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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/11/2026 has been entered.
Response to Arguments
Applicant's arguments filed 05/11/2026 regarding the rejection of the claims under 35 U.S.C. 102 and 103 have been fully considered but are moot because they do not apply to the new combination of the references being used in the current rejection.
Claim Rejections - 35 USC § 103
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 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.
Claims 1-8, 11-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Neff et al (US 2021/0192943), in view of Herman et al (US 2020/0055515).
Regarding Claim 1, Neff teaches a surveillance system (Figs. 1-3), comprising: a plurality of collection systems positioned at selected geographic areas ([0035-0036], Fig. 1, the parking structure 100 includes an entrance 102 and various sensor devices 120-126, which will be described in more detail later herein. One sensor device 120 is located at or near the entrance 102, and other sensor devices 122-126 are located in other areas of the parking structure 100. each sensor device 120-126 includes a sensor device housing, an image capture device such as a camera, and a wireless detector device), each comprising:
one or more sensors configured to capture visual identifiers for each of a plurality of targets ([0043], Fig. 3, an image capture device 310 captures images 312 of vehicles, [0045], The image 312 captured by the image capture device 310 can have any suitable format, such as JPEG or TIFF, among others. Also, the captured image 312 can include one vehicle or multiple vehicles. In accordance with aspects of the present disclosure, the storage and processing system (200, FIG. 2) processes the captured image 312 to extract license plate identifiers 314, and stores the license plate identifiers 312 in text format); and
one or more sensors configured to capture electronic signals associated with the plurality of targets ([0043], Fig. 3, the wireless detector device 320 detects wireless device identifiers 312 in the vicinity of the vehicles, [0046], The identifiers 322 captured by the wireless detector device 320 can have various formats and can include various types of information, such as media access control (MAC) addresses, physical addresses, user-assigned device names, ICCID numbers, and/or service set identifiers (SSID), among others. As used herein, the term “wireless device identifier” is intended to encompass all such types of information that can identify a wireless device. The wireless device identifiers 322 can be received by a storage and processing system (200, FIG. 2), which can separate different types of identifiers, as appropriate, and can store the various identifiers); and
an intelligence system in communication with each of the plurality of collection systems, the intelligence system including a correlation and search engine ([0044], Fig. 3, the association of the location identifier with those devices can be maintained by a storage and processing system (200, FIG. 2)), configured to:
receive captured visual identifiers for each target of the plurality of targets ([0045], Fig. 3, the storage and processing system (200, FIG. 2) processes the captured image 312 to extract license plate identifiers 314, and stores the license plate identifiers 312 in text format. Persons skilled in the art will recognize various ways to implement the extraction process, such as using statistical, machine vision, and/or machine learning techniques), and captured electronic signals associated with each target of the plurality of targets from each of the plurality of collection systems ([0046], The wireless device identifiers 322 can be received by a storage and processing system (200, FIG. 2), which can separate different types of identifiers, as appropriate, and can store the various identifiers);
filter the captured electronic signals associated with each target in view of one or more non-unique characteristics of the captured electronic signals and develop at least one electronic signature associated with each target ([0046], Fig. 3, The identifiers 322 captured by the wireless detector device 320 can have various formats and can include various types of information, such as media access control (MAC) addresses, physical addresses, user-assigned device names, ICCID numbers, and/or service set identifiers (SSID), among others, [0036], wireless detector device operates to scan for wireless device identifiers and can include, for example, components such as radio antennas and controllers and/or other components which persons skilled in the art will recognize as being present in a device that scans for wireless device identifiers. The wireless detector device can scan for any type of wireless device, including, without limitation, Wi-Fi device identifiers, Bluetooth device identifiers, and/or cellular device identifiers);
correlate the captured visual identifiers for each target with at least one electronic signature associated with the target ([0043], Fig. 3, the image capture device 310 and the wireless detector device 320 are positioned sufficiently close to each other such that the data they capture can be meaningfully associated with each other. As mentioned above, in various embodiments, the image capture device 310 and the wireless detector device 320 can be positioned approximately ten feet or less apart, but other distances are contemplated. The image capture device 310 and the wireless detector device 320 can be contained in a single housing or can be separated in different housings. In various embodiments, the image capture device 310 and the wireless detector device 320 can communicate with each other to capture information at substantially the same time or to capture information within a particular time period); and
generate an identification of one or more unknown targets based prior known factors associated with the target ([0062], Fig. 10, At block 1006, the operation processes the image containing the vehicle(s) to extract one or more license plate identifiers corresponding to the vehicle(s). At block 1008, the operation stores in an electronic storage at least one record associating the license plate identifier(s) with the wireless device identifier(s). At block 1010, the operation identifies, based on the at least one record, one of the wireless device identifiers that is to serve as a surrogate for or an alternative to one of the license plate identifiers. [0059-0061], Fig. 9, vehicle's properties 904 can form a fingerprint or signature for a vehicle whose license plate identifier 902 is unknown. In various embodiments, certain properties can be mandatory properties that are required for the vehicle fingerprint/signature to be used in place of an unknown license plate identifier, such as, for example, vehicle color, make, model, and type. In accordance with aspects of the present disclosure, when a vehicle signature/fingerprint is designated to be used in place of a license plate identifier, any aspects described herein relating to a license plate identifier also applies to a vehicle signature/fingerprint. For example, in relation to FIG. 7, a wireless device identifier that has the highest occurrence in data records having the same vehicle fingerprint/signature, can be designated as a surrogate for or as an alternative to the vehicle fingerprint/signature. In relation to FIG. 8, patterns indicative of tracking a vehicle through a parking structure can be applied to vehicle fingerprints/signatures in the same manner that they are applied to license plate identifiers);
determine a location of a selected target, an association of the selected target to one or more persons, association of the target to one or more locations, travel patterns of the selected target, or combinations thereof; or combinations thereof ([0044], Fig. 3, the image capture device 310 and the wireless detector device 320 can be associated with a location identifier, which in the example of FIG. 3 is designated as LOC 10 UNIT 5. In various embodiments, an image capture device 310 and a related wireless detector device 320 can both be associated with the same location identifier. [0052-0053], Figs. 6-7, As data records accumulate over time, it becomes more likely that patterns can become recognizable).
Neff fails to teach evaluate and combine one or more singular collection events with one or more catalogued events in the intelligence database to develop correlated information related to the intersection of multiple collected/captured electronic signals and/or visual identifiers that occurred at a selected time and geographical area or location; and based upon the correlated information, determine a predicted route or routes of the targets of interest.
In the same filed of endeavor, Herman teaches evaluate and combine one or more singular collection events with one or more catalogued events in the intelligence database to develop correlated information related to the intersection of multiple collected/captured electronic signals and/or visual identifiers that occurred at a selected time and geographical area or location ([0043], DNN 400 can process input image and environmental data 402 by sampling video sensors 210 each in turn periodically and processing the input image and environmental data 402 to sample the environment around vehicle 110. The environment around vehicle 110 can be sampled periodically in this fashion to provide cognitive maps 414 with a complete view of the environment around vehicle 110 covered by fields of view 220 of video sensors 210. In examples of techniques described herein, the image and environmental data can be pre-processed by additional neural networks to extract features for processing by DNN 400); and based upon the correlated information, determine a predicted route or routes of the targets of interest ([0044], where a plurality of vehicles are driving in proximity to vehicle 110, DNN 400 can be used to estimate the most probable driving behaviors of the human or ADAS drivers of the plurality of vehicles and a path polynomial for operating vehicle 110 per some increment of time into the future. This would then be updated by the interactions of vehicle 110 with the plurality of vehicles to predict the next time step of the trajectories of the plurality of vehicles up to the desired future time duration (e.g. 0.5 seconds time steps and up to 10 seconds into the future). This would provide information of the most probable path polynomial of the plurality of vehicles and allow computing device 115).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate cross-correlation of data to predict probable paths of devices in the sensing environment, as taught in Herman, in the system of Neff, in order to enhance motion planning and avoid future collisions and path disruptions.
Regarding Claim 2, Neff, modified by Herman, teaches the invention of Claim 1 above, Neff further comprising wherein at least some of the sensors of the one or more sensors configured to capture visual identifiers for each of the plurality of targets comprise an automated license plate reader positioned at one or more of the selected locations ([0045], The image 312 captured by the image capture device 310 can have any suitable format, such as JPEG or TIFF, among others. Depending on the location and configuration of the image capture device 310, the captured image 312 can include a single parking bay or multiple parking bays (110, FIG. 1). With reference also to FIG. 1, the captured image 312 can, for example, capture one or more parking bays on one side of a driving lane 112, or can parking bays on both sides of a driving lane 112. Also, the captured image 312 can include one vehicle or multiple vehicles. In accordance with aspects of the present disclosure, the storage and processing system (200, FIG. 2) processes the captured image 312 to extract license plate identifiers 314, and stores the license plate identifiers 312 in text format. Persons skilled in the art will recognize various ways to implement the extraction process, such as using statistical, machine vision, and/or machine learning techniques).
Regarding Claim 3, Neff, modified by Herman, teaches the invention of Claim 1 above, Neff further comprising wherein the intelligence system is further configured to track one or more targets of interest using updated real-time captures of the visual identifiers of the one or more targets of interest or the one or more electronic signatures associated targets at selected locations by additional ones of the one or more collection systems ([0052-0054], As data records accumulate over time, it becomes more likely that patterns can become recognizable. FIG. 6 illustrates an example of such a pattern where a particular license plate identifier FMK-321 appears frequently in data records for the reference wireless device identifier A0:B1:C2:D3:E4:F5. the determination can be subject to an occurrence threshold, such that an association of a license plate identifier with a wireless device identifier is created only if the highest occurrence occurs more than a particular number of times. In various embodiments, the occurrence threshold can be two or five, or another number. In various embodiments, the data records can be processed to determine a second-highest occurrence, or a third-highest occurrence, and so on. Such further determinations can also be subjected to an occurrence threshold).
Regarding Claim 4, Neff, modified by Herman, teaches the invention of Claim 1 above, Neff further comprising wherein each of the one or more collection systems comprise a sensor assembly, including an array of sensors each configured to detect and capture one or more electronic signals associated with the plurality of targets ([0036], In accordance with aspects of the present disclosure, each sensor device 120-126 includes a sensor device housing, an image capture device such as a camera, and a wireless detector device. The wireless detector device operates to scan for wireless device identifiers and can include, for example, components such as radio antennas and controllers and/or other components which persons skilled in the art will recognize as being present in a device that scans for wireless device identifiers. The wireless detector device can scan for any type of wireless device, including, without limitation, Wi-Fi device identifiers, Bluetooth device identifiers, and/or cellular device identifiers, among others. Because modern vehicles come equipped with wireless devices and drivers also carry wireless devices, the wireless detector device will almost certainly be able to detect a wireless device in the vicinity of a vehicle) .
Regarding Claim 5, Neff, modified by Herman, teaches the invention of Claim 4 above, Neff further comprising wherein the array of sensors includes one or more of a Bluetooth® antenna, a Wi-fi antenna, a RFID antenna, or other RF antenna ([0036], In accordance with aspects of the present disclosure, each sensor device 120-126 includes a sensor device housing, an image capture device such as a camera, and a wireless detector device. The wireless detector device operates to scan for wireless device identifiers and can include, for example, components such as radio antennas and controllers and/or other components which persons skilled in the art will recognize as being present in a device that scans for wireless device identifiers. The wireless detector device can scan for any type of wireless device, including, without limitation, Wi-Fi device identifiers, Bluetooth device identifiers, and/or cellular device identifiers, among others. Because modern vehicles come equipped with wireless devices and drivers also carry wireless devices, the wireless detector device will almost certainly be able to detect a wireless device in the vicinity of a vehicle).
Regarding Claim 6, Neff, modified by Herman, teaches the invention of Claim 1 above, Neff further comprising wherein at least some of the sensors of the one or more sensors configured to capture visual identifiers for each of the plurality of targets comprise one or more cameras configured to capture at least one of a plurality of vehicle identifiers of the plurality of targets ([0036], In accordance with aspects of the present disclosure, each sensor device 120-126 includes a sensor device housing, an image capture device such as a camera, and a wireless detector device. The image capture device and the wireless detector device will be described in more detail in connection with FIG. 3. For now, it is sufficient to note that the image capture device operates to capture images and can include, for example, components such as lenses, charge-coupled device light sensors, image signal processors, controllers, storage memory, and/or a communication interface, and/or other components which persons skilled in the art will recognize as being present in a device that captures and communicates images, [0045], The image 312 captured by the image capture device 310 can have any suitable format, such as JPEG or TIFF, among others. Depending on the location and configuration of the image capture device 310, the captured image 312 can include a single parking bay or multiple parking bays (110, FIG. 1)).
Regarding Claim 7, Neff, modified by Herman, teaches the invention of Claim 6 above, Neff further comprising wherein the visual identifiers include one or more of license plates, stickers, patterns, position(s) of component parts, after-market added parts, damage, or combinations thereof, of a vehicle ([0051], a data record can include multiple wireless device identifiers as well as multiple license plate identifiers. In various embodiments, a data record can include information about a vehicle in addition to a license plate identifier, such as color, make, model, and vehicle type, among other things. An example of using such additional information to form a “signature” for an unidentified vehicle).
Regarding Claim 8, Neff, modified by Herman, teaches the invention of Claim 1 above, Neff further comprising wherein the non-unique characteristics comprise a frequency of occurrence, relative representation, signal type, signal receipt location diversity, and signal strength profiling, and wherein filtering the captured electronic signals associated with each target in view of the one or more non-unique characteristics of the captured electronic signals further comprises determining whether a relative certainty value that the captured electronic signals is associated with the target exceed a prescribed threshold in view of the non-unique characteristics ([0052-0054], In the example of FIG. 6, if the license plate identifier FMK-321 has the most occurrences in the records, then wireless device identifier A0:B1:C2:D3:E4:F5 can be designated to be a surrogate or alternative to license plate identifier FMK-321. In the example of FIG. 7, if the wireless device identifier A0:B1:C2:D3:E4:F5 has the most occurrences in the records, then wireless device identifier A0:B1:C2:D3:E4:F5 can be designated to be a surrogate or alternative to license plate identifier FMK-321. In various embodiments, the determination can be subject to an occurrence threshold, such that an association of a license plate identifier with a wireless device identifier is created only if the highest occurrence occurs more than a particular number of times).
Regarding Claim 11, Neff, modified by Herman, teaches the invention of Claim 1 above, further comprising wherein the intelligence system is configured to prioritize one or more of the captured electronic signals for identification of a selected target ([0052-0054], In the example of FIG. 6, if the license plate identifier FMK-321 has the most occurrences in the records, then wireless device identifier A0:B1:C2:D3:E4:F5 can be designated to be a surrogate or alternative to license plate identifier FMK-321. In the example of FIG. 7, if the wireless device identifier A0:B1:C2:D3:E4:F5 has the most occurrences in the records, then wireless device identifier A0:B1:C2:D3:E4:F5 can be designated to be a surrogate or alternative to license plate identifier FMK-321. In various embodiments, the determination can be subject to an occurrence threshold, such that an association of a license plate identifier with a wireless device identifier is created only if the highest occurrence occurs more than a particular number of times (prioritized device that occurs most frequently as surrogate identifier)).
Regarding Claim 12, Neff teaches a method (Figs. 1-3), comprising: capturing, in real-time via a plurality of collection systems ([0043], Fig. 3, an image capture device 310 captures images 312 of vehicles, [0045], The image 312 captured by the image capture device 310 can have any suitable format, such as JPEG or TIFF, among others. Also, the captured image 312 can include one vehicle or multiple vehicles. In accordance with aspects of the present disclosure, the storage and processing system (200, FIG. 2) processes the captured image 312 to extract license plate identifiers 314, and stores the license plate identifiers 312 in text format), at least one visual identifier and associating the at least one visual identifier with a target ([0045], Fig. 3, the storage and processing system (200, FIG. 2) processes the captured image 312 to extract license plate identifiers 314, and stores the license plate identifiers 312 in text format. Persons skilled in the art will recognize various ways to implement the extraction process, such as using statistical, machine vision, and/or machine learning techniques);
capturing a plurality of electronic signals identified with a plurality of electronic devices and associating one or more of the electronic devices with the target ([0043], Fig. 3, the wireless detector device 320 detects wireless device identifiers 312 in the vicinity of the vehicles, [0046], The identifiers 322 captured by the wireless detector device 320 can have various formats and can include various types of information, such as media access control (MAC) addresses, physical addresses, user-assigned device names, ICCID numbers, and/or service set identifiers (SSID), among others. As used herein, the term “wireless device identifier” is intended to encompass all such types of information that can identify a wireless device. The wireless device identifiers 322 can be received by a storage and processing system (200, FIG. 2), which can separate different types of identifiers, as appropriate, and can store the various identifiers);
filtering the captured electronic signals of the one or more electronic devices associated with each target in view of one or more non-unique characteristics of the captured electronic signals and developing at least one electronic signature for at least one electronic device associated with each target ([0046], Fig. 3, The identifiers 322 captured by the wireless detector device 320 can have various formats and can include various types of information, such as media access control (MAC) addresses, physical addresses, user-assigned device names, ICCID numbers, and/or service set identifiers (SSID), among others, [0036], wireless detector device operates to scan for wireless device identifiers and can include, for example, components such as radio antennas and controllers and/or other components which persons skilled in the art will recognize as being present in a device that scans for wireless device identifiers. The wireless detector device can scan for any type of wireless device, including, without limitation, Wi-Fi device identifiers, Bluetooth device identifiers, and/or cellular device identifiers);
correlating the captured at least one visual identifier associated with the target with the at least one electronic signature associated with each target ([0043], Fig. 3, the image capture device 310 and the wireless detector device 320 are positioned sufficiently close to each other such that the data they capture can be meaningfully associated with each other. As mentioned above, in various embodiments, the image capture device 310 and the wireless detector device 320 can be positioned approximately ten feet or less apart, but other distances are contemplated. The image capture device 310 and the wireless detector device 320 can be contained in a single housing or can be separated in different housings. In various embodiments, the image capture device 310 and the wireless detector device 320 can communicate with each other to capture information at substantially the same time or to capture information within a particular time period);
identifying one or more unknown targets based on the at least one visual identifier associated with each of the one or more unknown targets, the at least one electronic signature associated with each of the one or more unknown targets, or a combination thereof, and one or more prior known factors associated with the target ([0062], Fig. 10, At block 1006, the operation processes the image containing the vehicle(s) to extract one or more license plate identifiers corresponding to the vehicle(s). At block 1008, the operation stores in an electronic storage at least one record associating the license plate identifier(s) with the wireless device identifier(s). At block 1010, the operation identifies, based on the at least one record, one of the wireless device identifiers that is to serve as a surrogate for or an alternative to one of the license plate identifiers. [0059-0061], Fig. 9, vehicle's properties 904 can form a fingerprint or signature for a vehicle whose license plate identifier 902 is unknown. In various embodiments, certain properties can be mandatory properties that are required for the vehicle fingerprint/signature to be used in place of an unknown license plate identifier, such as, for example, vehicle color, make, model, and type. In accordance with aspects of the present disclosure, when a vehicle signature/fingerprint is designated to be used in place of a license plate identifier, any aspects described herein relating to a license plate identifier also applies to a vehicle signature/fingerprint. For example, in relation to FIG. 7, a wireless device identifier that has the highest occurrence in data records having the same vehicle fingerprint/signature, can be designated as a surrogate for or as an alternative to the vehicle fingerprint/signature. In relation to FIG. 8, patterns indicative of tracking a vehicle through a parking structure can be applied to vehicle fingerprints/signatures in the same manner that they are applied to license plate identifiers); and
tracking one or more targets of interest based on real-time updated captures associated with the one or more targets of interest ([0044], Fig. 3, the image capture device 310 and the wireless detector device 320 can be associated with a location identifier, which in the example of FIG. 3 is designated as LOC 10 UNIT 5. In various embodiments, an image capture device 310 and a related wireless detector device 320 can both be associated with the same location identifier. [0052-0053], Figs. 6-7, As data records accumulate over time, it becomes more likely that patterns can become recognizable, [0035-0036], Fig. 1, the parking structure 100 includes an entrance 102 and various sensor devices 120-126, which will be described in more detail later herein. One sensor device 120 is located at or near the entrance 102, and other sensor devices 122-126 are located in other areas of the parking structure 100. each sensor device 120-126 includes a sensor device housing, an image capture device such as a camera, and a wireless detector device).
Neff fails to teach in response to a input of a selected time period or range, generate and display one or more listings of electronic signatures and/or visual identifiers received at various collection systems within the inputted time period/range, one or more maps or images showing movements of targets or convoys within the inputted time period based on their electronic signatures and/or visual identifiers received at the collection systems, or combinations thereof.
In the same filed of endeavor, Herman teaches in response to a input of a selected time period or range, generate and display one or more listings of electronic signatures and/or visual identifiers received at various collection systems within the inputted time period/range ([0043-0044], DNN 400 can process input image and environmental data 402 by sampling video sensors 210 each in turn periodically and processing the input image and environmental data 402 to sample the environment around vehicle 110. The environment around vehicle 110 can be sampled periodically in this fashion to provide cognitive maps 414 with a complete view of the environment around vehicle 110 covered by fields of view 220 of video sensors 210. In examples of techniques described herein, where a plurality of vehicles are driving in proximity to vehicle 110, DNN 400 can be used to estimate the most probable driving behaviors of the human or ADAS drivers of the plurality of vehicles and a path polynomial for operating vehicle 110 per some increment of time into the future. This would then be updated by the interactions of vehicle 110 with the plurality of vehicles to predict the next time step of the trajectories of the plurality of vehicles up to the desired future time duration (e.g. 0.5 seconds time steps and up to 10 seconds into the future). This would provide information of the most probable path polynomial of the plurality of vehicles and allow computing device 115), one or more maps or images showing movements of targets or convoys within the inputted time period based on their electronic signatures and/or visual identifiers received at the collection systems, or combinations thereof ([0027], Computing device 115 can input an image acquired by a video sensor 210 and environmental data including the identity and environmental data of a second vehicle included in the image, including which video sensor 210 acquired the image, and when the image was acquired, into a deep neural network. Computing device 115 can process image and environmental data using computing resources physically located in vehicle 110 or computing resources physically located at server computer 120, for example. The identity can include the identity of a human driver likely to be driving the second vehicle. The deep neural network is programmed based on the identity of the human driver by programming a style bank portion of a deep neural network with a style vector that describes the driving style of a human driver. A style bank and style vectors are described more fully in relation to FIG. 5, below. The style bank transforms image and environmental information from an encoding convolutional neural network into predictions of second vehicle motion to output to a decoding convolutional neural network to produce path polynomials with probabilities of occurrence, where a most likely path polynomial is accompanied by left and right less likely path polynomials as described in relation to FIG. 6).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate cross-correlation of data to predict probable paths of devices in the sensing environment, as taught in Herman, in the system of Neff, in order to enhance motion planning and avoid future collisions and path disruptions.
Regarding Claim 13, Neff, modified by Herman, teaches the invention of Claim 12 above, Neff further comprising wherein the real-time captures associated with the one or more targets of interest include updated captures of the visual identifiers and electronic signatures from electronic devices associated with the one or more targets of interest at successive times and locations ([0056], Referring now to FIG. 8, there is shown another set of records related to tracking a vehicle through a parking structure. With reference also to FIG. 1, and as mentioned above, the parking structure 100 includes various sensor devices 120-126 located in different locations of the parking structure 100, including a sensor device 120 located at the entrance 102 to the parking structure 100. The sensor devices 120-126 can capture information when there is motion in their respective fields of view, and a single vehicle that enters and travels through the parking structure 100 can be captured by multiple sensor devices. For example, a vehicle can be first captured by a sensor device 120 upon entering the entrance 102, then captured by a sensor device 122 upon proceeding from the entrance 102 to the driving lane 112, and then captured by a sensor device 124 upon proceeding through the driving lane 112 and parking in one of the parking bays 110. A progression of such information captures provides records having time stamps that are close in time and having location identifiers that may be sequential, as shown in FIG. 8).
Regarding Claim 14, Neff, modified by Herman, teaches the invention of Claim 12 above, Neff further comprising comparing the captured visual identifiers associated with the target of interest with identifying information for known targets of interest; and wherein tracking the one or more targets of interest comprises collecting one or more of the visual identifiers, electronic signatures, or a combination thereof, associated with the target of interest at a series of collection stations positioned at selected locations throughout a geographic area, and plotting movement of the target of interest throughout the geographic area ([0057], a storage and processing system can process data records to identify patterns that indicate the tracking of a vehicle travelling through a parking structure, such as the patterns embodied in FIG. 8. In various embodiments, the pattern can include a particular license plate identifier being captured by different sensor devices within a predetermined period of time, such as within a thirty-second or one-minute period of time, or another period of time. In various embodiment, the pattern can include a particular license plate identifier being captured by a sequence of adjacent sensor devices. [0058], a storage and processing system can process the records shown in FIG. 8 to determine whether a wireless device identifier can be identified as a surrogate for or as an alternative to a license plate identifier. In various embodiments, when a pattern indicative of tracking a vehicle through a parking structure is identified, a wireless device identifier that appears in all records of the identified pattern can be designated as a surrogate for or as an alternative to the reference license plate identifier).
Regarding Claim 15, Neff, modified by Herman, teaches the invention of Claim 14 above, Neff further comprising wherein the identifying information for known targets of interest includes vehicle identifiers comprising one or more of a license plate number, stickers, patterns, position(s) of component parts, after-market added parts, damage, other markings, or combinations thereof ([0051], a data record can include multiple wireless device identifiers as well as multiple license plate identifiers. In various embodiments, a data record can include information about a vehicle in addition to a license plate identifier, such as color, make, model, and vehicle type, among other things. An example of using such additional information to form a “signature” for an unidentified vehicle).
Regarding Claim 16, Neff, modified by Herman, teaches the invention of Claim 13 above, Neff further comprising wherein the non-unique characteristics comprise a frequency of occurrence, relative representation, signal type, signal receipt location diversity, and signal strength profiling, and wherein filtering the captured electronic signals associated with each target in view of the one or more non-unique characteristics of the captured electronic signals further comprises determining whether a relative certainty value exceeds a prescribed threshold, wherein the relative certainty value is based on determination of one or more captured electronic signals being associated with the identified target of interest in view of the non-unique characteristics ([0052-0054], In the example of FIG. 6, if the license plate identifier FMK-321 has the most occurrences in the records, then wireless device identifier A0:B1:C2:D3:E4:F5 can be designated to be a surrogate or alternative to license plate identifier FMK-321. In the example of FIG. 7, if the wireless device identifier A0:B1:C2:D3:E4:F5 has the most occurrences in the records, then wireless device identifier A0:B1:C2:D3:E4:F5 can be designated to be a surrogate or alternative to license plate identifier FMK-321. In various embodiments, the determination can be subject to an occurrence threshold, such that an association of a license plate identifier with a wireless device identifier is created only if the highest occurrence occurs more than a particular number of times).
Regarding Claim 20, Neff, modified by Herman, teaches the invention of Claim 12 above, Neff further comprising wherein tracking the one or more targets of interest comprises determining a location of a selected target, an association of the selected target to one or more persons, association of the target to one or more locations, travel patterns of the selected target, or combinations thereof ([0056-0058], Referring now to FIG. 8, there is shown another set of records related to tracking a vehicle through a parking structure. With reference also to FIG. 1, and as mentioned above, the parking structure 100 includes various sensor devices 120-126 located in different locations of the parking structure 100, including a sensor device 120 located at the entrance 102 to the parking structure 100. The sensor devices 120-126 can capture information when there is motion in their respective fields of view, and a single vehicle that enters and travels through the parking structure 100 can be captured by multiple sensor devices. For example, a vehicle can be first captured by a sensor device 120 upon entering the entrance 102, then captured by a sensor device 122 upon proceeding from the entrance 102 to the driving lane 112, and then captured by a sensor device 124 upon proceeding through the driving lane 112 and parking in one of the parking bays 110. A progression of such information captures provides records having time stamps that are close in time and having location identifiers that may be sequential, as shown in FIG. 8).
Claims 9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Neff et al (US 2021/0192943), and further in view of Kario et al (US 2024/0163402).
Regarding Claims 9 and 17, Neff, modified by Herman, teaches all aspects of the invention according to Claims 1 and 12 above, except the following, which in analogous art, Kario teaches wherein the intelligence system further comprises a user interface configured to display one or more of visual identifiers and electronic signatures associated with each of the identified targets of interest, relationships between the identified targets of interest and one or more electronic devices associated with the electronic signatures, or predicted routes of the targets of interest ([0195], Fig. 2, The surveillance unit software 200 comprises a communications engine 202 that communicates with, and/or is operably connected to, both a user interface engine 204 and a user interface 206. The user interface engine 204 is configured to communicate with, and/or is operably connected to, a system manager module 208. The system manager module is configured to communicate with, and/or is operably connected to, the following: a vision sensors processing engine 210, an audio sensors processing engine 212, a radio sensors processing engine 214, and a repository 216 for data storage. The individual processing engines 210, 212, and 214 are also each configured to communicate with, and/or are operably connected to, the repository 216. [0199], a user interface engine (such as user interface engine 204) may be configured to convert data received from the communications engine into data that can be used by a system manager (such as system manager module 208). For example, the user interface engine may manage the user interface (such as user interface 206) and enable the user to control the surveillance unit, set up operating parameters, start and stop the surveillance unit, and the like. [0083] The computer executable instructions further defining a user interface engine configured to generate and display a user interface for the surveillance device).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the user interface engine with display of sensor information, as taught in Kario, in the system of Neff and Herman, in order to enable the user to control the surveillance unit, set up operating parameters, and the like.
Claims 10 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Neff et al (US 2021/0192943), in view of Craig (US 2011/0267222).
Regarding Claim 10, Neff, modified by Herman, teaches all aspects of the invention according to Claim 1 above, except the following, which in analogous art, Craig teaches wherein one or more of the collection systems are configured to analyze a signal value of each captured electronic signal, a strength of each captured electronic signal, a spectrum of each captured electronic signal, embedded identification data of each captured electronic signal, or combinations thereof; determine whether each of the captured electronic signals are from likely-unrelated sources; and if one or more of the captured electronic signals are determined to be from likely-unrelated sources, filter out the one or more captured electronic signals ([0039], Target, e.g., a vehicle or electronic signal producer, 104 can include a mechanism that produces and unintentionally transmits electromagnetic radiation. Many electronic devices and circuits emit some signature electromagnetic radiation. Most vehicles that use electricity in some form are very noisy in parts of the radio frequency spectrum. The present inventor recognized this property of vehicles, e.g., aircraft and boat motors, and developed the structures and methods described herein to capitalize on such properties. The present inventor recognized this property of some electronic and electrical devices, e.g., radio transceivers, radio emitters, circuits that form part of device, etc. Moreover, the present inventor recognized that types of motors, vehicles, aircraft, and boats would have unique radio frequency signature that could be stored in detector structures described herein. A detector, as described herein, can passively sense these stray signals, filter the unique signal from background noise, identify the target, e.g., a vehicle, based at least in part of the stray signal, and locate the position of the target also based at least in part on the stray signal).
It would have been obvious to one having ordinary skill in the art before the effective filing date to incorporate the filtering of captured signals to determine those from likely-unrelated sources, as taught in Craig, in the system of Neff and Herman, in order to better recognize the unique device signatures of target devices amongst background noise and undesired signals.
Regarding Claim 18, Neff, modified by Herman, teaches all aspects of the invention according to Claim 12 above, except the following, which in analogous art, Craig teaches wherein filtering the captured electronic signals in view of one or more non-unique characteristics of the captured electronic signals comprises analyzing a signal value of each captured electronic signal, strength of each captured electronic signal, a spectrum of each captured electronic signal, embedded identification data of each captured electronic signal, or combinations thereof, and determining whether each of the captured electronic signals are from likely-unrelated sources ([0039], Target, e.g., a vehicle or electronic signal producer, 104 can include a mechanism that produces and unintentionally transmits electromagnetic radiation. Many electronic devices and circuits emit some signature electromagnetic radiation. Most vehicles that use electricity in some form are very noisy in parts of the radio frequency spectrum. The present inventor recognized this property of vehicles, e.g., aircraft and boat motors, and developed the structures and methods described herein to capitalize on such properties. The present inventor recognized this property of some electronic and electrical devices, e.g., radio transceivers, radio emitters, circuits that form part of device, etc. Moreover, the present inventor recognized that types of motors, vehicles, aircraft, and boats would have unique radio frequency signature that could be stored in detector structures described herein. A detector, as described herein, can passively sense these stray signals, filter the unique signal from background noise, identify the target, e.g., a vehicle, based at least in part of the stray signal, and locate the position of the target also based at least in part on the stray signal).
It would have been obvious to one having ordinary skill in the art before the effective filing date to incorporate the filtering of captured signals to determine those from likely-unrelated sources, as taught in Craig, in the system of Neff and Herman, in order to better recognize the unique device signatures of target devices amongst background noise and undesired signals.
Regarding Claim 19, Neff as modified by Herman and Craig teaches all aspects of the invention according to Claim 18 above, wherein Craig further teaches wherein filtering the captured electronic signals in view of one or more non-unique characteristics of the captured electronic signals is conducted at one or more of the collection systems ([0039], Target, e.g., a vehicle or electronic signal producer, 104 can include a mechanism that produces and unintentionally transmits electromagnetic radiation. Many electronic devices and circuits emit some signature electromagnetic radiation. Most vehicles that use electricity in some form are very noisy in parts of the radio frequency spectrum. The present inventor recognized this property of vehicles, e.g., aircraft and boat motors, and developed the structures and methods described herein to capitalize on such properties. The present inventor recognized this property of some electronic and electrical devices, e.g., radio transceivers, radio emitters, circuits that form part of device, etc. Moreover, the present inventor recognized that types of motors, vehicles, aircraft, and boats would have unique radio frequency signature that could be stored in detector structures described herein. A detector, as described herein, can passively sense these stray signals, filter the unique signal from background noise, identify the target, e.g., a vehicle, based at least in part of the stray signal, and locate the position of the target also based at least in part on the stray signal).
It would have been obvious to one having ordinary skill in the art before the effective filing date to incorporate the filtering of captured signals to determine those from likely-unrelated sources, as taught in Craig, in the system of Neff and Herman, in order to better recognize the unique device signatures of target devices amongst background noise and undesired signals.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Katz et al (US 2022/0013008) teaches information received from each connected road user typically includes the user's location (in geographic coordinate system), speed, acceleration, bearing, past and predicted future trajectory, similarly to the parameters calculated from the data received from the sensor 102. A class (e.g., private car, bus, pedestrian, etc.) and/or identification (e.g., V2X digital certificate, license plate number, etc.) of a road user may also be received via the V2X communication module 103. ([0050]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARGARET G WEBB whose telephone number is (571)270-7803. The examiner can normally be reached M-F 9:00-6:00 PM.
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/MARGARET G WEBB/ Primary Examiner, Art Unit 2641
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