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 . In communications filed on 06/23/2026. Claims 1, 7, 11, and 15 is amended. Claims 13, and 14 are cancelled. Claims 1-12, and 15-20 are pending in this examination.
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 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. This examination is in response to US Patent Application No. 17/940,677.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 has been entered.
Response to Arguments
Applicant's arguments filed 06/23/2026 have been fully considered but they are not persuasive:
Applicant submits on pages 7-9 of remarks filed on 06/23/2026 regarding claims 1 that the proposed combination of Boncyk and Baxley further fails to teach or suggest : “wherein the electronic signals of the captured electronic signature information do not include the images associated with each of the targets “, and “ manage security of the captured data, users, and lists of the target identifiers through application of a domain structure’, and “wherein the database is configured to remove or purge data classified as non-interest data after a selected period of time”.
Examiner respectfully disagrees with applicant argument for claim 1 filed on 06/23/2026 on pages 7-9 of remarks.
Baxley discloses:
wherein the electronic signals of the captured electronic signature information do not include the images associated with each of the targets [¶28, A network of sensors can collect radio frequency signals ( equated to electronic signature information do not include the images) . A network of signal processing engines can process those collected signals to identify, geolocate, group, determine intent of, and classify wireless devices in the area. Video streams may be co-processed along with the radio frequency information. Databases can manage and leverage libraries of signal and attack information. Security administrators may use a visualization console to monitor for wireless security threats and to visualize images or videos overlaid with radio frequency intelligence], and [0030] FIG. 1 is a block diagram depicting an electromagnetic environment and signature analysis system in accordance with one or more embodiments presented herein. Wireless devices 110A-110F may each engage in one or more modes of radio communication thereby generating electromagnetic signals. The technology presented herein can collect and analyze these signals. Sensors 120A-120E positioned within collection areas 105A-105E can collect and report radio frequency signals within the surrounding electromagnetic environment. A signal analysis system 130 can process the collected radio frequency signals.
manage security of the captured data, users, and lists of the target identifiers through application of a domain structure [ ¶28, A network of sensors can collect radio frequency signals. A network of signal processing engines can process those collected signals to identify, geolocate, group, determine intent of, and classify wireless devices in the area. Video streams may be co-processed along with the radio frequency information. Databases can manage and leverage libraries of signal and attack information. Security administrators may use a visualization console to monitor for wireless security threats and to visualize images or videos overlaid with radio frequency intelligence], and [¶¶38, 48, 229].
wherein the database is configured to remove or purge data classified as non-interest data after a selected period of time[¶209, According to certain embodiments, wireless devices 110 associated with a visitor may be detected when their electromagnetic personas are determined. As the visitor presents identification to an attendant or security officer, the visitor's identification information can be combined with the electromagnetic persona information to create a visitor super-persona. The visitor super-persona can persist in the system indefinitely or until purged according to data retention policies. If an electromagnetic persona associated with the visitor reappears on a subsequent occasion after the visit, rapid identification of the person carrying the wireless device 110 associated with the electromagnetic persona may be supported providing an improved security awareness].
Examiner Maintain the rejection.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 11-12, and 15-19, are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. (US2015/0205868) issued Boncyk filed in IDS 02/03/2023, and in view of US Patent No. (US2016/0124071) issued to Baxley.
Regarding claim 1, the system comprising: a plurality collection system, each of the collection systems including one or more sensors configured to: capture, data associated with a plurality of targets; wherein the captured data includes one or more target identifiers for each target of the plurality of targets: wherein the one or more target identifiers comprise visual identifiers, electronic signature information, or a combination thereof:
Boncyk discloses: [¶¶42, 63, The information transmitted to the server can comprise any relevant information (collection systems) regarding the contents of the image... the device captures one or more of single images, multiple images, motion imagery, and/or video (each and all of these information types are known henceforth as "imagery"). Indeed, the imagery can be captured by more than one electronic imaging device (equated to plurality of collection systems from plurality of targets), such as a digital camera, a camera-equipped mobile telephone, or a security camera, or multiple such devices (equated to one or more sensors with identifying information). An object or objects are identified in the imagery via image/object recognition techniques (software and/or hardware) (equated to plurality of targets). The identity of the object(s) is used to look up, in a table/database, a set of text keywords search terms, which are then provided to a search engine. The search engine returns information addresses (e.g., in the form of a web page with hyperlinks) that are pertinent to the objects identified in the imagery. The user then accesses information and/or computing resources based upon at least one of the information addresses], and [see FIG.4 and corresponding text for more details, ¶77]; and
wherein at least some of the collection systems include one or more cameras or camera system are configured to capture the visual identifiers of each target including images associated with each target of the plurality of targets
Boncyk discloses: [¶79, After the camera 103 captures the digital imagery of the target object 100, image preprocessing 104 software converts the digital imagery into image data 105 for transmission to and analysis by an identification server 106], and [¶63, The device captures one or more of single images, multiple images, motion imagery, and/or video (each and all of these information types are known henceforth as "imagery"). Indeed, the imagery can be captured by more than one electronic imaging device, such as a digital camera (produces electronic signal), a camera-equipped mobile telephone (produces electronic signal), or a security camera (produces electronic signal), or multiple such devices (equated to one or more sensors). An object or objects are identified in the imagery via image/object recognition techniques (software and/or hardware). The identity of the object(s) is used to look up, in a table/database, a set of text keywords search terms, which are then provided to a search engine. The search engine returns information addresses (e.g., in the form of a web page with hyperlinks) that are pertinent to the objects identified in the imagery. The user then accesses information and/or computing resources based upon at least one of the information addresses], and [¶92, It is usually desirable that the database 108 be scalable to enable identification of the target object 100 from a very large plurality (for example, millions) of known objects in the database 108. The algorithms, software, and computing hardware must be designed to function together to quickly perform such a search. An example software technique for performing such searching quickly is to use a metric distance comparison technique for comparing the image data 105 to data stored in the database 108, along with database clustering and multi-resolution distance comparisons], and [¶90]; and
an intelligence system comprising: one or more processors; one or more memories in communication with the one or more processors and one or more correlation and search engines including programming that, when accessed by the one or more processors is configured to
Boncyk discloses: [¶¶34, a Distal server 420(an intelligence system) ... The algorithms, software, and computing hardware must be designed to function together to quickly perform such a search], and [¶63, An object or objects are identified in the imagery via image/object recognition techniques (software and/or hardware). The identity of the object(s) is used to look up, in a table/database, a set of text keywords search terms, which are then provided to a search engine. The search engine returns information addresses (e.g., in the form of a web page with hyperlinks) that are pertinent to the objects identified in the imagery. The user then accesses information and/or computing resources based upon at least one of the information addresses], and [¶92, It is usually desirable that the database 108 be scalable to enable identification of the target object 100 from a very large plurality (for example, millions) of known objects in the database 108. The algorithms, software, and computing hardware must be designed to function together to quickly perform such a search. An example software technique for performing such searching quickly is to use a metric distance comparison technique for comparing the image data 105 to data stored in the database 108, along with database clustering and multi-resolution distance comparisons] and [¶¶82, 88, 90]; and
Boncyk discloses a database configured to: receive one or more of the data associated with each target and one or more correlations from the one or more correlation and search engines [¶63, The device captures one or more of single images, multiple images, motion imagery, and/or video (each and all of these information types are known henceforth as "imagery"). Indeed, the imagery can be captured by more than one electronic imaging device, such as a digital camera (produces electronic signal), a camera-equipped mobile telephone (produces electronic signal), or a security camera (produces electronic signal), or multiple such devices (equated to one or more sensors). An object or objects are identified in the imagery via image/object recognition techniques (software and/or hardware). The identity of the object(s) is used to look up, in a table/database, a set of text keywords search terms, which are then provided to a search engine. The search engine returns information addresses (e.g., in the form of a web page with hyperlinks) that are pertinent to the objects identified in the imagery. The user then accesses information and/or computing resources based upon at least one of the information addresses], and [¶92, It is usually desirable that the database 108 be scalable to enable identification of the target object 100 from a very large plurality (for example, millions) of known objects in the database 108. The algorithms, software, and computing hardware must be designed to function together to quickly perform such a search. An example software technique for performing such searching quickly is to use a metric distance comparison technique for comparing the image data 105 to data stored in the database 108, along with database clustering and multi-resolution distance comparisons], and [¶76, another technique employed to maximize speed is data indexing. This technique involves using a priori knowledge of where data resides to only search in those parts of the database that contain potential matches. Various forms of indexing may be used, such as hash tables, data compartmentalization (i.e., data within certain value ranges are stored in certain locations), data sorting, and database table indexing. An example of such techniques is, in the shape comparison algorithm, if a database is to be searched for an entry with an area with a value of A, the algorithm would know which database entries or data areas have this approximate value and would not need to search the entire database], and [¶¶ 90, 93]; and
Boncyk discloses classify the data based on the one or more correlations [¶92, It is usually desirable that the database 108 be scalable to enable identification of the target object 100 from a very large plurality (for example, millions) of known objects in the database 108. The algorithms, software, and computing hardware must be designed to function together to quickly perform such a search. An example software technique for performing such searching quickly is to use a metric distance comparison technique for comparing the image data 105 to data stored in the database 108, along with database clustering and multi-resolution distance comparisons]; and
Boncyk discloses store one or more of the data, the one or more correlations, or one or more classifications [¶63, The device captures one or more of single images, multiple images, motion imagery, and/or video (each and all of these information types are known henceforth as "imagery"). Indeed, the imagery can be captured by more than one electronic imaging device, such as a digital camera (produces electronic signal), a camera-equipped mobile telephone (produces electronic signal), or a security camera (produces electronic signal), or multiple such devices (equated to one or more sensors). An object or objects are identified in the imagery via image/object recognition techniques (software and/or hardware). The identity of the object(s) is used to look up, in a table/database, a set of text keywords search terms, which are then provided to a search engine. The search engine returns information addresses (e.g., in the form of a web page with hyperlinks) that are pertinent to the objects identified in the imagery. The user then accesses information and/or computing resources based upon at least one of the information addresses], and [¶9, It is usually desirable that the database 108 be scalable to enable identification of the target object 100 from a very large plurality (for example, millions) of known objects in the database 108. The algorithms, software, and computing hardware must be designed to function together to quickly perform such a search. An example software technique for performing such searching quickly is to use a metric distance comparison technique for comparing the image data 105 to data stored in the database 108, along with database clustering and multi-resolution distance comparisons]; and
Boncyk discloses according to one or more of partitioning, complex indexing, or pre-search statistical processing [¶76, another technique employed to maximize speed is data indexing. This technique involves using a priori knowledge of where data resides to only search in those parts of the database that contain potential matches. Various forms of indexing may be used, such as hash tables, data compartmentalization (i.e., data within certain value ranges are stored in certain locations), data sorting, and database table indexing. An example of such techniques is, in the shape comparison algorithm, if a database is to be searched for an entry with an area with a value of A, the algorithm would know which database entries or data areas have this approximate value and would not need to search the entire database], and [¶93]; and
Boncyk discloses wherein the data comprises high volumes of data received substantially continuously and in real-time [¶¶72, 78, The terminal 102 is a computing device that has an "image" capture device such as digital camera 103, a video camera, or any other device that a convert a physical object into a digital representation of the object. The imagery can be a single image, a series of images, or a continuous video stream. For simplicity of explanation this document describes the digital imagery generally in terms of a single image, however the invention and this system can use all of the imagery types described above]; and
Boncyk discloses and wherein the data includes one or more of time, date, and location data.
[¶71, The image can then be analyzed to determine the location, size, and nature of the symbols in the decode symbol 28. The symbols are preferably analyzed according to their type, and their content information is extracted. For example, barcodes and alphanumeric characters will result in numerical and/or text information], and [ see Claims 15,16,30]; and
Boncyk discloses and a user interface configured to: retrieve a subset of the data based on one or more of the time, date, and location data based on a user request [¶48, It should also be appreciated that there are embodiments in which the search engine never communicates with the portable device. For example, the server might do the search query, get results, and provide them to the portable device, or even to a television or other device besides the portable device.], and [Claims 15,16,30], and
Boncyk discloses generate one or more representations based on retrieved subsets of data to thereby enable targeted tracking and analysis[¶16, The present invention provides apparatus, systems and methods in which: (a) a digital photograph, video, MPEG, AVI, or other image is captured using a camera equipped cell phone, PDA, or other image capturing device; (b) key words or other search criteria are automatically extracted or derived from image; (c) the search criteria are submitted to a Search Engine to obtain information of interest; and (d) at least a portion of the resulting information is transmitted back locally to, or nearby, the device that captured the image], and [0079] After the camera 103 captures the digital imagery of the target object 100, image preprocessing 104 software converts the digital imagery into image data 105 for transmission to and analysis by an identification server 106. Typically, a network connection is provided capable of providing communications with the identification server 106. Image data 105 is data extracted or converted from the original imagery of the target object 100 and has information content appropriate for identification of the target object 100 by the object recognition 107, which can be software or hardware. Image data 105 can take many forms, depending on the particular embodiment of the invention. Specific examples are given in the priority documents].
A surveillance system for data and security management
While Boncyk discloses this limitation as: [Abstract, Search terms are derived automatically from images captured by a camera equipped cell phone, PDA, or other image capturing device, submitted to a search engine to obtain information of interest, and at least a portion of the resulting information is transmitted back locally to, or nearby, the device that captured the image], and [¶63, The device captures one or more of single images, multiple images, motion imagery, and/or video (each and all of these information types are known henceforth as "imagery"). Indeed, the imagery can be captured by more than one electronic imaging device, such as a digital camera, a camera-equipped mobile telephone, or a security camera(surveillance), or multiple such devices...].
Boncyk does not explicitly disclose, however Baxley discloses A surveillance system
[¶41, The sensors 120, or the antennas associated therewith, may be physically distributed around an area under surveillance. The collective coverage provided by the sensors 120 may define the effective extent of the area under surveillance], and [ see FIG. 7, Area under surveillance].
Boncyk does not explicitly disclose, however, Baxley discloses wherein at least some of the collection systems are configured to capture the electronic signature information of each of the targets including electronic signals emanating from (a) each target or (b) one or more electronic devices associated with each target; wherein the electronic signals of the captured the electronic signature information do not include the images associated with each of the target [ ¶28, A network of sensors can collect radio frequency signals ( equated to electronic signature information do not include the images) . A network of signal processing engines can process those collected signals to identify, geolocate, group, determine intent of, and classify wireless devices in the area. Video streams may be co-processed along with the radio frequency information. Databases can manage and leverage libraries of signal and attack information. Security administrators may use a visualization console to monitor for wireless security threats and to visualize images or videos overlaid with radio frequency intelligence], and [0030] FIG. 1 is a block diagram depicting an electromagnetic environment and signature analysis system in accordance with one or more embodiments presented herein. Wireless devices 110A-110F may each engage in one or more modes of radio communication thereby generating electromagnetic signals. The technology presented herein can collect and analyze these signals. Sensors 120A-120E positioned within collection areas 105A-105E can collect and report radio frequency signals within the surrounding electromagnetic environment. A signal analysis system 130 can process the collected radio frequency signals. Position estimates 115A-115B may be established, modeled, and tested to locate the wireless devices 110A-110F within the electromagnetic environment. Video cameras 160A-160C can provide video surveillance information associated with the electromagnetic environment. A console 140 can provide a user interface for configuring, controlling, or reviewing analysis results associated with the signal analysis system 130. One or more networks 150 may interconnect some or all of the sensors 120, the signal analysis system 130, and the console 140], and [see FIG.17 and corresponding text for more details, ¶¶300-309, In block 1710, the RF and video processing module 357 can receive positions and orientations for cameras 160 and RF sensors 120…. n block 1720, the RF and video processing module 357 can receive RF persona localizations… In block 1730, the RF and video processing module 357 can receive video streams from cameras the video cameras 160… In block 1740, the RF and video processing module 357 can compute correspondences between RF personas and elements within video streams… In block 1780, the RF and video processing module 357 can overlay video with radio frequency intelligence… n Fdatabasemismatches, and potential threats…], and [¶¶32, 140-143, 301-302].
correlate captured data associated with each of the plurality of targets received from the plurality of collection systems to associate one or more of visual identifiers associated with each target with the received electronic signature information or other visual identifiers of other targets to facilitate the identification of electronic signatures identifying for each of the targets
While Boncyk discloses this limitation as: [¶79, After the camera 103 captures the digital imagery of the target object 100, image preprocessing 104 software converts the digital imagery into image data 105 for transmission to and analysis by an identification server 106], and [¶63, The device captures one or more of single images, multiple images, motion imagery, and/or video (each and all of these information types are known henceforth as "imagery"). Indeed, the imagery can be captured by more than one electronic imaging device, such as a digital camera (produces electronic signal), a camera-equipped mobile telephone (produces electronic signal), or a security camera (produces electronic signal), or multiple such devices (equated to one or more sensors). An object or objects are identified in the imagery via image/object recognition techniques (software and/or hardware). The identity of the object(s) is used to look up, in a table/database, a set of text keywords search terms, which are then provided to a search engine. The search engine returns information addresses (e.g., in the form of a web page with hyperlinks) that are pertinent to the objects identified in the imagery. The user then accesses information and/or computing resources based upon at least one of the information addresses], and [¶92, It is usually desirable that the database 108 be scalable to enable identification of the target object 100 from a very large plurality (for example, millions) of known objects in the database 108. The algorithms, software, and computing hardware must be designed to function together to quickly perform such a search. An example software technique for performing such searching quickly is to use a metric distance comparison technique for comparing the image data 105 to data stored in the database 108, along with database clustering and multi-resolution distance comparisons], and [¶90]; and [¶63, The device captures one or more of single images, multiple images, motion imagery, and/or video (each and all of these information types are known henceforth as "imagery"). Indeed, the imagery can be captured by more than one electronic imaging device, such as a digital camera (produces electronic signal), a camera-equipped mobile telephone (produces electronic signal), or a security camera (produces electronic signal), or multiple such devices (equated to one or more sensors). An object or objects are identified in the imagery via image/object recognition techniques (software and/or hardware). The identity of the object(s) is used to look up, in a table/database, a set of text keywords search terms, which are then provided to a search engine. The search engine returns information addresses (e.g., in the form of a web page with hyperlinks) that are pertinent to the objects identified in the imagery. The user then accesses information and/or computing resources based upon at least one of the information addresses], and [¶¶70-71, input image contains recognizable symbols, such as barcodes, matrix codes, or alphanumeric characters (equated to visual identifiers]; and [¶69, If the server 20 is physically separate from the device 14, then user acquired images are transmitted from the device 14 to the Image Processor/server 20 using a conventional digital network or wireless network means], and [¶90, Other object recognition techniques also exist and include methods that store 3-dimensional models (rather than 2-dimensional images) of objects in a database and correlate input images with these models of the target object is performed by an object recognition technique of which many are available commercially and in the prior art. Such object recognition techniques usually consist of comparing a new input image to a plurality of known images and detecting correspondences between the new input image and one of more of the known images. The known images are views of known objects from a plurality of viewing angles and thus allow recognition of 2-dimensional and 3-dimensional objects in arbitrary orientations relative to the camera 103], and [¶¶92, 94, 110], and [¶104, FIG. 5 shows an embodiment that uses a cellular telephone, PDA, or such portable device equipped with computational capability, a digital camera, and a wireless network connection, as the terminal 202 corresponding to the terminal 102 in FIG. 4. In this embodiment, the terminal 202 communicates with the identification server 206 and the content server 211 via networks such as a cellular telephone network and the Internet].
Examiner Note: Taken from Internet: Does a camera produce an electrical signal? At the most basic level, a camera sensor is a solid-state device that absorbs particles of light (photons) through millions of light-sensitive pixels and converts them into electrical signals. These electrical signals are then interpreted by a computer chip, which uses them to produce a digital image.
From Wikipedia:
Wi-Fi (/ˈwaɪfaɪ/)[1][a] is a family of wireless network protocols based on the IEEE 802.11 family of standards, which are commonly used for local area networking of devices and Internet access, allowing nearby digital devices to exchange data by radio waves.
Boncyk does not explicitly disclose, however Baxley discloses correlate captured data associated with each of the plurality of targets received from the plurality of collection systems to associate one or more of visual identifiers associated with each target with the received electronic signature information or other visual identifiers of other targets to facilitate the identification of electronic signatures identifying for each of the targets [ ¶28, A network of sensors can collect radio frequency signals. A network of signal processing engines can process those collected signals to identify, geolocate, group, determine intent of, and classify wireless devices in the area. Video streams may be co-processed along with the radio frequency information. Databases can manage and leverage libraries of signal and attack information. Security administrators may use a visualization console to monitor for wireless security threats and to visualize images or videos overlaid with radio frequency intelligence], and [0030] FIG. 1 is a block diagram depicting an electromagnetic environment and signature analysis system in accordance with one or more embodiments presented herein. Wireless devices 110A-110F may each engage in one or more modes of radio communication thereby generating electromagnetic signals. The technology presented herein can collect and analyze these signals. Sensors 120A-120E positioned within collection areas 105A-105E can collect and report radio frequency signals within the surrounding electromagnetic environment. A signal analysis system 130 can process the collected radio frequency signals. Position estimates 115A-115B may be established, modeled, and tested to locate the wireless devices 110A-110F within the electromagnetic environment. Video cameras 160A-160C can provide video surveillance information associated with the electromagnetic environment. A console 140 can provide a user interface for configuring, controlling, or reviewing analysis results associated with the signal analysis system 130. One or more networks 150 may interconnect some or all of the sensors 120, the signal analysis system 130, and the console 140], and [see FIG.17 and corresponding text for more details, ¶¶300-309, In block 1710, the RF and video processing module 357 can receive positions and orientations for cameras 160 and RF sensors 120…. n block 1720, the RF and video processing module 357 can receive RF persona localizations… In block 1730, the RF and video processing module 357 can receive video streams from cameras the video cameras 160… In block 1740, the RF and video processing module 357 can compute correspondences between RF personas and elements within video streams… In block 1780, the RF and video processing module 357 can overlay video with radio frequency intelligence… n block 1790, the RF and video processing module 357 can Identify RF/Visual matches, mismatches, and potential threats…], and [¶¶32, 140-143, 301-302]; and
manage security of the captured data, users, and lists of the target identifiers through application of a domain structure [ ¶28, A network of sensors can collect radio frequency signals. A network of signal processing engines can process those collected signals to identify, geolocate, group, determine intent of, and classify wireless devices in the area. Video streams may be co-processed along with the radio frequency information. Databases can manage and leverage libraries of signal and attack information. Security administrators may use a visualization console to monitor for wireless security threats and to visualize images or videos overlaid with radio frequency intelligence], and [¶¶38, 48, 229]; and
wherein the database is configured to remove or purge data classified as non-interest data after a selected period of time[¶209, According to certain embodiments, wireless devices 110 associated with a visitor may be detected when their electromagnetic personas are determined. As the visitor presents identification to an attendant or security officer, the visitor's identification information can be combined with the electromagnetic persona information to create a visitor super-persona. The visitor super-persona can persist in the system indefinitely or until purged according to data retention policies. If an electromagnetic persona associated with the visitor reappears on a subsequent occasion after the visit, rapid identification of the person carrying the wireless device 110 associated with the electromagnetic persona may be supported providing an improved security awareness].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Boncyk, by incorporating “Diverse Radio Frequency Signature, Video, And Image Sensing for Detection and Localization”, as taught by Baxley. One could have been motivated to do so in order to support co-processing radio signals and video to identify and locate a radio transmitter. Positions and orientations for cameras and RF sensors may be maintained. An RF signature associated with the radio transmitter may be received from the RF sensors to determine an RF persona. A first physical location for the radio transmitter may be estimated according to a physical radio propagation model operating on RF signals. A video stream from one or more of the cameras may be received [ Baxley, Title, Abstract].
Regarding claims 2, and 19, Boncyk does not explicitly disclose, however, disclose Baxley wherein the one or more correlation and search engines are further configured to catalogue the electronic signatures for the targets with one or more characteristics associated with the of targets [¶104, The signal database 450 may provide a library of known signals to the raw signal analysis engines 240. According to certain embodiments, a feature vector of a known signal may be matched against or correlated to a received feature vector to classify modulation types and other parameters associated with the received signal. The signal database 450 may also provide codecs and drivers to the raw signal analysis engines 240. The codecs and drivers may be used for decoding content of the received signal once its modulation type has been classified. It should be appreciated that the signal database 450 may be provided as one of the analysis databases 380], and [ ¶134], and [¶195, The electromagnetic persona engine 720 can correlates multiple radio frequency fingerprints together to establish the existence of an electromagnetic persona. For example, many current smartphone models may have multiple different types of radios, which can be in various states of operation at various times. For example, these radios might include wireless cellular 2G/3G/4G/LTE for voice and data traffic (GSM/UMTS/CDMA), Wi-Fi, Bluetooth, NFC, positioning, and so forth].
Regarding claim 3, Boncyk discloses, wherein the plurality of targets comprises persons, vehicles, or electronic devices. [¶¶39-40, the three-dimensional shape of the vehicle, an iris of a person,], and [¶64].
Regarding claim 4, Boncyk discloses, wherein the characteristics comprise one or more of geographical coordinates, time stamps, source manufacturer, source type, unique ID, or combinations thereof [¶72, For object images, one can advantageously perform a "decomposition", in the input image decomposition step 34, of a high-resolution input image into several different types of quantifiable salient parameters. This allows for multiple independent convergent search processes of the database to occur in parallel, which greatly improves image match speed and match robustness in the database matching 36. The best match 38 from either the decode symbol 28, or the image database matching 36, or both, is then determined If a specific URL (or other online address) is associated with the image, then an URL Lookup 40 is performed, and the Internet address is returned by the URL Return 42. Code examples are set forth in the priority documents, as well as further detail, including segmentation, segment group generation, bounding box generation, geometric normalization, wavelet decomposition, color cube decomposition, shape decomposition, low-resolution grayscale image generation, grayscale comparison, wavelet comparison, color cube comparison, and calculation of combined match score], and [¶¶39-40, the three-dimensional shape of the vehicle , an iris of a person], and [¶¶61, 63, 70].
Regarding claim 5, Boncyk discloses, wherein the user request includes one or more of one or more specified targets, one or more times, one or more dates, one or more locations, or one or more characteristics [¶71, The image can then be analyzed to determine the location, size, and nature of the symbols in the decode symbol 28. The symbols are preferably analyzed according to their type, and their content information is extracted. For example, barcodes and alphanumeric characters will result in numerical and/or text information], and [ see Claims 15,16,30].
Regarding claim 6, Boncyk does not explicitly disclose, however, Baxley discloses, wherein the electronic signature comprises information from one or more of Bluetooth signals, wireless signals, RFID signals, Wi-Fi signals, or cellular signals [¶140, Wireless devices 110 are generally transmitting and/or receiving radio frequency signals and thus have an electromagnetic spectrum fingerprint. The electromagnetic spectrum fingerprint may be measured as a unique signature coming from a particular radio, antenna, or other radio frequency component associated with a device. Examples of such fingerprints may include, but are not limited to, Bluetooth signals, WiFi signals, cellular signal, passive or active radio frequencyID signals, AM/FM radio signal, spurious emissions, and so forth], and [¶¶141, 143].
Regarding claim 7, Boncyk, does not explicitly disclose, however Baxley discloses, wherein non-interest data comprises vehicle, person, electronic signal, transmission, image identification data, or combinations thereof, and is removed or purged on a daily basis [¶209, According to certain embodiments, wireless devices 110 associated with a visitor may be detected when their electromagnetic personas are determined. As the visitor presents identification to an attendant or security officer, the visitor's identification information can be combined with the electromagnetic persona information to create a visitor super-persona. The visitor super-persona can persist in the system indefinitely or until purged according to data retention policies. If an electromagnetic persona associated with the visitor reappears on a subsequent occasion after the visit, rapid identification of the person carrying the wireless device 110 associated with the electromagnetic persona may be supported providing an improved security awareness].
Regarding claim 11, this claim is interpreted and rejected for the same rational set forth in claim 1.
Regarding claim 12, Boncyk discloses, wherein the high volume of data comprises greater than about one million records per day [¶41, Image 412 is contemplated to be any array of pixels. In most cases the pixels will be regularly arranged, but that is not absolutely necessary. In most cases the pixels also will number greater than 19,200 (160.times.120), such as 78,800 (320.times.240) but they can number few than that. More preferred images have greater pixel counts, including for example, 256,000 (640.times.400), more preferably at least 2 million, and even more preferably at least 4 million. It is not necessary that the image be actually constructed at the portable device. Thus, a statement that "the portable device captures an image of an object" includes situations where the device receives and derives data from light emitted or reflected from the object, even if the data is never presented to a user as a visually perceptible image, and even if the data is sent to a distal server without ever being collected into an image by the device], and ¶39].
Regarding claim 15, Boncyk does not explicitly disclose, however Baxley discloses wherein removing or purging the non-interest data after the selected period of time comprises removing or purging the non-interest data on a daily basis [¶209, According to certain embodiments, wireless devices 110 associated with a visitor may be detected when their electromagnetic personas are determined. As the visitor presents identification to an attendant or security officer, the visitor's identification information can be combined with the electromagnetic persona information to create a visitor super-persona. The visitor super-persona can persist in the system indefinitely or until purged according to data retention policies. If an electromagnetic persona associated with the visitor reappears on a subsequent occasion after the visit, rapid identification of the person carrying the wireless device 110 associated with the electromagnetic persona may be supported providing an improved security awareness].
Regarding claim 16, Boncyk discloses, wherein the database is filterable based on the one or more of time, date, and location data [¶71, The image can then be analyzed to determine the location, size, and nature of the symbols in the decode symbol 28. The symbols are preferably analyzed according to their type, and their content information is extracted. For example, barcodes and alphanumeric characters will result in numerical and/or text information], and [ see Claims 15,16,30]
Regarding claim 17, Boncyk discloses, wherein the database is accessible via a user interface [¶16 The present invention provides apparatus, systems and methods in which: (a) a digital photograph, video, MPEG, AVI, or other image is captured using a camera equipped cell phone, PDA, or other image capturing device; (b) key words or other search criteria are automatically extracted or derived from image; (c) the search criteria are submitted to a Search Engine to obtain information of interest; and (d) at least a portion of the resulting information is transmitted back locally to, or nearby, the device that captured the image.], and [see NANT claim 30].
Regarding claim 18, Boncyk discloses, wherein retrieving a subset of the data comprises specifying, via the user interface, the one or more targets [¶18, In general, the present invention provides technology and processes that can accommodate linking objects and images to information via a network such as the Internet, which require no modification to the linked object. Traditional methods for linking objects to digital information, including applying a barcode, radio or optical transceiver or transmitter, or some other means of identification to the object, or modifying the image or object so as to encode detectable information in it, are not required because the image or object can be identified solely by its visual appearance. The users or devices can even interact with objects by "linking" to them. For example, a user can link to a vending machine by "pointing and clicking" on it. His device would be connected over the Internet to the company that owns the vending machine. The company would in turn establish a connection to the vending machine, and thus the user would have a communication channel established with the vending machine and could interact with it].
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. (US2015/0205868) issued Boncyk filed in IDS 02/03/2023, and in view of US Patent No. (US2016/0124071) issued to Baxley, and in view of US Patent No. (US2011/0053559) issued to Klein filed in IDS 02/03/2023.
Regarding claim 8, Boncyk, and Baxley do not explicitly disclose, however, Klein discloses wherein the user interface connects to the database via an application program interface (API) [¶56, In some implementations of the invention, voter 102 may update the voter information, for example, voter identity and registration related information, and/or other information, included in the SIM card 630 by directly entering the updated information using the mobile device's data entry screen or other integrated data entry keyboard. This updated information may be directly communicated to third-party service API software application 110 via third-party service API software application interaction module 610. In some implementations, the updated information may be communicated by a PC which in turn may communicate the updated information to third-party service API software application 110. Third-party service API software application 110 may receive and store the updated voter information in database 106 or in device server update module 618. In some implementations, the updated voter information from the voter may be received and stored in the SIM card 630 via SIM card interaction module 616].
It would have been obvious to one of ordinary skit in the art, at the time. the invention was made, to modify the teaching of Boncyk, and Baxley to Include wherein the user interface connects to the database via an application program interface: API” as discloses by Klein to gain the advantage of providing third party application program interfaces to facilitate processing of database requests.
Regarding claim 9, Boncyk, and Baxley do not explicitly disclose, however, Klein discloses, wherein the API comprises one of a RESTful API, a SQL based API, or an XML or JSON based API [¶115, Such digital pipe into each street address in the United States or worldwide location based on physical street addresses GPS latitude and longitude coordinate data, expressed in IP-based decimal degrees that are offered as a third-party API that can be integrated into computer implemented database methods described herein. The API works using REST over HTTP internet communication protocols. The term REST stands for "Representational State Transfer" and is a stateless protocol that includes the state with every communication. Additionally, REST provides access to Web services using HTTP; for internet-based database storage clouds, REST would be used to access storage resources as services to match postal mail address to matching GPS location(s)].
It would have been obvious to one of ordinary skit in the art, at the time. the invention was made, to modify the teaching of Boncyk, and Baxley to Include wherein the API comprises one of a RESTFUL API, SQL based API or an XML, or JSON based API as disclosed by Klein to gain the advantage of including the state with every communication for matching GPS locations.
Regarding claim 10, Boncyk , and Baxley do not explicitly disclose, however, Klein discloses wherein the user interface is configured to manage, based on an associated user's position, one or more of permission to update the database, permission to remove data from the database, or permission to allow other users to access the database [¶118, These aforementioned methods for data verification to enable voting teach a trustworthy and higher level for voter processing verification of identity then online banking because identity must be verified by multiple steps tied to Postal Service mail address GPS data coordinate and range permission(s) or other rules (i.e., dates and times for permitted voting by device method) and other individual identity data points that can be set under rules and permissions stored in the computer server under control of a voting authority or vote administrator organization]..
It would have been obvious to one of ordinary skit in the art, at the time. the invention was made, to modify the teaching of Boncyk, and Baxley to Include wherein the user interface is configured to manage, based on an associated user's position, one or more of permission to update the database, permission to remove Data from the database, or permission 1G allow other users to access the database as disclosed by KLEIN, to gain the advantage of using location parameters to enhance security.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. (US US2015/0205868) issued to Boncyk (filed in IDS 02/03/2023), and in view of US Patent No. (US2016/0124071) issued to Baxley, and further in view of Hannah (US2016/0097648).
Regarding claim 20, Boncyk, and Baxley do not explicitly disclose, however, Hannah discloses wherein one or more of the collection systems include Automated License Plate Readers ("ALPR") and sensors configured to capture the electronic signature information. [¶14, The one or more sensors can be configured to provide information which is used to detect one or more vehicles travelling on the roads without authorization; the sensors can provide or obtain data such as: camera images of one or more license plates (which can be processed with known optical character recognition techniques to derive a license plate number); radio frequency (RF) signals identifying one or more vehicles], and [¶17, The enforcement sensors can be fixed roadside sensors or sensors on vehicles, such as smart license plate holders, that can, in one embodiment, receive permission codes from vehicles along the one or more routes that have access controlled by a traffic control system. For example, in one embodiment, a fixed roadside enforcement sensor can receive a permission code containing the vehicle's license plate number (or an obfuscated version of that number, such as hash of that number) and then compare that license plate number (obtained from the permission code) to the license plate number captured by the enforcement sensor (which can be captured either with a camera or by an RF signal, containing the license plate number, transmitted by a device, such as a toll transponder or smart license plate holder, on the car). If the enforcement sensor determines that the two license plate numbers (or hashes of the numbers) do match, then the enforcement sensor, which is coupled to the traffic control system, does not report a violation, but if the enforcement sensor determines that the two license plate numbers do not match then the enforcement sensor does report a violation to one or more of the traffic control system or government agencies], and [¶44, If the system is implemented without permission codes, verification of permission is performed by checking the license number and location of each vehicle on controlled roads against the access permissions contained in the central database using a network of roadside verification sensors that include automated license plate readers (e.g., cameras and optical character recognition systems)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Boncyk, and Baxley by incorporating “network of roadside verification sensors that include automated license plate readers”, as taught by Hannah. One could have been motivated to do so in order to perform permission verification by checking the license number and location of each vehicle on controlled roads against the access permissions contained in the central database [ Hannah, ¶44].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See submitted 892 for relevant references.
Nadler (US2019/0325230) [0106] According to another embodiment, the plurality of sensors 102 comprise Bluetooth® beacons to be used to determine an accurate location of a plurality of electronic devices and the associated users thereof. It will be appreciated that Bluetooth® Low Energy (BLE) based beacons have two roles, namely broadcasting (by a beacon device) and receiving (by a sensing device). In an example, a surveillance area optionally includes three Bluetooth® sensing devices for triangulation. Triangulation calculates the intersection of common points of one set of coordinates using the properties of a triangle. Herein, the electronic device associated with a user optionally serves as a Bluetooth® beacon by transmitting low frequency Bluetooth® waves. In particular, a plurality of electronic devices optionally serves as beacons by transmitting Bluetooth® energy that is received by the Bluetooth® sensing devices.
[0107] To obtain an accurate position of the electronic device, the Bluetooth® sensing devices are optionally arranged, thereby forming a triangle in the surveillance area. When in operation, the electronic device transmits a low energy Bluetooth® signal. The signal is received by a first sensing device at a distance D1, by a second sensing device at a distance D2, and by a third sensing device at a distance D3. It will be appreciated that according to the first sensing device the electronic device is in a circle of radius D1, however the exact location is not determined. With respect to the first sensing device and the second device, the electronic device is optionally present at two points obtained by intersection of the circle of radius D1 and a circle of radius D2. Furthermore, the ambiguity of position of the electronic device is solved with respect to the third sensing device by determining a single intersection point (or location) which is obtained by intersection of the circle of radius D1, the circle of radius D2 and a circle of radius D3. Such an arrangement is employed for determining an accurate location of a plurality of electronic devices and the associated users thereof, by the principle of triangulation (even when the user is in motion).
[0108] In another example, the electronic device optionally serves a Bluetooth® sensing device and receives beacon messages to indicate a presence in a surveillance area. For example, a Bluetooth® beacon is arranged at the entrance of a shopping mall. Every electronic device that passes the entrance of the mall receives a Bluetooth signal from the Bluetooth® beacon device indicating a presence of an associated user at that particular location (here, shopping mall). Optionally, the plurality of sensors is implemented as an array of audio beacons arranged in the surveillance area. Notably, the audio beacons work in a same manner to the Bluetooth® beacons. However, the audio beacons transmit inaudible frequencies such as ultrasonic waves for communication between two or more devices. It will be appreciated that the process of triangulation to determine the accurate location of the objects to be tracked is same for both Bluetooth® beacons as well as audio beacons.
[0109] Referring to FIG. 2A, the surveillance area 200A comprises a plurality of sensors and a number of objects such as vehicles, people, trees, and obstacles. Furthermore, the surveillance area 200A includes an area of interest 202 within some pre-defined coordinates. Herein, the area of interest 202 is a portion of a street in the surveillance area 200A. The area of interest 202 is determined based on a user input. Furthermore, one or more sensors are selected from the plurality of sensors that efficiently cover the area of interest 202. Herein, a first sensor 204, a second sensor 206, a third sensor 208, and a fourth sensor 210 are selected that efficiently cover the area of interest 202. Optionally, the first sensor 204 is a camera, the second sensor 206 is a microphone, the third sensor 208 is a Bluetooth® beacon, and the fourth sensor 210 is a camera placed at a different location than the first sensor 204. The data acquired from the sensors 204, 206, 208 and 210 is transmitted to the server arrangement (such as the server arrangement 104 of FIG. 1) for further processing.
Anderson (US8,531, 523) (110) In one embodiment, the image data comprises video surveillance data. The location information may be received from a network-based wireless location system configured to locate wireless devices using radio emissions from the wireless devices. In various embodiments, the wireless communications network may be a local wireless communications network and comprise one of a WiFi (IEEE 802.11), RFID, Bluetooth or Ultra-Wideband network. These local wireless communications technologies are illustrative and other technologies may be used. In other embodiments, the wireless communications network may be a wide area wireless communications network and comprise one of GSM, WiMAN, WIMAX, CDMA, and W-CDMA. These wide area wireless communications technologies are illustrative and other technologies may be used. In one embodiment, the subject may be a vehicle.
Harel (US2010/0245585) [0034] The surveillance devices 110A-N can be any system, device, and/or any combination of devices/systems that is able to capture recordings of its surrounding environment and/or the events occurring in the surrounding environment and/or nearby areas. In general, the surveillance device 110 is portable such that each unit can be installed or uninstalled and moved to another location for use by a human without assistance from others or a vehicle. In addition, the surveillance device 110 generally has a form factor that facilitates ease of portability, installation, un-installation, deployment, and/or redeployment… The surveillance devices 110A-N can operate wired or wirelessly].
[0037] The surveillance devices 110A-N can include a capture unit with image, video, and/or audio capture capabilities].
[ see FIG 2A, [0094] The processing unit 226, in one embodiment, performs audio signal processing (e.g., digital signal processing) on captured audio of the surrounding environments and the nearby events. For example, frequency analysis can be performed on the captured audio. In addition, the processing unit 226, using the location data provided by the location sensor 208, can determine the location or approximate location of the source of the sound. In one embodiment, using the audio data captured using multiple surveillance devices 210, the location of the source of the sound can be determined via triangulation].
Renkis (US2015/0381946) [ ¶47, In one preferred embodiment of the present invention, the at least one wireless ICD (input capture devices) includes two antennas for providing a wireless signal for receiving and/or transmitting data with the cloud-based analytics platform or another ICD(s). The ICDs are operable for cross-communication with each other, including data exchange, wherein the data exchange includes information about the surveillance environment, settings, inputs, and combinations thereof.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHRIAR ZARRINEH whose telephone number is (571)272-1207. The examiner can normally be reached Monday-Friday, 8:30am-5:30pm.
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/SHAHRIAR ZARRINEH/Primary Examiner, Art Unit 2496
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