Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Fig. 1. Showing memory 131, image capturing device 132, while in description it describes memory 132 and an image capturing device 134 (US PGPub: US 2025/0080951 A1 Mar. 6, 2025, paragraphs 0032, 0036, 0038, 0040, 0072). Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 –
(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 – 3, 9 – 12 and 18 – 20 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by
Zhang US PGPub: US 2018/0183650 A1 Jun. 28, 2018.
Regarding claims 1, 10, Zhang discloses,
an object tracking system and method (an object tracking system and method, where at least one time series of channel information is extracted from a wireless signal transmitted between a Type 1 heterogeneous wireless device at a first position in a venue and a Type 2 heterogeneous wireless device at a second position in the venue through the wireless multipath channel. The wireless multipath channel is impacted by a current movement of an object in the venue. The method also includes determining a spatial-temporal information of the object based on at least one of: the at least one time series of channel information, a time parameter associated with the current movement, and a past spatial-temporal information of the object - ABSTRACT, Figs. 1 - 3, 6 - 8, paragraphs 0091, 0105 - 0115), comprising:
a transmitter (transmitter 108 – Fig. 1/108) configured to transmit wireless signals through a target space (multiple links each formed by one antenna of a wireless transmitter - e.g. 108 with at least one antenna and one antenna of a wireless receiver - e.g. 109 with at least one antenna – Figs. 1/108, 2, paragraph 0137);
a receiver (receiver 109 – Fig. 1/109) configured to receive the wireless signals (multiple links each formed by one antenna of a wireless transmitter - e.g. 108 with at least one antenna and one antenna of a wireless receiver - e.g. 109 with at least one antenna – Figs. 1/108, 2, paragraph 0137); and
at least one processing circuit configured to perform following processes (processors 100A and 100 B – Figs. 1/100A, 1/100B):
obtaining the wireless signals received by the receiver (the one or more time series of channel information – e.g., 111A, 111B is extracted/derived/obtained from a wireless signal – e.g., 140 transmitted between a Type 1 heterogeneous wireless device – e.g., 106A at first position 117 in a venue 142 and a Type 2 heterogeneous wireless device – e.g., 106B at a second position 119 in the venue 142 – Figs. 1, 5/502, paragraphs 0152, 0492);
generating motion information associated with at least one target object (the object 112 – Fig. 1, paragraph 0247. The method/device/system also determines a spatial-temporal information - e.g. location, speed, velocity, acceleration, a periodic motion, a time trend, a transient motion, a period, a characteristic, etc., of the object - e.g. 112 based on the at least one time series of channel information - e.g. 300, 111A, 111B, a time parameter associated with the current movement - e.g. 120, and/or a past spatial-temporal information of the object - e.g. 112 – Figs. 1, 5/504, paragraphs 0152, 0492) by executing a motion machine-learning model that processes the received wireless signals (machine learn, training, discriminative training, deep learning, neural network, continuous time processing, distributed computing, distributed storage, GPU/DSP/coprocessor/multicore/multiprocessing acceleration may be applied to any/every operation disclosed in the present teaching – paragraph 0151); and
generating three-dimensional (the map may be 2-dimension, 3-dimension and/or higher dimension. Points in the map may be associated with another spatial-temporal information such as longitude coordinate, latitude coordinate – paragraph 0403. A motion statistics …… a 3-dimensional statistics – paragraph 0376. A 3-dimensional description of the venue – e.g., 142 – paragraph 0404) tracking information of the at least one target object with respect to the target space (a task is performed based on the spatial-temporal information – Figs. 5/506, 6/602, 6/motion type, 6/motion classification, 6/motion characteristics, 6/predict motion, 7/object tracking, 7/detecting a periodic motion associated with the object, 7/graphical display of spatial-temporal information, 7/presenting the spatial-temporal information, paragraphs 0492, 0493. A graphical user interface GUI may be constructed to show that the where-about of the object in a house – paragraph 0495) by fusing spatial information of the target space (time series of channel information 111A/111B….distance of current movement of the object - Fig. 8/804 combined 8/806 to perform fusing and determine spatial information, paragraph 0494) and the motion information (estimated direction of current movement of the object - Fig. 8/806, paragraph 0494).
Regarding claims 2, 11, 20, Zhang discloses,
the object tracking system according to claim 1, further comprising:
an image capturing device configured to capture at least one panoramic image of the target space (a security camera – paragraph 0158);
wherein the at least one processing circuit is further configured to perform following processes:
generating the spatial information by executing a spatial machine-learning model that converts the at least one panoramic image into three-dimensional layout information of the target space (the map may be 2-dimension, 3-dimension and/or higher dimension. Points in the map may be associated with another spatial-temporal information such as longitude coordinate, latitude coordinate – paragraph 0403. A motion statistics …… a 3-dimensional statistics – paragraph 0376).
Regarding claims 3, 12, Zhang discloses,
the object tracking system according to claim 1, wherein the wireless signals are WI-FI signals (Wi-Fi based breathing monitoring – paragraph 0101).
Regarding claims 9, 18, Zhang discloses,
the object tracking system according to claim 1, wherein the image capturing device is included in a user equipment, the transmitter (Wi-Fi based breathing monitoring – paragraph 0101. One of the first Type 1 heterogeneous device and the first Type 2 heterogeneous device may be spatially close to and may move with the object during the movement of the object. One of the first Type 1 heterogeneous device and the first Type 2 heterogeneous device may be communicatively coupled with a network server, and/or communicatively coupled with a local device that may be communicatively coupled with the network server. The local device may be: a smart phone, a smart device, a smart speaker, a smart watch,…. – paragraph 0124. Type 1 device and/or a Type 2 device may be a smart speaker, a smart phone, an attachment to a phone, a portable dongle to plug into another device – paragraph 0158) or the receiver, and
the at least one processing circuit is included in the user equipment,
a network device located in the target space (processors 100A, 100B – Fig. 1) and/or
a cloud server (the multiple Type 1 devices/Type 2 devices may be communicatively coupled to same or different servers - e.g. cloud server, edge server, local server. Difference devices may communicate directly, and/or via another device/server/cloud server – paragraph 0160);
wherein the user equipment is configured to receive and display the three-dimensional tracking information of the at least one target object with respect to the target space (the map may be 2-dimension, 3-dimension and/or higher dimension. Points in the map may be associated with another spatial-temporal information such as longitude coordinate, latitude coordinate – paragraph 0403. A motion statistics …… a 3-dimensional statistics – paragraph 0376. The GUI may be a software for a computer/a tablet, an app on a smart phone - e.g., iPhone, Android phone, etc., an app in a smart device - e.g., smart glass, smart watch, etc., - paragraph 0495).
Regarding claim 19, Zhang discloses,
an object tracking method, adapted to a user equipment (Type 1 device and/or a Type 2 device may be a smart speaker, a smart phone, an attachment to a phone, a portable dongle to plug into another device – paragraph 0158. The GUI may be a software for a computer/a tablet, an app on a smart phone - e.g., iPhone, Android phone, etc., an app in a smart device - e.g., smart glass, smart watch, etc., - paragraph 0495) including a processor and a memory storing a plurality of executable instructions, and the object tracking method comprising: configuring the processor to execute the plurality of executable instructions to perform following processes: (an object tracking system and method, where at least one time series of channel information is extracted from a wireless signal transmitted between a Type 1 heterogeneous wireless device at a first position in a venue and a Type 2 heterogeneous wireless device at a second position in the venue through the wireless multipath channel. The wireless multipath channel is impacted by a current movement of an object in the venue. The method also includes determining a spatial-temporal information of the object based on at least one of: the at least one time series of channel information, a time parameter associated with the current movement, and a past spatial-temporal information of the object - ABSTRACT, Figs. 1 - 3, 6 - 8, paragraphs 0091, 0105 - 0115), comprising:
configuring the processor to execute the plurality of executable instructions to perform following processes:
configuring an image capturing device to capture at least one panoramic image of a target space (a security camera – paragraph 0158);
obtaining spatial information generated by executing a spatial machine-learning model (machine learn, training, discriminative training, deep learning, neural network, continuous time processing, distributed computing, distributed storage, GPU/DSP/coprocessor/multicore/multiprocessing acceleration may be applied to any/every operation disclosed in the present teaching – paragraph 0151) that converts the at least one panoramic image into three-dimensional layout information of the target space (the map may be 2-dimension, 3-dimension and/or higher dimension. Points in the map may be associated with another spatial-temporal information such as longitude coordinate, latitude coordinate – paragraph 0403. A motion statistics …… a 3-dimensional statistics – paragraph 0376);
transmitting the spatial information to a network device (Wi-Fi based breathing monitoring – paragraph 0101. One of the first Type 1 heterogeneous device and the first Type 2 heterogeneous device may be spatially close to and may move with the object during the movement of the object. One of the first Type 1 heterogeneous device and the first Type 2 heterogeneous device may be communicatively coupled with a network server, and/or communicatively coupled with a local device that may be communicatively coupled with the network server. The local device may be: a smart phone, a smart device, a smart speaker, a smart watch,…. – paragraph 0124. Type 1 device and/or a Type 2 device may be a smart speaker, a smart phone, an attachment to a phone, a portable dongle to plug into another device – paragraph 0158. The multiple Type 1 devices/Type 2 devices may be communicatively coupled to same or different servers - e.g. cloud server, edge server, local server. Difference devices may communicate directly, and/or via another device/server/cloud server – paragraph 0160) or a cloud server, wherein the network device or the cloud server is configured to obtain wireless signals received by a receiver (the multiple Type 1 devices/Type 2 devices may be communicatively coupled to same or different servers - e.g. cloud server, edge server, local server. Difference devices may communicate directly, and/or via another device/server/cloud server – paragraph 0160),
generate motion information associated with at least one target object (the object 112 – Fig. 1, paragraph 0247. The method/device/system also determines a spatial-temporal information - e.g. location, speed, velocity, acceleration, a periodic motion, a time trend, a transient motion, a period, a characteristic, etc., of the object - e.g. 112 based on the at least one time series of channel information - e.g. 300, 111A, 111B, a time parameter associated with the current movement - e.g. 120, and/or a past spatial-temporal information of the object - e.g. 112 – Figs. 1, 5/504, paragraphs 0152, 0492) by executing a motion machine-learning model that processes the received wireless signals (machine learn, training, discriminative training, deep learning, neural network, continuous time processing, distributed computing, distributed storage, GPU/DSP/coprocessor/multicore/multiprocessing acceleration may be applied to any/every operation disclosed in the present teaching – paragraph 0151), and
generating three-dimensional (the map may be 2-dimension, 3-dimension and/or higher dimension. Points in the map may be associated with another spatial-temporal information such as longitude coordinate, latitude coordinate – paragraph 0403. A motion statistics …… a 3-dimensional statistics – paragraph 0376. A 3-dimensional description of the venue – e.g., 142 – paragraph 0404) tracking information of the at least one target object with respect to the target space (a task is performed based on the spatial-temporal information – Figs. 5/506, 6/602, 6/motion type, 6/motion classification, 6/motion characteristics, 6/predict motion, 7/object tracking, 7/detecting a periodic motion associated with the object, 7/graphical display of spatial-temporal information, 7/presenting the spatial-temporal information, paragraphs 0492, 0493. A graphical user interface GUI may be constructed to show that the where-about of the object in a house – paragraph 0495) by fusing spatial information of the target space (time series of channel information 111A/111B….distance of current movement of the object - Fig. 8/804 combined 8/806 to perform fusing and determine spatial information, paragraph 0494) and the motion information (estimated direction of current movement of the object - Fig. 8/806, paragraph 0494) and
receiving the three-dimensional tracking information of the at least one target object with respect to the target space, and displaying the three-dimensional tracking information by a user interface of a tracking application program executed by the user equipment (the map may be 2-dimension, 3-dimension and/or higher dimension. Points in the map may be associated with another spatial-temporal information such as longitude coordinate, latitude coordinate – paragraph 0403. A motion statistics …… a 3-dimensional statistics – paragraph 0376. The GUI may be a software for a computer/a tablet, an app on a smart phone - e.g., iPhone, Android phone, etc., an app in a smart device - e.g., smart glass, smart watch, etc., - paragraph 0495).
Allowable Subject Matter
Claims 4 and 13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 5 – 8 and 14 – 17 are also objected due to their dependency on objected claims 4 and 13 respectively.
The prior arts made of record and not relied upon are considered pertinent to applicants disclosure.
Herman US PGPub: US 2023/0274386 A1 Aug. 31, 2023.
A vehicle, systems and methods for display stabilization including receiving a plurality of image data from sensors in a vehicle, receiving a plurality of measurements of road excitations from sensors in the vehicle, estimating a three-dimensional position of a driver or a passengers eyes based on the received plurality of image data, receiving apriori data from high definition maps, recorded accelerations of the vehicle, and stored data via a controller area network (CAN) bus, determining a prediction of motion of a display in the vehicle based on a fusion of the received image data, the plurality of measurements of road excitations and the apriori data, modeling the prediction of motion of the display in a convolutional neural network to form an initial estimate of a display stabilization position, and display computationally corrected images on the display based on the display stabilization position.
Kim US PGPub: US 2023/0186512116764 A1 Jun. 15, 2023.
An electronic device performs a method of obtaining Three-Dimensional (3D) skeleton data of an object obtained by using a first camera and a second camera. The method includes: obtaining a first image using the first camera and obtaining a second image using the second camera; obtaining, from the first image, a first Region Of Interest (ROI) comprising the object; obtaining, from the first ROI, first skeleton data comprising at least one keypoint of the object; obtaining a second ROI from the second image, based on the first skeleton data and information about a relative position between the first camera and the second camera; obtaining, from the second ROI, second skeleton data comprising at least one keypoint of the object; and obtaining 3D skeleton data of the object, based on the first skeleton data and the second skeleton data.
Chen US PGPub: US 2019/0026904 A1 Jan. 24, 2019.
The tracking system comprises a trackable device with an appearance including a feature pattern and a tracking device. The tracking device comprises an optical sensor module configured to capture a first image which covers the trackable device. The tracking device further comprises a processor coupled to the optical sensor module. The processor is configured to retrieve a region of interest (ROI) of the first image based on the feature pattern, and locate a position of each of a plurality of feature blocks in the ROI, where each feature block contains a portion of the feature pattern. The processor further calculates a pose data of the trackable object according to the positions of the feature blocks.
Chen US PGPub: US 2021/0041548 A1 Feb. 11, 2021.
A motion detection and classification using ambient wireless signals. A system for using radio frequency (RF) communication signals to extract situational awareness information thorough deep learning. Pre-processing is performed to maximally preserve discriminating features in spatial, temporal and frequency domains. A specially designed neural network architecture is used for handling complex RF signals and extracting spatial, temporal and frequency domain information. Data collection and training is used so that the learning system desensitizes from features orthogonal to the underlying classification problem.
For some learning systems, one would flatten the spatial dimension - i.e., combine the last two dimensions such that Ĥ becomes a three dimensional array with indices for time, subcarrier, and spatial dimension. The characteristics of the CSI array in the three dimensions – i.e., temporal, frequency, and spatial are largely dependent on the channel environment.
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/NIMESH PATEL/Primary Examiner, Art Unit 2642
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