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 .
Response to Amendment
The amendment filed on July 27, 2026, in response to the Non-Final Office Action mailed on March 26, 2026, has been entered. Claims 28-47 remain pending for examination. Examiner formally withdraws the rejection under 112 and the objections to the claims, drawings and specification.
Response to Arguments
Applicant’s arguments, see pages 12-19, filed on July 27, 2026, with respect to the rejection of claims 28-47 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Dumas.
Drawings
The corrected drawings for Figure 2 were received on July 27, 2026. These drawings are acceptable.
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 28-39, 41-43, and 45 are rejected under 35 U.S.C. 103 as being unpatentable over Leung (US20120163206A1 and Leung hereinafter) in view of Dumas (US 20190244498 A1 and Dumas hereinafter).
Regarding Claim 28, Leung suggests 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 profiling the presence and movements of users in crowed areas (i.e. clusters of pedestrian traffic (i.e. crowed areas), 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.) Para [0070], 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
receives and collects 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 Wi-Fi 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)),
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 identifiers extracted from all wireless transceivers (“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);
c) a data analysis module for accessing said memory or database and performing predefined analytics on the identifiers, to find a correlation between identifiers of different users, to detect that these 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 the newly added limitation “wherein a processor in a corresponding wireless transceiver processes the signals in one or more predefined communication protocols and extracts, for each transmitting device which is in communication range, a Received Signal Strength Indicator (RSSI) identifier to obtain location data relative to the corresponding transceiver and at least one additional identifier selected from the group of a wireless data-carrying signal transmitted in predetermined frequency and communication protocol, a MAC address, an IP address, an IMEI, an IMSI, traffic identifiers, cookies, sequence numbers and user identifiers of each transmitting device that were defined during a configuration process, along with an associated timestamp.”.
Although Leung teaches “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)).” Para [0038] and “where a relative distance between the sensor and the device is associated based on the relative signal strength of the device (a higher signal strength typically indicates that the device is closer to the sensor than a relatively lower signal strength).” Para [0076].
Leung doesn’t explicitly teach
wherein a processor in a corresponding wireless transceiver processes the signals in one or more predefined communication protocols and extracts, for each transmitting device which is in communication range, a Received Signal Strength Indicator (RSSI) identifier to obtain location data relative to the corresponding transceiver and at least one additional identifier selected from the group of a wireless data-carrying signal transmitted in predetermined frequency and communication protocol, a MAC address, an IP address, an IMEI, an IMSI, traffic identifiers, cookies, sequence numbers and user identifiers of each transmitting device that were defined during a configuration process, along with an associated timestamp.
However, in a similar field of endeavor Dumas suggests
wherein a processor in a corresponding wireless transceiver processes the signals in one or more predefined communication protocols and extracts, for each transmitting device which is in communication range, a Received Signal Strength Indicator (RSSI) identifier to obtain location data relative to the corresponding transceiver (i.e. The basic location services of the wireless access points are the location abilities of the digital system. 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.) Para [0050] and at least one additional identifier selected from the group of a wireless data-carrying signal transmitted in predetermined frequency and communication protocol, a MAC address, an IP address (i.e. 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.) Para [0076], an IMEI, an IMSI, traffic identifiers, cookies (i.e. The offline website data is stored in the mobile device and may include cache, history, cookies, and browser history information.) Para [0064], sequence numbers and user identifiers of each transmitting device that were defined during a configuration process, along with an associated timestamp (i.e. The digital and telemetry system server 214 may recognize that it is tracking a mobile device in same space or time with both the digital system and the telemetry system, and the information from both of these systems show a mobile device in the same space or time so the proper information can be saved to the system marker.) Para [0050].
Dumas also suggests (i.e. the digital and telemetry system server 214 stores in the system marker digital network related information for the mobile device such as date or time stamp of entrance or exit of network, device RSSI levels, Access Point (AP) communication or location information, MAC address, IDFA, web or browser or digital offline website data, and time inside the security system's network. The digital and telemetry system server 214 may also save telemetry system related information for the mobile devices in the network footprint. The telemetry system information may include verified location information collected by the security system for the entire time the mobile device was being tracked.) Para [0091].
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 teaching of Leung with the teaching 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 29, Leung in view of Dumas, Leung-Dumas 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-Dumas 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 Dumas teaches
in which the data analysis module resides on a computational cloud or on remote servers (i.e. The reflected signals are analyzed by the digital and telemetry system server 214 to locate and track the mobile devices.) Para [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 teaching of Leung with the teaching suggested by Dumas. The motivation is so the wireless device tracking system may track and locate a wireless device carried by a person or carried in a car as the person is visiting a venue, see Dumas at Abstract.
Regarding Claim 31, Leung-Dumas 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 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 teaching of Leung with the teaching suggested by Dumas. The motivation is so the wireless device tracking system may track and locate a wireless device carried by a person or carried in a car as the person is visiting a venue, see Dumas at Abstract.
Regarding Claim 32, Leung-Dumas 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-Dumas teaches all the limitations of claim 28 as described above. Further Leung teaches
The data analysis module finds correlations between identifiers, users, and mobile device to identify movement patterns of the users over time and to obtain information about the location and movements of users, vehicles, drones and connected devices in the sites 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-Dumas 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 Dumas teaches
wherein the calculation of directions is performed when the identifiers of the same mobile device were received by several wireless transceivers (i.e. The digital and telemetry system server 214 may have SDKs that obtains information from the telemetry system, the digital system, and the camera system to track a single device…The server system can be configured to determine directional information for the mobile devices. When the digital and telemetry system server 214 recognizes the telemetry system and the digital system have a match, it may begin tracking a user's mobile device and storing all the data on the system marker for the session. The system recognizes that it has a match when it begins to constantly follow the mobile device with the telemetry system, achieve a soft handshake to get the MAC Address, offline website data, and digital system location, such as location information derived from the mobile device RSSI. The telemetry system may input location data. The user's system marker information may be stored under her MAC address and available for future use.) Para [0088-0089].
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 teaching of Leung with the teaching suggested by Dumas. The motivation is so the wireless device tracking system may track and locate a wireless device carried by a person or carried in a car as the person is visiting a venue, see Dumas at Abstract.
Regarding Claim 35, Leung-Dumas 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 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 37, Leung-Dumas teaches all the limitations of claim 28 as described above.
Leung 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 teaching of Leung with the teaching suggested by Dumas. The motivation is so the wireless device tracking system may track and locate a wireless device carried by a person or carried in a car as the person is visiting a venue, see Dumas at Abstract.
Regarding Claim 38, Leung-Dumas teaches all the limitations of claim 28 as described above.
Leung 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; and
collecting and analyzing data and identifiers in order to profile the presence and movements of users in crowded areas collecting and analyzing data and identifiers in order to profile the presence and movements of users in crowded areas.
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);
collecting and analyzing data and identifiers in order to profile the presence and movements of users in crowded areas collecting and analyzing data and identifiers in order to profile the presence and movements of users in crowded areas (i.e. The camera system assists the wireless access points and telemetry systems by assigning each person with a mobile device an appropriate system marker. 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. As the camera system begins to track a person the system tags the system marker with the location information. When a server determines that a location information from the camera system matches the location information from the digital and telemetry systems, as described above, then the digital and telemetry system server can be notified of the camera system's system markers in queue.) Para [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 teaching of Leung with the teaching suggested by Dumas. The motivation is so the wireless device tracking system may track and locate a 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-Dumas 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 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]);
Leung 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 teaching of Leung with the teaching suggested by Dumas. The motivation is so the wireless device tracking system may track and locate a wireless device carried by a person or carried in a car as the person is visiting a venue, see Dumas at Abstract.
Regarding Claim 41, Leung-Dumas teaches all the limitations of claim 28 as described above.
Further Leung teaches
wherein each of the wireless transceiver comprises: at least one RF receiver module having appropriate hardware and operating software that 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]); and a local identifiers' extraction module, which analyzes the collected wireless data and extracts the identifiers of all mobile devices in range from collected data traffic (i.e. a set of one or more extrapolate data processes 56 are performed to extrapolate data from each raw data per sensor 48. For example, the extrapolate data process 56 may include the extract manufacturers process 420, the extract access points process 422, the encrypt unique identifier(s) process 424, and the extrapolate additional MAC addresses 426. The results of the reduced sample size process 54 and the extrapolate data process(es) 56 are stored in the filtered sensor data 58 (per sensor).) Para [0049].
Regarding Claim 42, Leung-Dumas teaches all the limitations of claim 28 as described above.
Further Leung teaches
wherein the extracted RSSI identifier allows estimating distance from the corresponding transceiver by measuring signal strength 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-Dumas 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)), while communicating with each other, with the database and with the data analysis module via wired communication channels (i.e. Sometime after detecting a wireless signal, each of the sensor (i.e. receivers) in the network of sensors 10 transmits its collected data to the data collection 12 via a wired or wireless data communication channel. ) Para [0037].
Regarding Claim 45, Leung-Dumas 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]).
Claim 40 is rejected under 35 U.S.C. 103 as being unpatentable over Leung-Dumas in view Volkerink et al. (US 12373660 B2 and Volkerink hereinafter)
Regarding Claim 40, Leung-Dumas 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])";
WiFiTM ("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 TM ("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])".
Leung-Dumas don’t explicitly teach
Near-Field Communication (NFC);
ZigBee TM;
LoRa TM;
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 TM ("ZigBee communication systems"[Col. 17, lns. 36-41]); and
LoRa TM ("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 teaching of Leung-Dumas with the teaching 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.
Claims 44 and 46-47 are rejected under 35 U.S.C. 103 as being unpatentable over Leung (US20120163206A1 and Leung hereinafter) in view of Dumas (US 20190244498 A1 and Dumas hereinafter) and further in view of Shen (US 20170111760 A1 and Shen hereinafter).
Regarding Claim 44, Leung-Dumas teaches all the limitations of claim 28 as described above.
Leung and Dumas don’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
However, in a similar field of endeavor 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 teaching of Leung-Dumas with the teaching 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 46, Leung-Dumas suggests all the limitations of claim 28 in method form rather than system form. Further Leung teaches
a method ([0004] ”The present invention is a method and apparatus to track pedestrian traffic and analyze the data”)
Leung and Dumas don’t explicitly teach
based on said correlations, obtaining location data to identify the location and movement patterns of said users over time.
However in a similar field of endeavor Shen suggests
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, the rejection of claim 28 applies equally as well to the limitations of claim 46.
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 teaching of Leung-Dumas with the teaching 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 47, Leung-Dumas in view of Shen, hereinafter Leung-Dumas-Shen, teaches all the limitations of claim 46 as described above. Further Leung teaches
wherein the wireless transceivers are replaced by transceivers with 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).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 Iyonda L. Lewis whose telephone number is (571)272-4440. The examiner can normally be reached Monday - Friday 8:00am - 4:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alison Slater can be reached at (571) 270-0375. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IYONDA L LEWIS/Examiner, Art Unit 2647
Iyonda.Lewis@USPTO.gov
/Alison Slater/Supervisory Patent Examiner, Art Unit 2647
Prosecution Insights