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 .
Response to Amendment
Prior to examination, claims 1-27 have been canceled.
Priority
This application claims priority to U.S. Provisional Patent Application No. 63/229,672 filed August 5, 2021.
This application claims priority to Patent Cooperation Treaty Application No. PCT/IL2022/050845 filed August 4, 2022.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/04/2024 and 01/23/2025 were filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because it appears figure 2 is in grayscale. Examiner suggests providing corrected drawings in black and white. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
In addition to Replacement Sheets containing the corrected drawing figure(s), applicant is required to submit a marked-up copy of each Replacement Sheet including annotations indicating the changes made to the previous version. The marked-up copy must be clearly labeled as “Annotated Sheets” and must be presented in the amendment or remarks section that explains the change(s) to the drawings. See 37 CFR 1.121(d)(1). Failure to timely submit the proposed drawing and marked-up copy will result in the abandonment of the application.
Specification
The disclosure is objected to because of the following informalities:
The use of the terms Wi-Fi, Bluetooth, Near-Field Communication (NFC), ZigBee, and LoRa, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Appropriate correction is required.
Claim Objections
Claims 28, 32, 39, 40-41, 43, and 47 objected to because of the following informalities:
Regarding Claims 28 and 41 the Examiner has noted the use of the clause “adapted to”. Examples of such claim language raise a question as to the limiting effect of the language in a claim. The claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. Notably, limitations recited after the phrases will be considered optional to the functionality of the claimed system. It is suggested to positively and concretely define the functionality of the claimed invention.
Regarding Claim 32, limitation “connected vehicles” is repeated twice.
Regarding Claim 40, Use of the term Wi-Fi, Bluetooth, Near-Field Communication (NFC), ZigBee, and LoRa which is are a trade name or a mark used in commerce. See Specification section above for usage of trade names and marks in patent applications.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 28-47 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors, see MPEP 2175.
For exemplary reasons:
Claim 39 recites “a wireless data carrying signal in any frequency and any communication protocol”, this rejected under 112 (b) because “any frequency” makes the claim indefinite.
Claim 47 recites “wherein the wireless transceiver are replaced by wireless receivers”, this rejected under 112 (b) for being indefinite and vague. It is unclear how the wireless transceivers are replaced with receivers.
The Examiner suggests reviewing all claims to correct grammar and indefiniteness issues to ensure clarity.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 28-30, 33-36, 43-47 are rejected under 35 U.S.C. 103 as being unpatentable over Leung (US20120163206A1 and Leung hereinafter) in view of Shen (US 20170111760 A1 and Shen hereinafter).
Regarding Claim 28, Leung discloses
a system (“FIG. 1 and 8 illustrates detecting user movement in a predefined area according to one embodiment. As illustrated in FIG. 8, the network of sensors 10 includes the sensors 800A-N. One or more of the sensors 800A-N detect a pedestrian at a physical location X1 (e.g., a retail establishment) based on signals emitted from one or more devices 810 carried by the pedestrian. ” [0073] and Fig 8) for obtaining location data (“At operation 1010, one or more sensors 800 receive a first signal from a mobile electronic device that has a unique identifier (e.g., a MAC address, etc.). The mobile electronic device may be a WiFi device, a Bluetooth device, a cellular device, or other radio device. Flow then moves to operation 1015 where the location of the device is determined based on the signal. “,[0079] and FIG 10, elements 1010 & 1015), based on identifiers transmitted from mobile devices (“receive a first signal from a mobile electronic device that has a unique identifier (e.g., a MAC address, etc.).”[0079]), comprising:
a) a plurality of stationary or moving (“the network of sensors 10 are located in a predefined area such as a commerce district”[0036]) wireless transceivers (“a network of one or more sensors (i.e., wireless transceiver) FIGs 1 (element 10), 2 (elements 34, 36, 38) & FIG 8 (element 800A-N) [0031]) being deployed in selected predetermined locations in sites of interest (“One or more of the sensors 800A-N detect a pedestrian at a physical location X1 (i.e. a predetermined location) (e.g., a retail establishment)“,[0073] and Fig 8 element 800A-N) and
adapted to receive and collect wireless signals transmitted during communication by mobile devices of users being within the vicinity of each wireless transceiver (“One or more of the sensors 800A-N detect a pedestrian at a physical location X1 (e.g., a retail establishment) based on signals emitted from one or more devices 810 carried by the pedestrian.” [0073] and Fig 8) which is in communication range (“At operation 1010, one or more sensors 800 receive a first signal from a mobile electronic device that has a unique identifier (e.g., a MAC address, etc.). The mobile electronic device may be a WiFi device, a Bluetooth device, a cellular device, or other radio device. Flow then moves to operation 1015 where the location of the device is determined based on the signal. In one embodiment, the location is estimated based upon the range of the sensor and the relative signal strength with the device. “,[0079] and Fig 10 (element 1010)),
where a processor in each wireless transceiver is adapted to process the signals in predefined protocols ("FIG. 1 the network of sensors 10 include multiple sensors that each detect wireless signals from a set of mobile electronic devices (e.g., WiFi enabled devices, cellular phones, Bluetooth enabled devices, etc. based on the capability of each sensor 10) when located in range of the sensors 10."[0036]) and
extract the identifiers of each transmitting device that were defined during a configuration process (“The extrapolate additional MAC address(es) process 426 (FIG 4) extracts and correlates MAC address(es) from the detected device and calculates a range of MAC addresses for a particular device. Exemplary operations for associating unique identifiers (e.g., MAC addresses) of a single device will be described with reference to FIG. 11.“,[0053] FIG 4 element 426 and FIG 11 (element 1120));
b) a memory or a database (“The data collection 12 (i.e. a database) stores the data received from the network of sensors 10. ”[0038] Fig 1), being in wired or wireless data communication with said plurality of wireless transceivers ("each of the sensor in the network of sensors 10 transmits its collected data to the data collection 12 via a wired or wireless data communication channel",[0037], FIG 2 elements 40, 42, and 44), for storing the extracted identifiers and/or the collected raw data from all wireless transceivers that are transmitted (“a data processing center 5 that includes a data collection store 12 that stores and processes the data collected by the network of sensors 10” [0031] FIG 1) and in along with their corresponding timestamps or source information (“The collected data from the network of sensors includes one or more of the following for each detected signal of each device: Media Access Control (MAC) address(es), signal strength, time of detection, and unique identifier (if different than the MAC address(es)).)“,[0038] and Fig 1, elements 45 and 47); and
Leung doesn’t explicitly teach
c) a data analysis module for accessing said a memory or a database and performing predefined analytics on the extracted identifiers and/or the collected raw data, to find correlations between identifiers, users and mobile devices and obtaining location data to identify the location and movement patterns of said users over time, based on said correlations.
However in a similar endeavor Shen teaches
c) a data analysis module for accessing said a memory or a database (“The mobility services server 45 (i.e. memory/database) forwards the aggregated data (including timestamp information, device identification, and location information) to the location data analysis server 47 (i.e. data analysis module)”[0021]) and performing predefined analytics on the extracted identifiers and/or the collected raw data, to find correlations between identifiers ("the location data analysis server 47 analyzes and aggregates the received plurality of individual wireless mobile device information to represent trends in crowd movement.”[0021] and Fig 1 element 47 and Fig 3 element 47), users and mobile devices ("A routine comprises location data for particular mobile wireless devices that has been consolidated into a single record, table, list, etc., based on the mobile device identifier. For a plurality of mobile devices, each device has a corresponding unique signal identifier (e.g., such as a MAC address) that may be used to group location data. (i.e. to find correlations between users/devices)"[0027]) and obtaining location data (“At operation 1010, location data from signals transmitted by a plurality of mobile wireless devices in a wireless network is obtained, wherein the plurality of mobile wireless devices are moving within a predefined space, and wherein the location data comprises a plurality of location data time points.”[0047] FIG 10]) to identify the location and movement patterns of said users over time, based on said correlations (“The mobility services server 45 forwards the aggregated data (including timestamp information, device identification, and location information) (i.e. user location data) to the location data analysis server 47, and the location data analysis server 47 analyzes and aggregates the received plurality of individual wireless mobile device information to represent trends in crowd movement (i.e. identify correlations between identifiers).“,[0021]).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Leung with the method suggested by Shen. The motivation is to collect and analyze data thus efficiently monitor crowd dynamics in a complex environment such as large number of individuals in an open or relatively open area, see Shen at [0003].
Regarding Claim 29, Leung in view of Shen, Leung-Shen hereinafter, teaches all the limitations of claim 28 as described above.
Further Leung teaches
in which the predefined analytics are performed using one or more of the following:
machine learning;
artificial intelligence (Al);
deep learning; and
signal processing (“the system includes the following: a network of one or more sensors 10 that collects data from mobile electronic devices of pedestrians when in signal range, a data processing center 5 that includes a data collection store 12 that stores and processes the data collected by the network of sensors 10“,[0031] FIG 1.)
Regarding Claim 30, Leung-Shen teaches all the limitations of claim 28 as described above.
Leung doesn’t explicitly teach
in which the data analysis module resides on a computational cloud or on remote servers
In a similar endeavor Shen teaches
in which the data analysis module resides on a computational cloud or on remote servers (“Moreover, the functions of the mobility services server 45 and the location data analysis server 47 may be implemented by one or more applications running in a cloud/data center environment. (i.e. remote server)“,[0022]).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Leung with the method suggested by Shen. The motivation is to collect and analyze data thus efficiently monitor crowd dynamics in a complex environment such as large number of individuals in an open or relatively open area, see Shen at [0003].
Regarding Claim 32, Leung-Shen teaches all the limitations of claim 28 as described above. Further, Leung teaches
in which the mobile devices are one or more of the following:
smartphones; - tablets; - connected vehicles; - wearable devices; - drones; - cameras; - connected vehicles; and -IoT devices (“a smartphone"[0042]).
Regarding Claim 33, Leung-Shen teaches all the limitations of claim 28 as described above. Further Leung teaches
wherein correlations between identifiers over time are used to obtain information about the location and movements of users, vehicles, drones and any connected devices in the areas of interest (“One or more of the sensors 800A-N detect a pedestrian at a physical location X1 (e.g., a retail establishment) based on signals emitted from one or more devices 810 carried by the pedestrian. For example, the location of the device may be determined based on the signals emitted from the device(s) 810 and the device location can be correlated with the physical location. The location of the pedestrian may be determined as previously described. The device location may also be correlated with the demographic attribute data associated with the physical location X1. Sometime later, one or more of the sensors 800A-N detect the pedestrian at a physical location X2 based on signals emitted from one or more devices 810 carried by the pedestrian. The device location may also be correlated with the demographic attribute data associated with the physical location X1. Based on these two locations, the movement of the pedestrian can be determined. “,[0073], FIG 8 elements 810).
Regarding Claim 34, Leung-Shen teaches all the limitations of claim 28 as described above.
Leung doesn’t explicitly teach
wherein the calculation of directions is performed when the identifiers of the same mobile device were received by several wireless transceivers
In a similar endeavor Shen teaches
wherein the calculation of directions is performed when the identifiers of the same mobile device were received by several wireless transceivers (“ At operation 415, the location data collection and aggregation logic 140 obtains location data from each mobile wireless device, the data comprising location readings from APs for each mobile wireless device. The mobility services server 45 aggregates received location data for each mobile device (e.g., based on an identifier, such as a MAC address) from a collection (i.e. several) of APs (i.e. wireless transceivers), and provides corresponding routines to location data analysis and subroutine aggregation logic 235 for analysis.“,[0033] and "subroutines are associated with a direction, e.g., a direction of an individual mobile wireless device user"[0029]).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Leung with the method suggested by Shen. The motivation is to collect and analyze data thus efficiently monitor crowd dynamics in a complex environment such as large number of individuals in an open or relatively open area, see Shen at [0003].
Regarding Claim 35, Leung-Shen teaches all the limitations of claim 28 as described above. Further Leung teaches wherein the extracted identifiers allow tracking the location and movements of a particular user or vehicle (Sometime later, one or more of the sensors 800A-N detect the pedestrian at a physical location X2 based on signals emitted from one or more devices 810 carried by the pedestrian. The device location may also be correlated with the demographic attribute data associated with the physical location X1. Based on these two locations, the movement of the pedestrian can be determined. “,[0073], FIG 8 elements 810).
Regarding Claim 36, Leung teaches all the limitations of claim 28 as described above. Further Leung teaches wherein the correlation between identifiers of different users allows:
detecting that said identifiers have the same movement pattern and determining that a particular smartphone belongs to a particular driver (Flow then moves to operation 1125 where it is determined whether there are unique device identifiers that have been received at different sensor types that are sequential. For example, a smartphone may include a WiFi transceiver, a cellular transceiver, and/or a Bluetooth transceiver, which each have their own unique MAC address that are often sequential. If there are, then flow moves to operation 1130 and those unique device identifiers are associated as belonging to the same mobile electronic device. “,[0081] FIG 11 elements 1125 & 1130); and
detecting that said users met each other, for how long and at which location (“In one embodiment, those unique device identifiers are stored in a device profile created for the device. The device profile may also include other items such as demographic attribute information, dwell time in retail location(s), ratio of in versus out of retail location(s), history of visit data, etc… In addition to determining whether unique device identifiers are sequential, the operations may also include determining whether those sequential device identifiers were detected in close proximity of time (e.g., within one hour, a day, etc.). A long period of time between detecting a unique device identifier that is sequential to another detected identifier increases the chances that the identifiers are on separate devices “,[0081-0082]).
Regarding Claim 41, Leung-Shen teaches all the limitations of claim 28 as described above.
Further Leung teaches
wherein the wireless transceiver comprises: at least one RF receiver module having appropriate hardware and operating software that are adapted to receive the wireless transmissions in different frequency bands (“The sensors 10 (i.e. wireless transceiver) may include Radio Frequency (RF) receivers 36 that detect RF signals produced by cell phones 30. “,[0043]);
Regarding Claim 42, Leung-Shen teaches all the limitations of claim 28 as described above.
Further Leung teaches
wherein the analyzed data includes RSSI level emitted from a mobile device that allows estimating the distance from a particular wireless transceiver that measures the signal strength, estimating the location and direction of movement using triangulation (“In one embodiment, the location is estimated based upon the range of the sensor and the relative signal strength (i.e. RSSI level) with the device. In one embodiment, the location is obtained by triangulating multiple signals received at multiple sensors from the same device.“,[0079] and Fig 10).
Regarding Claim 43, Leung-Shen teaches all the limitations of claim 28 as described above. Further Leung teaches
comprising wireless receivers (“At operation 1110, multiple sensors 10 of different sensor types (e.g., WiFi detector, RF receiver, Bluetooth receiver) receive multiple signals. “,[0080] and FIG 2 elements 34, 36, 38) which receive and collect data traffic (“a network of one or more sensors 10 that collects data from mobile electronic devices of pedestrians when in signal range, “,[0031] and Fig 10 (element 1010)),
Leung doesn’t explicitly teach
while communicating with each other, with the database and with the data analysis module via wired communication channels.
In a similar endeavor Shen teaches
while communicating with each other, with the database and with the data analysis module via wired communication channels (“Each of the APs 10-1 through 10-N are connected to a network (wired or wireless) which typically includes local area network and wide area network connectivity.“,[0020] FIG 1 elements 10-N, 30, and 47).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Leung with the method suggested by Shen. The motivation is to collect and analyze data thus efficiently monitor crowd dynamics in a complex environment such as large number of individuals in an open or relatively open area, see Shen at [0003].
Regarding Claim 44, Leung-Shen teaches all the limitations of claim 28 as described above.
Leung doesn’t explicitly teach
further adapted to generate data logs and alerts, based on events that are identified during performing analytics by the data analysis module
Further Shen teaches
further adapted to generate data logs and alerts, based on events that are identified during performing analytics by the data analysis module (“a visual representation (i.e. data log) of aggregated crowd movement and corresponding alerts are overlaid onto a map of the physical area begin monitored, providing a manner to easily assess aggregate crowd movement.“,[0016]).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Leung with the method suggested by Shen. The motivation is to collect and analyze data thus efficiently monitor crowd dynamics in a complex environment such as large number of individuals in an open or relatively open area, see Shen at [0003].
Regarding Claim 45, Leung-Shen teaches all the limitations of claim 28 as described above. Further Leung teaches
wherein all identifiers are encrypted before storing them and analytics are performed on the encrypted values (“…the sensors 10 encrypt the data (e.g., the unique identifiers such as the MAC addresses) and transmit the encrypted data to the data collection 12.“,[0052] and (" In at least certain embodiments, pedestrian traffic is passively detected, pedestrian traffic is tracked anonymously (e.g., the unique identifier may be encrypted), and pedestrian traffic can be detected and analyzed in real-time."[0093]), while still being able to correlate between them (" the associate unique identifier process 438 associates (i.e. correlates) the anonymized identifier(s) of the device (e.g., an encrypted MAC address of the device) with the unique identifier of the pedestrian, which may be stored in the profile associated with the device"[0058]).
Regarding Claim 46, Leung-Shen suggests all the limitations of claim 28 in method form rather than system form. Further Leung discloses
a method ([0004] ”The present invention is a method and apparatus to track pedestrian traffic and analyze the data”). Therefore, the rejection of claim 28 applies equally as well to the limitations of claim 46.
Regarding Claim 47, Leung-Shen teaches all the limitations of claim 46 as described above. Further Leung teaches
wherein the wireless transceiver are replaced by wireless receivers which are connected to other receivers and/or to the database via a wired connection ("each of the sensor in the network of sensors 10 transmits its collected data to the data collection 12 (i.e. database) via a wired or wireless data communication channel",[0037], FIG 2 element 40),
Claims 31 and 37-39 are rejected under 35 U.S.C. 103 as being unpatentable over Leung-Shen in view of Dumas (US 20190244498 A1 and Dumas hereinafter).
Regarding Claim 31, Leung-Shen teaches all the limitations of claim 28 as described above.
Further Leung teaches
shopping malls (“retail establishments” [0032]);
traffic junctions (“For example, visualizations include: clusters of pedestrian traffic, movement by pedestrians, extrapolation of demographics of pedestrians, dwell time of a retail location, ratio of in versus out of a retail location, and effectiveness of influencing traffic from the prediction engine”[0070]).
Leung-Shen doesn’t explicitly teach
transportation centers.
However in a similar field of endeavor Dumas teaches
transportation centers (“The camera system's recognition software can be utilized to process a person as he or she moves in or near a crowded location, such as a stadium or venue or an airport (i.e. transportation center)“,[0157]);
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Shen with the method suggested by Dumas. The motivation would be so the wireless device tracking system may track and locate wireless devices carried by a person or carried in a car as the person is visiting a venue, see Dumas at Abstract.
Regarding Claim 37, Leung-Shen teaches all the limitations of claim 28 as described above.
Leung-Shen doesn’t explicitly teach
wherein the identifiers of different users allow analyzing and detecting which type and model of the mobile device are owned by each user
However in a similar field of endeavor Dumas teaches
wherein the identifiers of different users allow analyzing and detecting which type and model of the mobile device are owned by each user (“The wireless device tracking system may also determine the identities 922a, 922b of both mobile devices, e.g., “iPhone 7” and “Disposable.”“,[0181]Figs 9A-9D).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Shen with the method suggested by Dumas. The motivation would be so security systems that are located at the venue can be used to identify security threats at the venue, see Dumas at [0003].
Regarding Claim 38, Leung-Shen teaches all the limitations of claim 28 as described above.
Leung doesn’t explicitly teach
collecting and analyzing data and identifiers in order to profile the presence and movements of users in crowded areas.
However in a similar endeavor Shen teaches
collecting (“At operation 1010, location data from signals transmitted by a plurality of mobile wireless devices in a wireless network is obtained”[0047] FIG 1010) and analyzing data and identifiers in order to profile the presence and movements of users in crowded areas (“The mobility services server 45 forwards the aggregated data (including timestamp information, device identification, and location information) to the location data analysis server 47, and the location data analysis server 47 analyzes and aggregates the received plurality of individual wireless mobile device information to represent trends in crowd movement.“,[0021]).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Leung with the method suggested by Shen. The motivation is to collect and analyze data thus efficiently monitor crowd dynamics in a complex environment such as large number of individuals in an open or relatively open area, see Shen at [0003].
Leung-Shen doesn’t explicitly teach
wherein in smart cities that are networked with deployed cameras, a correlation between identifiers of different users and vehicles that were captured by different cameras allows: detecting which user traveled in which vehicle and at what time.
However in a similar field of endeavor Dumas teaches
wherein in smart cities that are networked with deployed cameras (Some of the systems or components (i.e. cameras) of the security system 100 may be installed on a support structure 102, such as a cell tower, building wall or structure (i.e. where the cameras are deployed within the smart city), a mobile phone mast, or a base station) [0033] Fig 1, Fig 7 elements 700 and 716 and Figs 9A-9D), a correlation between identifiers of different users and vehicles that were captured by different cameras allows: detecting which user traveled in which vehicle and at what time (“(i) a wireless device tracking system that includes the various antennas and wireless access points shown in FIG. 1; (ii) a server system that includes the integration server 114; and (iii) a camera system 112. The security system can operate the wireless device tracking system and the server system to track and/or locate the mobile devices 118a-118d located in vehicles 116a-116c or carried by people visiting the venue (i.e. smart city).“,[0033] Fig 1, Fig 7 elements 700 and 716 and Figs 9A-9D);
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Shen with the method suggested by Dumas. The motivation would be so the wireless device tracking system may track and locate wireless device carried by a person or carried in a car as the person is visiting a venue, see Dumas at Abstract.
Regarding Claim 39, Leung-Shen teaches all the limitations of claim 28 as described above. Further Leung teaches
wherein the identifiers are selected from the group consisting of:
a wireless data carrying signal in any frequency and any communication protocol FIG. 1 the network of sensors 10 include multiple sensors that each detect wireless signals from a set of mobile electronic devices (e.g., WiFi enabled devices, cellular phones, Bluetooth enabled devices, etc. based on the capability of each sensor 10) “,[0036]);
a MAC address(“. The collected data from the network of sensors includes one or more of the following for each detected signal of each device: Media Access Control (MAC) address(es), signal strength, time of detection, and unique identifier (if different than the MAC address(es)).)“,[0038]);
sequence numbers (“MAC address (i.e. Sequence numbers in the MAC header)“,[0038]);
IMEI (“International Mobile Equipment Identity (IMEI) of the device“,[0045]);
IMSI (“International Mobile Subscriber Identity (IMSI) of the device“,[0045]);
user identifiers (“unique identifier (if different than the MAC address(es))“,[0038]);
signal strength (“ signal strength,“,[0038]);
However Leung-Shen doesn’t explicitly teach
an IP address;
traffic identifiers;
cookies;
RSSI.
In a similar field of endeavor Dumas teaches
an IP address (“This VLAN creation causes a log file to be created that may contain the target or tracking criteria information, MAC address, IP address, channel, location and date or time stamp“,[0076]);
traffic identifiers (“the target tracking module 708 can classify the target as a vehicle and the license plate recognition module 716 can start analyzing license plate information (i.e. traffic identifiers) obtained by the target tracking module 708.”[0135]);
cookies (“The offline website data is stored in the mobile device and may include cache, history, cookies, and browser history information.“,[0064]);
RSSI (“The tracked mobile device can send its RSSI to the wireless access points, so that the digital and telemetry system server can determine the location of the mobile device based off of the mobile device's RSSI“,[0050]).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Shen with the method suggested by Dumas. The motivation would be so security systems that are located at the venue can be used to identify security threats at the venue, see Dumas at [0003].
Claim 40 is rejected under 35 U.S.C. 103 as being unpatentable over Leung-Shen in view Volkerink et al. (US 12373660 B2 and Volkerink hereinafter)
Regarding Claim 40, Leung-Shen teaches all the limitations of claim 28 as described above. Further Leung teaches
Cellular ("FIG. 1 the network of sensors 10 include multiple sensors that each detect wireless signals from a set of mobile electronic devices (e.g., WiFi enabled devices, cellular phones, Bluetooth enabled devices, etc.” [0036])";
WiFi ("FIG. 1 the network of sensors 10 include multiple sensors that each detect wireless signals from a set of mobile electronic devices (e.g., WiFi enabled devices, cellular phones, Bluetooth enabled devices, etc.” [0036])";
Bluetooth ("FIG. 1 the network of sensors 10 include multiple sensors that each detect wireless signals from a set of mobile electronic devices (e.g., WiFi enabled devices, cellular phones, Bluetooth enabled devices, etc.” [0036])";
However Leung doesn’t explicitly teach
Near-Field Communication (NFC);
ZigBee;
LoRa;
In a similar field of endeavor Volkerink teaches
Near-Field Communication (NFC) ("a near field communication (NFC) scanner using an NFC protocol"[Col. 10, lns.9-10]);
ZigBee ("ZigBee communication systems"[Col. 17, lns. 36-41]); and
LoRa ("RF communication systems (e.g., LoRa)"[Col. 17, lns. 36-41]).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Shen with the method suggested by Volkerink. The motivation would be to seamlessly and accurately bridge different identification methodologies to enable advanced real-time tracking, see Volkerink at Abstract.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Alonso (US 20230164742 A1) which teaches the first signals to be received by a plurality of access points; operate in a receive mode during a second period of time to receive second signals from the plurality of access points; log device identifiers of the plurality of access points.
Powers (US 11181903 B1) which teaches the network nodes accurately and timely detects, identifies, locates, and responds to an unmanned aircraft system within a predetermined area.
Babu (US 20210320731 A1) which teaches receiving communication from each of the plurality of sensors for evaluation and determining the location of the asset coupled with the module by evaluating the communication received from each of the plurality of sensors.
Liu (US 20170105099 A1) which teaches Location data is obtained from signals transmitted by a first plurality of mobile wireless devices in a wireless network, wherein the first plurality of mobile wireless devices are moving within a predefined space, and wherein the location data comprises a plurality of location data time points, each location data time point including a timestamp, a unique mobile wireless device identifier, and location information indicating where in the predefined space an associated mobile wireless device is located.
Ogihara (US 20220067198 A1) which teaches leveraging collection and analysis of anonymized biological data, location data, individual IDs and time data from groups of individuals within a surrounding environment. The anonymized data can be combined with sources of map data and available historical data to help provide context about the surrounding environment of the users and stored for analysis.
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/IYONDA L LEWIS/Examiner, Art Unit 2647
/NIZAR N SIVJI/Primary Examiner, Art Unit 2647