIMA as applied to claim 8 above, and further in view of Meyer et al (US Publication No. 20130345524).
Regarding Claim 13
Sobol/NAKAJIMA teach the computer-implemented method of claim 8,
Sobol/NAKAJIMA do not teach wherein determining that the user activity corresponds with the clinical exam activity comprises receiving an input at a user device indicating that a user is beginning a virtual motor exam.
Meyer teaches wherein determining that the user activity corresponds with the clinical exam activity comprises receiving an input at a user device indicating that a user is beginning a virtual motor exam [Meyer at Para. 0064 teaches as illustrated in FIG. 3, the method starts at a point in time when a user interface element, in one of many forms noted below, is inputted into the terminal device 101. 0064] As illustrated in FIG. 3, the method starts at a point in time when a user interface element, in one of many forms noted below, is inputted into the terminal device 101].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Sobol, NAKAJIMA with the input of Meyer with the motivation to improve monitoring for cardiac patients.
Regarding Claim 29
Sobol teaches a computer-implemented method, comprising:
receiving, at a first time during a motor exam and by a wearable sensor system comprising a tri-axial accelerometer and a gyroscope [Sobol at Para. 0129, 0175 (see Claim 1 for explanation)], first sensor data indicative of a motor exam activity and comprising tri-axial accelerometer data from the tri-axial accelerometer and gyroscope data from the gyroscope, the first sensor data sampled at a predefined sampling rate [Sobol at Para. 0293 (see Claim 1 for explanation)];
determining, by the wearable sensor system, an activity window of the third sensor data that corresponds to the motor exam activity or the virtual motor exam by comparing the first sensor data and the second sensor data to a portion of the third sensor data [Sobol at Para. 0032 teaches in one form, an embodiment of the third aspect may include one or more of the previous forms, wherein at least a portion of the set of machine codes that are on at least one of the non-transitory computer readable medium of the wearable electronic device and the non-transitory computer readable medium of the backhaul server and that are operated upon by the respective processor further includes a machine code that compares at least a portion of the LEAP data to baseline data that forms a data structure that is stored on one or both of the non-transitory computer readable medium of the wearable electronic device and the non-transitory computer readable medium of the backhaul server (baseline data interpreted as first and second data); Sobol at Para. 0293 teaches in one form, other assessment tools, such as the Functional Independence Measure (FIM), may be used to form a score of an individual to show a degree of independence based on motor and cognitive functions. The score may be used to assess how well an individual can be expected to meet ADL minimums];
and generating, by the wearable sensor system using a machine learning algorithm trained using the first sensor data, the first annotation, the second sensor data, and the second annotation, a third annotation associated with the activity window and describing a user performance during the activity window [Sobol at Para. 0165 teaches likewise, applying temporal filters may help to extract suitable movement feature information. By way of example, the movement data (which in one form may include falls, high or low gait speed or other forms of movement) acquired from the accelerometer and gyroscope may be used in order to determine whether sudden changes in motion are present; such an approach may include the calculation of derivatives, moving averages or the like in order to make it useful for one or more classification or regression machine learning models such as a decision tree-based or random forest-based classifier. Likewise, a portion of the machine code may be dedicated to controlling sampling intervals for various forms of the LEAP data, particularly those data signals that have significant temporal components such as activity data. In one non-limiting example, such sampling intervals can be made to take place in various fractions of a second (such as 0.01 second, 0.1 second or the like), various multiples of a second (such as 1 second, 2 seconds, 5 seconds, 10 seconds or the like) or as fractions or multiples of larger time intervals, depending on the accuracy needs associated with the data being acquired. In one form, both the presently-acquired data and any historical or baseline data (including those with significant temporal components as discussed herein) may be placed in memory 173B in order to provide appropriate signatures that correspond to the LEAP data for subsequent comparison or analysis purposes].
Sobol does not teach receiving, by the wearable sensor system, a first annotation associated with the first sensor data, wherein the first annotation is appended to the first sensor data;
receiving, by the wearable sensor system, at a second time during a virtual motor exam and using the wearable sensor system, second sensor data;
receiving, by the wearable sensor system, a second annotation associated with the second sensor data, wherein the second annotation is appended to the second sensor data;
receiving, by the wearable sensor system, at a third time different from the first time and the second time, third sensor data indicative of user activity over an extended period of time;
NAKAJIMA teaches receiving, by the wearable sensor system, a first annotation associated with the first sensor data, wherein the first annotation is appended to the first sensor data [NAKAJIMA at Para. 0026 (see Claim 1 for explanation)];
receiving, by the wearable sensor system, a second annotation associated with the second sensor data, wherein the second annotation is appended to the second sensor data [NAKAJIMA at Para. Fig. 10; NAKAJIMA at Para. 0057 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine sensors of Sobol with the annotation of NAKAJIMA with the motivation to reduce the cost of annotations.
Sobol/NAKAJIMA do not teach receiving, by the wearable sensor system, at a second time during a virtual motor exam and using the wearable sensor system, second sensor data;
receiving, by the wearable sensor system, a second annotation associated with the second sensor data, wherein the second annotation is appended to the second sensor data;
receiving, by the wearable sensor system, at a third time different from the first time and the second time, third sensor data indicative of user activity over an extended period of time;
Meyer teaches receiving, by the wearable sensor system, at a third time different from the first time and the second time, third sensor data indicative of user activity over an extended period of time [Meyer at Para. 0064 (see Claim 22 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Sobol, NAKAJIMA with the sensor data of Meyer with the motivation to improve monitoring for cardiac patients.
Regarding Claim 30
Claim(s) 30 is/are analogous to Claim(s) 3, thus Claim(s) 30 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Regarding Claim 31
Sobol/NAKAJIMA/Meyer teach the computer-implemented method of claim 29,
Sobol/NAKAJIMA/Meyer further teach wherein the third annotation quantifies the user performance during the activity window [Sobol at Para. 0313 teaches FIG. 10D shows a daily performance in bar chart form of the frequency of room visits over the period of a notional week to help track an individual from room to room, as well as show where the individual spends his or her time on a daily basis. As such, FIGS. 10C and 10D may be read together to provide not only the number of times a monitored patient goes into a particular room, but also the amount of time spent in such room, which can further help to identify unusual trends or patterns].
Regarding Claim 32
Sobol/NAKAJIMA/Meyer teach the computer-implemented method of claim 31,
Sobol/NAKAJIMA/Meyer further teach wherein determining the activity window comprises selecting the activity window based on the first sensor data [Sobol at Para. 0303 teaches for example, because various activities may be performed over periods of time that are compared to the sampling rate of the sensors 121, it may be beneficial to recognize such activities over one or more time-sampled sliding windows. Because the received data is unlikely to be identical (even for the same individual performing the same activity), it may be helpful to use statistical or structural filters in order to transform the raw data into a set of feature vectors for each given window. For example, statistical-based feature extraction may be used on raw activity data, while structural-based feature extraction may be used on the raw environmental data].
Regarding Claim 33
Sobol/NAKAJIMA/Meyer teach the computer-implemented method of claim 32,
Sobol/NAKAJIMA/Meyer further teach wherein selecting the activity window comprises identifying a user activity using the machine learning algorithm trained using the first annotation and the second annotation [Sobol at Para. 0166 teaches for example, the individual may go through various sitting, standing, walking, running (if possible) and related movements that can be labeled for each activity where classification is desired. As will be discussed in more detail later, such labeling may be useful in performing supervised machine learning, particularly as it applies to training a machine learning model; Sobol at Para. 0232 teaches HAR and related ADL or IADL categorization may then be performed based at least in part on the clusters that were formed during training].
Regarding Claim 34
Sobol/NAKAJIMA/Meyer teach the computer-implemented method of claim 29,
Sobol/NAKAJIMA/Meyer further teach wherein the third annotation comprises a predicted performance score for a user during the activity window [Meyer at Para. 0018 (see Claim 25 for explanation)].
Regarding Claim 35
Sobol/NAKAJIMA/Meyer teach the computer-implemented method of claim 29,
Sobol/NAKAJIMA/Meyer further teach wherein the third annotation comprises an activity identification [Sobol at Para. 0166 teaches in one form, baseline activity data such as that acquired from sensors 121 that are in the form of accelerometers, gyroscopes and the like may be created through examples that can be correlated to known movements of the individual being monitored. For example, the individual may go through various sitting, standing, walking, running (if possible) and related movements that can be labeled for each activity where classification is desired].
Claims 22-27 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Meyer et al (US Publication No. 20130345524) in view of Sobol et al (US Publication No. 20190209022).
Regarding Claim 22
Meyer teaches a computer-implemented method, comprising:
receiving, at an input device of a wearable sensor system, a first user input identifying a beginning of a first time period in which a virtual motor exam is conducted [Meyer at Para. 0064 teaches (see Claim 13 for explanation)];
receiving, at the input device of the wearable sensor system, a second user input identifying an end of the first time period [Meyer at Para. 0078 teaches FIG. 14 shows an example of a “Visual Field” test that is viewed on a device such as 101 or 701. In this case, the patient must press each yellow dot, 1402 and 1403, and then press to end the test];
receiving, by the wearable sensor system, second signal data output by the first sensor of the wearable sensor system during a second time period [Meyer at Para. 0064; Meyer at Para. 0057 teaches for example, a list(s) of available tests, one or more graphs of the test results, and/or one or more tables with details of the tests. Such reports can be formatted to be printed. In some implementations, the report shows results for tests conducted at different times and days, for example, at various hours over a single day or over multiple days, weeks, months, or years];
and generating, by the wearable sensor system and based on the first signal data, the first annotation, and the second signal data, a second annotation associated with the second signal data indicative of a user performance [Meyer at Para. 0059 teaches means for evaluating the response information can include one or more processors and memory units on the control device, the one or more processors running programs that may process the response information and compare it to previous baselines for a patient, other baselines, or other desired data and identify differences and similarity. Means for determining the status of a patient based on evaluating the response information may include evaluating if the difference in the response information for a particular deficit assessment is significantly different than previous tests or a baseline, or other threshold information]… [ … ]
Meyer does not teach accessing, by the wearable sensor system and based on the virtual motor exam, first signal data output by a first sensor of the wearable sensor system during the first time period, the first signal data comprising one of tri-axial accelerometer data from the tri-axial accelerometer or gyroscope data from the gyroscope, the first signal data sampled at a predefined sampling rate;
receiving, by the wearable sensor system, a first annotation from a clinical provider associated with the first signal data, wherein the first annotation is appended to the first signal data;
[ … ] … wherein the second annotation is appended to the second signal data.
Sobol teaches accessing, by the wearable sensor system and based on the virtual motor exam, first signal data output by a first sensor of the wearable sensor system during the first time period, the first signal data comprising one of tri-axial accelerometer data from the tri-axial accelerometer or gyroscope data from the gyroscope, the first signal data sampled at a predefined sampling rate;
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine input of Meyer with the wearable sensors of Sobol with the motivation to improve the ability to track the location and associated environment, activity and physiological information of a person that is suffering from—or is manifesting symptoms associated with—dementia, infections, neuropsychiatric problems or other adverse health conditions in order to provide data-informed care insights for family members, nurses, doctors or other caregivers.
Meyer/Sobol do not teach receiving, by the wearable sensor system, a first annotation from a clinical provider associated with the first signal data, wherein the first annotation is appended to the first signal data;
[ … ] … wherein the second annotation is appended to the second signal data.
NAKAJIMA teaches receiving, by the wearable sensor system, a first annotation from a clinical provider associated with the first signal data, wherein the first annotation is appended to the first signal data [NAKAJIMA at Para. 0026 (see Claim 1 for explanation)];
[ … ] … wherein the second annotation is appended to the second signal data [NAKAJIMA at Para. 0026 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine sensors of Sobol with the annotation of NAKAJIMA with the motivation to reduce the cost of annotations.
Regarding Claim 23
Meyer/Sobol/NAKAJIMA teach the computer-implemented method of claim 22,
Meyer/Sobol/NAKAJIMA further teach wherein the first user input and the second user input are provided by a user during the virtual motor exam [Meyer at Para. 0064 teaches the input data may be in the form of direct patient entry such as touch input 217 using the touchscreen display 207 or direct input using onscreen keyboard 209 or keyboard buttons 210. The input data may also be in the form of indirectly assessed clinical parameters obtained via device vibration 115, movement sensation 113, pressure sensors 112, still photography 108, audio recordings 106, accelerometry 114 and global position sensors 113 (interpreted as during a virtual motor exam)].
Regarding Claim 24
Meyer/Sobol/NAKAJIMA teach the computer-implemented method of claim 22,
Meyer/Sobol/NAKAJIMA further teach wherein the first signal data comprises data from the tri-axial accelerometer [Sobol at Para. 0175 (see Claim 1 for explanation)].
Regarding Claim 25
Meyer/Sobol/NAKAJIMA teach the computer-implemented method of claim 22,
Meyer/Sobol/NAKAJIMA further teach wherein generating the second annotation comprises generating a predicted score that quantifies the user performance [Meyer at Para. 0018 teaches in some implementations, a user interface provides a user with the ability to interact with a device in order to determine a clinical examination score (clinical examination score interpreted as predicted score)].
Regarding Claim 26
Meyer/Sobol/NAKAJIMA teach the computer-implemented method of claim 25,
Meyer/Sobol/NAKAJIMA further teach wherein generating the second annotation comprises using a machine learning algorithm trained prior to receiving the second signal data using the first signal data, the first annotation, the machine learning algorithm having an input of the second signal data [Sobol at Para. 0232 teaches the acquired data (some of which may be taken from one or more of sensors 121) may then be processed to calculate signal spatio-temporal or other characteristics that may then be subjected to unlabeled clustering in order to assign them to an appropriate cluster depending on the movement being categorized. HAR and related ADL or IADL categorization may then be performed based at least in part on the clusters that were formed during training. As such, this hybrid approach may be understood to make up a K-means clustering-based approach].
Regarding Claim 27
Meyer/Sobol/NAKAJIMA teach the computer-implemented method of claim 22,
Meyer/Sobol/NAKAJIMA further teach wherein the first annotation comprises a user self-assessment score [Sobol at Para. 0280 teaches in particular, a quantifiable scoring system may be used, where Table 2 shows a functional decline may involve a 0 to 4 scale for an individual's self-performance of different ADLs for an activity occurring 3 or more times and that can be compared to baseline scores for the same individual where—as shown in FIG. 6—could be embodied as baseline data 1700. Guidelines are used to quantify the score as follows] and the computer-implemented method further comprises receiving a plurality of annotations associated with a plurality of segments of signal data, and wherein generating the second annotation is further based on the plurality of annotations and the plurality of segments of signal data [Sobol at Para. 0148 teaches In one form, the various data processing layers associated with models that provide analytics to system 1 may be included with—or formed to cooperate with—a lower (such as physical) layer that may include various hardware components such as sensors 121 that collect and convey signals that correlate to event data. In this way, the organization of events into data structures storable in memory 173B of the wearable electronic device 100 (or its equivalent memory 173B in system 1) may include various forms of data tables with labeling or identification, such as the type of event being sensed or detected, a particular activity category (such as ADL) of the person being monitored, spatio-temporal contextual information for an event, a scalar value of the sensed event, as well as others].
Claim 28 rejected under 35 U.S.C. 103(a) as being unpatentable over Meyer, Sobol as applied to claim 22 above, and further in view of CHHABRA et al (US Publication No. 20200051673).
Regarding Claim 28
Meyer/Sobol/NAKAJIMA teach the computer-implemented method of claim 22,
Meyer/Sobol/NAKAJIMA do not teach wherein the first annotation comprises an average of ratings from a plurality of clinical providers based on the virtual motor exam.
CHHABRA teaches wherein the first annotation comprises an average of ratings from a plurality of clinical providers based on the virtual motor exam [CHHABRA at Para. 0016 teaches thus, embodiments of the present disclosure provide a rigorous way of scoring a patient's medical state. Embodiments of the present disclosure are therefore a significant improvement over the experience-based assessment made by healthcare providers based on a patient's medical chart; CHHABRA at Para. 0049 teaches in some embodiments, the Scoring Component 250 determines the mean or median of the attribute group scores for the particular patient].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Meyer, Sobol, NAKAJIMA with the scores of CHHABRA with the motivation to improve medical care.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-35, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
Claim 1, as amended, includes an additional element that adds a meaningful limitation to the claim and thus integrates the alleged abstract idea into a practical application. This amendment limits the applicability of the amended claim to a narrowly defined context and thereby adds a meaningful limitation that precludes the amended claim from monopolizing any alleged abstract idea.
Amended claim 1 is analogous to the claims at issue in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057, 100 USPQ2d 1492 (Fed. Cir. 2011).
Moreover, amended claim 1 neither limits the use of the alleged abstract idea to a
particular technological environment nor adds insignificant extra-solution activity. Amended claim 1 recites a narrowly scoped context which enables the resolution of the technical problems described at paragraphs [0020]-[0021] of the specification, as described above. The narrowly scoped context claimed is not a mere "technological environment" or "field of use." See MPEP § 2106.05(h) (describing examples including "execution on a generic computer," "telephone network or the Internet," or "power grid monitoring" as limitations that merely limit to a technological environment or field of use).
Regarding (a), the Examiner respectfully disagrees. The additional elements do not provide a practical application. There is no technical problem caused by the computer or technological field supported in the Specification.
Regarding (b), the Examiner respectfully disagrees. The amended claims are not analogous because there is no technical problem caused by the computer or technological field found in the Specification. If no technical problem can be found, then no practical application can be found.
Regarding (c), the Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem is not a technological problem caused by the (the technological environment to which the claims are confined). The problem of tracking disease progression was not a problem cause by the computer/server, is it a problem that existed and/or exists regardless of whether a computer/server is involved in the process. At best, Applicant’s identified problem is a medical problem. Because no technological problem is present, the claims do not provide a practical application.
Rejection under 35 U.S.C. § 112
Regarding the indefiniteness rejection of Claims 1-35, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Applicant argues:
Amended claims overcome basis of rejection.
Regarding (a), the Examiner respectfully disagrees. It is still not clear whether the computer, as described by a computer-implemented method, or the wearable sensor system, which is interpreted as a separate device, is performing the limitations of receiving data, generating annotations and storing data. If there is no computer or processor recited in the claim, then it is unclear what is performing the steps in the claims other than the wearable sensor system. Furthermore, if computer is different from wearable system, then anything claimed that is done by the sensor is non-functional descriptive information.
Examiner recommends adding a computer/processor to the claims and having the computer performing the steps recited in the claims.
Rejection under 35 U.S.C. § 102/103
Regarding the rejection of Claims 1-35, the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment and/or afforded by the present RCE.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Johnstone et al (US Publication No. 20200275875) discloses a method for deriving and storing emotional conditions of humans.
Lillie et al (US Publication No. 20210067460) discloses a system, method, and computer program product for dynamically appending and transforming static activity data transmitted to a user device application.
Cook et al (US Publication No. 10896756) discloses methods, systems, and techniques for facilitating cognitive assessment.
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A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683