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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/16/2023 has been reconsidered by the examiner.
Response to Arguments
Applicant’s amendment filed on 9/9/2025 has been entered. Claims 1, 4,8, 17-19, 21, 23, 34, 36, 41, 57, 59 and 61 have been amended, no claims have been canceled, and claim 64 has been added
Applicant’s arguments with respect to claims 1 and 61 under 35 USC 102(a)(1) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument (please see rejections below).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4, 8-9, 17-19, 21, 23-25, 28-29, 34, 36 and 63 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al (US 20200300972 A1), hereinafter Wang.
Regarding claim 1, Wang discloses:
a mm-wave radar system for detecting an activity record from multiple targets comprising (Wang, para [0186], In this system, one can break down the limitation of 2.4 GHz/5 GHz WiFi by leveraging an opportunity in the emerging 60 GHz WiFi (e.g., 802.1 lad), which is already available in commercial routers. The disclosed ViMo is a first system that achieves multi-person stationary/non-stationary detection and vital signs monitoring using impulse-based commodity 60 GHz millimeter wave (mmWave) device. Different from 2.4 GHz/5 GHz radios, 60 GHz WiFi offers high directionality with large phased arrays in small size thanks to millimeter-wavelength and precise time-of-flight measurements brought by the large bandwidth. The advance in 60 GHz radios allows higher spatial resolution and range resolution, making it possible to monitor respiration as well as heart rate for multiple persons simultaneously):
i) at least one radar sensor configured to transmit a mm-wave radar signal waveform to receive backscattered radar signals from multiple targets (Wang, para [0152], A characteristics (e.g. characteristics of motion of an object in the venue) may comprise at least one of: an instantaneous characteristics, short-term characteristics, repetitive characteristics, recurring characteristics, history, incremental characteristics, changing characteristics, deviational characteristics, phase, magnitude, degree, time characteristics, frequency characteristics, time-frequency characteristics, decomposition characteristics, orthogonal decomposition characteristics, non-orthogonal decomposition characteristics, deterministic characteristics, probabilistic characteristics, stochastic characteristics, autocorrelation function (ACF), mean, variance, standard deviation, measure of variation, spread, dispersion, deviation, divergence, range, interquartile range, total variation, absolute deviation, total deviation, statistics, duration, timing, trend, periodic characteristics, repetition characteristics, long-term characteristics, historical characteristics, average characteristics, current characteristics, past characteristics, future characteristics, predicted characteristics, location, distance, height, speed, direction, velocity, acceleration, change of the acceleration, angle, angular speed, angular velocity, angular acceleration of the object, change of the angular acceleration, orientation of the object, angular of rotation, deformation of the object, shape of the object, change of shape of the object, change of size of the object, change of structure of the object, and/or change of characteristics of the object) Examiner notes that data such as range and velocity are extracted from backscatter signals,
and to measure range (Wang, para [0152]),
Doppler (Wang, para [0069], In the case of one or multiple Type 1 devices interacting with one or multiple Type 2 devices, any processing (e.g. time domain, frequency domain) may be different for different devices. The processing may be based on locations, orientation, direction, roles, user-related characteristics, settings, configurations, available resources, available bandwidth, network connection, hardware, software, processor, co-processor, memory, battery life, available power, antennas, antenna types, directional/unidirectional characteristics of the antenna, power setting, and/or other parameters/characteristics of the devices) Examiner notes that Doppler is inherently a frequency domain phenomenon wherein observations and measurements are obtained,
and/or angle information (Wang, para [0152]);
and ii) one or more processors configured to a) process the received backscattered signals (Wang, para [0037], In a different embodiment, a wireless monitoring system is described. The wireless monitoring system comprises: a plurality of pairs of transmitters and receivers in a venue, and processor. For each pair of the plurality of pairs: a respective transmitter of the pair is configured for asynchronously transmitting a respective wireless signal through a wireless multipath channel, while a first object in the venue is having a first repetitive motion, and a respective receiver of the pair is configured for asynchronously receiving the respective wireless signal through the wireless multipath channel, and extracting a respective time series of channel information (TSCI) of the wireless multipath channel from the respective wireless signal. The transmitter and the receiver of a first pair of the plurality of pairs are collocated at a same location in the venue. The transmitter and the receiver of a second pair of the plurality of pairs are positioned at two different locations in the venue. A plurality of TSCI is obtained by the receivers of the plurality of pairs. The processor is configured for: computing a first information of the first repetitive motion based on the plurality of TSCI, and monitoring the first repetitive motion of the first object based on the first information) and (para [0152]),
b) determine, for each target, radar data including radar-derived range (Wang, para [0152]),
Doppler (Wang, para [0144, col.1, line 30], fast FT (FFT)) Examiner notes that Fast Fourier Transform relates to range Doppler
and/or angle information data related to each target (Wang, para [0152]),
and c) process the radar-derived range Doppler (Wang, para [0144, col.1, line 30], fast FT (FFT)) Examiner notes that Fast Fourier Transform relates to range Doppler
and/or angle information of each target using a machine learning (MVL) engine that outputs an activity record related to each target, in which each activity record is linked to a timestamp (Wang, para [0037]) and (para [0134, p. 16, col. 2, lines 38-47], machine learning, supervised learning, unsupervised learning, semi-supervised learning, clustering, feature extraction, featuring training, principal component analysis, eigen-decomposition, frequency decomposition, time decomposition, time-frequency decomposition, functional decomposition, other decomposition, training, discriminative training, supervised training, unsupervised training, semi-supervised training, neural network,);
in which the activity record comprises a time-ordered sequence a record of events or activities related to the detected target and associated with a series of timestamps (Wang, para [0037]);
and in which the mm-wave radar system is configured to predict, based on the activity record, a next event or activity of the detected target or predict a subsequent location of the detected target or identify an abnormal event or change of behavior (Wang, para [0152], A characteristics (e.g. characteristics of motion of an object in the venue) may comprise at least one of: an instantaneous characteristics, short-term characteristics, repetitive characteristics, recurring characteristics, history, incremental characteristics, changing characteristics, deviational characteristics, phase, magnitude, degree, time characteristics, frequency characteristics, time-frequency characteristics, decomposition characteristics, orthogonal decomposition characteristics, non-orthogonal decomposition characteristics, deterministic characteristics, probabilistic characteristics, stochastic characteristics, autocorrelation function (ACF), mean, variance, standard deviation, measure of variation, spread, dispersion, deviation, divergence, range, interquartile range, total variation, absolute deviation, total deviation, statistics, duration, timing, trend, periodic characteristics, repetition characteristics, long-term characteristics, historical characteristics, average characteristics, current characteristics, past characteristics, future characteristics, predicted characteristics, location, distance, height, speed, direction, velocity, acceleration, change of the acceleration, angle, angular speed, angular velocity, angular acceleration of the object, change of the angular acceleration, orientation of the object, angular of rotation, deformation of the object, shape of the object, change of shape of the object, change of size of the object, change of structure of the object, and/or change of characteristics of the object).
Regarding claim 4, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which radar data related to a target includes one or more of the following (Wang, para [0186]):
3D radar data cube (Wang para [0172], The user-interface (UI) device may be a smart phone (e.g. iPhone, Android phone), tablet (e.g. iPad), laptop (e.g. notebook computer), personal computer (PC), device with graphical user interface (GUI), smart speaker, device with voice/audio/speaker capability, virtual reality (VR) device, augmented reality (AR) device, smart car, display in the car, voice assistant, voice assistant in a car, etc. The map (or environmental model) may be 2-dimensional, 3-dimensional and/or higher-dimensional. (e.g. a time varying 2D/3D map/environmental model) Walls, windows, doors, entrances, exits, forbidden areas may be marked on the map or the model),
micro-Doppler parameters or signature (Wang, (para [0062], The packet may comprise a control data and/or a motion detection probe. A data (e.g. ID/parameters/characteristics/settings/control signal/command/instruction/notification/broadcasting-related information of the Type 1 device) may be obtained from the payload. The wireless signal may be transmitted by the Type 1 device. It may be received by the Type 2 device. A database (e.g. in local server, hub device, cloud server, storage network) may be used to store the TSCI, characteristics, STI, signatures, patterns, behaviors, trends, parameters, analytics, output responses, identification information, user information, device information, channel information, venue (e.g. map, environmental model, network, proximity devices/networks) information, task information, class/category information, presentation (e.g. UI) information, and/or other information),
point cloud of scene (Wang, paras [0186]( and (para [0355, p. 41, col. 2, line 20], 3D-CFAR filtering) Examiner notes that point clouds of a scene are fundamental to 3-dimensional mmWave and also 3D CFAR,
points assigned to the target by a clustering algorithm (Wang, para [0193], In order to detect human subjects at various distances, one can apply a reflecting object detector that adaptively estimates the noise level at various distances and thus detects the presence of reflecting objects. To further differentiate the human subjects from static objects, one can design a motion detector that identifies static objects, stationary human subjects and human with large body motion. A target clustering module is implemented to further identify the number of human subjects and their respective locations. Moreover, to make a robust estimate of the heart rate, one can first devise a breathing signal eliminator to reduce the interference from the respiration signal after the breathing rate is estimated. The eliminator can remove the harmonics of the breathing signal, as well as deal with the spread of the breathing frequency component when the breathing period slightly changes. To tackle with the random measurement noise, one can leverage the stationary property of the heart rate and apply dynamic programming to estimate the heart rate utilizing both the frequency and time diversity);
and, in which micro-Doppler parameters are derived from a cluster of points assigned to the target by a clustering algorithm and from the properties of each of the points (Wang, para [0354, p. 40 col. 2, lines 51-63], gait cycle, gesture, handwriting, head motion, mouth motion, hand motion, leg motion, body motion, heart motion, internal organ motion, tool motion, machine motion, complex motion, combination of multiple motions, motion trend, repeatedness, periodicity, pseudo-periodicity, impulsiveness, sudden-ness, fall-down occurrence, recurrence, transient event, behavior, transient behavior, period, time trend, temporal profile, temporal characteristics, occurrence, time, timing, starting time, initiating time, ending time, duration, history, motion classification, motion type, change, temporal change, frequency change, CI change, DI change, timing change, gait cycle change) Examiner notes that micro-Doppler parameters relate to such events as a fall-down event indicative a large Doppler shift, transient motion indicative of non-periodic Doppler bursts, or a gait cycle that is indicative micro-Doppler pattern of the target’s extremities while walking,
including location (Wang, para [0058], The expression may comprise placement, placement of moveable parts, location, position, orientation, identifiable place, region, spatial coordinate, presentation, state, static expression, size, length, width, height, angle, scale, shape, curve, surface, area, volume, pose, posture, manifestation, body language, dynamic expression, motion, motion sequence, gesture, extension, contraction, distortion, deformation, body expression (e.g. head, face, eye, mouth, tongue, hair, voice, neck, limbs, arm, hand, leg, foot, muscle, moveable parts), surface expression (e.g. shape, texture, material, color, electromagnetic (EM) characteristics, visual pattern, wetness, reflectance, translucency, flexibility), material property (e.g. living tissue, hair, fabric, metal, wood, leather, plastic, artificial material, solid, liquid, gas, temperature), movement, activity, behavior, change of expression, and/or some combination),
velocity (Wang, para [0152]),
signal-to-noise ratio (Wang, para [0033, lines 17-20], Moreover, since the RF signal is reflected by multiple scatters, the embedded heartbeat signal has extremely low signal-to-noise ratio (SNR). As such, it is extremely difficult, if possible, to use commodity WiFi to estimate the heart rate),
as well as mathematical operations performed on those properties over a series of timestamps (Wang, para [0035 lines 12-18], Each of the plurality of TSCI is associated with a transmit antenna of the transmitter and a receive antenna of the receiver. The processor is configured for: computing a first information of the first repetitive motion based on the plurality of TSCI, and monitoring the first repetitive motion of the first object based on the first information).
Regarding claim 8, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm-wave radar system is also configured to determine, from the radar data, a vital sign or other physiological parameter related to the target (Wang, para [0186]) Examiner notes heart rate and respirations as example of vital signals and physiological related information of the targets,
and in which an event or activity is associated with a vital sign or other physiological parameters (Wang, para [0186]).
Regarding claim 9, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm-wave radar system is configured to send the activity record to a dashboard or application or a web page (Wang, para [0173], The user-interface (UI) device may be a smart phone (e.g. iPhone, Android phone), tablet (e.g. iPad), laptop (e.g. notebook computer), personal computer (PC), device with graphical user interface (GUI), smart speaker, device with voice/audio/speaker capability, virtual reality (VR) device, augmented reality (AR) device, smart car, display in the car, voice assistant, voice assistant in a car, etc. The map (or environmental model) may be 2-dimensional, 3-dimensional and/or higher-dimensional. (e.g. a time varying 2D/3D map/environmental model) Walls, windows, doors, entrances, exits, forbidden areas may be marked on the map or the model) and (para [0182], The summary may comprise: analytics, output response, selected time window, subsampling, transform, and/or projection. The presenting may comprise presenting at least one of: monthly/weekly/daily view, simplified/detailed view, cross-sectional view, small/large form-factor view, color-coded view, comparative view, summary view, animation, web view, voice announcement, and another presentation related to the periodic/repetition characteristics of the repeating motion),
and in which the mm-wave radar system is configured to output a digital representation of each target within an environment (Wang, para [0173]).
Regarding claim 17, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the ML engine is configured to extract features related to the target such as including limb length (Wang ,para [0058, lines 1-10], The expression may comprise placement, placement of moveable parts, location, position, orientation, identifiable place, region, spatial coordinate, presentation, state, static expression, size, length, width, height, angle, scale, shape, curve, surface, area, volume, pose, posture, manifestation, body language, dynamic expression, motion, motion sequence, gesture, extension, contraction, distortion, deformation, body expression (e.g. head, face, eye, mouth, tongue, hair, voice, neck, limbs, arm, hand, leg, foot, muscle, moveable parts)),
posture (Wang, para [0058, lines 1-10]),
movement rates of limbs or any other related parameters (Wang, para [0058, lines 1-10]).
Regarding claim 18, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the ML engine is configured to predict physical change or to identify abnormality or pathology based on the activity record (Wang, para [0134, pg. 17, col. 1 lines 10-26], A first part of the task may comprise at least one of: preprocessing, processing, signal conditioning, signal processing, post-processing, processing sporadically/continuously/simultaneously/contemporaneously/dynamically/adaptive/on-demand/as-needed, calibrating, denoising, feature extraction, coding, encryption, transformation, mapping, motion detection, motion estimation, motion change detection, motion pattern detection, motion pattern estimation, motion pattern recognition, vital sign detection, vital sign estimation, vital sign recognition, periodic motion detection, periodic motion estimation, repeated motion detection/estimation, breathing rate detection, breathing rate estimation, breathing pattern detection, breathing pattern estimation, breathing pattern recognition, heart beat detection, heart beat estimation, heart pattern detection, heart pattern estimation, heart pattern recognition, gesture detection, gesture estimation, gesture recognition, speed detection, speed estimation, object locationing, object tracking, navigation, acceleration estimation, acceleration detection, fall-down detection).
Regarding claim 19, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the ML engine is configured to identify long-term trends based on the activity record (Wang, para [0062], The packet may comprise a control data and/or a motion detection probe. A data (e.g. ID/parameters/characteristics/settings/control signal/command/instruction/notification/broadcasting-related information of the Type 1 device) may be obtained from the payload. The wireless signal may be transmitted by the Type 1 device. It may be received by the Type 2 device. A database (e.g. in local server, hub device, cloud server, storage network) may be used to store the TSCI, characteristics, STI, signatures, patterns, behaviors, trends, parameters, analytics, output responses, identification information, user information, device information, channel information, venue (e.g. map, environmental model, network, proximity devices/networks) information, task information, class/category information, presentation (e.g. UI) information, and/or other information).
Regarding claim 21, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which a lightweight ML classifier that is located on the sensor is first used to determine if a selected portion of radar data should be transmitted off-sensor for additional processing or classification (Wang, para [0092, lines 1- 28], The classifier may be applied to classify at least one current TSCI obtained from the series of current probe signals by the at least one second Type 2 device, to classify at least one portion of a particular current TSCI, and/or to classify a combination of the at least one portion of the particular current TSCI and another portion of another TSCI. The classifier may partition TSCI (or the characteristics/STI or other analytics or output responses) into clusters and associate the clusters to specific events/objects/subjects/locations/movements/activities. Labels/tags may be generated for the clusters. The clusters may be stored and retrieved. The classifier may be applied to associate the current TSCI (or characteristics/STI or the other analytics/output response, perhaps associated with a current event) with: a cluster, a known/specific event, a class/category/group/grouping/list/cluster set of known events/subjects/locations/movements/activities, an unknown event, a class/category/group/grouping/list/cluster/set of unknown events/subjects/locations/movements/activities, and/or another event/subject/location/movement/activity/class/category/group/grouping/list/cluster/set. Each TSCI may comprise at least one CI each associated with a respective timestamp. Two TSCI associated with two Type 2 devices may be different with different: starting time, duration, stopping time, amount of CI, sampling frequency, sampling period. Their CI may have different features. The first and second Type 1 devices may be at same location in the venue. They may be the same device) and (para [0134, p. 16, col. 2, lines 38-47]),
such as to a gateway connected to the sensor (Wang, para [0130 lines 1-5], The nearby device may be smart phone, iPhone, Android phone, smart device, smart appliance, smart vehicle, smart gadget, smart TV, smart refrigerator, smart speaker, smart watch, smart glasses, smart pad, iPad, computer, wearable computer, notebook computer, gateway),
or to a server connected to the sensor or gateway (Wang, para [0062]) and (para [0174 lines 17-20], Logical segmentation of the venue may be done using the at least one heterogeneous Type 2 device, or a server (e.g. hub device), or a cloud server, etc.)),
or distributed across any permutation of these (Wang, para [0092 pg. 9, col. 2 lines 2-7], The at least one second Type 2 device and/or a subset of the at least one second Type 2 device may be a permutation of a subset of the at least one first Type 2 device. The at least one second Type 2 device and/or a subset of the at least one second Type 2 device may be at same respective location as a subset of the at least one first Type 2 device) and (para [0174 lines 17-20]).
Regarding claim 23, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm-wave radar system is configured to monitor the vital signs of one or more people in bed using the radar data (Wang, para [0134, pg. 17, col. 1 lines 10-26]),
and also monitors motions (Wang, para [0058]),
which can be used to diagnose sleep quality (Wang, para [0134, pg. 17, col. 1 lines 10-26]).
Regarding claim 24, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm- wave radar system includes a communication subsystem that is configured to send or receive radar data or command or telemetry data to or from a remote server (Wang, para [0183, p. 26, col.1 lines 5-10] , The Type 1 device and/or the Type 2 device may operate with local control, can be controlled by another device via a wired/wireless connection, can operate automatically, or can be controlled by a central system that is remote (e.g. away from home)).
Regarding claim 25, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm- wave radar system includes multiple sensors that each scan individually or that scan different sections of an environment (Wang, para [0171, lines 1-20], In the presentation, information may be displayed with a map (or environmental model) of the venue. The information may comprise: location, zone, region, area, coverage area, corrected location, approximate location, location with respect to (w.r.t.) a map of the venue, location w.r.t. a segmentation of the venue, direction, path, path w.r.t. the map and/or the segmentation, trace (e.g. location within a time window such as the past 5 seconds, or past 10 seconds; the time window duration may be adjusted adaptively (and/or dynamically); the time window duration may be adaptively (and/or dynamically) adjusted w.r.t. speed, acceleration, etc.), history of a path, approximate regions/zones along a path, history/summary of past locations, history of past locations of interest, frequently-visited areas, customer traffic, crowd distribution, crowd behavior, crowd control information, speed, acceleration, motion statistics, breathing rate, heart rate, presence/absence of motion, presence/absence of people or pets or object, presence/absence of vital sign, gesture, gesture control (control of devices using gesture),
in order to show the target moving through the environment (Wang, para [0171, lines 1-20]).
Regarding claim 28, Wang discloses:
the mm-wave radar system of Claim 25 (Wang, para [0186]),
in which the environment includes one or more indoor area with walls (Wang, para [0132, col. 1, line 1 – col. 2, line 15], The venue may be a space such as a sensing area, room, house, office, property, workplace, hallway, walkway, lift, lift well, escalator, elevator, sewage system, air ventilations system, staircase, gathering area, duct, air duct, pipe, tube, enclosed space, enclosed structure, semi-enclosed structure, enclosed area, area with at least one wall, plant, machine, engine, structure with wood, structure with glass, structure with metal, structure with walls, structure with doors, structure with gaps, structure with reflection surface, structure with fluid, building, rooftop, store, factory, assembly line, hotel room, museum, classroom, school, university, government building, warehouse, garage, mall, airport, train station, bus terminal, hub, transportation hub, shipping terminal, government facility, public facility, school, university, entertainment facility, recreational facility, hospital, pediatric/neonatal wards, seniors home, elderly care facility, geriatric facility, community center).
Regarding claim 29, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which when a specific event or activity is detected, such as including an abnormal event or change of behavior (Wang, para [0134, col. 1 line 14 – col. 2, line 40]),
the mm-wave radar system is configured to send an instruction or alert together with the activity record that captures the detected event to an application running on a connected device (Wang, para [0136, lines 1-11], The task may include: detect a user returning home, detect a user leaving home, detect a user moving from one room to another, detect/control/lock/unlock/open/close/partially open a window/door/garage door/blind/curtain/panel/solar panel/sun shade, detect a pet, detect/monitor a user doing something (e.g. sleeping on sofa, sleeping in bedroom, running on treadmill, cooking, sitting on sofa, watching TV, eating in kitchen, eating in dining room, going upstairs/downstairs, going outside/coming back, in the rest room), monitor/detect location of a user/pet, do something (e.g. send a message, notify/report to someone) automatically upon detection).
Regarding claim 34, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm-wave radar system does not output the entire stream of the received backscattered signals and only outputs a selected portion of the received backscattered signals based on a detected event or activity (Wang, para [0152]).
Regarding claim 36, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the sensor includes an inertial sensor such as including an accelerometer or gyroscope, such that a change of position of the sensor is automatically detected (Wang, para [0065, p. 4, col. 2, line 14 – p. 5, col. 1, line 12 ], The motion of the object may be monitored actively (in that Type 1 device, Type 2 device, or both, are wearable of/associated with the object) and/or passively (in that both Type 1 and Type 2 devices are not wearable of/associated with the object). It may be passive because the object may not be associated with the Type 1 device and/or the Type 2 device. The object (e.g. user, an automated guided vehicle or AGV) may not need to carry/install any wearables/fixtures (i.e. the Type 1 device and the Type 2 device are not wearable/attached devices that the object needs to carry in order perform the task). It may be active because the object may be associated with either the Type 1 device and/or the Type 2 device. The object may carry (or installed) a wearable/a fixture (e.g. the Type 1 device, the Type 2 device, a device communicatively coupled with either the Type 1 device or the Type 2 device)) Examiner notes that gyroscopes and accelerometers are fundamental to device such as wearables wherein motions, falls, gait, posture, etc. may be detected.
Regarding claim 54, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm- wave radar system is configured to automatically indicate when the sensors are correctly or incorrectly positioned (Wang, para [0130, p. 14, col. 2, line 9 – p. 15, col. 1, line 9], The portable device, the nearby device, a local server (e.g. hub device) and/or a cloud server may share the computation and/or storage for a task (e.g. obtain TSCI, determine characteristics/STI of the object associated with the movement (e.g. change in position/location) of the object, computation of time series of power (e.g. signal strength) information, determining/computing the particular function, searching for local extremum, classification, identifying particular value of time offset, de-noising, processing, simplification, cleaning, wireless smart sensing task, extract CI from signal, switching, segmentation, estimate trajectory/path/track, process the map, processing trajectory/path/track based on environment model s/constraints/limitations, correction, corrective adjustment, adjustment, map-based (or model-based) correction, detecting error, checking for boundary hitting, thresholding) and information (e.g. TSCI)) Examiner notes that corrective adjustment relates to real-time or post-processing calibration/filtering to improve accurate detection and classification of position, motion, and/or physiological information of the target(s)..
Regarding claim 55, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm- wave radar system is configured to automatically indicate a dead area, in which a dead area refers to an area that the mm-wave radar sensor cannot correctly scan (Wang, para [0173 ]).
Regarding claim 56, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm- wave radar system is configured to automatically indicate when the sensors are correctly or incorrectly positioned to detect specific events or activities or vital signs or other physiological data (Wang, para [0130, p. 14, col. 2, line 9 – p. 15, col. 1, line 9]) Examiner notes that corrective adjustment relates to real-time or post-processing calibration/filtering to improve accurate detection and classification of position, motion, and/or physiological information of the target(s).
Regarding claim 57, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm-wave radar system is configured to detect an abnormal event or change of behavior and in which the mm-wave radar system is configured to send an instruction or alert together with the activity record that captures a specific detected event, such as including abnormal event or change of behaviour (Wang, para [0134, p. 16, col. 2, lines 46- 59], sudden motion detection, fall-down detection, danger detection, life-threat detection, regular motion detection, stationary motion detection, cyclo-stationary motion detection, intrusion detection, suspicious motion detection, security, safety monitoring, navigation, guidance, map-based processing, map-based correction, model-based processing/correction, irregularity detection, locationing, room sensing, tracking, multiple object tracking, indoor tracking, indoor position, indoor navigation, energy management, power transfer, wireless power transfer, object counting, car tracking in parking garage, activating a device/system (e.g. security system, access system, alarm, siren, speaker, television, entertaining system, camera, heater/air-conditioning (HVAC) system).
Regarding claim 59, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm-wave radar system is configured to integrate with other sensor devices, such as including an audio monitoring sensor (Wang, para [0173]),
that automatically turns on when a specific event or activity is detected (Wang, para [0134, p. 16, col. 2, lines 46- 59]),
such as including a fall (Wang, para [0134, p. 16, col. 2, lines 46- 59]).
Claim 61 is rejected under the same analysis as claim 1.
Regarding claim 63, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the system is configured to (Wang, para [0186]):
i) analyse how long it takes for a person to perform a defined activity (Wang, para [0160], The threshold adjustment may be automatic, semi-automatic and/or manual. The threshold adjustment may be applied once, sometimes, often, periodically, repeatedly, occasionally, sporadically, and/or on demand. The threshold adjustment may be adaptive (and/or dynamically adjusted). The threshold adjustment may depend on the object, object movement/location/direction/action, object characteristics/STI/size/property/trait/habit/behavior, the venue, feature/fixture/furniture/barrier/material/machine/living thing/thing/object/boundary/surface/medium that is in/at/of the venue, map, constraint of the map (or environmental model), the event/state/situation/condition, time, timing, duration, current state, past history, user, and/or a personal preference, etc.);
ii) generate a metric of long-term health or mobility trends (Wang, para [0353, p. 39, col. 2, line 1- p. 40, col. 1, line 6], Clause 53: The method of the wireless monitoring system of clause 1 or clause 2: wherein at least one of: the first information (info) and the second info, comprising at least one of: a periodicity info, rhythm info, timing info, intensity info, regularity info, transient info, statistical info, normal info, deviation-from-normal info, state info, state-transition info, instantaneous info, local info, moving info, sliding info, weighted info, motion info, proximity info, presence info, movement info, gesture info, gait info, gait cycle info, action info, activity info, behavior info, daily routine info, location info, navigation info, locationing info, localization info, tracking info, coordinate info, spatial info, temporal info, trend info);
and iii) determine whether a referral to a third party, such as a health practitioner is needed (Wang, para [0180, lines 28-33], The Type 1 device and Type 2 devices may be deployed by emergency service at disaster area to search for trapped victims in debris. The Type 1 device and Type 2 devices may be deployed in an area to detect breathing of any intruders. There are numerous applications of wireless breathing monitoring without wearables).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 41, 46, 48-49, 51-52, 55, 57, 59, 61 and 64 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (US 20200300972 A1), hereinafter Wang in view of Santra et al (US 20190195728 A1), hereinafter Santra.
Regarding claim 41, Wang discloses:
the mm-wave radar system of Claim 1 (Wang, para [0186]),
in which the mm-wave radar system includes configuration parameters that can be remotely updated (Wang, para [0138, lines 18-28], When the user leaves home for work, the task may be to detect the user leaving, play a farewell and/or have-a-good-day message, open/close garage door, turn on/off garage light and driveway light, turn off/dim lights to save energy (just in case the user forgets), close/lock all windows/doors (just in case the user forgets), turn off appliance (especially stove, oven, microwave oven), turn on/arm the home security system to guard the home against any intruder, adjust air conditioning/heating/ventilation systems to “away-from-home” profile to save energy, send alerts/reports/updates to the user's smart phone, etc.) ;
and in which the configuration parameters include one or more of the following (Wang, para [0170], A current parameter (e.g. time offset value) may be initialized based on a target value, target profile, trend, past trend, current trend, target speed, speed profile, target speed profile, past speed trend, the motion or movement (e.g. change in position/location) of the object, at least one characteristics and/or STI of the object associated with the movement of object, positional quantity of the object, initial speed of the object associated with the movement of the object, predefined value, initial width of the regression window, time duration, value based on carrier frequency of the signal, value based on subcarrier frequency of the signal, bandwidth of the signal, amount of antennas associated with the channel, noise characteristics, signal h metric, and/or an adaptive (and/or dynamically adjusted) value. The current time offset may be at the center, on the left side, on the right side, and/or at another fixed relative location, of the regression window):
(Wang, para [0120], The Type 1 device and Type 2 device may support WiFi, WiMax, 3G/beyond 3G, 4G/beyond 4G, LTE, LTE-A, 5G, 6G, 7G, Bluetooth, NFC, BLE, Zigbee, UWB, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA, mesh network, proprietary wireless system, IEEE 802.11 standard, 802.15 standard, 802.16 standard, 3GPP standard, and/or another wireless system) Examiner notes that the list of various modulation/formats wherein Near Field Communication (NFC) uses frequency modulation (FM). Examiner further notes that with frequency modulation the instantaneous frequency of the signal changes over time and a chirp is a controlled form of frequency modulation. Examiner notes that this a general disclosure of FM, and it would be obvious that any other form of FM could be used, such as FMCW.
ADC sampling rate (Wang, para [0313 lines 1-5], Clause 14: The method of the wireless monitoring system of clause 11, further comprising: wherein the beamforming is computed based on at least one of: analog beamforming, digital beamforming, non-adaptive beamforming, adaptive beamforming) Examiner notes that analog-to-digital sampling is fundamental to digital beamforming,
waveform duration or sampling time (Wang, para [0144, col.1, line 30]) Examiner notes that FFT relates to sampling time in that it interprets frequency content from sampled data,
and software parameters such as including point clustering parameters (Wang, para [0347], Clause 48: The method of the wireless monitoring system of clause 27, further comprising: associating the number of clusters with the at least one object; and computing at least one of: a count of the at least one objects, a location of an object based on a geometric medoid of all the points belonging to an associated cluster, and a height of the first object based on a maximum of height associated with all the points belonging to the associated cluster),
tracking filter parameters (Wang, para [0144, lines 3-24], An operation may comprise preprocessing, processing, post-processing, scaling, computing a confidence factor, computing a line-of-sight (LOS) quantity, computing a non-LOS (NLOS) quantity, a quantity comprising LOS and NLOS, computing a single link (e.g. path, communication path, link between a transmitting antenna and a receiving antenna) quantity, computing a quantity comprising multiple links, computing a function of the operands, filtering, linear filtering, nonlinear filtering, folding, grouping, energy computation, lowpass filtering, bandpass filtering, highpass filtering, median filtering, rank filtering, quartile filtering, percentile filtering, mode filtering, finite impulse response (FIR) filtering, infinite impulse response (IIR) filtering, moving average (MA) filtering, autoregressive (AR) filtering, autoregressive moving averaging (ARMA) filtering, selective filtering, adaptive filtering, interpolation, decimation, subsampling, upsampling, resampling, time correction, time base correction, phase correction, magnitude correction, phase cleaning, magnitude cleaning, matched filtering,),
monitoring zones (Wang, para [0174], The map may comprise floor plan of a facility. The map or model may have one or more layers (overlays). The map/model may be a maintenance map/model comprising water pipes, gas pipes, wiring, cabling, air ducts, crawl-space, ceiling layout, and/or underground layout. The venue may be segmented/subdivided/zoned/grouped into multiple zones/regions/geographic regions/sectors/sections/territories/districts/precincts/localities/neighborhoods/areas/stretches/expanse such as bedroom, living room, storage room, walkway, kitchen, dining room, foyer, garage, first floor, second floor, rest room, offices, conference room, reception area, various office areas, various warehouse regions, various facility areas, etc. The segments/regions/areas may be presented in a map/model. Different regions may be color-coded. Different regions may be presented with a characteristic (e.g. color, brightness, color intensity, texture, animation, flashing, flashing rate, etc.). Logical segmentation of the venue may be done using the at least one heterogeneous Type 2 device, or a server (e.g. hub device), or a cloud server, etc.),
or information related to the room or environment that needs to be monitored, such as including locations of sensors within an environment or specific area of interest (Wang, para [0171, lines 1-20], In the presentation, information may be displayed with a map (or environmental model) of the venue. The information may comprise: location, zone, region, area, coverage area, corrected location, approximate location, location with respect to (w.r.t.) a map of the venue, location w.r.t. a segmentation of the venue, direction, path, path w.r.t. the map and/or the segmentation, trace (e.g. location within a time window such as the past 5 seconds, or past 10 seconds; the time window duration may be adjusted adaptively (and/or dynamically); the time window duration may be adaptively (and/or dynamically) adjusted w.r.t. speed, acceleration, etc.), history of a path, approximate regions/zones along a path, history/summary of past locations, history of past locations of interest, frequently-visited areas, customer traffic, crowd distribution, crowd behavior, crowd control information, speed, acceleration, motion statistics, breathing rate, heart rate, presence/absence of motion, presence/absence of people or pets or object, presence/absence of vital sign, gesture, gesture control (control of devices using gesture),
floor plans (Wang, para [0174]),
specific locations of objects (Wang, paras [0171, lines 1-20] and [0174]),
specific areas of interest (Wang, paras [0171, lines 1-20] and [0174]),
specific activity records or events or activities (Wang, paras [0171, lines 1-20] and [0174]),
or number of targets of interest that are to be detected (Wang, paras [0171, lines 1-20] and [0174]).
Santra discloses:
the radar waveform parameters such as including chirp (Santra, para [0035], Radar circuitry 206 may receive a baseband radar signal from processing circuitry 204 and control a frequency of an RF oscillator based on the received baseband signal. In some embodiments, this received baseband signal may represent a FMCW frequency chirp to be transmitted. Radar circuitry 206 may adjust the frequency of the RF oscillator by applying a signal proportional to the received baseband signal to a frequency control input of a phase locked loop. Alternatively, the baseband signal received from processing circuitry 204 may be upconverted using one or more mixers. Radar circuitry 206 may transmit and digitize baseband signals via a digital bus (e.g., a USB bus), transmit and receive analog signals via an analog signal path, and/or transmit and/or receive a combination of analog and digital signals to and from processing circuitry 204).
It would have been obvious to someone in the art prior to the effective filing date of the claimed invention to modify Wang with Santra to incorporate the features of: the radar waveform parameters such as including chirp. Both arts are considered analogous arts as they both disclose a mm Wave radar systems. Wang does not fully disclose chirp but does provide examples of frequency modulation such as NFC; however, Santra discloses frequency modulated continuous waves (FMCW) frequency chirp to transmitted. The modification would render the predictable results of improved simultaneous range-velocity capabilities for improve resolutions; improved signal-to-nose; and improved performance.
Regarding claim 46, Wang discloses:
the mm-wave radar system of Claim 41 (Wang, para [0186]),
in which the configuration parameters further include parameters related to a virtual area, and in which the virtual area is generated by a software module to define a specific area of interest (Wang, para [0172]).
Regarding claim 48, Wang discloses:
the mm-wave radar system of Claim 41 (Wang, para [0186]),
in which the mm- wave radar system includes an application running on a gateway hub (Wang, para [0174]),
server (Wang, para [0174]),
or cloud that is configured to wirelessly send the configuration parameters to the sensor (Wang, para [0174]).
Regarding claim 49, Wang discloses:
the mm-wave radar system of Claim 41 (Wang, para [0186]),
in which the configuration parameters are stored on the gateway hub (Wang, para [0174]),
server or cloud on which the application is running (Wang, para [0174]).
Regarding claim 51, Wa