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 12/05/2025 has been entered. Claims 1-20 remain pending in the application.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis
for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4-8, and 12-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by de Hoog US 20200364819 A1.
Regarding Claim 1, de Hoog teaches a method of predicting an emergency evacuation time of a facility, comprising: capturing location information from an access control system in a facility of each of a plurality of people associated with the facility; identifying one or more historical movement patterns of one or more of the plurality of people associated with the facility based at least in part on the location information from the access control system ([0019] Using collected personal data, the PVE
system [an access control system] may generate an evacuation plan that may consider factors such as a user's proximity to a dangerous condition, such as a fire, and whether the user may require a wheelchair
accessible evacuation route. The PVE system may also obtain, in real-time, venue condition data, or information about dynamic conditions at the venue, such as crowd movement patterns within the venue and adjacent areas outside of the venue, an arrival of first responders, crowd bottlenecks, temperature changes, air quality, and weather conditions. Venue condition data may be monitored by a set of recording devices and a set of sensing devices at the venue. By obtaining venue condition data and personal data, the PVE system may accurately determine a safe time and route for a user to evacuate according to the user's abilities, location, and circumstances. [0020] the PVE system may monitor one or more venue conditions during a variety of time periods so that it may use artificial intelligence and machine learning techniques predict one or more venue conditions. For example, in some embodiments the PVE system may monitor a flow rate and a flow pattern of a crowd during a non-emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during an emergency evacuation of the venue. In some embodiments, the PVE system may monitor a flow rate and a flow pattern of a crowd during a first emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during a second emergency evacuation of the venue. By monitoring venue conditions during emergency and non-emergency time periods, the PVE system may continuously develop its analyses of venue conditions and continuously improve its prediction accuracy);
identifying one or more predefined evacuation routes for the facility ([0044] The route generation submodule 330 may generate PVE plans by analyzing obtained data, predicting evacuation conditions and metrics, and ranking evacuation routes based on metrics such as safety and efficiency); and
predicting the emergency evacuation time for evacuating the facility using the one or more predefined evacuation routes in response to an evacuation event based at least in part on the one or more historical movement patterns of one or more of the plurality of people associated with the facility ([0025] during a non-evacuation period, video cameras and motion sensors may capture images and motion data of visitors, and computing devices may analyze the images and motion data to detect and characterize special mobility attributes. For example, computing devices may apply artificial intelligence and machine learning processes to determine that a visitor is pushing a stroller and to predict both an average pace of motion and a maximum pace of motion for such a visitor during an evacuation period. In some embodiments, during a non-emergency evacuation period, video cameras and motion sensors may collect data regarding the occupancy of venue areas and flow patterns of visitors in passageways and on stairways and escalators. Computing devices may analyze such data to determine where crowd bottlenecks have developed and make predictions such as a maximum pace that a group of visitors may progress on a crowded stairway, a minimum time in which the venue may be completely evacuated by all visitors, or a crowd flow pattern, during an emergency evacuation period. Thus, by monitoring venue conditions at one or more time periods, the PVE system may train itself to predict a number of evacuation conditions and evacuation metrics during the same time periods or during different time periods. Those predictions may be used by the PVE system to generate personalized venue evacuation plans that may be used for evacuation during non-evacuation periods, non-emergency evacuation periods, or emergency evacuation periods).
Regarding Claim 4, de Hoog teaches the limitations of independent claim 1, as discussed above. de Hoog further teaches predicting the emergency evacuation time for evacuating the facility using the one or more predefined evacuation routes in response to an evacuation event based at least in part on the one or more historical movement patterns of the people that are identified as being currently in the facility ([0020] the PVE system may monitor a flow rate and a flow pattern of a crowd during a first emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during a second emergency evacuation of the venue. By monitoring venue conditions during emergency and non-emergency time periods, the PVE system may continuously develop its analyses of venue conditions and continuously improve its prediction accuracy. [0025] during a non-evacuation period, video cameras and motion sensors may capture images and motion data of visitors, and computing devices may analyze the images and motion data to detect and characterize special mobility attributes. For example, computing devices may apply artificial intelligence and machine learning processes to determine that a visitor is pushing a stroller and to predict both an average pace of motion and a maximum pace of motion for such a visitor during an evacuation period. In some embodiments, during a non-emergency evacuation period, video cameras and motion sensors may collect data regarding the occupancy of venue areas and flow patterns of visitors in passageways and on stairways and escalators. Computing devices may analyze such data to determine where crowd bottlenecks have developed and make predictions such as a maximum pace that a group of visitors may progress on a crowded stairway, a minimum time in which the venue may be completely evacuated by all visitors, or a crowd flow pattern, during an emergency evacuation period. Thus, by monitoring venue conditions at one or more time periods, the PVE system may train itself to predict a number of evacuation conditions and evacuation metrics during the same time periods or during different time periods. Those predictions may be used by the PVE system to generate personalized venue evacuation plans that may be used for evacuation during non-evacuation periods, non-emergency evacuation periods, or emergency evacuation periods).
Regarding Claim 5, de Hoog teaches the limitations of independent claim 1, as discussed above. de Hoog further teaches wherein capturing location information of each of the plurality of people associated with the facility comprises capturing location information of two or more of the plurality of people associated with the facility during one or more previous evacuation events, and wherein at least one of the historical movement patterns is based at least in part on the location information captured during the one or more previous evacuation events ([0042] By sharing and using historical data 310, PVE systems of the present disclosure may, in effect, “collaborate” to develop highly effective PVE plans over time. Also, the sharing of historical data 310 may allow a newer venue to more quickly develop an effective PVE system, as the PVE system at the newer venue may benefit from machine learning correlations that have been developed by more established PVE systems, rather than developing machine learning correlations from scratch. ([0045] The PVE system may also obtain historical data such as statistical information regarding a percentage of people who rushed to a central set of escalators during a previous evacuation and a percentage of people who rushed to a staircase during a previous evacuation).
Regarding Claim 6, de Hoog teaches the limitations of independent claim 1, as discussed above. de Hoog further teaches wherein predicting the emergency evacuation time for evacuating people from the facility during the evacuation event comprises processing at least part on the captured location information using a trained Artificial Intelligence (AI) model to identify the predicted emergency evacuation time ([0020] the PVE system may monitor one or more venue conditions during a variety of time periods so that it may use artificial intelligence and machine learning techniques predict one or more venue conditions. For example, in some embodiments the PVE system may monitor a flow rate and a flow pattern of a crowd during a non-emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during an emergency evacuation of the venue. In some embodiments, the PVE system may monitor a flow rate and a flow pattern of a crowd during a first emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during a second emergency evacuation of the venue. By monitoring venue conditions during emergency and non-emergency time periods, the PVE system may continuously develop its analyses of venue conditions and continuously improve its prediction accuracy).
Regarding Claim 7, de Hoog teaches the limitations of independent claim 6, as discussed above. de Hoog further teaches wherein the trained AI model is trained to account for a number and location of people in the facility at the time of the evacuation event and crowd dynamics in the facility ([0020] the PVE system may monitor one or more venue conditions during a variety of time periods so that it may use artificial intelligence and machine learning techniques predict one or more venue conditions. For example, in some embodiments the PVE system may monitor a flow rate and a flow pattern of a crowd during a non-emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during an emergency evacuation of the venue. In some embodiments, the PVE system may monitor a flow rate and a flow pattern of a crowd during a first emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during a second emergency evacuation of the venue. By monitoring venue conditions during emergency and non-emergency time periods, the PVE system may continuously develop its analyses of venue conditions and continuously improve its prediction accuracy. [0025] during a non-evacuation period, video cameras and motion sensors may capture images and motion data of visitors, and computing devices may analyze the images and motion data to detect and characterize special mobility attributes. For example, computing devices may apply artificial intelligence and machine learning processes to determine that a visitor is pushing a stroller and to predict both an average pace of motion and a maximum pace of motion for such a visitor during an evacuation period. In some embodiments, during a non-emergency evacuation period, video cameras and motion sensors may collect data regarding the occupancy of venue areas and flow patterns of visitors in passageways and on stairways and escalators. Computing devices may analyze such data to determine where crowd bottlenecks have developed and make predictions such as a maximum pace that a group of visitors may progress on a crowded stairway, a minimum time in which the venue may be completely evacuated by all visitors, or a crowd flow pattern, during an emergency evacuation period. Thus, by monitoring venue conditions at one or more time periods, the PVE system may train itself to predict a number of evacuation conditions and evacuation metrics during the same time periods or during different time periods. Those predictions may be used by the PVE system to generate personalized venue evacuation plans that may be used for evacuation during non-evacuation periods, non-emergency evacuation periods, or emergency evacuation periods).
Regarding Claim 8, de Hoog teaches the limitations of independent claim 7, as discussed above. de Hoog further teaches wherein the number and location of people in the facility is based at least in part on one or more of the historical movement patterns of people in the facility ([0002] systems that can monitor crowd quantity, density, and flow direction, as well as systems capable of warning of potential crowd congestion based on monitored crowd data).
Regarding Claim 12, de Hoog teaches the limitations of independent claim 1, as discussed above. de Hoog further teaches wherein identifying one or more historical movement patterns of one or more of the plurality of people associated with the facility comprises processing at least part on the captured location information from the access control system using a trained Artificial Intelligence (AI) model to identify one or more historical movement patterns of one or more of the plurality of people associated with the facility ([0020] the PVE system may monitor one or more venue conditions during a variety of time periods so that it may use artificial intelligence and machine learning techniques predict one or more venue conditions. For example, in some embodiments the PVE system may monitor a flow rate and a flow pattern of a crowd during a non-emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during an emergency evacuation of the venue. In some embodiments, the PVE system may monitor a flow rate and a flow pattern of a crowd during a first emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during a second emergency evacuation of the venue. By monitoring venue conditions during emergency and non-emergency time periods, the PVE system may continuously develop its analyses of venue conditions and continuously improve its prediction accuracy).
Regarding Claim 13, de Hoog teaches the limitations of independent claim 1, as discussed above. de Hoog further teaches comprising: determining one or more updated predefined evacuation routes for the facility by predicting the emergency evacuation time for each of a plurality of possible predefined evacuation routes ([0032] the PVE system may generate a PVE route based on one or more of obtained personal data, obtained venue characteristic data, and obtained venue condition data. The PVE route may be updated continuously or intermittently to account for changes in obtained data. For example, in some embodiments, during an emergency evacuation, a PVE system may initially instruct a group of visitors to use a set of escalators to evacuate a venue, based on the set of escalators being operational and a low number of visitors detected near the set of escalators. However, at a later time, the set of escalators may stop operating due to an excessive number of visitors using them at once. As a result, sensing devices and/or recording devices near the set of escalators may indicate to the PVE system that a flowrate of the group of visitors in that area has changed, causing a bottleneck of visitors there. In response, the PVE system may begin instructing visitors to use one or more stairways to evacuate the venue).
Regarding Claim 14, de Hoog teaches the limitations of independent claim 13, as discussed above. de Hoog further teaches comprising: notifying one or more of the plurality of people associated with the facility of one or more of the updated predefined evacuation routes for the facility ([0032] the PVE system may generate a PVE route based on one or more of obtained personal data, obtained venue characteristic data, and obtained venue condition data. The PVE route may be updated continuously or intermittently to account for changes in obtained data. For example, in some embodiments, during an emergency evacuation, a PVE system may initially instruct a group of visitors to use a set of escalators to evacuate a venue, based on the set of escalators being operational and a low number of visitors detected near the set of escalators. However, at a later time, the set of escalators may stop operating due to an excessive number of visitors using them at once. As a result, sensing devices and/or recording devices near the set of escalators may indicate to the PVE system that a flowrate of the group of visitors in that area has changed, causing a bottleneck of visitors there. In response, the PVE system may begin instructing visitors to use one or more stairways to evacuate the venue).
Regarding Claim 15, de Hoog teaches an emergency evacuation prediction system for a facility, comprising: an access control system for capturing location information of each of a plurality of people associated with the facility; a controller operatively coupled to the access control system, the controller configured to: identify one or more historical movement patterns of one or more of the plurality of people associated with the facility based at least in part on the location information captured by the access control system ([0019] Using collected personal data, the PVE system may generate an evacuation plan that may consider factors such as a user's proximity to a dangerous condition, such as a fire, and whether the user may require a wheelchair accessible evacuation route. The PVE system may also obtain, in real-time, venue condition data, or information about dynamic conditions at the venue, such as crowd movement patterns within the venue and adjacent areas outside of the venue, an arrival of first responders, crowd bottlenecks, temperature changes, air quality, and weather conditions. Venue condition data may be monitored by a set of recording devices and a set of sensing devices at the venue. By obtaining venue condition data and personal data, the PVE system may accurately determine a safe time and route for a user to evacuate according to the user's abilities, location, and circumstances. [0020] the PVE system may monitor one or more venue conditions during a variety of time periods so that it may use artificial intelligence and machine learning techniques predict one or more venue conditions. For example, in some embodiments the PVE system may monitor a flow rate and a flow pattern of a crowd during a non-emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during an emergency evacuation of the venue. In some embodiments, the PVE system may monitor a flow rate and a flow pattern of a crowd during a first emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during a second emergency evacuation of the venue. By monitoring venue conditions during emergency and non-emergency time periods, the PVE system may continuously develop its analyses of venue conditions and continuously improve its prediction accuracy); and
predict an emergency evacuation time for evacuating the facility using one or more predefined evacuation routes based at least in part on the one or more historical movement patterns of one or more of the plurality of people associated with the facility ([0025] during a non-evacuation period, video cameras and motion sensors may capture images and motion data of visitors, and computing devices may analyze the images and motion data to detect and characterize special mobility attributes. For example, computing devices may apply artificial intelligence and machine learning processes to determine that a visitor is pushing a stroller and to predict both an average pace of motion and a maximum pace of motion for such a visitor during an evacuation period. In some embodiments, during a non-emergency evacuation period, video cameras and motion sensors may collect data regarding the occupancy of venue areas and flow patterns of visitors in passageways and on stairways and escalators. Computing devices may analyze such data to determine where crowd bottlenecks have developed and make predictions such as a maximum pace that a group of visitors may progress on a crowded stairway, a minimum time in which the venue may be completely evacuated by all visitors, or a crowd flow pattern, during an emergency evacuation period. Thus, by monitoring venue conditions at one or more time periods, the PVE system may train itself to predict a number of evacuation conditions and evacuation metrics during the same time periods or during different time periods. Those predictions may be used by the PVE system to generate personalized venue evacuation plans that may be used for evacuation during non-evacuation periods, non-emergency evacuation periods, or emergency evacuation periods).
Regarding Claim 16, de Hoog teaches the limitations of independent claim 15, as discussed above. de Hoog further teaches wherein the controller is configured to: identify which of the plurality of people associated with the facility are currently in the facility; and predict the emergency evacuation time for evacuating the facility using the one or more predefined evacuation routes based at least in part on the one or more historical movement patterns of the people that are identified as being currently in the facility ([0020] the PVE system may monitor a flow rate and a flow pattern of a crowd during a first emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during a second emergency evacuation of the venue. By monitoring venue conditions during emergency and non-emergency time periods, the PVE system may continuously develop its analyses of venue conditions and continuously improve its prediction accuracy. [0025] during a non-evacuation period, video cameras and motion sensors may capture images and motion data of visitors, and computing devices may analyze the images and motion data to detect and characterize special mobility attributes. For example, computing devices may apply artificial intelligence and machine learning processes to determine that a visitor is pushing a stroller and to predict both an average pace of motion and a maximum pace of motion for such a visitor during an evacuation period. In some embodiments, during a non-emergency evacuation period, video cameras and motion sensors may collect data regarding the occupancy of venue areas and flow patterns of visitors in passageways and on stairways and escalators. Computing devices may analyze such data to determine where crowd bottlenecks have developed and make predictions such as a maximum pace that a group of visitors may progress on a crowded stairway, a minimum time in which the venue may be completely evacuated by all visitors, or a crowd flow pattern, during an emergency evacuation period. Thus, by monitoring venue conditions at one or more time periods, the PVE system may train itself to predict a number of evacuation conditions and evacuation metrics during the same time periods or during different time periods. Those predictions may be used by the PVE system to generate personalized venue evacuation plans that may be used for evacuation during non-evacuation periods, non-emergency evacuation periods, or emergency evacuation periods).
Regarding Claim 17, de Hoog teaches the limitations of independent claim 15, as discussed above. de Hoog further teaches wherein the controller is configured to: process at least part on the captured location information using a trained Artificial Intelligence (AI) model to identify the predicted emergency evacuation time ([0020] the PVE system may monitor one or more venue conditions during a variety of time periods so that it may use artificial intelligence and machine learning techniques predict one or more venue conditions. For example, in some embodiments the PVE system may monitor a flow rate and a flow pattern of a crowd during a non-emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during an emergency evacuation of the venue. In some embodiments, the PVE system may monitor a flow rate and a flow pattern of a crowd during a first emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during a second emergency evacuation of the venue. By monitoring venue conditions during emergency and non-emergency time periods, the PVE system may continuously develop its analyses of venue conditions and continuously improve its prediction accuracy).
Regarding Claim 18, de Hoog teaches the limitations of independent claim 17, as discussed above. de Hoog further teaches wherein the controller is configured to train the AI model to account for a number and location of people in the facility and crowd dynamics in the facility ([0020] the PVE system may monitor one or more venue conditions during a variety of time periods so that it may use artificial intelligence and machine learning techniques predict one or more venue conditions. For example, in some embodiments the PVE system may monitor a flow rate and a flow pattern of a crowd during a non-emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during an emergency evacuation of the venue. In some embodiments, the PVE system may monitor a flow rate and a flow pattern of a crowd during a first emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during a second emergency evacuation of the venue. By monitoring venue conditions during emergency and non-emergency time periods, the PVE system may continuously develop its analyses of venue conditions and continuously improve its prediction accuracy. [0025] during a non-evacuation period, video cameras and motion sensors may capture images and motion data of visitors, and computing devices may analyze the images and motion data to detect and characterize special mobility attributes. For example, computing devices may apply artificial intelligence and machine learning processes to determine that a visitor is pushing a stroller and to predict both an average pace of motion and a maximum pace of motion for such a visitor during an evacuation period. In some embodiments, during a non-emergency evacuation period, video cameras and motion sensors may collect data regarding the occupancy of venue areas and flow patterns of visitors in passageways and on stairways and escalators. Computing devices may analyze such data to determine where crowd bottlenecks have developed and make predictions such as a maximum pace that a group of visitors may progress on a crowded stairway, a minimum time in which the venue may be completely evacuated by all visitors, or a crowd flow pattern, during an emergency evacuation period. Thus, by monitoring venue conditions at one or more time periods, the PVE system may train itself to predict a number of evacuation conditions and evacuation metrics during the same time periods or during different time periods. Those predictions may be used by the PVE system to generate personalized venue evacuation plans that may be used for evacuation during non-evacuation periods, non-emergency evacuation periods, or emergency evacuation periods).
Regarding Claim 19, de Hoog teaches a non-transitory computer readable medium storing instructions thereon that when executed by one or more processors causes the one or more processors to ([0053] The Memory 420 of the Computer System 401 may be comprised of a Memory Controller 422 and one or more memory modules for temporarily or permanently storing data (not depicted). In some embodiments, the Memory 420 may comprise a random-access semiconductor memory, storage device, or storage medium (either volatile or non-volatile) for storing data and programs. The Memory Controller 422 may communicate with the Processor 410, facilitating storage and retrieval of information in the memory modules. The Memory Controller 422 may communicate with the I/O Interface 430, facilitating storage and retrieval of input or output in the memory modules. In some embodiments, the memory modules may be dual in-line memory modules):
receive location information from an access control system in a facility of each of a plurality of people associated with the facility; identify one or more historical movement patterns of one or more of the plurality of people associated with the facility based at least in part on the location information from the access control system ([0019] Using collected personal data, the PVE system may generate an evacuation plan that may consider factors such as a user's proximity to a dangerous condition, such as a fire, and whether the user may require a wheelchair accessible evacuation route. The PVE system may also obtain, in real-time, venue condition data, or information about dynamic conditions at the venue, such as crowd movement patterns within the venue and adjacent areas outside of the venue, an arrival of first responders, crowd bottlenecks, temperature changes, air quality, and weather conditions. Venue condition data may be monitored by a set of recording devices and a set of sensing devices at the venue. By obtaining venue condition data and personal data, the PVE system may accurately determine a safe time and route for a user to evacuate according to the user's abilities, location, and circumstances. [0020] the PVE system may monitor one or more venue conditions during a variety of time periods so that it may use artificial intelligence and machine learning techniques predict one or more venue conditions. For example, in some embodiments the PVE system may monitor a flow rate and a flow pattern of a crowd during a non-emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during an emergency evacuation of the venue. In some embodiments, the PVE system may monitor a flow rate and a flow pattern of a crowd during a first emergency evacuation of a venue and use that information to predict a flow rate and a flow pattern of a crowd during a second emergency evacuation of the venue. By monitoring venue conditions during emergency and non-emergency time periods, the PVE system may continuously develop its analyses of venue conditions and continuously improve its prediction accuracy);
predict an emergency evacuation time for evacuating the facility using one or more predefined evacuation routes based at least in part on the one or more historical movement patterns of one or more of the plurality of people associated with the facility ([0025] during a non-evacuation period, video cameras and motion sensors may capture images and motion data of visitors, and computing devices may analyze the images and motion data to detect and characterize special mobility attributes. For example, computing devices may apply artificial intelligence and machine learning processes to determine that a visitor is pushing a stroller and to predict both an average pace of motion and a maximum pace of motion for such a visitor during an evacuation period. In some embodiments, during a non-emergency evacuation period, video cameras and motion sensors may collect data regarding the occupancy of venue areas and flow patterns of visitors in passageways and on stairways and escalators. Computing devices may analyze such data to determine where crowd bottlenecks have developed and make predictions such as a maximum pace that a group of visitors may progress on a crowded stairway, a minimum time in which the venue may be completely evacuated by all visitors, or a crowd flow pattern, during an emergency evacuation period. Thus, by monitoring venue conditions at one or more time periods, the PVE system may train itself to predict a number of evacuation conditions and evacuation metrics during the same time periods or during different time periods. Those predictions may be used by the PVE system to generate personalized venue evacuation plans that may be used for evacuation during non-evacuation periods, non-emergency evacuation periods, or emergency evacuation periods).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
I. Claims 2-3 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over de Hoog in view of in view of Carbonell US 20150347829 A1.
Regarding Claim 2, de Hoog teaches the limitations of independent claim 1, as discussed above. de Hoog does not explicitly teach, however Carbonell teaches wherein the location information comprises access control data collected by an access control system of the facility ([0019] tracking program 115 also determines the location of an individual based on a code entry at a keypad. In such scenarios, the location of the keypad is known to or is determinable by tracking program 115. As such, the location of the individual is implied via the entry of the code using the keypad and tracking program 115 logs the location of the individual to coincide with that of the keypad. In some embodiments, tracking program 115 also determines the location of an individual based on an RFID reader that scans a card or RFID-enabled key chain. In some embodiments, tracking program 115 also determines the location of an individual based on the determined presence of a particular cell phone or another like portable computing device such as a laptop. In some embodiments, tracking program 115
also determines the location of an individual based on a barcode scanner that scans a keychain. In some embodiments, tracking program 115 also determines the location of an individual based on a type of biometric identification, such as a fingerprint taken using a fingerprint reader. In short, tracking program 115 identifies individuals based on facial recognition techniques and can, depending on the embodiment and type of information available, further verify that identification using any of a number of other techniques, such as those described above).
It would have been obvious to person having ordinary skill in the art before the effective filing date of the claimed invention to substitute the location information in de Hoog with the location information in Carbonell. See KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) (simple substitution of one known element for another to obtain predictable results). Such a simple substitution would yield the predictable result of location information comprising access control data collected by an access control system of the facility.
Regarding Claim 3, the combination of de Hoog and Carbonell teaches the limitations of independent claim 2, as discussed above. de Hoog does not explicitly teach, however Carbonell teaches wherein the access control data comprises a person identifier, an access event time and an access location for each person that passes through an access control point of the access control system ([0019] tracking program 115 also determines the location of an individual based on a code entry at a keypad. In such scenarios, the location of the keypad is known to or is determinable by tracking program 115. As such, the location of the individual is implied via the entry of the code using the keypad and tracking program 115 logs the location of the individual to coincide with that of the keypad. In some embodiments, tracking program 115 also determines the location of an individual based on an RFID reader that scans a card or RFID-enabled key chain. In some embodiments, tracking program 115 also determines the location of an individual based on the determined presence of a particular cell phone or another like portable computing device such as a laptop. In some embodiments, tracking program 115
also determines the location of an individual based on a barcode scanner that scans a keychain. In some embodiments, tracking program 115 also determines the location of an individual based on a type of biometric identification, such as a fingerprint taken using a fingerprint reader. In short, tracking program
115 identifies individuals based on facial recognition techniques and can, depending on the embodiment and type of information available, further verify that identification using any of a number of other techniques, such as those described above. [0031] data sources 117, tracking program 115 creates a number of entries that correspond to each location, which indicate that individual A was identified at that location, and saves those entries as part of user profiles 116. In some embodiments, a time stamp is included in such entries to indicate the time at which individual A was identified at a particular location) (see claim 2 rejection above for combination rationale).
Regarding Claim 20, de Hoog teaches the limitations of independent claim 19, as discussed above. de Hoog does not explicitly teach, however Carbonell teaches wherein the location information comprises access control data collected by an access control system of the facility ([0019] tracking program 115 also determines the location of an individual based on a code entry at a keypad. In such scenarios, the location of the keypad is known to or is determinable by tracking program 115. As such, the location of the individual is implied via the entry of the code using the keypad and tracking program 115 logs the location of the individual to coincide with that of the keypad. In some embodiments, tracking program 115 also determines the location of an individual based on an RFID reader that scans a card or RFID-enabled key chain. In some embodiments, tracking program 115 also determines the location of an individual based on the determined presence of a particular cell phone or another like portable computing device such as a laptop. In some embodiments, tracking program 115
also determines the location of an individual based on a barcode scanner that scans a keychain. In some embodiments, tracking program 115 also determines the location of an individual based on a type of biometric identification, such as a fingerprint taken using a fingerprint reader. In short, tracking program 115 identifies individuals based on facial recognition techniques and can, depending on the embodiment and type of information available, further verify that identification using any of a number of other techniques, such as those described above).
It would have been obvious to person having ordinary skill in the art before the effective filing date of the claimed invention to substitute the location information in de Hoog with the location information in Carbonell. See KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) (simple substitution of one known element for another to obtain predictable results). Such a simple substitution would yield the predictable result of location information comprising access control data collected by an access control system of the facility.
II. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over de Hoog in view of Malthurkar US 20230111264 A1.
Regarding Claim 9, de Hoog teaches the limitations of independent claim 7, as discussed above. de Hoog does not explicitly teach, however Malthurkar teaches wherein the number and location of people in the facility is based at least in part on real time location data collected by an access control system of the facility ([0012] an access control system that is configured to determine a number of
people in a location corresponding to receiving a card swipe or inputting credentials to access lock or access reader).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the number and location of people in the facility being based at least in part on real time location data collected by an access control system of the facility as taught in Malthurkar with the method of de Hoog because such a combination enables the system to monitor the users in the facility (Malthurkar [0031]).
III. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over de Hoog in view of Benjamin US 20200334615 A1.
Regarding Claim 10, de Hoog teaches the limitations of independent claim 6, as discussed above. de Hoog does not explicitly teach, however Benjamin teaches comprising training the AI model by one or more of: feeding back an actual emergency evacuation time following each of one or more previous evacuation events; and feeding back actual location information of one or more of the plurality of people associated with a facility during each of one or more previous evacuation events ([0034] the training data 202T passes to a wait time trainer 204 for training the wait time predictor model 270. Based on the training data 202T, the wait time trainer 204 is able to model support request parameters
206 to train the wait time predictor model 270. Once trained, the wait time predictor model (e.g., trained model) 270 is used by the wait time predictor 260 during inference for predicting estimated wait times 130 for corresponding pending support requests 120. Thus, using training data 202T associated with a corpus of historical support requests 120H each including a corresponding plurality of high-level features 202 and/or a known corresponding actual wait time 202f, the wait time predictor
model 270 is trained to predict estimated wait times 130).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the process of training the AI model by one or more of: feeding back an actual emergency evacuation time following each of one or more previous evacuation events; and feeding back actual location information of one or more of the plurality of people associated with a facility during each of one or more previous evacuation events as taught in Benjamin with the method of de Hoog because such a combination enables the system to “increas[e] the efficacy of the deep neural network approach” (Benjamin [0035]).
IV. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over de Hoog in view of LaPorte US 20180357385 A1.
Regarding Claim 11, de Hoog teaches the limitations of independent claim 1, as discussed above. de Hoog does not explicitly teach, however LaPorte teaches wherein the location information of each of the plurality of people associated with the facility include entry and exit times at preferred access points of the facility ([0037] The building monitors 120E may be configured to capture the monitoring metadata 113 pertaining to particular building location(s) of the respective PD 180 (e.g., specific office, examination room, laboratory, and/or the like). The building monitors 120E may be configured to capture location information at a lower granularity than the device monitor 120A and/or network monitor 120D. The building monitors 120E may comprise one or more low-range communication devices, such as BLUETOOTH® communication devices, near-field communication (NFC) communication devices, radio frequency identifier (RFID) devices, bar code scanners, and/or the like. The building monitors 120E may capture the device identifier 181 of the PDs 180 when such PDs 180 enter and/or exit particular building locations (e.g., rooms, offices, examination rooms, labs, operating rooms, and/or the like). The building monitors 120E may capture the device identifiers 181 using any suitable mechanism including, but not limited to: scanning a barcode and/or QR code on the PD 180 (and/or presented on a display of the PD 180), reading an RFID tag on the PD 180, and capturing the device identifier 181 via BLUETOOTH®, NFC, and/or the like. The building monitors 120E may be further configured to monitor the time the PD 180 remains in particular locations (e.g., track the time the PD 180 enters and exits particular building locations).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include in the method of de Hoog the location information of each of the plurality of people associated with the facility including entry and exit times at preferred access points of the facility as taught by LaPorte since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination is predictable. Such a combination would yield the predictable result of a method where the location information of each of the plurality of people associated with the facility includes entry and exit times at preferred access points of the facility.
Response to Arguments
Applicant’s arguments with respect to the prior art rejections have been fully considered but they are not persuasive.
Applicant argues that the prior art “does not disclose, teach, or suggest access control systems” (p. 7). As described more fully above, such features are taught by De Hoog. For example, De Hoog teaches that “[u]sing collected personal data, the PVE system may generate an evacuation plan that may consider factors such as a user's proximity to a dangerous condition, such as a fire, and whether the user may require a wheelchair accessible evacuation route” [0019].
Applicant argues that:
Access control systems use credentialing mechanisms such as RFID badges, card readers, and biometric scanners to grant or deny access to secure areas through discrete entry and exit points. In doing so, these systems track identified individuals and maintain records of access events, including person identifiers, access event times, and access locations
(p. 8). The Examiner notes that these features are not recited in the present claims.
Applicant argues that:
De Hoog consistently describes monitoring "a crowd," "flow patterns of visitors," "crowd
bottlenecks," and "a group of visitors." The claim language, in contrast, requires location
information "of each of a plurality of people" and historical movement patterns "of one or more of the plurality of people." The Action has not shown where de Hoog captures location information "of each" person or identifies patterns "of' particular people as the claim language requires. De Hoog's video cameras and motion sensors observe
aggregate crowd dynamics without identifying or tracking specific individuals.
(p. 9). The Examiner disagrees. Contrary to the position taken by Applicant, the present claims do not require identification of movement patterns for “particular people.” Furthermore, the Examiner notes that monitoring "flow patterns of visitors" teaches monitoring flow patterns of “each of a plurality of people.”
Applicant argues that:
claim 3 for example, recites details of the access control data that is used: "wherein the access control data comprises a person identifier, an access event time and an access location for each person that passes through an access control point of the access control system." The anonymous crowd data of de Hoog does not contemplate any of these details, and so the anticipation rejection of claim 3 should be withdrawn
(p10). As described more fully above, such features are taught by the combination of de Hoog and Carbonell.
Applicant argues that “Carbonell makes no mention of emergency evacuation prediction, evacuation time calculation, evacuation route optimization, or any of the concerns addressed by Applicant's disclosure” (p. 11). As described more fully above, such features are taught by de Hoog
Applicant argues that “one of ordinary skill in the art would have no motivation to look
to Carbonell - a reference about tracking individuals in metropolitan areas for safety monitoring
purposes - when developing a system for predicting emergency evacuation times in facilities using
access control systems” (p. 12). The Examiner disagrees. It is not clear how, in Applicant’s own words, a reference about tracking individuals in metropolitan areas for safety is not similar to the claimed method of tracking individuals for safety (evacuation from a venue).
Applicant argues that “[t]he Action has not articulated why one of ordinary skill in the art would look to Malthurkar - a reference about air quality monitoring in indoor spaces - when developing a system for predicting emergency evacuation times in facilities using access control systems” (p. 13). The Examiner disagrees. Similar to de Hoog and the claimed invention, Malthurkar solves the problem of tracking the location of people for safety purposes (air quality).
Applicant argues that “as Benjamin seeks to inform customers how long they will wait for support and Applicant's disclosure seeks to predict emergency evacuation times based on historical movement patterns of people tracked through an access control system, there is no reasonable motivation to combine these references to arrive at the claimed invention” (pp. 13-14). The Examiner disagrees. Similar to de Hoog and the claimed invention, Benjamin solves the problem of training AI models for time predictions.
Applicant argues that:
LaPorte seeks to ensure that portable devices are sanitized according to a sanitization policy based on potential contamination exposure. See paras [0024], [0042]. Applicant's disclosure seeks to predict emergency evacuation times based on historical movement patterns of people tracked through an access control system. There is no reasonable motivation to combine these references from disparate technical fields to arrive at the claimed invention
(pp. 14-15). The Examiner disagrees. Similar to de Hoog and the claimed invention, LaPorte solves the problem of monitoring location information of people, including the entry and exit times at preferred access points of a facility.
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 extension fee 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 date of this final action.
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/D.N.M./Examiner, Art Unit 3628
/GEORGE CHEN/Primary Examiner, Art Unit 3628
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