DETAILED ACTION
Acknowledgement
This non-final office action is in response to claims filed on 06/04/2025.
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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 08/01/2025 and 10/22/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, 3, 8-10, 12, and 17-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by O’Neill et al. (US 2005/0209770 A1).
As per claims 1, 10, and 19, O’Neill teaches a method for smart city integrated emergency management and control, being realized based on an Internet of Things large model system, wherein the Internet of Things large model system comprises; An Internet of Things large model system for smart city integrated emergency management and control, wherein the system includes; and a non-transitory computer-readable storage medium, wherein the storage medium stores computer instructions, and when a computer reads the computer instructions in the storage medium, the computer performs the method of claim 1 (O’Neill e.g. A knowledge-based system and method for establishing and managing routes for emergency vehicles (Abstract). A knowledge-based system for design, management, and deployment of routes used by emergency response vehicles is described herein. Such a system is referred to herein as a SAFER Route system (SAFER is a trademark of SAFER Technologies, L.L.C.) [0052]. FIG. 1 is a schematic illustration of the design phase components of the SAFER Route system 100 [0056]. FIG. 5a is a block diagram illustrating a possible network architecture for an implementation of a SAFER Route solution. This architecture example consists of networked computer systems for the various functions involved in designing and executing SAFER Route routes. The SAFER System Server 202, which, as described herein above, provides access to the various databases that make up the knowledge base 521 used for emergency response according to the invention, is a software system residing on a SAFER Route Server computer 501. Typically the SAFER System Server 202 and the SAFER knowledge base 521 would be stored on some form of secondary storage, e.g., a disk drive 517 that is connected to a central processing unit (CPU) 513. The CPU 513 would be able to load the requisite components into a memory 515 for execution [0144].).
O’Neill teaches an emergency supervision user platform, an emergency supervision service platform, an emergency supervision management platform, an emergency supervision sensor network platform, and an emergency supervision object platform, the emergency supervision user platform includes a third-party terminal, the emergency supervision service platform includes a communication terminal, the emergency supervision management platform includes a processor, a master data center, and an emergency sub-platform, the master data center is configured with a memory and a data processing model library, the emergency supervision sensor network platform includes a communication transmission network and a routing device, the emergency supervision object platform is configured with a sensor and a memory, and the emergency supervision object platform is configured to operate based on a plurality of emergency inspection vehicles and communication devices; (O’Neill e.g. The knowledge-based system further includes a routes database for storing routes designed using the information useful in establishing and managing routes, and one or more modules operable to retrieve information from the knowledge base and to perform an operation selected from designing one or more routes between a first location and a second location, selecting a route from the routes database, and modifying a route in the routes database [0014]. The invention provides a system that integrates several components of the existing traffic control and emergency response infrastructure to enhance efficiency and safety in the use of access and evacuation routes by emergency/incident response units [0034]. The main components leveraged by a preferred embodiment of the invention include: A grid of fixed points with sensors (e.g., cameras, vehicle counters, flood level sensors) and actuators (e.g., camera controls, traffic light sequencers, highway message boards) [0036]. A grid management system that allows remote access and management of sets of sensors and actuators [0037]. Mobile computers for First Responders to provide them with two-way access to dispatch and command centers with standard software clients, communications and applications [0038]. Information servers connected to the grid [0039]. Information models for describing, classifying, organizing, storing, accessing and presenting information in context [0040]. Information security including user authentication and access control policies [0041]. Workflow, collaboration and knowledge management applications [0042]. FIG. 1 is a schematic illustration of the design phase components of the SAFER Route system 100 [0056]. FIG. 2 is a schematic illustration depicting each of the components, interfaces and operations of SAFER Route system 100 used in the activation phase, the relationship between these components, interfaces and operations, and their interaction when a situation requires the activation of a SAFER Route route [0085]. The SAFER Route system 100 is implemented using a client-server paradigm. In that embodiment, the SAFER Route Control module 201 is implemented as a client system for use by SAFER Route coordinators. Conversely, the system functions, e.g., database access, are implemented as a server system 202. The SAFER Route server 202 contains the various databases used by the SAFER System 100 and software modules for managing the access to those databases [0089]. A Traffic Statistics Collection and Characterization activity 101 assembles and organizes all of the relevant information about traffic flow in a region under consideration. The information can come from a variety of sources such as data from daily ITS traffic monitoring, surveys taken by municipal, regional, state-wide or federal traffic entities, observations made by traffic management and control professionals, etc. Examples include data observed by traffic monitoring equipment 109, sensors associated with traffic signals 111, and on-site observers 113 [0059]. A road grid database 103 is a computer model of the public road network within a region and contains information specifying which intersections have some form of traffic control (e.g., stop signs, stop lights), the nature of the control (e.g., fixed sequencing, programmable sequencing, emergency override, ITS control), and whether certain segments have special traffic control/flow capabilities (e.g., high occupancy vehicle (HOV) lanes, contra-flow lanes, programmable message boards, etc.) [0063]. A Route Planner module 119 provides SAFER Route planners mechanisms to define access and evacuation routes between various locations that have been identified as At-Risk and Responder sites in the SAFER Grid database 107 [0076]. The SAFER Route Control module 201 oversees the behavior of the SAFER Route system 100 during the deployment and management of a SAFER Route mission. The SAFER Route Control module 201 also provides a control console to the SAFER Route coordinators who control and manage the operation of the SAFER Route Control module 201 [0088]. The Traffic Management interface 211 provides interfaces to the SAFER Route system 100 and the SAFER Route coordinators (who control the SAFER Route system 100 via the SAFER Route Control module 201) to communicate with a local/regional traffic management control center 213 [0099]. The Responder Unit interface 227 receives the route information from the Route Loader 225 and causes the route information to be delivered to the Responder Unit 223 [0120]. The Responder Unit vehicles 223 are equipped with their own computer systems 505 on which the responder client software 507 resides and executes. The responder unit computer systems 505 are equipped to communicate wirelessly to the SAFER Route Server computer 501. This communication may be via the Internet. [0146].)
the method is performed based on the emergency supervision management platform and comprises:
O’Neill teaches acquiring a plurality pieces of emergency management and control data through the communication transmission network of the emergency supervision sensor network platform configured in a plurality of geographic regions and storing the plurality pieces of emergency management and control data in the memory in the master data center; (O’Neill e.g. A Traffic Statistics Collection and Characterization activity 101 assembles and organizes all of the relevant information about traffic flow in a region under consideration. The information can come from a variety of sources such as data from daily ITS traffic monitoring, surveys taken by municipal, regional, state-wide or federal traffic entities, observations made by traffic management and control professionals, etc. Examples include data observed by traffic monitoring equipment 109, sensors associated with traffic signals 111, and on-site observers 113 [0059]. The traffic monitoring equipment 109 may be in the form of video cameras mounted to produce continuous video streams of traffic conditions that may be interpreted by an operator and entered into supporting databases. The sensors associated with traffic signals include such sensors as traffic counters and sensors that may indicate that a vehicle is waiting for a traffic light to change, e.g., to make a left turn [0059].)
O’Neill teaches retrieving, from the memory in the master data center, upload times and data types of the plurality of emergency management and control data and geographic regions to which the plurality of emergency management and control data belongs, and generating data sets to be processed; (O’Neill e.g. A Traffic Analysis and Road Grid Characterization activity 105 examines the traffic statistics and road grid information and uses this information to identify patterns in the data (e.g., 25% of the vehicles approaching intersection X from the south turn left between 7:00 AM and 8:30 AM on weekdays) and computes the average time to transit between designated locations at various times (e.g., every fifteen minutes, by day of the week, etc.) [0066]. The Traffic Analysis and Road Grid Characterization activity 105 augments the Road Grid database 103 with the traffic pattern data from the Traffic Statistics Collection 101 and with the average transit time data. This augmented data is stored in an augmented road grid database, and referred to herein as the SAFER Grid database 107 [0066]. The SAFER Grid database 107 contains all of the information that was captured by the Traffic Analysis and Road Grid Characterization activity 105. Each entity in the Road Grid (i.e., road segment, type of intersection, type of traffic signal, etc.) is augmented with the time-based information gathered from the Traffic Analysis and Road Grid Characterization activity 105. The augmented grid database 107 is organized to support route definition and analysis by other components of the SAFER system 100 [0068]. The SAFER Grid database 107 also contains information describing locations that presents particular high risk of being a location having an emergency requiring response by emergency vehicles and information describing responder sites [0069].)
O’Neill teaches marking, in an interactive terminal of a smart city geographic information system (GIS), an emergency management and control range corresponding to a temporary management and control region according to a geographic scope of the geographic regions involved in the data sets to be processed, and displaying the emergency management and control range in an interface of an emergency management and control terminal; (O’Neill e.g. There are numerous ways in which a road grid may be represented in a road grid database 103. One example is the ESRI GIS platform which is a fairly wide-spread platform for capturing and managing road grid representations [0063]. The various databases of the SAFER Route system 100 that are used to store data abstractions for SAFER Route routes use a common abstract data model for modeling the routes that are used to conduct SAFER Route missions. In a preferred embodiment the abstract model is implemented using a Geographical Information System (GIS). That embodiment is described, below in the section entitled “GIS Embodiment of the SAFER Route Abstract Model”. The manner in which the various operational modules use the abstract model is mapped into a preferred embodiment the SAFER Route system 100 [0149]. In a preferred embodiment, the present invention may be implemented on top of a computer-based Geographical Information Systems (GIS). In such an implementation, the SAFER Route Abstract model is mapped into the ESRI ArcView system [0169]. In the preferred embodiment, the Road Grid database 103 for an area being modeled is contained in an ArcView geodatabase. The Road Grid database 103 is established using a coordinate system that is compatible with the bulk of the existing GIS information that is being used by the local governments GIS systems. These entities are the source for background information (GIS shapefiles, feature classes, geodatabases, etc.) that describes the area (roads, highways, waterways, railroads, etc.) [0241]. Multiple simultaneous SAFER missions are managed via separate maps. A combined view of the overall environment can be provided by using the ESRI server-based GIS products [0295]. In system-assisted route selection, the SAFER Route operator reviews the relevant routes by displaying them on the map 901 and then selects the most appropriate route. The SAFER Route system 100 supports this operation by labeling each route with both its L (length) and D (estimated duration) values along with the name of the route [0332].)
O’Neill teaches generating a first traveling route based on an emergency management and control street within the emergency management and control range, and a size of a crowd corresponding to the emergency management and control street, and sending the first traveling route to the emergency inspection vehicles to control the emergency inspection vehicles to execute the first traveling route and carry out inspection; (O’Neill e.g. The knowledge-based system also consist of one or more software modules operable to retrieve information from the knowledge base and to perform operations such as designing routes between location involved in emergency response, selecting between such routes, and modifying such routes all using information in the knowledge base (Abstract). A Route Planner module 119 provides SAFER Route planners mechanisms to define access and evacuation routes between various locations that have been identified as At-Risk and Responder sites in the SAFER Grid database 107 [0076]. The Activation Phase selects the best route for the current conditions and makes the route available to both a Responder Unit that will use the SAFER Route route and the affected traffic control organizations [0085]. The Current Conditions database 215 is used to store information about current conditions and factors in the region that can affect traffic flow, which may be entered by Traffic Management 213 via the Traffic Management interface 211. In addition to information provided by the local Traffic Management Center(s) 213, which may include location of accidents, traffic congestion, smoke plumes blocking particular sections of the road grid, flooding, sinkholes, ruptured gas and water mains, downed power lines, time impact on traffic, etc., it also contains information about scheduled activities in the region which can also affect traffic (e.g., scheduled road/lane closures, schedules and venues for major events, prime shopping days at regional malls, parades); information which typically would be gathered from sources outside of the SAFER Route system 100 [0101]. The Route Loader 225 causes the activated route, activated by the Route Activation module 219, to be transferred from the Active Routes database 221 to the designated Responder Unit 223 via a Responder Unit interface 227. Depending on the communications and computer capabilities of the Responder Unit 223, loading of the route may take a variety of forms including computer-to-computer communications, human uploading of the route to a Mobile Display Terminal (MDT), or voice instructions [0118]. During the SAFER Route Execution Phase, the Responder Unit interface 227 is used to transmit to the SAFER Route system 100 the real time position of the Responder Unit vehicle 223 as it follows the active route and to receive route updates from the SAFER Route system 100 in case such updates are required [0378].
O’Neill teaches updating signal light phases in conjunction with travel needs of the emergency inspection vehicles and a traffic flow on the emergency management and control street; and controlling operations of traffic signals within the emergency management and control range based on the updated signal light phases, and controlling signboards of variable lanes within the emergency management and control range to update a traveling direction (O’Neill e.g. A knowledge-based system makes use of the control capabilities for traffic control devices so as to provide responders to emergency events such efficient and safe routes to and from incident locations [0013]. Of particular interest are those traffic signals that can be remotely controlled via human or computer request. These form the basis for the most effective use of the SAFER Route approach [0063]. Another key factor that the Route Planner module 119 considers is the kind of traffic control signals there are available along a candidate route. In regions with significant implementations of ITS or some other form of centralized traffic control, designated routes can be chosen and pre-configured to facilitate rapid implementation of key SAFER Routes by, for example, storing the commands needed to re-program a set of traffic signals to “open” a SAFER Route and storing them along with the route definition [0079]. The Traffic Control interface 301 provides mechanisms whereby the SAFER Route system 100 controls physical actuators 303 (e.g., traffic lights, highway message boards) and sensors (e.g., traffic cameras) in the regional road grid to facilitate the progress of a SAFER Route mission. In particular, the Traffic Control interface 301 is used to allow Responder Unit vehicles 223 operating under control of the SAFER Route system 100 to request certain traffic lights to “turn green” well in advance of the arrival of the vehicle, and to release the light to normal operation once the vehicle passe [0124]. In regions with automated traffic control signals, the Unit Tracker 305 further provides the Traffic Control interface 301 with information required to use the Traffic Control assets 303 along the designated route to “open and close” the SAFER Route as the Responder Unit 223 approaches and leaves intersections under control by the SAFER Route system 100 [0126]. The SAFER system 100 also translates the route to an activation pattern for traffic control boxes along the selected route. This activation pattern, which is designed to provide Responder Units 223 with a wave of green lights along the route, is transmitted to the Responder Units 223 and the Traffic Management authority 213 [0423]. The activation pattern, which may also include giving the Responder Unit free access to a designated lane on that portion of 1-45 that the Responder Unit will travel on its SAFER Route mission, overrides the designated traffic lights thereby providing the wave of green lights using an established protocol (e.g., ITS control messages) [0434]. In situations of extreme emergency (e.g., mass casualties, major infrastructure failures), human route planners have the ability to override these restrictions and can create routes to be put under control of the SAFER Route system 100 and that disregard the conventional constraints (e.g., reverse the direction of travel on a major freeway to support regional evacuations) [0164].)
As per claims 3 and 12, O’Neill teaches the method of claim 1 and the system of claim 10, wherein the retrieving, from the memory in the master data center, upload times and data types of the plurality of emergency management and control data and geographic regions to which the plurality of emergency management and control data belongs includes:
O’Neill teaches retrieving, from the data processing model library, a data classification model based on the master data center; categorizing the plurality pieces of emergency management and control data based on the data classification model to generate the data sets to be processed; (O’Neill e.g. The main components leveraged by a preferred embodiment of the invention include [0035]: Information models for describing, classifying, organizing, storing, accessing and presenting information in context [0040]. The Responder Site Identification activity 117 provides SAFER Route planners mechanisms to identify sites which are potential sources and destinations for an evacuation supported by the SAFER Route system 100.There are at least two classes of Responder Sites: Responder Bases (e.g., Police Stations, Firehouses, EMS Stations, etc.) and Responder Havens (e.g., hospitals and storm shelters) [0073]. Current conditions that may be supplied to the SAFER Route system 100 may be categorized into several different classes of current conditions, e.g., conditions that block all traffic (e.g., road cut by security forces in response to a public security incident or the threat of such an incident), conditions that allow emergency responder vehicles passage at higher risk or reduced speed (e.g., road partially blocked because of traffic accident, road segment occupied by large numbers of demonstrators or people being evacuated), and conditions that permit passage only to properly equipped vehicles (e.g., if condition is toxic fumes—crews with appropriate suits and breathing equipment, if flooded roads—amphibious vehicles, if snipers on rooftop—armored personnel carrier) [0101]. In the preferred embodiment, the Road Grid database 103 for an area being modeled is contained in an ArcView geodatabase. The Road Grid database 103 is established using a coordinate system that is compatible with the bulk of the existing GIS information that is being used by the local governments GIS systems. These entities are the source for background information (GIS shapefiles, feature classes, geodatabases, etc.) that describes the area (roads, highways, waterways, railroads, etc.) [0241]. The SAFER Grid database 107 of the SAFER Route system 100 is a set of specifically defined ESRI domains, feature classes, and associated data tables stored in a separate ESRI geodatabase that is used to model and implement the SAFER Route system 100 solution [0242].)
O’Neill teaches generating routing information based on the data sets to be processed, and controlling based on the routing information, the routing device of the emergency supervision sensor network platform to send the data sets to be processed to a sub- data center of the emergency sub-platform in a corresponding geographic region (O’Neill e.g. The Route Planner module 119 provides mechanisms, e.g., user interface and query logic, to the SAFER Route planners to query the road grid database 103 and the SAFER Grid database 107 described above to assist in the process to create candidate routes between at-risk sites and responder sites, and to analyze the routes for suitability, and to designate certain routes for incorporation into the set of SAFER Route pre-planned routes [0076]. The Route Selection module 207 allows a SAFER Route operator to either select a Pre-Planned Route or request that the Route Planner 119 generate a new, Ad Hoc Route to satisfy the SAFER Route request. The Route Planner 119 takes into account: the target location for a SAFER Route mission, the location (and type) of the Responder Unit, current conditions, potential events that may occur during the likely duration of the mission, and the set of available routes ([0103]-[0108]). The Route Activation module 219 causes a selected SAFER Route to become active within the SAFER Route system 100. The Route Activation module 219 notifies the affected Traffic Management Center(s) 213 which route is being put into effect, and making the route itself available to other concerned parties by storing it in the Active Routes database 221 [0113]. The Route Loader 225 causes the activated route, activated by the Route Activation module 219, to be transferred from the Active Routes database 221 to the designated Responder Unit 223 via a Responder Unit interface 227. Depending on the communications and computer capabilities of the Responder Unit 223, loading of the route may take a variety of forms including computer-to-computer communications, human uploading of the route to a Mobile Display Terminal (MDT), or voice instructions [0118]. The SAFER system 100 also translates the route to an activation pattern for traffic control boxes along the selected route. This activation pattern, which is designed to provide Responder Units 223 with a wave of green lights along the route, is transmitted to the Responder Units 223 and the Traffic Management authority 213 [0423].).
As per claims 8 and 17, O’Neill teaches the method of claim 1 and the system of claim 10, wherein the method further comprises: generating a second traveling route by adjusting the first traveling route based on the association value of the plurality pieces of emergency management and control data with the emergency event in conjunction with the emergency management and control street; controlling the emergency inspection vehicles to execute the second traveling route and conduct an inspection to collect updated emergency management and control data (O’Neill e.g. The Route Update module 307 is used to modify or augment a currently active SAFER Route route. The Route Update module 307 may be used, for example, by a SAFER Route coordinator to respond to unforeseen conditions affecting the current route or an alternative route (e.g., an accident has been cleared) [0128]. Automated route creation provides for excellent primary routes for a given time of the day. However, some manual intervention is often required to identify alternate or secondary routes, especially if certain portions of a primary route are potentially subject to non-time-based disruptions (e.g., flooding, railway accident.) [0290]. Knowledge of local conditions, risks, historical perspective, etc. are used by the Route Planner module 119 to generate very effective alternate/secondary routes between key At Risk Sites and Responder locations [0292]. The Current Conditions database 215 is used to provide real-time modification to the Route Selection and Route Update operations. It provides a way for the Traffic Management infrastructure to indicate changes to the “steady state” SAFER Grid [0300]. During the SAFER Route Execution Phase, the Responder Unit interface 227 is used to transmit to the SAFER Route system 100 the real time position of the Responder Unit vehicle 223 as it follows the active route and to receive route updates from the SAFER Route system 100 in case such updates are required [0378]. The present invention presents a way to collect and use information that is useful to design and select the best possible routes for emergency vehicles to use in such missions. The present invention further presents a way to use these routes during emergency missions and update the routes in a real-time manner [0460].).
As per claims 9 and 18, O’Neill teaches the method of claim 8 and the system of claim 17, wherein the method further comprises: adjusting the second traveling route to generate a third traveling route based on the updated emergency management and control data in combination with the association value and the emergency management and control street (O’Neill e.g. The knowledge-based system also consist of one or more software modules operable to retrieve information from the knowledge base and to perform operations such as designing routes between location involved in emergency response, selecting between such routes, and modifying such routes all using information in the knowledge base (Abstract). The method further includes retrieving information from the knowledge base and performing an operation selected from designing one or more routes between a first location and a second location, selecting a route from the routes database, and modifying a route in the routes database [0015]. The SAFER Grid database 107 also contains information describing locations that presents particular high risk of being a location having an emergency requiring response by emergency vehicles and information describing responder sites [0069]. A Route Planner module 119 provides SAFER Route planners mechanisms to define access and evacuation routes between various locations that have been identified as At-Risk and Responder sites in the SAFER Grid database 107 [0076]. The Route Planner module 119 assists the route designers in the task of defining pre-planned routes up to and including suggesting modifications to routes proposed by the route planners or alternative routes to those proposed routes [0078]. The Current Conditions database 215 is used to store information about current conditions and factors in the region that can affect traffic flow, which may be entered by Traffic Management 213 via the Traffic Management interface 211. In addition to information provided by the local Traffic Management Center(s) 213, which may include location of accidents, traffic congestion, smoke plumes blocking particular sections of the road grid, flooding, sinkholes, ruptured gas and water mains, downed power lines, time impact on traffic, etc., it also contains information about scheduled activities in the region which can also affect traffic (e.g., scheduled road/lane closures, schedules and venues for major events, prime shopping days at regional malls, parades); information which typically would be gathered from sources outside of the SAFER Route system 100 [0101]. The route generation request, which may originate from the Dispatcher interface 205 or the SAFER Route Control module 201, might be due to, for example, an accident blocking a pre-planned route, or a new target or responder location being requested. In addition to selecting a preferred route, one or more alternate access routes and one or more potential evacuation routes can be designated and forwarded to the requester for selection [0109]. The Route Update module 307 is used to modify or augment a currently active SAFER Route route. The Route Update module 307 may be used, for example, by a SAFER Route coordinator to respond to unforeseen conditions affecting the current route or an alternative route (e.g., an accident has been cleared) [0128]. Knowledge of local conditions, risks, historical perspective, etc. are used by the Route Planner module 119 to generate very effective alternate/secondary routes between key At Risk Sites and Responder locations [0292].)
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.
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 2, 4-7, 11, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over O’Neill et al. (US 2005/0209770 A1) in view of Brand et al. (US 2023/0038260 A1).
As per claims 2 and 11, O’Neill teaches the method of claim 1 and the system of claim 10 , wherein the method further comprises: generating the updated signal light phases based on location information of the emergency inspection vehicles, street information of the emergency management and control street, and current signal light phases using a signal light phase model (O’Neill e.g. A knowledge-based system makes use of the control capabilities for traffic control devices so as to provide responders to emergency events such efficient and safe routes to and from incident locations [0013]. In addition to their end points, Route objects in the Pre-Planned Routes database 121 also contain descriptive information such as the utility/value of the route under different conditions (e.g., time of day, day of the week, weather conditions), and the commands/steps needed to control the traffic signals along the route [0083]. The Traffic Control interface 301 provides mechanisms whereby the SAFER Route system 100 controls physical actuators 303 (e.g., traffic lights, highway message boards) and sensors (e.g., traffic cameras) in the regional road grid to facilitate the progress of a SAFER Route mission [0124]. In regions with automated traffic control signals, the Unit Tracker 305 further provides the Traffic Control interface 301 with information required to use the Traffic Control assets 303 along the designated route to “open and close” the SAFER Route as the Responder Unit 223 approaches and leaves intersections under control by the SAFER Route system 100 [0126]. The SAFER Route abstract model is a representation of the local road grid as it is used by conventional civilian traffic. The philosophy embodied in SAFER Routes is that responder vehicles will complete their trips in shorter elapsed times, more safely, by leveraging knowledge of the dynamic traffic environment and the ability to proactively control the traffic management infrastructure along the route [0163]. FIG. 6 is a graphical illustration of a directed graph illustrating an example 600 of the SAFER Route abstract model. The example model 600 is a model of a portion of a road grid with vertices V1-V8 and arcs A1-A12 [0166]. The SAFER Grid database 107 of the SAFER Route system 100 is a set of specifically defined ESRI domains, feature classes, and associated data tables stored in a separate ESRI geodatabase that is used to model and implement the SAFER Route system 100 solution [0242]. The SAFER Grid model has the properties of a directed graph, and there are a number of well-known algorithms, e.g., A*, Dijkstra's Single Source Shortest Paths, Floyd's algorithm to find the shortest paths between all pairs of nodes in a directed graph, that can be used to generate paths through a directed graph and/or select an optimal one [0284]. In the preceding code example of Table 2, the parameter LeadDistance specifies how far ahead of the Responder Unit vehicle 223 the algorithm should look to find the next traffic light to be requested. This enables the SAFER Route system 100 to request control of traffic lights that are outside of the direct line of sight of the Responder Vehicle 223, e.g., around corners, through tunnels. This type of control is a significant advantage over prior art systems which require a clear line of sight to a signal in order to request control (e.g., 3M's Opticom Priority Control System) [0411]. The parameter LagDistance specifies how far the Responder Unit vehicle 223 must travel past the signal before the signal is released to standard control [0412]. In a preferred embodiment, wherein a region has a mature implementation of the Intelligent Transportation Systems (ITS) signals, controllers, and centralized computer-based management systems, traffic signals 303 are controlled using “request signal” and “release signal” messages using the standard ITS protocols for remote signal control and management [0415].), O’Neill does not explicitly teach that the signal light phase model/algorithm being a machine learning model.
However, Brand teaches using a machine learning model in a first response system (Brand e.g. FIG. 2 is a diagram illustrating an example 200 of training and using a machine learning model in connection with autonomous first response routing for improved public safety. The machine learning model training and usage described herein may be performed using a machine learning system. The machine learning system may include or may be included in a computing device, a server, a cloud computing environment, and/or the like, such as the first response system 115 described in more detail elsewhere herein [0036]. As shown by reference number 205, a machine learning model may be trained using a set of observations. The set of observations may be obtained from historical data, such as data gathered during one or more processes described herein. In some implementations, the machine learning system may receive the set of observations (e.g., as input) from the first response system 115, as described elsewhere herein [0037]. As shown by reference number 220, the machine learning system may train a machine learning model using the set of observations and using one or more machine learning algorithms, such as a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, a support vector machine algorithm, and/or the like. After training, the machine learning system may store the machine learning model as a trained machine learning model 225 to be used to analyze new observations [0043].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine O’Neill’s Knowledge-based emergency response system models with Brand’s first response machine learning system in order to train the model to predict, provide a recommendation, and/or perform an automated action (Brand e.g. [0045]).
As per claims 4 and 13, O’Neill teaches the method of claim 3 and the system of claim 12, O’Neill does not explicitly teach however, Brand teaches wherein the plurality pieces of emergency management and control data inputted into the data classification model is preprocessed and filtered, the preprocessing and filtering of the plurality pieces of emergency management and control data being based on history processing information corresponding to the plurality pieces of emergency management and control data (Brand e.g. As shown in FIG. 5, process 500 may include receiving emergency data, traffic data, network performance data, crime data, and gunshot data associated with a geographical area (block 510) [0067]. As further shown in FIG. 5, process 500 may include determining based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data, and using a risk classifier model, a risk level for a location within the geographical area (block 520) [0068]. In some implementations, process 500 includes parsing the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data to generate parsed data in a particular format; performing data cleansing of the parsed data to generate processed data; and storing the processed data in a data structure [0077].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify O’Neill’s Knowledge-based emergency response data to include preprocessing and filtering data prior to use in the classification model as taught by Brand in order to ensure accuracy (Brand e.g. [0014]).
As per claims 5 and 14, O’Neill teaches the method of claim 3 and the system of claim 12, O’Neill does not explicitly teach, however, Brand teaches wherein the method further comprises:
Brand teaches generating an association value of the plurality pieces of emergency management and control data with an emergency event based on a value range of the plurality pieces of emergency management and control data in a preset time period, the geographic regions to which the plurality pieces of emergency management and control data belongs, a type of the emergency event, and a location of the emergency event; (Brand e.g. For example, the first response system may receive emergency data, traffic data, network performance data, crime data, and gunshot data associated with a geographical area and may process the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data, with a risk classifier model, to identify a location within the geographical area. The first response system may utilize the risk classifier model to determine, based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data for the location, a risk level for the location and may process the risk level, the traffic data, and the network performance data for the location, with a deployable location model, to identify an autonomous vehicle to deploy to the location [0009]. The first response system 115 may initially define parameters for categorizing crime risk, and over time may utilize machine learning to modify these parameters to better visualize risk level output based on a normal bell curve distribution. For example, for the risk of “crime,” red may correspond to “high crime,” orange may correspond to “moderate crime,” yellow may correspond to “marginal crime,” and green may correspond to “low crime.” Geographical zones may be further rated based on time of day. For example, a residential zone may be designated a yellow zone from 8 am-8 pm and a red zone 8 pm-8 am [0019].)
Brand teaches determining, based on the association value, the data sets to be processed corresponding to different time periods; (Brand e.g. As shown in FIG. 1B, and by reference number 130, the first response system 115 may determine that a risk classification frequency is satisfied. In some implementations, the risk classification frequency may include a risk classification frequency timer that is satisfied after a particular time period (e.g., in hours, days, weeks, and/or the like). The first response system 115 may determine whether the risk classification frequency timer has been satisfied (e.g., exceeded) before processing the emergency data, the traffic data, the network performance data, the crime data, and/or the gunshot data with one or more models [0015].)
Brand teaches delineating the emergency management and control region corresponding to the temporary management and control region based on the data sets to be processed, and the geographic scope of the geographic regions involved in the data sets to be processed; and (Brand e.g. As further shown in FIG. 1B, and by reference number 135, the first response system 115 may process the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data, with a risk classifier model, to identify one or more locations within the geographical area [0016]. For example, when processing the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data, with the risk classifier model, to identify the one or more locations within the geographical area, the first response system 115 may determine or receive a location sizing parameter to define a size of each of the one or more locations [0016]. In some implementations, the first response system 115 may combine individual risk entries into a “risk block” of addresses with a size that is determined based on a house number grid parameter (e.g., a quantity of risk entries into blocks of one-hundred addresses) [0019].)
Brand teaches controlling the interactive terminal of the smart city GIS to display an electronic map corresponding to the temporary management and control region according to the temporary management and control region (Brand e.g. The first response system 115 may initially define parameters for categorizing crime risk, and over time may utilize machine learning to modify these parameters to better visualize risk level output based on a normal bell curve distribution. For example, for the risk of “crime,” red may correspond to “high crime,” orange may correspond to “moderate crime,” yellow may correspond to “marginal crime,” and green may correspond to “low crime.” Geographical zones may be further rated based on time of day. For example, a residential zone may be designated a yellow zone from 8 am-8 pm and a red zone 8 pm-8 am [0019]. In some implementations, performing the one or more actions includes generating a risk report that identifies the location and the risk level for the location, generating a risk visualization map based on the risk report, and providing the risk report and the risk visualization map for display [0073].).
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine O’Neill’s Knowledge-based emergency response system with Brand’s first response system that determines a risk association value of geographic regions in order to identify an autonomous vehicle to deploy to the location (Brand e.g. [0022]).
As per claims 6 and 15, O’Neill in view of Brand teach the method of claim 5 and the system of claim 13, wherein the generating an association value of the plurality pieces of emergency management and control data with an emergency event based on a value range of the plurality pieces of emergency management and control data in a preset time period, the geographic regions to which the plurality pieces of emergency management and control data belongs, a type of the emergency event, and a location of the emergency event includes:
O’Neill does not explicitly teach, however, Brand teaches generating the association value of the plurality pieces of emergency management and control data with the emergency event using an emergency association model based on the value range of the plurality pieces of emergency management and control data in the preset time period, the geographic regions to which the plurality pieces of emergency management and control data belongs, the type of emergency event, and the location of the emergency event, wherein the emergency association model is a machine learning model, and the emergency association model after being trained is stored in a data processing model library (Brand e.g. The first response system may receive emergency data, traffic data, network performance data, crime data, and gunshot data associated with a geographical area and may process the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data, with a risk classifier model, to identify a location within the geographical area [0009]. The first response system may utilize the risk classifier model to determine, based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data for the location, a risk level for the location and may process the risk level, the traffic data, and the network performance data for the location, with a deployable location model, to identify an autonomous vehicle to deploy to the location [0009]. As shown in FIG. 1B, and by reference number 130, the first response system 115 may determine that a risk classification frequency is satisfied. In some implementations, the risk classification frequency may include a risk classification frequency timer that is satisfied after a particular time period (e.g., in hours, days, weeks, and/or the like). The first response system 115 may determine whether the risk classification frequency timer has been satisfied (e.g., exceeded) before processing the emergency data, the traffic data, the network performance data, the crime data, and/or the gunshot data with one or more models [0015]. The first response system 115 may initially define parameters for categorizing crime risk, and over time may utilize machine learning to modify these parameters to better visualize risk level output based on a normal bell curve distribution. For example, for the risk of “crime,” red may correspond to “high crime,” orange may correspond to “moderate crime,” yellow may correspond to “marginal crime,” and green may correspond to “low crime.” Geographical zones may be further rated based on time of day. For example, a residential zone may be designated a yellow zone from 8 am-8 pm and a red zone 8 pm-8 am [0019]. In some implementations, to determine a risk level for a location of the one or more locations, the first response system 115 utilizes the risk classifier model to count, over a time period, incidents identified in the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data associated with the location. The time period may be in minutes, an hour, hours, and/or the like [0021]. The first response system 115 may conserve computing resources associated with identifying, obtaining, and/or generating historical data for training the one or more of the risk classifier model or the deployable location model relative to other systems for identifying, obtaining, and/or generating historical data for training machine learning models [0033].).
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine O’Neill’s Knowledge-based emergency response system with Brand’s first response system that determines a risk association value of geographic regions in order to identify an autonomous vehicle to deploy to the location (Brand e.g. [0022]).
As per claims 7 and 16, O’Neill in view of Brand teach the method of claim 6 and the system of claim 15, O’Neill does not explicitly teach, however, Brand teaches wherein training data of the emergency association model comprises a plurality of training samples with labels, the labels being determined based on data sets to be processed corresponding to a plurality pieces of sample emergency management and control data included in the training samples, and (Brand e.g. FIG. 2 is a diagram illustrating an example of training and using a machine learning model in connection with autonomous first response routing for improved public safety [0003]. The first response system 115 may conserve computing resources associated with identifying, obtaining, and/or generating historical data for training the one or more of the risk classifier model or the deployable location model relative to other systems for identifying, obtaining, and/or generating historical data for training machine learning models [0033]. The machine learning model training and usage described herein may be performed using a machine learning system [0036]. As shown by reference number 205, a machine learning model may be trained using a set of observations. The set of observations may be obtained from historical data, such as data gathered during one or more processes described herein. In some implementations, the machine learning system may receive the set of observations (e.g., as input) from the first response system 115, as described elsewhere herein [0037]. As an example, a feature set for a set of observations may include a first feature of emergency data, a second feature of traffic data, a third feature of network performance data, and so on. As shown, for a first observation, the first feature may have a value of emergency data 1, the second feature may have a value of traffic data 1, the third feature may have a value of network performance data 1, and so on. These features and feature values are provided as examples and may differ in other examples [0039].)
the method further comprises: obtaining event processing results corresponding to the training samples; predicting association prediction values corresponding to the training samples based on the event processing results; in response to the association prediction values being greater than a predetermined sample value, setting predetermined label values of the labels corresponding to the training samples, the predetermined label values being greater than a predetermined threshold value; classifying the plurality of the training samples into a plurality of training sets based on a plurality of values of the labels corresponding to a plurality of the training samples; and training the emergency association model based on the plurality of training sets (Brand e.g. As shown by reference number 215, the set of observations may be associated with a target variable. The target variable may represent a variable having a numeric value, may represent a variable having a numeric value that falls within a range of values or has some discrete possible values, may represent a variable that is selectable from one of multiple options (e.g., one of multiple classes, classifications, labels, and/or the like), may represent a variable having a Boolean value, and/or the like. A target variable may be associated with a target variable value, and a target variable value may be specific to an observation. In example 200, the target variable is a risk level, which has a value of risk level 1 for the first observation [0040]. The target variable may represent a value that a machine learning model is being trained to predict, and the feature set may represent the variables that are input to a trained machine learning model to predict a value for the target variable. The set of observations may include target variable values so that the machine learning model can be trained to recognize patterns in the feature set that lead to a target variable value. A machine learning model that is trained to predict a target variable value may be referred to as a supervised learning model [0041]. As an example, the trained machine learning model 225 may predict a value of risk level A for the target variable of the risk level for the new observation, as shown by reference number 235. Based on this prediction, the machine learning system may provide a first recommendation, may provide output for determination of a first recommendation, may perform a first automated action, may cause a first automated action to be performed (e.g., by instructing another device to perform the automated action), and/or the like [0045].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine O’Neill’s Knowledge-based emergency response system models with Brand’s first response machine learning system in order to train the model to predict, provide a recommendation, and/or perform an automated action (Brand e.g. [0045]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure include FOR: Zhao, Li-li (CN-106652437-A) “Comprehensive Real-Time Command Intelligent Traffic Management And Control System” and NPL: Y. Xiao, P. Zhang, T. Wang, T. Li and Z. Song, "A Study of the Framework of Smart City Management System Construction," 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE), Hangzhou, China, 2021, pp. 566-569.
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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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