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 .
Status of Claims
This action is in reply to the Appeal Brief filed on 09/02/2025.
Claims 1-20 are currently pending and have been examined.
Claims 1-5 and 11-20 are allowed.
Claims 6-10 are currently rejected.
This action is made NON-FINAL.
Response to Arguments
Applicant’s arguments filed 09/02/2025 have been fully considered an are partially persuasive.
In light of the arguments regarding claims 1 and 11, the previous rejections have been withdrawn and the claims allowed.
Regarding the rejection of claim 6, the claims are broader in scope and still stand rejected as shown in the updated rejections below, specifically the geographical relationship required by claims 1 and 11 for the data that makes up the first and second variables are not present in claim 6.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 6-7 and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nasburg (US 5,696,503), herein Nasburg ‘503 in view of Tatourian et. al. (US 2016/0284212), herein Tatourian and Su et. al. (US 2021/0366279), herein Su.
Regarding claim 6:
Nasburg ‘503 teaches:
A method (The illustrated embodiment of the present invention includes a system capable of performing wide area traffic surveillance and the methods for processing gathered data to achieve this [col 4, lines 36-39]) comprising:
receiving, at a first computing device associated with a first geographic area (The functions of the bottom MATS layer 130 are implemented in three processes entitled tracking node 1 (310), tracking node 2 (320), and tracking node 3 (330) [col 12, lines 45-47]), first vehicle data from a plurality of vehicles (The bottom MATS layer 130 converts OSMs from the SSI layer 120 into a first stage of traffic flow information. The bottom MATS layer 130 develops flow information for individual vehicles by reduction of the data from the SSI layer 120. Flow information developed at the bottom MATS layer 130 includes vehicle velocity, vehicle densities, vehicle classification (e.g. motorcycle, car/pickup, truck, bus, etc.), vehicle behavior (e.g. lane changes), and link times. In addition, a path history is developed for individual vehicles. To develop a path history, a list of sensors which have sensed a particular vehicle is entered in a database. The path histories may be used to assess flow patterns within the roadway system. [col 7, lines 51-64]);
determining a first variable (vehicle velocity, vehicle densities, vehicle classification (e.g. motorcycle, car/pickup, truck, bus, etc.), vehicle behavior (e.g. lane changes), and link times [col 7, lines 51-64]) associated with the first vehicle data (The bottom MATS layer 130 converts OSMs from the SSI layer 120 into a first stage of traffic flow information. The bottom MATS layer 130 develops flow information for individual vehicles by reduction of the data from the SSI layer 120. Flow information developed at the bottom MATS layer 130 includes vehicle velocity, vehicle densities, vehicle classification (e.g. motorcycle, car/pickup, truck, bus, etc.), vehicle behavior (e.g. lane changes), and link times. In addition, a path history is developed for individual vehicles. To develop a path history, a list of sensors which have sensed a particular vehicle is entered in a database. The path histories may be used to assess flow patterns within the roadway system. [col 7, lines 51-64]);
receiving, [at the first computing device], second vehicle data (The bottom MATS layer 130 hence acts as a data source for the model fitting of the top MATS layer 140. Other aggregate flow information (ancillary information) such as data from magnetic loops is also employed in the model fitting process. The result of this processing is a flow assessment for each link in terms of state variables, typically velocity and vehicle density. State variables are described in P. Maybeck, Stochastic models, estimation, and control; 1979; Academic Press; NY, N.Y., which is incorporated herein by reference. [col 8, lines 28-41]) from a second computing device (Information utilized by this process includes aggregated individual flow information 341, 342, 343 from each of the tracking nodes 310, 320, 330, hence, from each of the sensors. This aggregated information is used within the process 340 to model the flow by use of a macro modeling technique. [col 13, lines 61-67]) associated with a second geographic area (Each SSI 210, 220, 230 preferably contains hardware to interface with the attached sensor, firmware and software to detect vehicles and measure a vehicle's fingerprint, a real-time clock to attach the time (time tag) of the vehicle detections, and an interface to a communications system which links the SSIs 210, 220, 230 to the MATS 240 [col 11, lines 9-14]);
determining the first variable (information about the individual vehicle flows is aggregated in the tracking nodes [col 13, lines 51-52]) associated with the first vehicle data (Tracking node 1 (310) provides an understanding, at the individual vehicle level, of the flow within the FOV of sensor 1 [col 12, lines 56-58]);
determining a second variable (information about the individual vehicle flows is aggregated in the tracking nodes [col 13, lines 51-52]) associated with the second vehicle data (Similarly, tracking node 2 (320) is fed directly from SSI 2 (220), and tracking node 3 (330) is fed directly from SSI 3 (230) [col 12, lines 59-61]);
determining whether the first variable and the second variable conform to a predetermined dependency among the variables (an analyze link flow 1-3 process 1910 provides flow analysis of the link between sensor 1 and sensor 3 [col 28, lines 4-5]; a flow parameters 1-2 data flow 1914 provides data to flow analysis of the link between sensor 1 and sensor 2 [col 28, lines 12-14]; This data is utilized in the section models of the analyze section flow process 1940 to develop both operator information and flow assessments for area signal control 270. Flow parameters from the analyze link flow processes 1910, 1920, 1930 to the analyze section flow process 1940 could include lane capacity estimates, traffic incidents detected, and percentage of vehicles leaving one sensor site and arriving at the other sensor site [col 28, lines 43-51]);
identifying an anomaly based on the determination (the link flow state provides a representation of the flow within the link between sensor 1 FOV and sensor 2 FOV. Consequently, a detect traffic incidents process 2030 may detect flow disruptions or traffic incidents upon the link. The signal processing literature contains many well-known algorithms well suited to implement the function of the detect traffic incidents process 2030 [col 29, lines 21-27]); and
Tatourian also teaches:
receiving, at a first computing device associated with a first geographic area, first vehicle data from a plurality of vehicles (In some embodiments, the system 100 may include one or more mobile computing devices 116, typically belonging to an occupant (e.g., a driver, an operator, a passenger, etc.) of a vehicle 102. As discussed in more detail below, the mobile computing devices 116 may be capable of providing additional vehicle data 102 to the traffic analysis server 108. For example, an application may be executed on a mobile computing device 116 that may also provide speed, trajectory, location, and/or other vehicle 102 related information to the traffic analysis server 108, which the traffic analysis server 108 may use to verify the vehicle data received from the in-vehicle computing system 104. [0014]; the traffic analysis server 108 receives the vehicle data and the infrastructure data, and determines traffic patterns based on an analysis of the aggregated vehicle and infrastructure data over time. [0015]);
receiving, [at the first computing device], second vehicle data from a second computing device associated with a second geographic area (In use, the traffic analysis server 108 receives the vehicle data and the infrastructure data, and determines traffic patterns based on an analysis of the aggregated vehicle and infrastructure data over time. [0015]; means for determining whether an anomaly has occurred in the present traffic behavior based on a comparison of the present traffic behavior and the expected traffic behavior. [0094]; examiner notes that data is continually being received and compared to make traffic determinations.);
determining a first variable (The vehicles 102 each include an in-vehicle computing system 104 that is capable of transmitting vehicle data (e.g., speed, trajectory, location, etc.) to the traffic analysis server 108 via one or more of the networks 106. [0013]) associated with the first vehicle data (In use, the traffic analysis server 108 receives the vehicle data and the infrastructure data, and determines traffic patterns based on an analysis of the aggregated vehicle and infrastructure data over time. [0015]);
Nasburg ‘503 does not explicitly teach, however Tatourian teaches:
determining whether the first variable and the second variable conform to a predetermined dependency among the variables (At block 506, the traffic analysis server 108 compares the present traffic behavior determined at block 504 to historical traffic patterns to detect anomalies for the road segment 114. [0045]; a computing device for monitoring vehicle traffic, the computing device comprising a network communication module to receive infrastructure data from one or more infrastructure sensors associated with a road segment of a road vehicle data from one or more vehicles located on the road segment, wherein the infrastructure data is indicative of a characteristic of the road segment, and wherein the vehicle data is indicative of operational characteristics of the corresponding vehicle while the corresponding vehicle traverses the road segment; a traffic pattern determination module to (i) determine a present traffic behavior for the road segment based on the vehicle data and the infrastructure data and (ii) determine an expected traffic behavior for the road segment based on a historical traffic pattern associated with the road segment, wherein the historical traffic pattern is based on historical vehicle data and historical infrastructure data captured during a prior time period; and a traffic pattern analysis module to determine whether an anomaly has occurred in the present traffic behavior based on a comparison of the present traffic behavior and the expected traffic behavior. [0054]); and
identifying an anomaly (The traffic pattern analysis module 440 is configured to identify anomalies for each road segment 114. To do so, the traffic pattern analysis module 440 includes an anomaly detection module 442, an anomaly pattern determination module 444, an anomaly probability calculation module 446, and an anomaly priority determination module 448. The anomaly detection module 442 is configured to detect, or identify, anomalies based on a comparison between the expected traffic behavior and the present traffic behavior. The anomaly pattern determination module 444 is configured to create anomaly patterns for each road segment 114 based on the determined anomalies. The anomaly patterns may be any type of pattern that is indicative of a behavior of the anomaly over a period of time. The anomaly probability calculation module 446 is configured to calculate an anomaly probability for each of the identified anomalies. The anomaly probability is indicative of the likelihood that the corresponding anomaly would occur in the present traffic behavior. The anomaly priority determination module 448 is configured to sort the identified anomalies. In some embodiments, the detected anomalies with the highest probabilities may be sorted such that they are addressed first. For example, the detected anomalies may be sorted based on highest to lowest probability as determined by the calculated anomaly probability for each identified anomaly. [0041]) based on the determination (At block 508, the traffic analysis server 108 determines whether an anomaly was detected. [0045]);
updating driving behavior of a vehicle to autonomously navigate the vehicle to avoid the anomaly (The vehicle control module 464 is configured to assume control of the identified vehicle(s) and take an action based on the response policy associated with the anomaly. For example, the vehicle control module 464 may send a kill command to the identified vehicle(s) that causes the identified vehicle(s) to reduce speed and/or change direction. [0043]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Nasburg ‘503 to include the teachings as taught by Tatourian with a reasonable expectation of success. Tatourian teaches “The traffic analysis server 108 additionally determines whether the received vehicle data and/or the infrastructure data is indicative of an anomaly, or deviation, from the expected traffic behavior based on the road segment 114 and a present time. To detect the anomaly, the traffic analysis server 108 compares the historical traffic patterns to present vehicle data and/or present infrastructure data. The traffic analysis server 108 further monitors the present vehicle data and/or present infrastructure data of adjacent road segments to the road segment 114 in which the anomaly was identified. Accordingly, the traffic analysis server 108 can track the identified anomaly and/or evaluate whether the detected anomaly is valid (e.g., a malicious hack of software of the vehicle 102, a malfunctioning component of the vehicle 102, etc.). Additionally, the traffic analysis server 108 evaluates whether the identified anomaly is associated with a particular vehicle 102, or group of vehicles 102, and may take further action (e.g., notify authorities, disable the vehicle(s) 102, etc.), if further action is required, based on a response policy associated with the anomaly. [Tatourian, 0016]” This allows for a system to identify and correct issues with traffic flow and road conditions.
Su more explicitly teaches:
receiving, at a first computing device (fig. 1, a respective edge gateway module (EGW) 120) associated with a first geographic area (each defined local geographical area 110A, B being managed by and/or in data communication with a respective edge gateway module (EGW) 120 [0036]), first vehicle data from a plurality of vehicles (Among other things, each ICGW 150 is preferably configured to provide V2X communication system access and information exchange with other ICGWs 150 and road infrastructure in the defined local geographical area 110A, B, to collect data from the vehicle on-board modules such as, for example, the speedometer and satellite positioning system, directly or indirectly exchange vehicle collected data with other local ICGWs 150, RSUs 130 and its respective EGW 120 [0039]);
Nasburg ‘503 in view of Tatourian does not explicitly teach, however Su teaches:
receiving, at the first computing device, second vehicle data from a second computing device associated with a second geographic area (use the vehicle collected data and data received from other local ICGWs 150, RSUs 130 and EGW 120 to determine threats and generate alarms, etc., and receive and issue V2X alarms and notifications as well as receive traffic status information and recommendations. [0039]; The RSUs 130 and EGWs 120 preferably use V2N to exchange data with each other [0047]);
determining a first variable associated with each of the plurality of vehicles at the first time based on the first vehicle data (the influence area or map associated with the type of the event E.sub.1 determined at said event RSU.sub.1 may comprise one of a hierarchy of influence area or maps at said event RSU, where each level of the hierarchy defines a respective influence area or map identifying one or more selected RSUs from said remaining RSUs. The set of hierarchy or graded influence area or maps may comprise a primary influence area or map comprising the map area defined for determined event E.sub.1 encompassing remaining RSUs reporting interest values I.sub.E1 in determined event E.sub.1 above a predetermined or calculated level, a secondary influence area or map comprising a map area defined for determined event E.sub.1 which encompasses some of the remaining RSUs reporting interest values I.sub.E1 in determined event E.sub.1 below the predetermined or calculated level but above a lower predetermined or calculated secondary level, and a tertiary influence area or map comprising a map area defined for determined event E.sub.1 which encompasses some of the remaining RSUs reporting interest values I.sub.E1 in determined event E.sub.1 below the lower predetermined or calculated secondary level. [0069]);
determining a second variable associated with the second vehicle data (each ICGW 150 is preferably configured to provide V2X communication system access and information exchange with other ICGWs 150 and road infrastructure in the defined local geographical area 110A, B, to collect data from the vehicle on-board modules such as, for example, the speedometer and satellite positioning system, directly or indirectly exchange vehicle collected data with other local ICGWs 150, RSUs 130 and its respective EGW 120, use the vehicle collected data and data received from other local ICGWs 150, RSUs 130 and EGW 120 to determine threats and generate alarms, etc., and receive and issue V2X alarms and notifications as well as receive traffic status information and recommendations. [0039]);
determining whether the first variable and the second variable conform to a predetermined dependency among the variables (the influence area or map associated with the type of the event E.sub.1 determined at said event RSU.sub.1 may comprise one of a hierarchy of influence area or maps at said event RSU, where each level of the hierarchy defines a respective influence area or map identifying one or more selected RSUs from said remaining RSUs. The set of hierarchy or graded influence area or maps may comprise a primary influence area or map comprising the map area defined for determined event E.sub.1 encompassing remaining RSUs reporting interest values I.sub.E1 in determined event E.sub.1 above a predetermined or calculated level, a secondary influence area or map comprising a map area defined for determined event E.sub.1 which encompasses some of the remaining RSUs reporting interest values I.sub.E1 in determined event E.sub.1 below the predetermined or calculated level but above a lower predetermined or calculated secondary level, and a tertiary influence area or map comprising a map area defined for determined event E.sub.1 which encompasses some of the remaining RSUs reporting interest values I.sub.E1 in determined event E.sub.1 below the lower predetermined or calculated secondary level. [0069]);
identifying an anomaly based on the determination (the EGWs 120 and/or RSUs 130 are configured to process local, real-time and/or low latency data to assist or provide alarms and/or determine threats to road users. [0048]); and
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Nasburg ‘503 in view of Tatourian to include the teachings as taught by Su with a reasonable expectation of success. Su teaches the benefits of “enhancing safety alarm generation and/or threat detection accuracy for vehicles, for example, multiple sources of information such as vehicles, pedestrian devices, roadside infrastructure, and communications network(s), etc. are required at low latency signal processing and delivery levels. The present invention provides an end-to-end V2X network system having a multi-tier system architecture which utilizes information and algorithms performed at different tiers of the V2X network system to enable low latency generation of vehicle/road safety alarms and/or low latency determination of vehicle/road threats. More particularly, the present invention provides an improved method and system for relaying event information in a V2X system. [Su, 0013]”
Regarding claim 7:
Nasburg ‘503 in view of Tatourian and Su teaches all the limitations of claim 6, upon which this claim is dependent.
Tatourian further teaches:
wherein the predetermined dependency among the variables comprises an expected relationship between the variables when an anomaly is not present (The traffic analysis server determines whether anomalies are present in the traffic data through the road segment based on an expected traffic behavior for the road segment. The traffic analysis server determines the expected traffic behavior for the road segment in a particular time window based on a historical traffic pattern associated with the road segment, based on historical vehicle data and historical infrastructure data captured during a prior time window corresponding to the particular time window for that road segment. [abstract]; The expected traffic behavior determination module 434 is configured to determine expected traffic behavior (e.g., expected traffic flow patterns) based on the traffic patterns. The expected traffic behavior may be any type of behaviors exhibited by the vehicles 102 travelling through a road segment 114 at a particular time that corresponds to a time in the future (i.e., one year later than the previous time the historical traffic data was analyzed). For example, the expected traffic behavior may include a characteristic of the traffic flow of the vehicles 102 travelling through the road segment 114, such as a density of the vehicles 102, an average rate of speed of the vehicles 102, an average distance between the vehicles 102, etc. In some embodiments, the expected traffic behavior determination module 434 may use hysteresis and/or various machine learning algorithms to predict expected traffic behavior for the road segment 114 based on the time (i.e., time and date) in which the traffic data was received and detect the anomalies based on the expected traffic behaviors. [0039]).
Regarding claim 9:
Nasburg ‘503 in view of Tatourian and Su teaches all the limitations of claim 6, upon which this claim is dependent.
Tatourian further teaches:
further comprising using machine learning techniques to determine the predetermined dependency among the variables based on previously received vehicle data (In such embodiments, the traffic analysis server 108 may separate the road segments 114 using a machine learning algorithm, which may update the lengths of the particular road segments over time. Additionally, the traffic analysis server 108 determines the traffic patterns for each road segment 114 based on an analysis of the historical vehicle and infrastructure data for that road segment 114 at a given time, or for a given time window (e.g., a one-hour window of time, rush hour, morning, evening, etc.). [0015]; In some embodiments, the expected traffic behavior determination module 434 may use hysteresis and/or various machine learning algorithms to predict expected traffic behavior for the road segment 114 based on the time (i.e., time and date) in which the traffic data was received and detect the anomalies based on the expected traffic behaviors. [0039]).
Regarding claim 10:
Nasburg ‘503 in view of Tatourian and Su teaches all the limitations of claim 6, upon which this claim is dependent.
Tatourian further teaches:
at the first computing device (fig. 4, the traffic analysis server 108), determining a driving rule based on the identified anomaly and transmitting the driving rule to one or more of the plurality of vehicles (In some embodiments, at block 562, the traffic analysis server 108 may additionally or alternatively take control of the vehicle(s) 102 determined to be associated with the validated anomaly. For example, the traffic analysis server 108 may send a kill command to the vehicle(s) 102 determined to be associated with the validated anomaly to force the vehicle(s) 102 to slow down and pull over to the side of the road 112. [0052]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nasburg (US 5,696,503), herein Nasburg ‘503 in view of Tatourian et. al. (US 2016/0284212) and Su et. al. (US 2021/0366279), herein Su and in further view of Nasburg (US 5,801,943), herein Nasburg ‘943.
Regarding claim 8:
Nasburg ‘503 in view of Tatourian and Su teaches all the limitations of claim 6, upon which this claim is dependent.
Nasburg ‘503 in view of Tatourian does not explicitly teach, however Nasburg ‘943 teaches:
wherein the predetermined dependency among the variables is determined by a traffic engineer (A number of traffic micromodels have been developed and are used by traffic engineers for the analysis and explanation of roadway conditions. One typical traffic micromodel is known as the GM car following micromodel, named after research performed by General Motors. The GM car following micromodel is described in Adolf May, Traffic Flow Fundamentals at 167-177, published by Prentice Hall, Englewood Cliffs, N.J. (1990), which is incorporated herein by reference. The users of vehicle tracking systems and simulations are typically traffic engineers. The GM micromodel is both intuitive and simple to setup for typical traffic engineers. [col 3, lines 15-26]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Nasburg ‘503 in view of Tatourian to include the teachings as taught by Nasburg ‘943 with a reasonable expectation of success. Nasburg ‘943 teaches “The micromodel provides the traffic engineer with an intuitive and efficient method for describing or modeling complex traffic intersections and interchanges so that the kinematic behavior of all vehicles within that interchange can be predicted. Parameters provided as inputs by the traffic engineer are mapped directly to a set of differential equations governing the movement through time of all vehicles within the interchange. The result is a compact dynamic model usable in vehicle tracking applications and in graphic simulations, including real time simulations, of vehicles as they proceed through the interchange. [Nasburg ‘943, col 8, lines 4-13]”.
Allowable Subject Matter
Claims 1-5 and 11-20 are allowed.
The following is an examiner’s statement of reasons for allowance: “determining a second variable associated with a second plurality of vehicles in the second geographic area based on the first aggregated vehicle data and the second aggregated vehicle data; determining whether the first and second variables conform to a predetermined dependency among the variables; and identifying an anomaly based on the determination” was unable to be found in an updated search.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Du (US 2020/0320873) discloses systems and methods for determining traffic information of a region. The method may include determining a first region and a second region. The method may also include obtaining a set of links associated with the first region and the second region. The method may also include obtaining a plurality of driving routes of a plurality of vehicles in the first region and the second region in a predetermined time period. The method may also include selecting one or more driving routes that traverse a first boundary of the first region and a second boundary of the second region based on the set of links associated with the first region and the second region. The method may also include determining traffic information of the first region based on information related to the one or more selected driving routes.
Wrobel (US 10,118,604) discloses Methods, systems, and apparatus for managing energy efficiency of a vehicle. The system includes a GPS unit configured to detect a current location of the vehicle. The system includes a network access device configured to receive traffic data. The system includes a navigation unit configured to determine whether traffic is upcoming along a route based on the traffic data. The system includes a speed sensor configured to detect a current speed of the vehicle. The system includes an electronic control unit configured to activate a pre-charge mode when the upcoming slowdown in vehicle speed is determined, the pre-charge mode causing the engine to charge the battery via the motor/generator. The electronic control unit is configured to deactivate the pre-charge mode to prevent charging of the battery by the engine when the detected current speed of the vehicle is below a speed threshold for a set period of time or distance.
Xu (US 11,671,436) discloses a system for producing indicators and warnings of adversarial activities. The system receives multiple networks of transactional data from different sources. Each node of a network of transactional data represents an entity, and each edge represents a relation between entities. A worldview graph is generated by merging the multiple networks of transactional data. Suspicious subgraph regions related to an adversarial activity are identified in the worldview graph through activity detection. The suspicious subgraph regions are used to generate and transmit an alert of the adversarial activity.
Li (US 10,284,619) discloses methods for performing distributed data aggregation include receiving Internet Protocol (IP) traffic from only a first portion of the network. The methods further include utilizing a big data tool to generate a summary of the IP traffic from the first portion of the network, wherein a summary of IP traffic from a second portion of the network is generated by a second network device utilizing its local big data tool. The methods include sending the summary of the IP traffic of the first portion of the network to the third network device, causing the third network device to utilize its local big data tool to generate a summary of the IP traffic of the first and second portion of the network based on the summaries received from the first and second network devices, thereby allowing the IP traffic in the network to be characterized in a distributed manner.
Gratton (US 2021/0081559) discloses methods, systems, and computer program products for managing roadway incidents. A probable origination location of a roadway incident is identified from features of a normalized signal. One or more additional normalized signals within a specified distance of the probable origination location are accessed. The probable origination location is validated, from features of the one or more additional signals, to establish a validated origination location. An event associated with the roadway incident is detected from the features of the normalized signal based on the validated origination location. The detected event includes the validated origination location and a probability that the event is true. Dispatch of resources responding to the roadway incident event is tailored based on the validated origination location and the calculated probability.
Rolf (US 2020/0090503) discloses An approach is provided for detecting traffic anomalies in real-time using sparse probe-data. The approach involves processing probe data collected from a partition of a digital map to determine a probe origin point, a probe destination point, or a combination thereof. The approach also involves generating an origin/destination matrix for the partition based on the origin point, destination point, or combination thereof. The approach further involves calculating an estimated traffic flow for road segments of the partition based on the matrix. The approach also involves determining a road segment from among the plurality for which the estimated traffic flow differs by more than a threshold value from an observed traffic flow indicated by the probe data for the least one road segment. The approach further involves providing data to indicate a detection of the traffic anomaly on the at least one road segment based on the difference.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Scott R Jagolinzer whose telephone number is (571)272-4180. The examiner can normally be reached M-Th 8AM - 4PM Eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at (571)272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Scott R. Jagolinzer
Examiner
Art Unit 3665
/S.R.J./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665