DETAILED ACTION
Status of the Application
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 the Claims
This action is in response to the applicant’s filing on August 06, 2025. Claim 1 has been cancelled. Claims 1 – 52 are new. Claims 2 – 52 are pending and examined below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 03, 2025 have been considered by the Examiner.
Claim Rejections - 35 USC § 102
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 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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 31 and 46 – 52 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by U.S. Patent Application Publication No. US 2018/0374113 A1 to RAMIREZ et al. (herein after "Ramirez"), already of record from IDS.
(Note: Claim language is in bold typeface, and the Examiner’s comments and cited passages from the prior art reference(s) are in normal typeface.)
As to Claim 1, (Cancelled)
As to Claim 2, (New)
Ramirez’ road segment safety rating system discloses a machine (see at least Fig. 1 ~ illustrates a general arrangement of a road segment safety rating system comprising a computing device 102,
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see Fig. 2 ~ outlines a process flow chart defining process method steps for computing hazard measures for vehicle road segments, and
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see ¶0047 ~ computing device 102) comprising:
a data interface that receives traffic object data from sensors including one or more of a camera, a Global Positioning System, Lidar and radar (see Fig. 7 ~ illustrates an exemplary arrangement of the road segment safety rating system environment consisting of V2X communications between vehicles-based devices, a personal mobile device, and an insurance system server, see
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see ¶0047 ~ computing device 102 and ¶0107 ~ "Vehicle sensor 711 also may include cameras and/ or proximity sensors capable of recording... Conditions inside or outside of the vehicle 710");
a processor connected to the data interface (see at least ¶0052, ¶0059 ~ "computer-executable instructions may be configured for execution by a processor ( e.g., a processor in personal navigation device 110) and stored in a memory (e.g., flash memory in device 110)", and ¶0076 ~ processor 114); and
a memory connected to the processor (see at least ¶0072 ~ "upon completion of the trip, the route risk value may be calculated and stored in memory along with the other information related to the route risk score and mileage traveled"), the memory storing instructions executed by the processor to:
process the traffic object data to form geometric and kinematic data (see at least Fig. 2 ~ process method steps 206 - 212, Fig. 3 ~ process method steps 306 - 310, ¶0038 ~ computing device 102 uses the information from the data sources 104, 106 to generate values that may be used to calculate an estimated route risk, ¶0043 ~ "road segment may be defined by the latitude and longitude of its endpoints and/or an area defined by the road shape and a predetermined offset that forms a polygon. Segments may comprise intersections, bridges, tunnels, rail road crossings or other roadway types and features", ¶0044 ~ "information may also include information collected through one or more in vehicle devices or systems such as an event data recorder (EDR), onboard diagnostic system, or global positioning satellite (GPS) device; examples of this information include speed at impact, brakes applied, throttle position, ¶0045 ~ "the computing device 102 with accident information... generate[s] values (e.g., create new values and/or update existing values)... use at least part of the received accident information to calculate a value, associate the value with a road segment", and ¶0051 ~ speed data between a first traffic object; thereby teaching wherein the computing device forms geometric (polygon segmentation) and kinematic (speed) data); and
process the geometric and kinematic data to form a hazard measure (see at least Figs. 1 - 3, ¶0044 - ¶0045 and ¶0046 ~ "Attributes associated with roadways may also be used in part to generate risk values", and ¶0088 ~ teaches a road hazard measure formula (road segment safety rating)), where the hazard measure is computed through operations to:
obtain a relative speed between a first traffic object and a second traffic object (see at least Figs. 1 - 3, and ¶0051 ~ acquires speed between a first traffic object and),
receive a separation distance between the first traffic object and the second traffic object (see at least Figs. 2 - 3, ¶0051 ~ acquires speed between a first traffic object and other traffic objects, ¶0106 ~ "external cameras and proximity sensors 711 may detect other nearby vehicles, vehicle spacing... animals, cyclists, pedestrians, and other conditions that may factor into a driving data/behavior analysis, ¶111 ~ “V2V communications… for a driving analysis computing device 714 in one vehicle to determine that another vehicle… to determine… types of driving behaviors (e.g., vehicle spacing… to a safety hazard, etc.) and driving conditions”, ¶0151 ~ "Processor(s) 1303… allow the system 1301 to… receive and analyze driver data, vehicle data, traffic data and/or accident data, determine a safety rating for a road segment", and ¶0166 ~ "vehicle 1410 may include one or more additional sensors (e.g., sensors 711) that may detect, for example... vehicle spacing"),
combine the relative speed and the separation distance to form a quantitative measure of hazard encountered by the first traffic object (see at least ¶0046, ¶072, ¶0095, ¶0106-¶0108, ¶0111-¶0112, ¶0125, ¶0134, ¶0166-¶0168), wherein the instructions to combine include instructions to:
apply a compensating factor to the relative speed to form a compensated relative speed (see at least ¶0134 ~ "recommendations generated… based… on driving behaviors of the user… behaviors such as speed relative to the speed limit, speed relative to other drivers,… following distance… have been determined… by a driving behaviors module… to identify road segments... suited to... have a safer rating" and ¶0166 ~ "sensors 711... detect other nearby vehicles, vehicle spacing"), and
divide the compensated relative speed by the separation distance (see at least Figs. 2 – 3 and ¶0134),
repeat the obtain, receive and combine operations to form cumulative measures of hazard associated with the first traffic object (see at least Figs. 2 – 3 and ¶0134),
analyze the cumulative measures of hazard to derive a first traffic object safety score for the first traffic object (Figs. 2 – 3 and ¶0125 ~ "a vehicle based driving analysis computer 71… may continuously receive and analyze driver data, vehicle data, driving trip data, and the like to determine certain events and characteristics ( e.g., commencement of a driving trip, identification of a driver, determination of a driving route, road segment, or intended destination, driving data and behaviors during driving trips, etc.),"); and
adjust or affect the behavior of a traffic object in response to the first traffic object safety score. (See at least Figs. 2 - 3, ¶0113 ~ "data collected by vehicle sensor 711 may be transmitted to... telematics device 713", ¶0115 ~ "For example, in autonomous driving, a vehicle control computer 717 may be configured to operate all or some aspects of the vehicle driving, including but not limited to acceleration, braking, steering, and/or route navigation", ¶0119 ~ "mobile computing device 730 may be used in conjunction with the vehicle control computers 717 for purposes of autonomous driving... analyze vehicle driving data, analyze driving trips, and perform other related functions", and ¶0169 ~ "the sensor data may be transmitted via a telematics device (e.g., the telematics device 713) to one or more remote computing devices, such as… the road segment safety rating server 1450").
As to Claim 3, (New)
Ramirez discloses the machine of claim 2, wherein the compensating factor is the square of the relative speed. (See at least Figs. 2 – 3, 15, ¶0134, and ¶0189 ~ “At 1508, a safest route is identified based on a formula, or other quantitative means, where one or more of the plurality of routes, including the safest route, is presented… the road segment safety rating system”; Ramirez, teaches the computation of a compensating factor).
As to Claim 4, (New)
Ramirez discloses the machine of claim 2 wherein the instructions to
combine the relative speed and the separation distance are executed for each new value of the relative speed and the separation distance. (See at least Figs. 2 – 3, ¶0051, ¶0106, ¶0111, ¶0151, and ¶0166; Ramirez ~ computation of separation distance with relative speed is a continuous process by processor(s) 1303 thereby allowing system 1301 to perform new updated values.)
As to Claim 5, (New)
Ramirez discloses the machine of claim 2 wherein the instructions to
combine the relative speed and the separation distance include instructions to incorporate a deceleration measure. (See at least Figs. 2 - 3, ¶0088 ~ teaches a road hazard measure formula (road segment safety rating) incorporating a driver braking / deceleration, ¶0092, ¶0105 ~ vehicle data re: speed and velocity changes, ¶0111, and ¶0134 ~ relative speed and separation distance).
As to Claim 6, (New)
Ramirez discloses the machine of claim 2 wherein the instructions to
combine the relative speed and the separation distance include instructions to incorporate a change of direction measure. (See at least Figs. 2 - 3, ¶0088 ~ teaches a road hazard measure formula (road segment safety rating) incorporating a lane change merge, ¶0092, ¶0105 ~ vehicle data re: speed and direction changes, ¶0111, and ¶0134 ~ relative speed and separation distance).
As to Claim 7, (New)
Ramirez discloses the machine of claim 2 wherein the instructions to
combine the relative speed and the separation distance include instructions to incorporate a road condition measure. (See at least Figs. 2 - 3, ¶0092, and ¶0111 ~ " a driving analysis computing device 714 in one vehicle to determine that another vehicle is tailgating or cut-off the vehicle, whereas longer communications may allow the device 714 to determine... types of driving behaviors (e.g., vehicle spacing..., proper response to a safety hazard, etc.) and driving conditions (e.g.,... traffic, road conditions, weather conditions, etc.)").
As to Claim 8, (New)
Ramirez discloses the machine of claim 2
wherein the cumulative measures of hazard are collected over time or distance for a transportation session completed by the first traffic object. (See at least Figs. 2 – 3, ¶0104 ~ "driving analysis system 700 including additional aspects of the road segment safety rating system 700", and ¶0105 ~ "Vehicle 710 in the system 700 may be, for… a bus, a recreational vehicle… sensor 711 may detect and store data corresponding to the vehicle's location (e.g., GPS coordinates), time, travel time, speed, and direction").
As to Claim 9, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
collect cumulative measures of hazard over time or distance for multiple traffic objects. (See at least Figs. 2 - 3, ¶0041, and ¶0112 ~ "the nodes in a V2V communication system ( e.g., vehicles and other reception devices) may use internal clocks with synchronized time signals, and may send transmission times within V2V communications, so that the receiver may calculate its distance from the transmitting node based on the difference between the transmission time and the reception time").
As to Claim 10, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
collect cumulative measures of hazard over time or distance for a defined region. (See at least ¶0041 and ¶0112; Ramirez ~ "the nodes in a V2V communication system ( e.g., vehicles and other reception devices) may use internal clocks with synchronized time signals, and may send transmission times within V2V communications, so that the receiver may calculate its distance from the transmitting node based on the difference between the transmission time and the reception time").
As to Claim 11, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
collect cumulative measures of hazard for a road intersection. (See at least ¶0040, ¶0042; Ramirez ~ "examples of geographic information include, but are not limited to, location information and attribute information. Examples of attribute information include... road feature (e.g., intersection, gentle curve, blind curve, bridge, tunnel), number of intersections," and ¶0063; Ramirez ~ "another embodiment may combine accident frequency and severity to form a rating for a segment or intersection").
As to Claim 12, (New)
Ramirez discloses the machine of claim 2 wherein the cumulative measures of hazard are associated with a route. (See at least ¶0045; Ramirez ~ "computing device 102… calculating an estimated route risk").
As to Claim 13, (New)
Ramirez discloses the machine of claim 2
wherein the cumulative measures of hazard are associated with specified road conditions. (See at least ¶0092 and ¶0111; Ramirez ~ " a driving analysis computing device 714 in one vehicle to determine that another vehicle is tailgating or cut-off the vehicle, whereas longer communications may allow the device 714 to determine... types of driving behaviors (e.g., vehicle spacing..., proper response to a safety hazard, etc.) and driving conditions (e.g.,... traffic, road conditions, weather conditions, etc.)")
As to Claim 14, (New)
Ramirez discloses the machine of claim 13
wherein the specified road conditions are specified weather conditions. (See at least ¶0092 and ¶0111; Ramirez ~ " a driving analysis computing device 714 in one vehicle to determine that another vehicle is tailgating or cut-off the vehicle, whereas longer communications may allow the device 714 to determine... types of driving behaviors (e.g., vehicle spacing..., proper response to a safety hazard, etc.) and driving conditions (e.g.,... traffic, road conditions, weather conditions, etc.)")
As to Claim 15, (New)
Ramirez discloses the machine of claim 13
wherein the specified road conditions are visibility conditions. (See at least ¶0092, ¶0106; Ramirez ~ "sensors 711 may detect and store the external driving conditions, for example, external temperature, rain, snow, light levels, and sun position for driver visibility", and ¶0111; Ramirez).
As to Claim 16, (New)
Ramirez discloses the machine of claim 13
wherein the specified road conditions are traffic conditions. (See at least ¶0092 and ¶0111; Ramirez ~ " a driving analysis computing device 714 in one vehicle to determine that another vehicle is tailgating or cut-off the vehicle, whereas longer communications may allow the device 714 to determine... types of driving behaviors (e.g., vehicle spacing..., proper response to a safety hazard, etc.) and driving conditions (e.g.,... traffic, road conditions, weather conditions, etc.)"
As to Claim 17, (New)
Ramirez discloses the machine of claim 13
wherein the specified road conditions are angular sun measures. (See at least Figs. 2 - 3, ¶0085 ~ teaches weighting factors applied to each variable and ¶0088 ~ teaches a road hazard measure formula (road segment safety rating), and incorporates external values such as road conditions and/or driver visibilities affected by sun intensities and positions as further described in ¶0106).
As to Claim 18, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
compare the cumulative measures of hazard to a hazard threshold to derive periods above or below the hazard threshold. (See at least Figs. 2 - 3 and ¶0083 - ¶0088 ~ teaches a road hazard measure formula (road segment safety rating),
As to Claim 19, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
compute one or more extremal values of the cumulative measures of hazard. (See Figs. 2 - 3, ¶0085 ~ teaches weighting factors applied to each variable and ¶0088 ~ teaches a road hazard measure formula (road segment safety rating), and incorporates external values such as road conditions and/or geometries).
As to Claim 20, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
compute one or more averages of the cumulative measures of hazard. (See at least Figs. 2 - 3, ¶0072, and ¶0083 - ¶0088).
As to Claim 21, (New)
Ramirez discloses the machine of claim 2
further comprising instructions executed by the processor to
compute the cumulative measures of hazard from a moving average. (See at least Figs. 2 - 3, ¶0085 ~ teaches weighting factors applied to each variable and ¶0088 ~ teaches a road hazard measure formula (road segment safety rating)).
As to Claim 22, (New)
Ramirez discloses the machine of claim 2
wherein the cumulative measures of hazard weigh older values less than more recent values. (See at least Figs. 2 - 3, ¶0083 ~ road hazard measure formula (road segment safety rating) applied relative to time, ¶0085 ~ teaches weighting factors applied to each variable and ¶0088 ~ teaches a road hazard measure formula (road segment safety rating)).
As to Claim 23, (New)
Ramirez discloses the machine of claim 2
wherein an exponentially weighted moving average is applied to the cumulative measures of hazard. (See at least Figs. 2 - 3, ¶0085 ~ teaches weighting factors applied to each variable and ¶0088 ~ teaches a road hazard measure formula (road segment safety rating)).
As to Claim 24, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
correlate the cumulative measures of hazard with historical traffic data. (See at least Figs. 1 - 3, ¶0096 ~ "road segment safety rating system 600… may generate… historical road segment safety rating data may be stored, for instance, in a data table" and ¶0127 ~ "historical data may include traffic volume data, accident data, severity of accidents, type of road on which accidents occurred, road type, and the like").
As to Claim 25, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
correlate the cumulative measures of hazard with historical traffic risk data. (See at least Figs. 1 - 3, ¶0096, and ¶0127; Ramirez ~ determining a road segment safety rating using historical traffic risk data).
As to Claim 26, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
correlate the cumulative measures of hazard with historical loss data. (See at least Figs. 1 - 3, ¶0096, and ¶0127 ~ determining a road segment safety rating using historical loss data).
As to Claim 27, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
correlate the cumulative measures of hazard with frequency of occurrence (see at least Figs. 2 - 3, ¶0088; Ramirez ~ teaches a road hazard measure formula (road segment safety rating) with frequency of occurrence) and
severity of damage or harm measures from aggregated traffic object data. (See at least Figs. 2 - 3, ¶0096, and ¶0127; Ramirez).
As to Claim 28, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
correlate the cumulative measures of hazard with a risk of damage or harm for aggregated traffic object data. (See at least Figs. 1 - 3, ¶0088, ¶0096, and ¶0127).
As to Claim 29, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
forecast a frequency of occurrence and severity of damage or harm from the cumulative measures of hazard. (See at least Figs. 2 - 3, ¶0088, ¶0096, and ¶0127; Ramirez).
As to Claim 30, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
forecast a frequency of occurrence and severity of damage or harm from the cumulative measures of hazard. (See at least Figs. 2 - 3, ¶0088, ¶0096, and ¶0127; Ramirez).
As to Claim 31, (New)
Ramirez discloses the machine of claim 2 further comprising instructions executed by the processor to
associate the first traffic object safety score with an operator. (See at least Fig. 9 ~ process method step 908, Fig. 15 ~ process method step 1506, ¶0044 ~ driver information inputted into road segment safety controller, ¶0072 ~ "for rating purposes the route risk value may consider the driving information of the driver/vehicle", and ¶0131 ~ "the safety rating determined in step 908 may be particular or unique to the driver, because it relies on driving behaviors of the driver to determine the rating").
As to Claim 46, (New)
Ramirez discloses a machine (see at least Fig. 1 ~ illustrates a general arrangement of a road segment safety rating system comprising a computing device 102, Fig. 2 ~ outlines a process flow chart defining process method steps for computing hazard measures for vehicle road segments, and ¶0047 ~ computing device 102), comprising:
a data interface that receives traffic object data from sensors including one or more of a camera, a Global Positioning System, Lidar and radar (see at least Fig. 7 ~ illustrates an exemplary arrangement of the road segment safety rating system environment consisting of V2X communications between vehicles-based devices, a personal mobile device, and an insurance system server, ¶0047 ~ computing device 102 and ¶0107 ~ "Vehicle sensor 711 also may include cameras and/ or proximity sensors capable of recording... Conditions inside or outside of the vehicle 710");
a processor connected to the data interface; and a memory connected to the processor (see at least ¶0052, ¶0059 ~ "computer-executable instructions may be configured for execution by a processor ( e.g., a processor in personal navigation device 110) and stored in a memory (e.g., flash memory in device 110)", and ¶0172), the memory storing instructions executed by the processor to:
process the traffic object data to form geometric and kinematic data (see at least Fig. 2 ~ process method steps 206 - 212, Fig. 3 ~ process method steps 306 - 310, ¶0038 ~ computing device 102 uses the information from the data sources 104, 106 to generate values that may be used to calculate an estimated route risk, ¶0043 ~ "road segment may be defined by the latitude and longitude of its endpoints and/or an area defined by the road shape and a predetermined offset that forms a polygon. Segments may comprise intersections, bridges, tunnels, rail road crossings or other roadway types and features", ¶0044 ~ "information may also include information collected through one or more in vehicle devices or systems such as an event data recorder (EDR), onboard diagnostic system, or global positioning satellite (GPS) device; examples of this information include speed at impact, brakes applied, throttle position, ¶0045 ~ "the computing device 102 with accident information... generate[s] values (e.g., create new values and/or update existing values)... use at least part of the received accident information to calculate a value, associate the value with a road segment", and ¶0051 ~ speed data between a first traffic object; thereby teaching wherein the computing device forms geometric (polygon segmentation) and kinematic (speed) data); and
process the geometric and kinematic data to form a hazard measure (see at least Figs. 1 - 3, ¶0044 - ¶0045 and ¶0046 ~ "Attributes associated with roadways may also be used in part to generate risk values", and ¶0088 ~ teaches a road hazard measure formula (road segment safety rating)), where the hazard measure is computed through operations to:
obtain a relative speed between a first traffic object and a second traffic object (see at least Figs. 2 – 3 and ¶0051 ~ acquires speed between a first traffic object and),
receive a separation distance between the first traffic object and the second traffic object (see at least Figs. 2 – 3, ¶0051 ~ acquires speed between a first traffic object and other traffic objects, ¶0106 ~ "external cameras and proximity sensors 711 may detect other nearby vehicles, vehicle spacing... animals, cyclists, pedestrians, and other conditions that may factor into a driving data/behavior analysis, ¶111 ~ “V2V communications… for a driving analysis computing device 714 in one vehicle to determine that another vehicle… to determine… types of driving behaviors (e.g., vehicle spacing… to a safety hazard, etc.) and driving conditions”, ¶0151 ~ "Processor(s) 1303… allow the system 1301 to… receive and analyze driver data, vehicle data, traffic data and/or accident data, determine a safety rating for a road segment", and ¶0166 ~ "vehicle 1410 may include one or more additional sensors (e.g., sensors 711) that may detect, for example... vehicle spacing"),
combine the relative speed and the separation distance to form a quantitative measure of hazard encountered by the first traffic object (see at least Figs. 2 – 3 and ¶0134), wherein the instructions to
combine the relative speed and the separation distance include instructions to incorporate a perception-reaction time (PRT) measure and wherein the separation distance is reduced by the PRT measure (see at least Figs. 2 – 3 and ¶0134),
repeat the obtain, receive and combine operations to form cumulative measures of hazard associated with the first traffic object (see at least Figs. 2 – 3 and ¶0134),
analyze the cumulative measures of hazard to derive a first traffic object safety score for the first traffic object (see at least Figs. 2 – 3 and ¶0125 ~ "a vehicle based driving analysis computer 71… may continuously receive and analyze driver data, vehicle data, driving trip data, and the like to determine certain events and characteristics ( e.g., commencement of a driving trip, identification of a driver, determination of a driving route, road segment, or intended destination, driving data and behaviors during driving trips, etc.),"); and
adjust or affect the behavior of a traffic object in response to the first traffic object safety score. (See at least Figs. 2 – 3, ¶0113 ~ "data collected by vehicle sensor 711 may be transmitted to... telematics device 713", ¶0115 ~ "For example, in autonomous driving, a vehicle control computer 717 may be configured to operate all or some aspects of the vehicle driving, including but not limited to acceleration, braking, steering, and/or route navigation", ¶0119 ~ "mobile computing device 730 may be used in conjunction with the vehicle control computers 717 for purposes of autonomous driving... analyze vehicle driving data, analyze driving trips, and perform other related functions", and ¶0169 ~ "the sensor data may be transmitted via a telematics device (e.g., the telematics device 713) to one or more remote computing devices, such as… the road segment safety rating server 1450").
As to Claim 47, (New)
Ramirez discloses the machine of claim 46, wherein the instructions to combine include instructions to:
apply a compensating factor to the relative speed to form a compensated relative speed (see at least ¶0134 ~ "recommendations generated… based… on driving behaviors of the user… behaviors such as speed relative to the speed limit, speed relative to other drivers,… following distance… have been determined… by a driving behaviors module… to identify road segments... suited to... have a safer rating" and ¶0166 ~ "sensors 711... detect other nearby vehicles, vehicle spacing"); and
divide the compensated relative speed by the separation distance. (See at least Figs. 2 – 3, ¶0088 ~ teaches a road hazard measure formula (road segment safety rating), and ¶0134).
As to Claim 48, (New)
Ramirez discloses the machine of claim 47,
wherein the compensating factor is the square of the relative speed. (See at least Figs. 2 – 3, ¶0088, and ¶0134; Ramirez, teaches the computation of a compensating factor).
As to Claim 49, (New)
Ramirez discloses the machine of claim 46 wherein the instructions to
combine the relative speed and the separation distance are executed for each new value of the relative speed and the separation distance. (See at least Figs. 2 – 3, ¶0051, ¶0106, ¶0111, ¶0151, and ¶0166; Ramirez ~ computation of separation distance with relative speed is a continuous process by processor(s) 1303 thereby allowing system 1301 to perform new updated values).
As to Claim 50, (New)
Ramirez discloses the machine of claim 46 wherein the instructions to
combine the relative speed and the separation distance include instructions to incorporate a deceleration measure. (See at least Figs. 2 - 3, ¶0088 ~ teaches a road hazard measure formula (road segment safety rating) incorporating a driver braking / deceleration, ¶0092, ¶0105 ~ vehicle data re: speed and velocity changes, ¶0111, and ¶0134 ~ relative speed and separation distance).
As to Claim 51, (New)
Ramirez discloses the machine of claim 46 wherein the instructions to
combine the relative speed and the separation distance include instructions to incorporate a change of direction measure. (See at least Figs. 2 - 3, ¶0088 ~ teaches a road hazard measure formula (road segment safety rating) incorporating a lane change merge, ¶0092, ¶0105 ~ vehicle data re: speed and direction changes, ¶0111, and ¶0134 ~ relative speed and separation distance).
As to Claim 52, (New)
Ramirez discloses the machine of claim 46 wherein the instructions to
combine the relative speed and the separation distance include instructions to incorporate a road condition measure. (See at least Figs. 2 - 3, ¶0092, and ¶0111 ~ " a driving analysis computing device 714 in one vehicle to determine that another vehicle is tailgating or cut-off the vehicle, whereas longer communications may allow the device 714 to determine... types of driving behaviors (e.g., vehicle spacing..., proper response to a safety hazard, etc.) and driving conditions (e.g.,... traffic, road conditions, weather conditions, etc.)").
Allowable Subject Matter
Claims 32 and 39 are allowable.
Claims 33 – 38 and 40 - 45 are allowed to as being dependent upon a rejected base claim.
In particular, the prior art does not teach or explicitly disclose wherein the instructions to combine the relative speed and the separation distance include instructions to incorporate an absolute speed measure and to
combine the square of the absolute speed measure and the relative speed and then divide by the separation distance. Emphasis added.
The prior art does not appear to explicitly teach or disclose the above recited claim limitations.
To that end and although further search and consideration would always need to be performed based upon any submitted amendments by the Applicant, it is the Examiner’s position that incorporating these above recited claim limitations into independent claims 2 and 46 may/might possibly advance prosecution.
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ASHLEY L. REDHEAD, JR. whose telephone number is (571) 272 - 6952. The Examiner can normally be reached on weekdays, Monday through Thursday, between 7 a.m. and 5 p.m.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Peter Nolan can be reached Monday through Friday, between 9 a.m. and 5 p.m. at (571) 270 – 7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHLEY L REDHEAD JR./Primary Examiner, Art Unit 3661
Prosecution Insights