DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/23/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the Claims
Claims 1-21 have been examined.
Claims 20 and 22 have been cancelled.
Abstract
The abstract of the disclosure is objected to because The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 wards while current abstract contain 171 words. The form and legal phraseology often used in patient claims, such as “means” and “said,” should be avoided. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details. Correction is required. See MPEP § 608.01 (b).
Note:- following words used interchangeable such as “fiber/fibre” and “meter/metres”.
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.
Claim(s) 1-5, 8-13, 16-19, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ionescu (US20230343209A1), and further in view of Crickmore (US20160078760A1).
Claim.1 Ionescu discloses a method of estimating one or more properties of traffic passing along segments of a road network, the traffic comprising a plurality of vehicles (see at least abstract, a method for generating traffic data indicative of traffic volume and/or traffic density within a navigate network in an area covered by an electronic map, generally comprises obtaining data indicative of a count of devices associated with vehicles traversing the navigable element represented by the segment in respect of a given time, p1, generating data indicative of traffic volume within a navigable network. The navigable networks is in an area covered by an electronic map, the electronic map comprising a plurality of segments representing navigable elements of the navigable network), the method comprising: receiving, from a radio navigation receiver located at each vehicle of a subset of said vehicles, one or more first properties of each of the vehicles in the subset of vehicles (see at least fig.1-3, p8-9, a certain number of vehicles are associated with devices including position detecting means (such as a GPS device). Such devices may transmit positional data indicative of their position, and hence that of the vehicle, with respect to time, the probe data transmitted by devices associated with vehicles therefore provides an indication of the movement of vehicles through the network, the devices associated with vehicles that transmit probe data may be devices running navigation applications, probe data may include positional data obtained from any device associated with a vehicle, and having position determining capability. For example, the device may comprise means for accessing and receiving information from WiFi access points or cellular communication networks, such as a GSM device, and using this information to determine its location, p256, the GPS is a satellite-radio based navigation system, p30, obtaining data indicative of a count of devices associated with vehicles traversing the navigable element represented by the segment in respect of a given time, wherein the count of devices is based on positional data and associated timing data relating to the movement of a plurality of devices associated with vehicles along the navigable element represented by the segment); using the signals representing acoustic vibration caused by said traffic to determine one or more second properties of said traffic in the subset of segments (see at least fig.1-2,8-10, p22-25, traffic flow detectors, such as induction loops, may be used to directly measure a count of vehicles along a navigable element, the measured total traffic volume for a given navigable segment s at a time t is expressed as Y (s,t), the total traffic volume Y (s,t) for a segment may be obtained corresponding to the measured count of vehicles passing along the road element represented by the segment at the relevant time, p29-31, using the determined count data and a scaling coefficient to obtain data indicative of an estimated traffic volume for the segment in respect of the given time, wherein the scaling coefficient is a time dependent scaling coefficient, and the method comprises using the scaling coefficient in respect of the given time in obtaining the estimated traffic volume for the segment, using two sets of induction loops, the first set of induction loops resulted in a coefficient k=5.68, the observed probe data and induction loop data for the second set, p57, a scaling coefficient for a segment of interest is derived for different times based on a comparison of traffic counts based on probe data and traffic detector data for at least some segments for which both types of data are available for different times, p229, a signal such as an electronic signal over wires, an optical signal or a radio signal such as to a satellite or the like); and combining the received first properties of the vehicles in the subset of vehicles with the determined second properties of the traffic in the subset of segments, to estimate one or more third properties of the traffic (see at fig.9-10, p319-326, The remaining road segments in this map area form a second subset M={s.sub.r|0≤i<R} of R road segments s.sub.r that have no association with a traffic flow detector. Thus, L={s.sub.i|0≤i<N} and M={s.sub.r|0≤r<R} and S=L∪M with |S|=N+R, time dependent values of the scaling coefficient determined for segments which are associated with induction loops, through comparison of traffic count data based on probe data and measured induction loop data, may be used to infer time dependent scaling coefficient values for use in determining a traffic volume for segments for which there is no measured traffic information, p21, properties such as traffic volume, measured traffic volume or count of traversals, whether measured of according to probe data, may be referred to herein in relation to the navigate segment e.g., road segment of the electronic map representing the navigable element e.g., road element of the navigate network e.g. road work to which they relate).
Ionescu does not discloses using distributed acoustic sensing to generate, as a function of time and of position along a subset of the segments of the road network, signals representing acoustic vibration caused by said traffic at one or more sensing optical fibers that extend along the subset of segments.
However, Crickmore discloses using distributed acoustic sensing to generate, as a function of time and of position along a subset of the segments of the road network, signals representing acoustic vibration caused by said traffic at one or more sensing optical fibers that extend along the subset of segments (see at least fig.1-5, abstract, the noise feature (104) is arranged to generate a characteristic acoustic signature when traversed by the wheels of a vehicle (105) traveling within a lane of the road, a distributed acoustic sensor (102,103) is deployed to detect occurrences of the characteristic acoustic signature, the acoustic signals from a wheel crossing both elements can be detected and used to determine the vehicle speed, a plurality of noise features may be located in different lanes of a multi-lane road with noise features in different lanes arranged to generate different characteristic acoustic signatures, p1, for road traffic monitoring using distributed acoustic fiber optic sensing, p6, to monitor the overall volume of traffic and the flow of traffic but also the make-up of the traffic, i.e., the types of vehicles, e.g. heavy goods vehicles/trucks, light commercial vehicles/vans, large passenger cars/SUVs, midsize/compact passenger cars or motorcycles for example, determining the relative proportion of various types of vehicles may also be useful for control, planning and maintenance purpose, p73-77, a given sensing portion of optical fibre will only detect a strong acoustic response from one noise feature).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Ionescu to include using distributed acoustic sensing to generate, as a function of time and of position along a subset of the segments of the road network, signals representing acoustic vibration caused by said traffic at one or more sensing optical fibers that extend along the subset of segments by Crickmore in order to detect the number, speed and/or type of vehicles travelling on a road (see Crickmore’s abstract).
Claim.2 Ionescu discloses wherein the one or more first properties of each of the vehicles in the subset of vehicles comprise one or more of: positions of said vehicles; positions of said vehicles to a spatial precision of worse than 5 metres or worse than 10 metres; directions of travel of said vehicles; and velocities or speeds of said vehicles (see at least p135, the selection of the subset of one or more reference segments may be based at least in part on a proximity of a reference segment to the given segment, the proximity may be a temporal and/or spatial proximity, predefined number of the closet reference segments in terms of travel time or distance, p308, the position data sample may include a longitude value and a latitude value (both with a typical accuracy of around 10 meters), p256, determining continuous position, velocity, time, and in some instances direction information for an unlimited number of users, p319-326, The remaining road segments in this map area form a second subset M={s.sub.r|0≤i<R} of R road segments s.sub.r that have no association with a traffic flow detector. Thus, L={s.sub.i|0≤i<N} and M={s.sub.r|0≤r<R} and S=L∪M with |S|=N+R, time dependent values of the scaling coefficient determined for segments which are associated with induction loops, through comparison of traffic count data based on probe data and measured induction loop data, may be used to infer time dependent scaling coefficient values for use in determining a traffic volume for segments for which there is no measured traffic information).
Claim.3 Ionescu discloses wherein the one or more first properties of each of the vehicles in the subset of vehicles are sent by, or received from, each vehicle no more than every 10 seconds(see at least fig.16, p135, the selection of the subset of one or more reference segments may be based at least in part on a proximity of a reference segment to the given segment, the proximity may be a temporal and/or spatial proximity, predefined number of the closet reference segments in terms of travel time or distance, p308, the position data sample may include a longitude value and a latitude value (both with a typical accuracy of around 10 meters), p303, an update rate averaging around 1000 updates per second, processes and distribute real time traffic information, p324, a high resolution time T(counting microseconds since a reference time)).
Claim.4 Ionescu discloses wherein the one or more second properties of the traffic in the subset of segments comprise one or more of: a count or density of vehicles in the traffic; and a flow rate or velocity of the traffic (see at least p335, the traffic information server uses the probe profile for road segments to determine a parameter indicative of profile similarity. The similarity value establishes links between a road segment s.sub.r of interest and all road segments with a traffic detector {s.sub.i|i=1 . . . M}. The road segments with a traffic detector may be referred to as “reference segments”. In other words a probe profile of a road segment that has no association with a traffic flow detector is matched to each of the probe profiles of road segments s.sub.i with a traffic detector).
Claim.5 Ionescu discloses wherein the one or more second properties of the traffic in the subset of segments comprise one or more of: positions of particular vehicles; positions of particular vehicles to a spatial precision of better than 10 metres or of better than 5 metres; velocities of particular vehicles; and categories or sizes of particular vehicles (see at least p135, the selection of the subset of one or more reference segments may be based at least in part on a proximity of a reference segment to the given segment, the proximity may be a temporal and/or spatial proximity, predefined number of the closet reference segments in terms of travel time or distance, p308, the position data sample may include a longitude value and a latitude value (both with a typical accuracy of around 10 meters), p256, determining continuous position, velocity, time, and in some instances direction information for an unlimited number of users, p319-326, The remaining road segments in this map area form a second subset M={s.sub.r|0≤i<R} of R road segments s.sub.r that have no association with a traffic flow detector. Thus, L={s.sub.i|0≤i<N} and M={s.sub.r|0≤r<R} and S=L∪M with |S|=N+R, time dependent values of the scaling coefficient determined for segments which are associated with induction loops, through comparison of traffic count data based on probe data and measured induction loop data, may be used to infer time dependent scaling coefficient values for use in determining a traffic volume for segments for which there is no measured traffic information).
Claim.8 Ionescu discloses wherein the one or more third properties of the traffic comprise one or more of: an estimate of a proportion of the vehicles of the traffic within one or more particular segments of the segments subset, that are also within the vehicles subset(see at fig.9-10, p319-326, The remaining road segments in this map area form a second subset M={s.sub.r|0≤i<R} of R road segments s.sub.r that have no association with a traffic flow detector. Thus, L={s.sub.i|0≤i<N} and M={s.sub.r|0≤r<R} and S=L∪M with |S|=N+R, time dependent values of the scaling coefficient determined for segments which are associated with induction loops, through comparison of traffic count data based on probe data and measured induction loop data, may be used to infer time dependent scaling coefficient values for use in determining a traffic volume for segments for which there is no measured traffic information, p21, properties such as traffic volume, measured traffic volume or count of traversals, whether measured of according to probe data, may be referred to herein in relation to the navigate segment e.g., road segment of the electronic map representing the navigable element e.g., road element of the navigate network e.g. road work to which they relate).
Ionescu does not discloses an estimate of the proportion of the vehicles of the vehicles subset, within one or more particular segments, that fall into each of two or more different categories or sizes of vehicles; and an estimate of the proportion of vehicles in each of two or more different categories, within one or more particular segments, that are found in the vehicles subset.
However, Crickmore discloses an estimate of the proportion of the vehicles of the vehicles subset, within one or more particular segments, that fall into each of two or more different categories or sizes of vehicles; and an estimate of the proportion of vehicles in each of two or more different categories, within one or more particular segments, that are found in the vehicles subset (see at least fig.1-5, abstract, the noise feature (104) is arranged to generate a characteristic acoustic signature when traversed by the wheels of a vehicle (105) traveling within a lane of the road, a distributed acoustic sensor (102,103) is deployed to detect occurrences of the characteristic acoustic signature, the acoustic signals from a wheel crossing both elements can be detected and used to determine the vehicle speed, a plurality of noise features may be located in different lanes of a multi-lane road with noise features in different lanes arranged to generate different characteristic acoustic signatures, p1, for road traffic monitoring using distributed acoustic fiber optic sensing, p6, to monitor the overall volume of traffic and the flow of traffic but also the make-up of the traffic, i.e., the types of vehicles, e.g. heavy goods vehicles/trucks, light commercial vehicles/vans, large passenger cars/SUVs, midsize/compact passenger cars or motorcycles for example, determining the relative proportion of various types of vehicles may also be useful for control, planning and maintenance purpose, p73-77, a given sensing portion of optical fibre will only detect a strong acoustic response from one noise feature).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Ionescu to include an estimate of the proportion of the vehicles of the vehicles subset, within one or more particular segments, that fall into each of two or more different categories or sizes of vehicles; and an estimate of the proportion of vehicles in each of two or more different categories, within one or more particular segments, that are found in the vehicles subset by Crickmore in order to detect the number, speed and/or type of vehicles travelling on a road (see Crickmore’s abstract).
Claim.9 Ionescu discloses wherein the one or more third properties of the traffic are estimated from the one or more first properties with compensation using an estimate of a proportion of the vehicles of the traffic within one or more particular segments of the segments subset, that are also within the vehicles subset (see at fig.9-10, p319-326, The remaining road segments in this map area form a second subset M={s.sub.r|0≤i<R} of R road segments s.sub.r that have no association with a traffic flow detector. Thus, L={s.sub.i|0≤i<N} and M={s.sub.r|0≤r<R} and S=L∪M with |S|=N+R, time dependent values of the scaling coefficient determined for segments which are associated with induction loops, through comparison of traffic count data based on probe data and measured induction loop data, may be used to infer time dependent scaling coefficient values for use in determining a traffic volume for segments for which there is no measured traffic information, p21, properties such as traffic volume, measured traffic volume or count of traversals, whether measured of according to probe data, may be referred to herein in relation to the navigate segment e.g., road segment of the electronic map representing the navigable element e.g., road element of the navigate network e.g. road work to which they relate).
Claim.10 Ionescu does not discloses wherein the one or more third properties of the traffic comprise one or more of: a count, density, flow rate or velocity of the traffic within one or more segments of the segments subset; a count, density, flow rate or velocity of the traffic in one or more segments outside the segments subset; an estimated journey time across the road network; an optimised vehicle route across the road network; and routes of particular vehicles.
However, Crickmore discloses wherein the one or more third properties of the traffic comprise one or more of: a count, density, flow rate or velocity of the traffic within one or more segments of the segments subset; a count, density, flow rate or velocity of the traffic in one or more segments outside the segments subset; an estimated journey time across the road network; an optimised vehicle route across the road network; and routes of particular vehicles (see at least fig.1-5, abstract, the noise feature (104) is arranged to generate a characteristic acoustic signature when traversed by the wheels of a vehicle (105) traveling within a lane of the road, a distributed acoustic sensor (102,103) is deployed to detect occurrences of the characteristic acoustic signature, the acoustic signals from a wheel crossing both elements can be detected and used to determine the vehicle speed, a plurality of noise features may be located in different lanes of a multi-lane road with noise features in different lanes arranged to generate different characteristic acoustic signatures, p1, for road traffic monitoring using distributed acoustic fiber optic sensing, p6, to monitor the overall volume of traffic and the flow of traffic but also the make-up of the traffic, i.e., the types of vehicles, e.g. heavy goods vehicles/trucks, light commercial vehicles/vans, large passenger cars/SUVs, midsize/compact passenger cars or motorcycles for example, determining the relative proportion of various types of vehicles may also be useful for control, planning and maintenance purpose, p73-77, a given sensing portion of optical fibre will only detect a strong acoustic response from one noise feature).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Ionescu to include wherein the one or more third properties of the traffic comprise one or more of: a count, density, flow rate or velocity of the traffic within one or more segments of the segments subset; a count, density, flow rate or velocity of the traffic in one or more segments outside the segments subset; an estimated journey time across the road network; an optimised vehicle route across the road network; and routes of particular vehicles by Crickmore in order to detect the number, speed and/or type of vehicles travelling on a road (see Crickmore’s abstract).
Claim.11 Ionescu discloses wherein by using the second properties, the one or more third properties of the traffic are estimated at a higher temporal and/or a higher spatial resolution than the corresponding temporal and/or spatial resolutions of the first properties (see at fig.9-10, p319-326, The remaining road segments in this map area form a second subset M={s.sub.r|0≤i<R} of R road segments s.sub.r that have no association with a traffic flow detector. Thus, L={s.sub.i|0≤i<N} and M={s.sub.r|0≤r<R} and S=L∪M with |S|=N+R, time dependent values of the scaling coefficient determined for segments which are associated with induction loops, through comparison of traffic count data based on probe data and measured induction loop data, may be used to infer time dependent scaling coefficient values for use in determining a traffic volume for segments for which there is no measured traffic information, p21, properties such as traffic volume, measured traffic volume or count of traversals, whether measured of according to probe data, may be referred to herein in relation to the navigate segment e.g., road segment of the electronic map representing the navigable element e.g., road element of the navigate network e.g. road work to which they relate).
Claim.12 Ionescu discloses wherein by using the second properties, the one or more third properties define a particular lane of a roadway within which a particular vehicle is travelling (see at least p40, traffic volumes are reported in vehicles per hour (or even vehicles per hour per lane for multi-lane roadways)).
Claim.13 Ionescu discloses further comprising providing route guidance to a driver of a vehicle based on the one or more third properties of the traffic estimated at a higher temporal and/or a higher spatial resolution than the corresponding temporal and/or spatial resolutions of the first properties (see at least p135, the selection of the subset of one or more reference segments may be based at least in part on a proximity of a reference segment to the given segment, the proximity may be a temporal and/or spatial proximity, predefined number of the closet reference segments in terms of travel time or distance, p308, the position data sample may include a longitude value and a latitude value (both with a typical accuracy of around 10 meters), p256, determining continuous position, velocity, time, and in some instances direction information for an unlimited number of users, p319-326, The remaining road segments in this map area form a second subset M={s.sub.r|0≤i<R} of R road segments s.sub.r that have no association with a traffic flow detector. Thus, L={s.sub.i|0≤i<N} and M={s.sub.r|0≤r<R} and S=L∪M with |S|=N+R, time dependent values of the scaling coefficient determined for segments which are associated with induction loops, through comparison of traffic count data based on probe data and measured induction loop data, may be used to infer time dependent scaling coefficient values for use in determining a traffic volume for segments for which there is no measured traffic information).
Claim.16 Ionescu discloses apparatus for estimating one or more properties of traffic passing along segments of a road network, the traffic comprising a plurality of vehicles (see at least abstract, a method for generating traffic data indicative of traffic volume and/or traffic density within a navigate network in an area covered by an electronic map, generally comprises obtaining data indicative of a count of devices associated with vehicles traversing the navigable element represented by the segment in respect of a given time, p1, generating data indicative of traffic volume within a navigable network. The navigable networks is in an area covered by an electronic map, the electronic map comprising a plurality of segments representing navigable elements of the navigable network), the apparatus being arranged: to receive, from a radio navigation receiver located at each vehicle of a subset of said vehicles, one or more first properties of each of the vehicles in the subset of vehicles (see at least fig.1-3, p8-9, a certain number of vehicles are associated with devices including position detecting means (such as a GPS device). Such devices may transmit positional data indicative of their position, and hence that of the vehicle, with respect to time, the probe data transmitted by devices associated with vehicles therefore provides an indication of the movement of vehicles through the network, the devices associated with vehicles that transmit probe data may be devices running navigation applications, probe data may include positional data obtained from any device associated with a vehicle, and having position determining capability. For example, the device may comprise means for accessing and receiving information from WiFi access points or cellular communication networks, such as a GSM device, and using this information to determine its location, p256, the GPS is a satellite-radio based navigation system, p30, obtaining data indicative of a count of devices associated with vehicles traversing the navigable element represented by the segment in respect of a given time, wherein the count of devices is based on positional data and associated timing data relating to the movement of a plurality of devices associated with vehicles along the navigable element represented by the segment); to use the signals representing acoustic vibration caused by said traffic to determine one or more second properties of said traffic in the subset of segments(see at least fig.1-2,8-10, p22-25, traffic flow detectors, such as induction loops, may be used to directly measure a count of vehicles along a navigable element, the measured total traffic volume for a given navigable segment s at a time t is expressed as Y (s,t), the total traffic volume Y (s,t) for a segment may be obtained corresponding to the measured count of vehicles passing along the road element represented by the segment at the relevant time, p29-31, using the determined count data and a scaling coefficient to obtain data indicative of an estimated traffic volume for the segment in respect of the given time, wherein the scaling coefficient is a time dependent scaling coefficient, and the method comprises using the scaling coefficient in respect of the given time in obtaining the estimated traffic volume for the segment, using two sets of induction loops, the first set of induction loops resulted in a coefficient k=5.68, the observed probe data and induction loop data for the second set, p57, a scaling coefficient for a segment of interest is derived for different times based on a comparison of traffic counts based on probe data and traffic detector data for at least some segments for which both types of data are available for different times, p229, a signal such as an electronic signal over wires, an optical signal or a radio signal such as to a satellite or the like); and to combine the received first properties of the vehicles in the subset of vehicles with the determined second properties of the traffic in the subset of segments, to estimate one or more third properties of the traffic (see at fig.9-10, p319-326, The remaining road segments in this map area form a second subset M={s.sub.r|0≤i<R} of R road segments s.sub.r that have no association with a traffic flow detector. Thus, L={s.sub.i|0≤i<N} and M={s.sub.r|0≤r<R} and S=L∪M with |S|=N+R, time dependent values of the scaling coefficient determined for segments which are associated with induction loops, through comparison of traffic count data based on probe data and measured induction loop data, may be used to infer time dependent scaling coefficient values for use in determining a traffic volume for segments for which there is no measured traffic information, p21, properties such as traffic volume, measured traffic volume or count of traversals, whether measured of according to probe data, may be referred to herein in relation to the navigate segment e.g., road segment of the electronic map representing the navigable element e.g., road element of the navigate network e.g. road work to which they relate).
Ionescu does not discloses to receive, from one or more distributed acoustic sensors each comprising one or more sensing optical fibers that extend along a subset of the segments of the road network, signals representing acoustic vibration caused by said traffic as a function of time and of position along the subset of segments.
However, Crickmore discloses to receive, from one or more distributed acoustic sensors each comprising one or more sensing optical fibers that extend along a subset of the segments of the road network, signals representing acoustic vibration caused by said traffic as a function of time and of position along the subset of segments (see at least fig.1-5, abstract, the noise feature (104) is arranged to generate a characteristic acoustic signature when traversed by the wheels of a vehicle (105) traveling within a lane of the road, a distributed acoustic sensor (102,103) is deployed to detect occurrences of the characteristic acoustic signature, the acoustic signals from a wheel crossing both elements can be detected and used to determine the vehicle speed, a plurality of noise features may be located in different lanes of a multi-lane road with noise features in different lanes arranged to generate different characteristic acoustic signatures, p1, for road traffic monitoring using distributed acoustic fiber optic sensing, p6, to monitor the overall volume of traffic and the flow of traffic but also the make-up of the traffic, i.e., the types of vehicles, e.g. heavy goods vehicles/trucks, light commercial vehicles/vans, large passenger cars/SUVs, midsize/compact passenger cars or motorcycles for example, determining the relative proportion of various types of vehicles may also be useful for control, planning and maintenance purpose, p73-77, a given sensing portion of optical fibre will only detect a strong acoustic response from one noise feature).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Ionescu to include to receive, from one or more distributed acoustic sensors each comprising one or more sensing optical fibers that extend along a subset of the segments of the road network, signals representing acoustic vibration caused by said traffic as a function of time and of position along the subset of segments by Crickmore in order to detect the number, speed and/or type of vehicles travelling on a road (see Crickmore’s abstract).
Claim.17 Ionescu does not discloses further comprising said one or more distributed acoustic sensors.
However, Crickmore discloses further comprising said one or more distributed acoustic sensors (see at least fig.1-5, abstract, the noise feature (104) is arranged to generate a characteristic acoustic signature when traversed by the wheels of a vehicle (105) traveling within a lane of the road, a distributed acoustic sensor (102,103) is deployed to detect occurrences of the characteristic acoustic signature, the acoustic signals from a wheel crossing both elements can be detected and used to determine the vehicle speed, a plurality of noise features may be located in different lanes of a multi-lane road with noise features in different lanes arranged to generate different characteristic acoustic signatures, p1, for road traffic monitoring using distributed acoustic fiber optic sensing, p6, to monitor the overall volume of traffic and the flow of traffic but also the make-up of the traffic, i.e., the types of vehicles, e.g. heavy goods vehicles/trucks, light commercial vehicles/vans, large passenger cars/SUVs, midsize/compact passenger cars or motorcycles for example, determining the relative proportion of various types of vehicles may also be useful for control, planning and maintenance purpose, p73-77, a given sensing portion of optical fibre will only detect a strong acoustic response from one noise feature).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Ionescu to include further comprising said one or more distributed acoustic sensors by Crickmore in order to detect the number, speed and/or type of vehicles travelling on a road (see Crickmore’s abstract).
Claim.18 Ionescu discloses wherein the one or more first properties of each of the vehicles in the subset of vehicles comprise one or more of: positions of said vehicles; positions of said vehicles to a spatial precision of worse than 5 metres or worse than 10 metres; directions of travel of said vehicles; and velocities or speeds of said vehicles (see at least p135, the selection of the subset of one or more reference segments may be based at least in part on a proximity of a reference segment to the given segment, the proximity may be a temporal and/or spatial proximity, predefined number of the closet reference segments in terms of travel time or distance, p308, the position data sample may include a longitude value and a latitude value (both with a typical accuracy of around 10 meters), p256, determining continuous position, velocity, time, and in some instances direction information for an unlimited number of users, p319-326, The remaining road segments in this map area form a second subset M={s.sub.r|0≤i<R} of R road segments s.sub.r that have no association with a traffic flow detector. Thus, L={s.sub.i|0≤i<N} and M={s.sub.r|0≤r<R} and S=L∪M with |S|=N+R, time dependent values of the scaling coefficient determined for segments which are associated with induction loops, through comparison of traffic count data based on probe data and measured induction loop data, may be used to infer time dependent scaling coefficient values for use in determining a traffic volume for segments for which there is no measured traffic information).
Claim.19 Ionescu discloses wherein the one or more second properties of the traffic in the subset of segments comprise one or more of: a count or density of vehicles in the traffic; a flow rate or velocity of the traffic (see at least p335, the traffic information server uses the probe profile for road segments to determine a parameter indicative of profile similarity. The similarity value establishes links between a road segment s.sub.r of interest and all road segments with a traffic detector {s.sub.i|i=1 . . . M}. The road segments with a traffic detector may be referred to as “reference segments”. In other words a probe profile of a road segment that has no association with a traffic flow detector is matched to each of the probe profiles of road segments s.sub.i with a traffic detector); positions of particular vehicles; velocities of particular vehicles; categories or sizes of particular vehicles (see at least p135, the selection of the subset of one or more reference segments may be based at least in part on a proximity of a reference segment to the given segment, the proximity may be a temporal and/or spatial proximity, predefined number of the closet reference segments in terms of travel time or distance, p308, the position data sample may include a longitude value and a latitude value (both with a typical accuracy of around 10 meters), p256, determining continuous position, velocity, time, and in some instances direction information for an unlimited number of users, p319-326, The remaining road segments in this map area form a second subset M={s.sub.r|0≤i<R} of R road segments s.sub.r that have no association with a traffic flow detector. Thus, L={s.sub.i|0≤i<N} and M={s.sub.r|0≤r<R} and S=L∪M with |S|=N+R, time dependent values of the scaling coefficient determined for segments which are associated with induction loops, through comparison of traffic count data based on probe data and measured induction loop data, may be used to infer time dependent scaling coefficient values for use in determining a traffic volume for segments for which there is no measured traffic information).
Ionescu does not discloses one or more properties of queues of said traffic, and wherein the one or more third properties of the traffic comprise or more of an estimate of a proportion of the vehicles of the traffic within one or more particular segments of the segments subset that are also within the vehicles subset, a count, density, flow rate or velocity of the traffic within one or more segments of the segments subset; a count, density, flow rate of velocity of the traffic in one or more s outside the segments subset; an estimated journey time across the road network; an optimised vehicle route across the road network; and routes of particular vehicles.
However, Crickmore discloses one or more properties of queues of said traffic, and wherein the one or more third properties of the traffic comprise or more of an estimate of a proportion of the vehicles of the traffic within one or more particular segments of the segments subset that are also within the vehicles subset, a count, density, flow rate or velocity of the traffic within one or more segments of the segments subset; a count, density, flow rate of velocity of the traffic in one or more s outside the segments subset; an estimated journey time across the road network; an optimised vehicle route across the road network; and routes of particular vehicles (see at least fig.1-5, abstract, the noise feature (104) is arranged to generate a characteristic acoustic signature when traversed by the wheels of a vehicle (105) traveling within a lane of the road, a distributed acoustic sensor (102,103) is deployed to detect occurrences of the characteristic acoustic signature, the acoustic signals from a wheel crossing both elements can be detected and used to determine the vehicle speed, a plurality of noise features may be located in different lanes of a multi-lane road with noise features in different lanes arranged to generate different characteristic acoustic signatures, p1, for road traffic monitoring using distributed acoustic fiber optic sensing, p6, to monitor the overall volume of traffic and the flow of traffic but also the make-up of the traffic, i.e., the types of vehicles, e.g. heavy goods vehicles/trucks, light commercial vehicles/vans, large passenger cars/SUVs, midsize/compact passenger cars or motorcycles for example, determining the relative proportion of various types of vehicles may also be useful for control, planning and maintenance purpose, p73-77, a given sensing portion of optical fibre will only detect a strong acoustic response from one noise feature).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Ionescu to include one or more properties of queues of said traffic, and wherein the one or more third properties of the traffic comprise or more of an estimate of a proportion of the vehicles of the traffic within one or more particular segments of the segments subset that are also within the vehicles subset, a count, density, flow rate or velocity of the traffic within one or more segments of the segments subset; a count, density, flow rate of velocity of the traffic in one or more s outside the segments subset; an estimated journey time across the road network; an optimised vehicle route across the road network; and routes of particular vehicles by Crickmore in order to detect the number, speed and/or type of vehicles travelling on a road (see Crickmore’s abstract).
Claim.21 Ionescu does not discloses further comprising one or more of: a traffic signal control system arranged to receive said estimated one or more third properties and to provide control signals to the traffic using the estimated one or more third properties; and a satellite navigation service 28 arranged to receive said estimated one or more third properties and to provide navigation services to one or more vehicles within the road network using the estimated one or more third properties.
However, Crickmore discloses further comprising one or more of: a traffic signal control system arranged to receive said estimated one or more third properties and to provide control signals to the traffic using the estimated one or more third properties; and a satellite navigation service 28 arranged to receive said estimated one or more third properties and to provide navigation services to one or more vehicles within the road network using the estimated one or more third properties (see at least fig.1-5, abstract, the noise feature (104) is arranged to generate a characteristic acoustic signature when traversed by the wheels of a vehicle (105) traveling within a lane of the road, a distributed acoustic sensor (102,103) is deployed to detect occurrences of the characteristic acoustic signature, the acoustic signals from a wheel crossing both elements can be detected and used to determine the vehicle speed, a plurality of noise features may be located in different lanes of a multi-lane road with noise features in different lanes arranged to generate different characteristic acoustic signatures, p1, for road traffic monitoring using distributed acoustic fiber optic sensing, p6, to monitor the overall volume of traffic and the flow of traffic but also the make-up of the traffic, i.e., the types of vehicles, e.g. heavy goods vehicles/trucks, light commercial vehicles/vans, large passenger cars/SUVs, midsize/compact passenger cars or motorcycles for example, determining the relative proportion of various types of vehicles may also be useful for control, planning and maintenance purpose, p73-77, a given sensing portion of optical fibre will only detect a strong acoustic response from one noise feature).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Ionescu to include further comprising one or more of: a traffic signal control system arranged to receive said estimated one or more third properties and to provide control signals to the traffic using the estimated one or more third properties; and a satellite navigation service 28 arranged to receive said estimated one or more third properties and to provide navigation services to one or more vehicles within the road network using the estimated one or more third properties by Crickmore in order to detect the number, speed and/or type of vehicles travelling on a road (see Crickmore’s abstract).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ionescu (US20230343209A1), and Crickmore (US20160078760A1) as applied to claim 1 above, and further in view of Narisetty (US20220375338A1).
Claim.6 Ionescu and Crickmore do not discloses wherein the one or more second properties of the traffic in the subset of segments comprise one or more properties of queues of said traffic, wherein said one or more properties of queues of said traffic optionally comprise one or more of: the presence of a queue, the spatial positions of front and/or back boundaries of a queue, the spatial length of a queue, and the time expected for a vehicle to remain in the queue.
However, Narisetty discloses wherein the one or more second properties of the traffic in the subset of segments comprise one or more properties of queues of said traffic, wherein said one or more properties of queues of said traffic optionally comprise one or more of: the presence of a queue, the spatial positions of front and/or back boundaries of a queue, the spatial length of a queue, and the time expected for a vehicle to remain in the queue (see at least fig.1-6, p72, the enhancements 420 correspond to the traffic flow properties including, but not limited to, occupancy of a lane/roadway, weight and dimensions of vehicle(s), damage incurred by the roadway. This correspondence can be understood as an extraction of information essential for estimating the effect of a vehicle's presence on a roadway. Such effects can be estimated by analyzing the amplitude and characteristics of the vibration patterns of each vehicle. From each of the lines in the enhanced waterfall data 421 and 422, it is possible to determine the dimensions and/or weight of a corresponding vehicle. Dimensions of a vehicle in waterfall data 421 and 422 should, in general, be proportional to the width of said vehicle's vibration pattern. Weight of a vehicle in waterfall 421 and 422 should, in general, be proportional to the total amount of vibration amplitude measured at a given location or at a given time instant. By estimating the dimensions of each of the vehicles in waterfall data 421 and 422, it is possible to determine how much of a lane or roadway is occupied, which in turn can be a metric to determine congestion).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Ionescu and Crickmore to include to wherein the one or more second properties of the traffic in the subset of segments comprise one or more properties of queues of said traffic, wherein said one or more properties of queues of said traffic optionally comprise one or more of: the presence of a queue, the spatial positions of front and/or back boundaries of a queue, the spatial length of a queue, and the time expected for a vehicle to remain in the queue by Narisetty in order to for estimate at least one traffic flow property of the roadway from the enhancements of the processed waterfall data (see Narisetty’s abstract).
Claim(s) 7, 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ionescu (US20230343209A1), and Crickmore (US20160078760A1) as applied to claim 1 above, and further in view of Narisetty (US20210241615A1).
Claim.7 Ionescu and Crickmore do not discloses wherein the one or more second properties of said traffic in the subset of segments are determined more frequently than every 10 seconds.
However, Narisetty discloses wherein the one or more second properties of said traffic in the subset of segments are determined more frequently than every 10 seconds (see at least fig.1, p33, the traffic flow property is output periodically, e.g., every minute. In some embodiments, the traffic flow property is output continuously).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Ionescu and Crickmore to include to wherein the one or more second properties of said traffic in the subset of segments are determined more frequently than every 10 seconds by Narisetty in order to process each of the plurality of patches to estimate at least one traffic flow property of the roadway (see Narisetty’s abstract).
Claim.14 Ionescu and Crickmore do not discloses wherein the one or more third properties of the traffic comprise one or more of: extended tracks of particular vehicles within the subset of segments; and extended tracks of particular vehicles which traverse segments both within and outside of the subset of segments.
However, Narisetty discloses wherein the one or more third properties of the traffic comprise one or more of: extended tracks of particular vehicles within the subset of segments; and extended tracks of particular vehicles which traverse segments both within and outside of the subset of segments (see at least fig.1, p24, the waterfall data, individual vehicles along the roadway are more easily identified and tracked in order to more precisely determine traffic flow properties during different time periods, p33, the traffic flow property is output periodically, e.g., every minute. In some embodiments, the traffic flow property is output continuously).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Ionescu and Crickmore to include wherein the one or more third properties of the traffic comprise one or more of: extended tracks of particular vehicles within the subset of segments; and extended tracks of particular vehicles which traverse segments both within and outside of the subset of segments by Narisetty in order to process each of the plurality of patches to estimate at least one traffic flow property of the roadway (see Narisetty’s abstract).
Claim.15 Ionescu and Crickmore do not discloses wherein each extended track for a particular vehicle comprises multiple track segments determined using the second properties, which are known to be associated to form an extended track of the particular vehicle by using the first properties.
However, Narisetty discloses wherein each extended track for a particular vehicle comprises multiple track segments determined using the second properties, which are known to be associated to form an extended track of the particular vehicle by using the first properties (see at least fig.1, p24, the waterfall data, individual vehicles along the roadway are more easily identified and tracked in order to more precisely determine traffic flow properties during different time periods, p33, the traffic flow property is output periodically, e.g., every minute. In some embodiments, the traffic flow property is output continuously).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Ionescu and Crickmore to include wherein each extended track for a particular vehicle comprises multiple track segments determined using the second properties, which are known to be associated to form an extended track of the particular vehicle by using the first properties by Narisetty in order to process each of the plurality of patches to estimate at least one traffic flow property of the roadway (see Narisetty’s abstract).
Conclusion
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/SHARDUL D PATEL/Primary Examiner, Art Unit 3664