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) was filed on 09/11/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 38 is objected to because of the following informalities: “a battlefield” shows after the sentence period, it is interpreted as a typographical error. Appropriate correction is required.
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.
Claims 22 and 44 are 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.
Claims 22 and 44 recite the limitation "said signature information". There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 6-24, 33-35 and 37-45 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Goldstein et. al. (US Pub. No. 20220057519 A1).
As per claim 1, Goldstein teaches “A method for use in defeating and testing camouflage strategies, comprising:
providing a processing platform operative for one or both of a spatial analysis and a temporal analysis of sensor data; and
operating a processing platform for: obtaining first sensor information of an area of interest;” (See fig. 1 and paragraphs 98-106, paragraphs 55 “[0055] Embodiments disclosed herein may detect entry of persons or animals into subject areas and respond consistently with determined behavior descriptors, object recognition, or rulesets using a graduated deterrence system. Embodiments may use a combination of imaging and other sensors, such as optical cameras, infrared cameras, 3D cameras, multispectral cameras, hyperspectral cameras, polarized cameras, chemical sensors, motion sensors, ranging sensors, light radar component, such as lidar, detection or imaging using radio frequencies component, such as radar, terahertz or millimeter wave imagers, seismic sensors, magnetic sensors, weight/mass sensors, ionizing radiation sensors, and/or acoustical sensors, to accurately recognize and spatially determine entrance into the subject area, to distinguish between known or whitelisted persons, children, animals, and potential threats…” See also paragraphs 63-70 and 76-91 “[0089] Still referring to FIG. 3, apparatus 100 may use one or more imaging devices 104 to determine a baseline condition of subject area. Imaging device 104 may map points within subject area to a coordinate system. Coordinate system may include x and y coordinates, which may correspond to axes on one or more focal planes of imaging device 104, substantially horizontal and vertical axes, or the like, and a z coordinate corresponding to depth and/or distance from imaging device 104… cameras may each be used to register boundaries of subject area and/or a geometric center thereof to coordinate system. Objects within subject area may then be located within coordinate system to establish a baseline condition.” See also paragraphs 527-532 “[0527] Still referring to FIG. 26, apparatus 2604 is configured to identify at least a spatiotemporal element 2628 related to individual 2612 and deterrent 2616. As used in this disclosure a “spatiotemporal element” is datum relating to position, velocity, and/or or acceleration of an individual's physical being…” See also paragraphs 127, 138, 1143-145, 189, 191. Goldstein )
“first processing the sensor information to provide enhanced sensor information,
said enhanced sensor information providing enhanced detection of a target in said sensor information;”
“second processing said enhanced sensor information to provide an output concerning a presence or absence of said target of interest.” (See paragraphs 78, 87, 91, 93, 99-105, 138, 162, 163, 229, 239, 252, 263, 296, 306, 313, 390, 532, 562 and 611, there are several enhancements made to the sensor information for enhanced detection of a subject/target “[0078] Still referring to FIG. 2, imaging device 104 may include components for detection or imaging using radio frequencies, such without limitation a radar component 208 and/or a wavelength detector 212, where a wavelength detector may include a millimeter wave sensor or imager and/or terahertz sensor or imager… An antenna and/or one or more elements of optics may include a plurality of configurations, materials, meta-materials, geometries, structures, and/or methods to specify, enhance, reject, amplify, focus, filter, provide directionally, and/or further modify frequencies of the RF spectrum for an RF detector to receive…without limitation, determination of range to an object, determination of chemical makeup of an object, detection and/or identification of hidden objects, determination of speed of an object, creation of an image an object and/or scene in the respective frequency, and/or change detection.” “[0087] In an embodiment, and with further reference to FIG. 2, two or more of optical camera 108, infrared camera 112, light radar component 116, ultrasound device 120, 3D detector 204, Radar Component 208, Wavelength Detector 212, Polarized Camera 216, Multispectral Camera 220, Hyperspectral Camera 224, and/or other sensor data, for instance, and without limitation, including audio sensor 124, chemical sensor 128, motion sensor 132, ionizing radiation sensor 228, seismic sensor 232, mass or weight sensor 236, magnetic sensor 240, and/or global positioning system receiver 244 may function together as a fusion camera…ss a further non-limiting example, edge detection using ToF as described above may be enhanced and/or corrected using edge detection image processing algorithms as described in further detail below.” “[0100] In an embodiment, processor 136 and/or remote device 140 may apply algorithms to smooth or enhance tracking of moving objects in the subject area to enhance accuracy of pointing devices and minimize error, hysteresis, and/or latency of deterrents. These include, but are not limited to, Kalman filters, derivative controls, proportional/integral/derivative (PID) controls, moving averages, weighted averages, and/or other noise and error mitigation techniques.” “0306] With further reference to FIG. 15, apparatus may communicated with and/or participate in a cloud computing architecture and/or system. Apparatus and/or a network thereof may connect to a cloud processing facility that may amalgamate all data, for instance according to a single user's site or two or more user sites, according to a common customer, according to a region, or the like. Cloud processing facility may use this data to process better performance, learn new threats, learn better methods for countermeasures, enhance sensor performance, enhance sensor fusion, or any other improvements or corrections that may be gained from the data.” “[0390] With continued reference to FIG. 15… System may use lasers to create light patterns that can be used to further enhance a single or multiple imaging sensor ability to determine 3D position of people or objects in the area.” “[0252] Still referring to FIG. 14, beam steering parameters may be calculated in real time by a dedicated implementation of a previously explained algorithm, and a current direction of a main beam may be assigned by an external control. A camera and/or other imaging device as described above, which may be dedicated to steerable array 1400 and/or may include any imaging device of apparatus 100, may be able to recognize presence of a person in an area of interest, and may be capable of determining a position in space of such person and/or subject and a distance therefrom.” See also paragraphs 424 “[0424] In an embodiment, and still referring to FIG. 18, a cumulative energy value may include a per-engagement value. A per-engagement value, as used in this disclosure, is a cumulative energy value that accumulates over the course of an engagement…or instance, apparatus 1800 and/or a component thereof may include a variable that is set when interaction with individuals and/or a particular individual begin, indicating initiation of an engagement, and may be cleared and/or reset, indicating the end of an engagement, when interaction generally or with a specific individual has ceased for a threshold period of time. For instance, termination of an engagement may be recorded when a given person has been absent from and/or not interacting with apparatus 1800 for one hour, one day, or any other suitable period…” See also paragraphs 106-117 and 127-135 which also shows sensor information enhancement. Goldstein)
Claim 23 is rejected under the same analysis as claim 1.
Claim 45 is rejected under the same analysis as claim 1. (Examiner interprets “signature information” as sensed information. See also paragraphs 98-109 which also shows image segmentation. Goldstein)
As per claim 2, Goldstein teaches “(Original) The method of claim 1, wherein said spatial analysis comprises a computer vision analysis including one or more of object detection, edge detection, blob detection, multi-scale analysis, feature fusion, and an attention mechanism.” (See all of the paragraphs presented in the rejection of claim 1. See also paragraphs 101 “[0101] Still referring to FIG. 1, object detection and/or edge detection may alternatively or additionally be performed using light radar data, RF sensor or radar component data, and/or 3D camera, sensor or computational method data…” Paragraphs 296, 87, 306, 73, 75, 79, 80-82, 87 and 101 show feature fusion. Paragraphs 367-369, 381, 495, 503, 614, 167-181, 216, 220-230 show attention mechanisms. Paragraphs 175-178, 267, 396, 493, 606-608 and 661 show multi-scale analysis. Goldstein )
Claim 24 is rejected under the same analysis as claim 2.
As per claim 3, Goldstein teaches “The method of claim 1, wherein said target of interest comprises a human being.” (See all of the paragraphs presented in the rejection of claim 1. See also paragraphs 118 and 130, “[0118]… or instance, and without limitation, object classifier may identify a first object detected using computer vision techniques and/or ToF as described above as a human body, which may be further classified as an adult and/or child, a second object as an animal such as a dog, rat, racoon, bird, or the like, a third object as an inanimate object, which may be further classified as described below, or the like.” )
As per claim 4, Goldstein teaches “The method of claim 3, wherein said processing platform is operative for distinguishing a human being from another living being.” (See all of the paragraphs presented in the rejection of claim 1. See also paragraphs 118 and 130, 133, 213, 217 and 392 “[0118]… or instance, and without limitation, object classifier may identify a first object detected using computer vision techniques and/or ToF as described above as a human body, which may be further classified as an adult and/or child, a second object as an animal such as a dog, rat, racoon, bird, or the like, a third object as an inanimate object, which may be further classified as described below, or the like.” See also paragraph 55 “…sensors, to accurately recognize and spatially determine entrance into the subject area, to distinguish between known or whitelisted persons, children, animals, and potential threats…. ” See also paragraph 133 “[0133] Still referring to FIG. 4, processor 136 and/or remote device 140 may use any or all sensor feedback and/or machine-learning to perform liveliness detection, defined as a process used to distinguish a person from a static image or inanimate object, for instance by tracking movements, classifying behavior, sensing body temperature, or the like… Processor 136 and/or remote device 140 may be configured to distinguish people from other animals such as dogs.” Goldstein)
As per claim 6, Goldstein teaches “(Original) The method of claim 3, wherein said processing platform is operative for determining information concerning one or both of signs of life and status of life.” (See all of the paragraphs presented in the rejection of claim 1. See also paragraphs 118 and 130, 133, 213, 217 and 392 “[0118]… or instance, and without limitation, object classifier may identify a first object detected using computer vision techniques and/or ToF as described above as a human body, which may be further classified as an adult and/or child, a second object as an animal such as a dog, rat, racoon, bird, or the like, a third object as an inanimate object, which may be further classified as described below, or the like.” See also paragraph 55 “…sensors, to accurately recognize and spatially determine entrance into the subject area, to distinguish between known or whitelisted persons, children, animals, and potential threats…. ” See also paragraph 87 “[0087]… Infrared imaging may be used to verify that a subject 308 depicted in an image created by other means is likely a living human through body temperature detection…” See also paragraph 133 “[0133] Still referring to FIG. 4, processor 136 and/or remote device 140 may use any or all sensor feedback and/or machine-learning to perform liveliness detection, defined as a process used to distinguish a person from a static image or inanimate object, for instance by tracking movements, classifying behavior, sensing body temperature, or the like… Processor 136 and/or remote device 140 may be configured to distinguish people from other animals such as dogs.” See paragraph 323 “Indicia may include, as a non-limiting example, geo-rectified icons that convey location based information, messaging from other devices and/or people, current status of systems, locations of threats, friendlies, neutrals, unknowns or the like, location and/or direction of detected gunshots or other events, warnings, “keep-out” zones of countermeasures or other devices, or the like, while also providing two-way communication back apparatus and/or a system thereof to provide real-time status of a user, such as their location, ammo remaining, current vital signs, status, radios, or the like…” Goldstein)
As per claim 7, Goldstein teaches “The method of claim 1, wherein said target of interest comprises one or more of an object, equipment, machinery, vehicles, weapons systems, RADAR and communication installations, and the like.” ((See all of the paragraphs presented in the rejection of claim 1. See also paragraphs 118, 89, “[0118]… or instance, and without limitation, object classifier may identify a first object detected using computer vision techniques and/or ToF as described above as a human body, which may be further classified as an adult and/or child, a second object as an animal such as a dog, rat, racoon, bird, or the like, a third object as an inanimate object, which may be further classified as described below, or the like.” See paragraph 93 “… Chemical sensor 128 may be used to detect one or more hazardous chemicals and/or chemicals associated with weaponry, such as nitroaromatic compounds for detection of explosives, gun powder and/or gun oils. Chemical sensor 128 may be used to detect one or more hazardous chemicals and/or chemicals associated with chemical and/or biological warfare, such as nerve agents (such as sarin, soman, cyclohexylsarin, tabun, VX), blistering agents (such as mustards, lewisite), choking agents or lung toxicants (such as chlorine, phosgene, diphosgene), cyanides, incapacitating agents (such as anticholinergic compounds), lacrimating agents (such as pepper gas, chloroacetophenone, CS), vomiting agents (such as adamsite), and/or biological agents (such as anthrax, smallpox, plague, tularemia, and/or other detrimental bacteria, viruses, prions).” See paragraph 119 “[0119] Still referring to FIG. 4, Classifier may include a tool classifier 440. Tool classifier 440 may identify one or more objects held by or on a person of subject 308 by classification to one or more categories of object, such as tools, weapons, communication devices, or the like. One or more categories may identify such an object as, and/or distinguish object from, a weapon, a tool usable for a break-in, a tool designed to damage objects and/or people, a tool capable of damaging objects and/or people, and/or an innocuous object such as a sandwich or coffee cup.” See also paragraph 85. Goldstein)
As per claim 8, Goldstein teaches at least one of “The method of claim 1, wherein said target of interest comprises one or more of a building, trench or tunnel system, airfield, infrastructure and other physical phenomenon.” (See all of the paragraphs presented in the rejection of claim 1. See paragraph 88 “…apparatus 100 may use automated detection using any imaging device 104 or the like to image and/or scan the subject area for processing to determine the locations of walls, objects, animals, features, or other boundary delineators to find potential boundaries, which a user may confirm from a user computing device.” See paragraph 91 “0091] With further reference to FIG. 3, one or more subjects 308 may be detected via detection of changes to a baseline condition. A “subject,” as used herein, is a person, animal, object phenomenon, and/or substance introduced into subject area after baseline has been established.” See paragraph 389 “[0389] Still referring to FIG. 15, in a non-limiting embodiment, one or more lasers and/or other light output devices may be deployed in a swimming pool, such as beneath the water and/or on the surface. Light sources may generate images, illumination, and the like beneath and/or on the water, on walls or floors of a pool, on bodies of one or more persons within the pool, or the like.” See also the rest of paragraph 88 “[0088] Referring now to FIG. 3, apparatus 100 may be mounted and/or deployed in a subject area. A “subject area,” as used in this disclosure, is region within which apparatus 100 is configured to enforce one or more security objectives. In an embodiment, subject area may include one or more buildings, shopping centers, office spaces, zones, fields, and the like thereof. In an embodiment, and without limitation, subject area may include one or more residential homes and/or residential buildings…” See also paragraph 287 “… Apparatus may alternatively or additionally be provided with and/or store data describing one or more mission objectives. For instance, where apparatus when a mission objective includes finding a particular person, object, or building, apparatus may use image classification to aid in identification thereof. Similarly, apparatus 100 may identify routes and/or hazards along routes, and may provide guidance for combatants to traverse such routes.” See also paragraph 378. Goldstein )
As per claim 9, Goldstein teaches “The method of claim 1, wherein said camouflage strategies comprise one or more of a cover, a concealment, and a camouflage material.” (See all of the paragraphs presented in the rejection of claim 1. See paragraphs 187-190 “[0187] Further referring to FIG. 10, apparatus 100 may be configured to detect countermeasures by subject. Countermeasures may include, without limitation, protective behaviors such as aversion of eyes, covering ears, crawling on the ground, using cover, or the like, protective equipment such as eye protection goggles and/or other eyewear, eye protection such as protective and/or noise-cancelling earphones and/or headsets, or the like. Apparatus 100 may be configured to select deterrents to bypass countermeasures… As a non-limiting example, where countermeasure blocks or otherwise avoids light deterrents, apparatus 100 may be configured to select and/or output another deterrent such as an audio deterrent. Alternatively, a frequency of an audio and/or light deterrent may be modified to circumvent protection against other frequencies; for instance, and without limitation, where a subject is wearing eyewear that selectively reflects a first wavelength, apparatus may output a second wavelength that the eyewear does not selectively reflect. As a further example, where apparatus detections hearing protection, apparatus may output a low-frequency sound, which subject may feel as a result of bone conduction or the like; in an embodiment, lower-frequency sound may also heighten a psychological effect and/or “fear factor” from those lower-frequency sounds, even with hearing protection, because of the unsettling sensation of vibration in apparent silence... ” See paragraph 78 “… without limitation, determination of range to an object, determination of chemical makeup of an object, detection and/or identification of hidden objects, determination of speed of an object, creation of an image an object and/or scene in the respective frequency, and/or change detection.” See also paragraphs 157-160. Goldstein )
As per claim 10, Goldstein teaches “The method of claim 9, wherein said camouflage strategies comprise a visual camouflage.” (See all of the paragraphs presented in the rejection of claim 1. See paragraphs 187-194 “[0187] Further referring to FIG. 10, apparatus 100 may be configured to detect countermeasures by subject. Countermeasures may include, without limitation, protective behaviors such as aversion of eyes, covering ears, crawling on the ground, using cover, or the like, protective equipment such as eye protection goggles and/or other eyewear, eye protection such as protective and/or noise-cancelling earphones and/or headsets, or the like. Apparatus 100 may be configured to select deterrents to bypass countermeasures… As a non-limiting example, where countermeasure blocks or otherwise avoids light deterrents, apparatus 100 may be configured to select and/or output another deterrent such as an audio deterrent. Alternatively, a frequency of an audio and/or light deterrent may be modified to circumvent protection against other frequencies; for instance, and without limitation, where a subject is wearing eyewear that selectively reflects a first wavelength, apparatus may output a second wavelength that the eyewear does not selectively reflect. As a further example, where apparatus detections hearing protection, apparatus may output a low-frequency sound, which subject may feel as a result of bone conduction or the like; in an embodiment, lower-frequency sound may also heighten a psychological effect and/or “fear factor” from those lower-frequency sounds, even with hearing protection, because of the unsettling sensation of vibration in apparent silence... ” See paragraph 78 “… without limitation, determination of range to an object, determination of chemical makeup of an object, detection and/or identification of hidden objects, determination of speed of an object, creation of an image an object and/or scene in the respective frequency, and/or change detection.” See also paragraphs 157-160. Goldstein )
As per claim 11, Goldstein teaches at least one of “The method of claim 9, wherein said camouflage strategies comprise one or more of electromagnetic spectrum, to include ultraviolet, visible, infrared, thermal, and radio frequency, to include RADAR, acoustic, or other signature masking technique.” (See all of the paragraphs presented in the rejection of claim 1. See paragraphs 187-194 “[0187] Further referring to FIG. 10, apparatus 100 may be configured to detect countermeasures by subject. Countermeasures may include, without limitation, protective behaviors such as aversion of eyes, covering ears, crawling on the ground, using cover, or the like, protective equipment such as eye protection goggles and/or other eyewear, eye protection such as protective and/or noise-cancelling earphones and/or headsets, or the like. Apparatus 100 may be configured to select deterrents to bypass countermeasures… As a non-limiting example, where countermeasure blocks or otherwise avoids light deterrents, apparatus 100 may be configured to select and/or output another deterrent such as an audio deterrent. Alternatively, a frequency of an audio and/or light deterrent may be modified to circumvent protection against other frequencies; for instance, and without limitation, where a subject is wearing eyewear that selectively reflects a first wavelength, apparatus may output a second wavelength that the eyewear does not selectively reflect. As a further example, where apparatus detections hearing protection, apparatus may output a low-frequency sound, which subject may feel as a result of bone conduction or the like; in an embodiment, lower-frequency sound may also heighten a psychological effect and/or “fear factor” from those lower-frequency sounds, even with hearing protection, because of the unsettling sensation of vibration in apparent silence... ” See paragraph 78 “… without limitation, determination of range to an object, determination of chemical makeup of an object, detection and/or identification of hidden objects, determination of speed of an object, creation of an image an object and/or scene in the respective frequency, and/or change detection.” See also paragraphs 157-160. Goldstein )
Claim 33 is rejected under the same analysis as claim 11.
As per claim 12, Goldstein teaches “The method of claim 1, wherein said sensor information comprises one or more still images.” (See paragraph 63 “[0063] Referring now to FIG. 1, an exemplary embodiment of an automated threat detection and deterrence apparatus 100 is illustrated. Apparatus 100 includes an imaging device 104 configured to detect a subject 308 in a subject area. Imaging device 104 may include an optical camera 108. An “optical camera,” as used in this disclosure, is a device that generates still, video, and/or event-based images by capturing senses electromagnetic radiation in the visible spectrum…” Goldstein)
Claim 34 is rejected under the same analysis as claim 12.
As per claim 13, Goldstein teaches The method of claim 1, wherein said sensor information comprises a live video.” (See paragraph 252 “…A camera and/or other imaging device as described above, which may be dedicated to steerable array 1400 and/or may include any imaging device of apparatus 100, may be able to recognize presence of a person in an area of interest, and may be capable of determining a position in space of such person and/or subject and a distance therefrom. These data may be sent in a continuous real time stream to beam steering algorithm, updating in real time a position in space where a main beam may be sent… ” See also paragraph 297 “[0297] Still referring to FIG. 15, remote device 140 may and alternatively or additionally communicate with further devices under services, such as cloud services, which may perform, without limitation, cross platform data aggregation and/or analysis, data storage, or other tasks, such as updated safety regulation and/or settings, as well as software, FPGA and/or firmware updates. Find stamps, and without limitation, remote device 140 and or processor 136 may regularly, iteratively, are continuously, update stream and or otherwise provide video, sensor, and other data, to one or more remote services such as cloud services…” See also paragraphs 311, 610, 613, 202 and 323, (real-time sensing) “[0311]..These countermeasures may be steered or not. One pucks and/or a separate device may house a CPU and/or other processor for real-time processing of the sensor feeds.” Goldstein)
As per claim 14, Goldstein teaches The method of claim 1, wherein said sensor information comprises a recorded video.” (See paragraph 63 “[0063] Referring now to FIG. 1, an exemplary embodiment of an automated threat detection and deterrence apparatus 100 is illustrated. Apparatus 100 includes an imaging device 104 configured to detect a subject 308 in a subject area. Imaging device 104 may include an optical camera 108. An “optical camera,” as used in this disclosure, is a device that generates still, video, and/or event-based images by capturing senses electromagnetic radiation in the visible spectrum…” See also paragraph 98 “… Processor 136 may periodically and/or continuously poll imaging device 104 and/or other sensors to determine whether a change from baseline has occurred; for instance, apparatus 100 may periodically scan room using light radar, take photo/video data, or the like. Processor 136 may iteratively compare baseline data to polled and/or event-driven data to detect changes.” See also paragraph 297 “[0297] Still referring to FIG. 15, remote device 140 may and alternatively or additionally communicate with further devices under services, such as cloud services, which may perform, without limitation, cross platform data aggregation and/or analysis, data storage, or other tasks, such as updated safety regulation and/or settings, as well as software, FPGA and/or firmware updates. Find stamps, and without limitation, remote device 140 and or processor 136 may regularly, iteratively, are continuously, update stream and or otherwise provide video, sensor, and other data, to one or more remote services such as cloud services…” See also paragraph 301. Goldstein)
Claim 35 is rejected under the same analysis as claims 13 and 14.
As per claim 15, Goldstein teaches “The method of claim 1, wherein said sensor information comprises passive sensor information including one or more of a red-green-blue (RGB) video, a grayscale video, a thermal, infrared, or night-vision video, and an ultraviolet video.” (See all of the paragraphs presented in the rejection of claim 1. See paragraphs 63-76. “[0063] Referring now to FIG. 1, an exemplary embodiment of an automated threat detection and deterrence apparatus 100 is illustrated. Apparatus 100 includes an imaging device 104 configured to detect a subject 308 in a subject area. Imaging device 104 may include an optical camera 108. An “optical camera,” as used in this disclosure, is a device that generates still, video, and/or event-based images by capturing senses electromagnetic radiation in the visible spectrum, having wavelengths between approximately 380 nm and 740 nm, which radiation in this range may be referred to for the purposes of this disclosure as “visible light,” wavelengths approximately between 740 nm and 1,100 nm, which radiation in this range may be referred to for the purposes of this disclosure as “near-infrared light” or “NIR,” and wavelengths approximately between 300 nm and 380 nm, which radiation in this range may be referred to for the purposes of this disclosure as “ultraviolet light” or “UV”. Optical camera 108 may include a plurality of optical detectors, visible photodetectors, or photodetectors, where an “optical detector,” “visible photodetector,” or “photodetector” is defined as an electronic device that alters any parameter of an electronic circuit when contacted by visible, UV, and/or NIR light...” “[0064] Still referring to FIG. 1, individual photodetectors in optical camera 108 may be sensitive to specific wavelengths of light… Combinations of photodetectors specifically sensitive to red, green, and blue wavelengths may correspond to wavelength sensitivity of human retinal cone cells, which detect light in similar frequency ranges. Photodetectors may be grouped into a three-dimensional array of pixels, each pixel including a red photodetector, a blue photodetector, and a green photodetector.” See paragraph 65 “0065] With continued reference to FIG. 1, imaging device 104 may include an infrared camera 112…” “[0075] Continuing to refer to FIG. 1, imaging device 104 may include an ultrasound device 120…” See paragraph 98 “Processor 136 may periodically and/or continuously poll imaging device 104 and/or other sensors to determine whether a change from baseline has occurred; for instance, apparatus 100 may periodically scan room using light radar, take photo/video data, or the like. Processor 136 may iteratively compare baseline data to polled and/or event-driven data to detect changes.” Goldstein )
Claim 37 is rejected under the same analysis as claim 15.
As per claim 16, Goldstein teaches “The method of claim 1, wherein said processing platform comprises a mobile edge device for deployment on or in one or more of a battlefield, an austere environment, or an operationally constrained environment.” (See all of the paragraphs presented in the rejection of claim 1. See paragraph 306 “[0306] With further reference to FIG. 15, apparatus may communicated with and/or participate in a cloud computing architecture and/or system. Apparatus and/or a network thereof may connect to a cloud processing facility that may amalgamate all data, for instance according to a single user's site or two or more user sites, according to a common customer, according to a region, or the like. Cloud processing facility may use this data to process better performance, learn new threats, learn better methods for countermeasures, enhance sensor performance, enhance sensor fusion, or any other improvements or corrections that may be gained from the data. Cloud processing facility may then distribute this back to on-premises and/or edge device across an entire deployment…” See paragraphs 94-101, 183 and 436 “[0183]… Accordingly, where apparatus 100 is being used in a mobile setting such as a hand-held and/or drone device, a red wavelength may be used to create longer-lasting impairment to help a user in escaping from and/or subduing subject 308…” Paragraphs 375, 552, 588, 646 demonstrate deployment in an austere (strict) environment and operationally constrained environments “[0646]… As a further non-limiting example, a first threat level may include entering a restricted area, and/or trespassing into a secured area. As a further non-limiting example a first threat level may include a subject's presence in a subject area, wherein the subject is not authorized to be in the subject area.” “[0375] Still referring to FIG. 15, apparatus may use one or more behavior classifiers to determine whether a given drone is a threat and/or risk; for instance, behavior classifier may determine whether a drone is attempting to damage property, is crossing a restricted space where it could collide with aircraft or sensitive equipment, or the like…” [0066] Continuing to refer to FIG. 1, infrared camera 112 may use a separate aperture and/or focal plane from optical camera 108, and/or may be integrated together with optical camera 108. There may be a plurality of optical cameras 108 and/or a plurality of infrared cameras 112, for instance with different angles, magnifications, and/or fields-of-view of perspective on a subject area. Alternatively or additionally, two or more apparatuses coordinated using a communication network, as described in further detail below, may be combined to generate two or more images from varying perspectives to aid in multi-dimensional imaging and/or analysis.” See also paragraphs 88-92. “[0088] Referring now to FIG. 3, apparatus 100 may be mounted and/or deployed in a subject area. A “subject area,” as used in this disclosure, is region within which apparatus 100 is configured to enforce one or more security objectives… Security objectives may include exclusion of unauthorized persons from subject area, prevention of unauthorized persons from entering a door in subject area, prevention of unauthorized persons from accessing an item to be protected 304, such as a valuable and/or dangerous item, protection of a person or object in subject area from harm, prevention of harm to apparatus 100, or the like.” Goldstein)
Claim 38 is rejected under the same analysis as claim 16.
As per claim 17, Goldstein teaches “The method of claim 1, wherein said sensor information comprises information from multiple sensor systems and said output is based on information from one or more of said multiple sensor systems.” (See all of the paragraphs presented in the rejection of claim 1. See paragraph 81 “[0081] More generally, and continuing to refer to FIG. 2, apparatus 100 may combine multiple sensors together to detect subject and/or make any determinations as described in this disclosure. Combinations may include any combinations described below…” See paragraphs 73- 87. “[0087] In an embodiment, and with further reference to FIG. 2, two or more of optical camera 108, infrared camera 112, light radar component 116, ultrasound device 120, 3D detector 204, Radar Component 208, Wavelength Detector 212, Polarized Camera 216, Multispectral Camera 220, Hyperspectral Camera 224, and/or other sensor data, for instance, and without limitation, including audio sensor 124, chemical sensor 128, motion sensor 132, ionizing radiation sensor 228, seismic sensor 232, mass or weight sensor 236, magnetic sensor 240, and/or global positioning system receiver 244 may function together as a fusion camera. A “fusion camera,” as used in this disclosure, is an imaging device 104 that receives two or more different kinds of imaging or other spatially derived data, which may be combined to form an image combining the two or more different kinds of imaging or other spatially derived data. For instance, and without limitation, light radar data may be superimposed upon and/or combined with data captured using an optical and/or infrared camera 112, enabling a coordinate system more accurately to capture depth in a resulting three-dimensional image.” See paragraph 66 [0066] Continuing to refer to FIG. 1, infrared camera 112 may use a separate aperture and/or focal plane from optical camera 108, and/or may be integrated together with optical camera 108. There may be a plurality of optical cameras 108 and/or a plurality of infrared cameras 112, for instance with different angles, magnifications, and/or fields-of-view of perspective on a subject area. Alternatively or additionally, two or more apparatuses coordinated using a communication network, as described in further detail below, may be combined to generate two or more images from varying perspectives to aid in multi-dimensional imaging and/or analysis.” See paragraph 306. )
Claim 39 is rejected under the same analysis as claim 17.
As per claim 18, Goldstein teaches “The method of claim 1, wherein said first processing comprises aggregating the outputs from multiple sensors with different angles and fields of view relative to the target of interest to provide a more complete spatial picture of said target of interest.” (See all of the paragraphs presented in the rejection of claim 1. See paragraph 81 “[0081] More generally, and continuing to refer to FIG. 2, apparatus 100 may combine multiple sensors together to detect subject and/or make any determinations as described in this disclosure. Combinations may include any combinations described below…” See paragraphs 73- 87. “[0087] In an embodiment, and with further reference to FIG. 2, two or more of optical camera 108, infrared camera 112, light radar component 116, ultrasound device 120, 3D detector 204, Radar Component 208, Wavelength Detector 212, Polarized Camera 216, Multispectral Camera 220, Hyperspectral Camera 224, and/or other sensor data, for instance, and without limitation, including audio sensor 124, chemical sensor 128, motion sensor 132, ionizing radiation sensor 228, seismic sensor 232, mass or weight sensor 236, magnetic sensor 240, and/or global positioning system receiver 244 may function together as a fusion camera. A “fusion camera,” as used in this disclosure, is an imaging device 104 that receives two or more different kinds of imaging or other spatially derived data, which may be combined to form an image combining the two or more different kinds of imaging or other spatially derived data. For instance, and without limitation, light radar data may be superimposed upon and/or combined with data captured using an optical and/or infrared camera 112, enabling a coordinate system more accurately to capture depth in a resulting three-dimensional image.” See paragraph 66 [0066] Continuing to refer to FIG. 1, infrared camera 112 may use a separate aperture and/or focal plane from optical camera 108, and/or may be integrated together with optical camera 108. There may be a plurality of optical cameras 108 and/or a plurality of infrared cameras 112, for instance with different angles, magnifications, and/or fields-of-view of perspective on a subject area. Alternatively or additionally, two or more apparatuses coordinated using a communication network, as described in further detail below, may be combined to generate two or more images from varying perspectives to aid in multi-dimensional imaging and/or analysis.” See also paragraphs 306, 297. Paragraphs 76-77 also show great examples of using the imaging device to create a more complete environment. Goldstein)
Claim 40 is rejected under the same analysis as claim 18.
As per claim 19, Goldstein teaches “The method of claim 1, wherein said second processing comprises processing said first information using artificial intelligence/machine learning.” (See all of the paragraphs presented in the rejection of claim 1. See paragraphs 106-117 and 127-135. “[0106] Referring now to FIG. 4, processor 136 and/or remote device 140 may be configured to perform one or more machine-learning processes to analyze data captured by sensors, feedback loops, and/or imaging devices 104. Such processes may be performed using a machine-learning module 400, which may include any processor 136…” “[0127] Now back to FIG. 4, machine-learning module 400 may include additional elements that use identification of anatomical features, objects, and/or other visual data to determine and/or estimate further information concerning phenomena detected using sensor and/or imaging device 104.” Goldstein)
Claim 41 is rejected under the same analysis as claim 19.
As per claim 20, Goldstein teaches “The method of claim 1, wherein said processing platform further receives sensor inputs separate from said sensor information.” (See paragraph 103 “[0103] Further referring to FIG. 1, homography matrices or other similar methods may alternatively or additionally be used to calibrate reflective or other beam steering devices such as galvanometers, fast-steering mirrors, and/or other beam steering devices used to aim directed light or other emissive deterrents, camera and/or imaging inputs, ToF inputs or outputs…” See also paragraphs 111-113. See also paragraphs 79-87 and 92-95, they show several sensor inputs and the sensor information utilized depends on which sensor inputs are used. “[0080] With continued reference to FIG. 2, imaging device 104 may include both high-resolution and low-resolution visual sensors. In an embodiment, imaging device 104 and/or one or more processors, computing devices, logic circuits, or the like in and/or communicating with apparatus 100 may select low-resolution or high-resolution sensors as a function of one or more determinations based on accuracy, speed, and resource allocation…” Goldstein )
Claim 42 is rejected under the same analysis as claim 20.
As per claim 21, Goldstein teaches “The method of claim 1, wherein said processing platform implements one or both of region of interest and signal of interest processing.” (See paragraph 88 “0088] Referring now to FIG. 3, apparatus 100 may be mounted and/or deployed in a subject area. A “subject area,” as used in this disclosure, is region within which apparatus 100 is configured to enforce one or more security objectives. In an embodiment, subject area may include one or more buildings, shopping centers, office spaces, zones, fields, and the like thereof. In an embodiment, and without limitation, subject area may include one or more residential homes and/or residential buildings. Security objectives may include exclusion of unauthorized persons from subject area, prevention of unauthorized persons from entering a door in subject area, prevention of unauthorized persons from accessing an item to be protected 304, such as a valuable and/or dangerous item, protection of a person or object in subject area from harm, prevention of harm to apparatus 100, or the like.” See also paragraphs 611 and 127-147. See also paragraphs 78, 82- 86 and 99-101 for signal of interest processing. Goldstein)
Claim 43 is rejected under the same analysis as claim 21.
As per claim 22, Goldstein teaches “The method of claim 1, wherein said signature information concerns at least one of motion, vibration, emission, color change, reflectance, or a bio-physiologic signature.” (See paragraph 87 [0087] In an embodiment, and with further reference to FIG. 2, two or more of optical camera 108, infrared camera 112, light radar component 116, ultrasound device 120, 3D detector 204, Radar Component 208, Wavelength Detector 212, Polarized Camera 216, Multispectral Camera 220, Hyperspectral Camera 224, and/or other sensor data, for instance, and without limitation, including audio sensor 124, chemical sensor 128, motion sensor 132, ionizing radiation sensor 228, seismic sensor 232, mass or weight sensor 236, magnetic sensor 240, and/or global positioning system receiver 244 may function together as a fusion camera.” See paragraph 120 “[0120] With further reference to FIG. 4, once objects, persons, and/or other subjects 308 are defined, imaging device 104 and/or processor 136 may be configured to track motion and/or actions of such persons and/or objects relative to apparatus 100. For instance, and without limitation, a label may be associated with each identified object and/or person, which may be tracked subsequently.” See paragraph 83 “… One or more seismic sensors may be configured to detect vibrations made by subjects in a subject area, such as people, animals, and/or machines…” See paragraph 84 “One or more mass sensors may be configured to detect mass or weight changes made by subjects in a subject area, such as people, animals, and/or machines..”. See paragraphs 81-88. Goldstein )
Claim 44 is rejected under the same analysis as claim 22.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Goldstein in view of Zhang et. al. (US Pub. No. 20200364478 A1).
As per claim 5, Goldstein teaches “The method of claim 3, wherein said processing platform is operative for distinguishing a living human being from a non-living human being or non-living entity.” (See all of the paragraphs presented in the rejection of claim 1. See also paragraphs 118 and 130, 133, 213, 217 and 392 “[0118]… or instance, and without limitation, object classifier may identify a first object detected using computer vision techniques and/or ToF as described above as a human body, which may be further classified as an adult and/or child, a second object as an animal such as a dog, rat, racoon, bird, or the like, a third object as an inanimate object, which may be further classified as described below, or the like.” See also paragraph 55 “…sensors, to accurately recognize and spatially determine entrance into the subject area, to distinguish between known or whitelisted persons, children, animals, and potential threats…. ” See also paragraph 87 “[0087]… Infrared imaging may be used to verify that a subject 308 depicted in an image created by other means is likely a living human through body temperature detection…” See also paragraph 133 “[0133] Still referring to FIG. 4, processor 136 and/or remote device 140 may use any or all sensor feedback and/or machine-learning to perform liveliness detection, defined as a process used to distinguish a person from a static image or inanimate object, for instance by tracking movements, classifying behavior, sensing body temperature, or the like… Processor 136 and/or remote device 140 may be configured to distinguish people from other animals such as dogs.” Goldstein), however while Goldstein does teach all of the limitations in an implicit way, Zhang does teach it in an explicit manner.
Zhang teaches “distinguishing a living human being from a non-living human being or non-living entity.” (See paragraphs 46, 47, 84, 137-150 and 84-85 “[0140] The input image includes the sample image in the training set or the generative image obtained via the generative network based on the sample image, the annotation information of the sample image indicates that the sample image is a living real image or a non-living real image, and the annotation information of the generative image indicates that the generative image is a generated image. The second classification predication result includes living, non-living, and generative, which correspond to the living real image, the non-living real image, or the generative image, respectively.” The images are of humans as seen in paragraphs 2, 3, 80. Zhang)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Goldstein with the teachings of Zhang to distinguish between a living human and a non-living human. The modification would have been motivated by the desire to defend against spoofing attacks and improve security, therefore it is an improvement, as suggested by Zhang (See paragraphs 2, 3, 10. “[0003] In face anti-spoofing detection, due to the characteristics of easy acquisition and easy spoofing of faces, it is necessary to determine whether a face image in front of a camera comes from a real person by liveness detection, so as to improve the security of face recognition. At present, how to perform liveness detection for various possible characteristics of easy spoofing is a research hotspot in the art.” See paragraph 10 “…and then a classification result that the target object is living or non-living is obtained based on the image to be detected and the reconstruction error, thereby effectively distinguishing whether the target object in the image to be detected is living or non-living, effectively defending against unknown types of spoofing attacks, and improving anti-spoofing performance.” Zhang)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN J MENDEZ MUNIZ whose telephone number is (703)756-5672. The examiner can normally be reached M-F, 8AM - 5PM ET.
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/DYLAN JOHN MENDEZ MUNIZ/Examiner, Art Unit 2675
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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