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 .
Drawings
The drawings were received on September 24th 2024. These drawings are accepted.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware of, in the specification.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
The certified copy has been filed on October 29th 2024.
Status of Claims
This Final rejection is in response to the applicant’s filing on February 20th 2026;
Claims 1-17 are pending and examined below.
Response to Arguments
Applicant’s amendments with respect to the claim’s objection have been fully considered. Therefore, the claim objection has been withdrawn.
Applicant’s amendments with respect to the rejection of claims under 35 USC § 101 have been fully considered and are persuasive. Therefore, the rejection of claims under 35 USC § 101 has been withdrawn. Specifically, The Declaration under 37 CFR 1.132 filed 2/20/26 is sufficient to overcome the rejection of claims 1-17 based upon lack of subject matter eligibility under 35 USC 101 in view of the following:
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Applicant’s amendments with respect to the rejection of claims under 35 USC § 103 have been fully considered but are moot. While the Examiner notes that the applicant is arguing the claim limitations recite " … receiving cartographic data in a data format comprising a plurality of road segments, each road segment associated with indexed coefficients stored in the memory, wherein the indexed coefficients are assigned using an attribute and type for efficient access; … by accessing the indexed coefficients from the memory and performing a summation of the indexed coefficients for each link and intersection to generate a stress value; and combining, by the stress index determination system, the stress of the one or more links and/or intersections to obtain an overall stress index of the one or more routes in real-time…”. Therefore, the rejection has been withdrawn; However, upon further consideration a new ground(s) of rejection is made for Claims 1 and 6 Filimonov (Patent No. WO2023128784A1) in view of Chintakindi (Patent No. US10132644B2) and Soni (Paten No US11645839B2).
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.
Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Filimonov (Patent No. WO2023128784A1) in view of Chintakindi (Patent No. US10132644B2) and Soni (Paten No US11645839B2).
Regarding claim 1 Filimonov teaches, a vehicle, comprising: a navigation system for navigating a route; (See Filimonov paragraph 0034 and 0029; “…system in a vehicle, or using a cloud service or existing navigation system services… The representation of roads may be a navigation map, a data grid or similar…”);
determining, based on a cartographic representation, at least one route from a first point to a second point; dividing, by a stress index determination system, the at least one route into one or more links and/or intersections; (See Filimonov paragraph 0031-0032; “…determining respective individual stress level indicators associated with each of the route properties (step 108) and assigning a total stress level indicator to the route properties based on the individual stress level indicators (step 110).
For example, certain (individual) stress level indicators may be determined for the number of lanes, intersections or traffic lights on a route…”);
the link is a road segment having substantially constant road properties along the road segment and the stress of the link is determined based on a complexity of the link; (See Filimonov paragraph 0013; “ Implementing any number or combination of such data, a more precise determination of the total stress level value/indicator of the routes is enabled and a better suggestion of a route with less or the least stress associated to it may be possible. In particular, including road conditions enables avoiding routes with e.g. a large number of traffic lights or complex intersections, thereby reducing stress for the driver.”);
and a display to display the overall stress index; (See Filimotov paragraph 0034; “…For example, the stress level values of the route or part of the route may in indicated using labels or color representations in a map on a display in a car…”).
Filimotov does not explicitly teach but Chintakindi teaches, wherein the navigation system comprises a processor and memory with instructions stored therein; (See Chintakindi column 8, line 8-14; “…the one or more processors 120, including instructions 134 and data 132 that may be executed or otherwise used by the processor 120. The memory 130 may be of any type capable of storing information accessible by the processor, including a computing device-readable medium,…”).
wherein the indexed coefficients are assigned using an attribute and type for efficient access; (See Chintakindi column 20, line 38-53; “The frustration risk profile may include information to generate weighting factors or other such information to customize a mathematical algorithm for use in generating a personalized road frustration index value associated with each of a plurality of road segments for a particular driver. For example, a total route road frustration index value may be customized for the particular road segments comprising the route and/or the individual traveling the route. For example, a road frustration index value may be calculated for a route as a sum of the road frustration index value values for the route segments that comprise the total route. Further, each route segment may be calculated as a weighted combination of road frustration risks that may be encountered as part of the particular route segment and may be customized using weighting factors (e.g., coefficient) customized for each driver.”);
wherein dividing the at least one route comprises identifying road segments having substantially constant road properties; (See Chinatakindi column 7, line 16-23; “The frustration index value may also be used to represent a pattern of risk across a plurality of route segments of a route, where a transition between a route segment having a low road frustration index value and a route segment having a higher road frustration index value may be made more gradual to avoid a quick transition between road frustration index levels.”); determining, by the stress index determination system, the stress for the at least one or more links and/or intersections by accessing the indexed coefficients from the memory and performing a summation of the indexed coefficients for each link and intersection to generate a stress value; (See Chintakindi column 20, line 30-53; “ the road frustration index analysis engine 252 may analyze the frustration risk information entered or otherwise obtained about a particular driver. Using this frustration risk information, a frustration risk profile may be built for each driver where the frustration risk profile includes personalized frustration risk information that may be used to generate a road frustration index value for one or more road segments or types of road segments. The frustration risk profile may include information to generate weighting factors or other such information to customize a mathematical algorithm for use in generating a personalized road frustration index value associated with each of a plurality of road segments for a particular driver. For example, a total route road frustration index value may be customized for the particular road segments comprising the route and/or the individual traveling the route. For example, a road frustration index value may be calculated for a route as a sum of the road frustration index value values for the route segments that comprise the total route. Further, each route segment may be calculated as a weighted combination of road frustration risks that may be encountered as part of the particular route segment and may be customized using weighting factors (e.g., coefficient) customized for each driver.”); and combining, by the stress index determination system, the stress of the one or more links and/or intersections to obtain an overall stress index of the one or more routes in real-time; (See Chintakindi column 12, line 28-42; “one or more application programs 119, such as a road frustration index determination application, may be used by one or more computing devices (e.g., the computing device 101) within the system 100, including computer executable instructions for identifying a road frustration index being experienced by a driver (or owner, passenger, parent of the driver, etc.) of a vehicle in near-real time, predicting one or more road segments upon which the driver may experience some level of road frustration, generating a road frustration index value associated with the driver corresponding to a driving speed on a road segment having an associated road classification type, and generating one or more travel routes each predicted to have an associated road frustration index value based on information received from a plurality of drivers.”);
wherein the complexity of the link is determined based on attributes of the link that are indexed based on a type and provide a resulting indexed coefficient; (See Chintakindi column 33, line 22-49; “The route frustration index risks described above may be variables in a multivariate model of insurance losses, frequencies, severities, and/or pure premiums. Interactions of the variables would also be considered. The coefficient the model produces for each variable (along with the coefficient for any interaction terms) would be the value to apply to each subjective risk type. The user device and/or the personal navigation device 110 may initially provide the quickest/shortest route from a start location A to an end location B, and then determine the route frustration index value by determining either the sum product of the number of each route frustration index parameters and the value for that route frustration index or the overall product of the number of each route frustration index and the value for that route frustration index. (Traffic and weather conditions could either be included or excluded from the determination of the route frustration index value for comparison of routes. If not included, an adjustment may be made to the route risk value once the route has been traveled). The driver may be presented with an alternate route which is less risky than the initial route calculated. The user device and/or the personal navigation device 110b may display the difference in route frustration between the alternate routes and permit the driver to select the preferred route. In some embodiments in accordance with this disclosure, a driver/vehicle may be provided a monetary benefit (e.g., a credit towards a future insurance policy) for selecting a route having lesser route frustration index values.”).
Both Filimotov and Chintakindi are in the same field of system and method for rout stress index determination. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Filimotov vehicle and navigation system for a route with Chintakindi navigation system comprises a processor and memory with instructions stored as well as stress index of the one or more routes in real-time. No new functionality would arise from the combination and the combination would improve usability of Filimotov by adding a navigation system comprises a processor and memory with instructions stored as well as real time updating the stress index. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Filimotov does not explicitly teach but Soni teaches, wherein the instructions include instructions for receiving cartographic data in a data format comprising a plurality of road segments each road segment associated with indexed coefficients stored in the memory; (See Soni column 16, line 23-44; “The databases may also include other attributes of or about the roads such as, for example, geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and/or other navigation related attributes (e.g., one or more of the road segments is part of a highway or toll way, the location of stop signs and/or stoplights along the road segments), as well as points of interest (POIs), such as gasoline stations, hotels, restaurants, museums, stadiums, offices, automobile dealerships, auto repair shops, buildings, stores, parks, etc. The databases may also contain one or more node data record(s) which may be associated with attributes (e.g., about the intersections) such as, for example, geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and other navigation related attributes, as well as POIs such as, for example, gasoline stations, hotels, restaurants, museums, stadiums, offices, automobile dealerships, auto repair shops, buildings, stores, parks, etc. The geographic data may additionally or alternatively include other data records such as, for example, POI data records, topographical data records, cartographic data records, routing data, and maneuver data.”).
Both Filimotov and Soni are in the same field of system and method for rout stress index determination. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Filimotov vehicle and navigation system for a route with Soni cartographic data of each road segment associated with indexed coefficients. No new functionality would arise from the combination and the combination would improve usability of Filimotov by adding a cartographic data of each road segment associated with indexed coefficients will allow for more detailed data to determine the stress index of the roads. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2 Filimotov in view of Chintakindi and Soni teaches the vehicle according to claim 1, Filimotov further teaches, wherein the complexity of the link is determined based on attributes of the link relating to a number of lanes of the link and whether it is possible to change from one lane to another; (See Filimotov paragraph 0013; “…the one or more route properties comprise route or road conditions and/or sensor data associated with at least the respective part of the one or more routes. For example, the route properties may comprise a number of lanes, a number of traffic lights, a number and/or complexity of intersections and/or the current traffic situation along the one or more routes… Implementing any number or combination of such data, a more precise determination of the total stress level value/indicator of the routes is enabled and a better suggestion of a route with less or the least stress associated to it may be possible. In particular, including road conditions enables avoiding routes with e.g. a large number of traffic lights or complex intersections, thereby reducing stress for the driver. Including traffic information allows for a real-time determination of stress related to a respective route…”).
Regarding claim 3 Filimotov in view of Chintakindi and Soni teaches the vehicle according to claims 2, Filimonov further teaches, wherein the stress of the link is determined based on each of the following attributes: - time used by passing the link; (See Filimonov paragraph 0013; “…indicate a stress level of the driver or similar data of other drivers of other vehicles on one of the one or more routes, respective parts of the routes or proximate locations. Implementing any number or combination of such data, a more precise determination of the total stress level value/indicator of the routes is enabled and a better suggestion of a route with less or the least stress associated to it may be possible. In particular, including road conditions enables avoiding routes with e.g. a large number of traffic lights or complex intersections, thereby reducing stress for the driver. Including traffic information allows for a real-time determination of stress related to a respective route. Thereby, certain routes may be avoided during rush hour, thus, reducing stress for the driver…).
Filimotov does not explicitly teach but Chintakindi teaches, - whether the link is at least part of a highway, tunnel, bridge or normal street; (See Chintakindi column 8, line 13-25; “The road frustration risk analysis system may further incorporate objective risks (e.g., construction areas, wildlife areas, accident prone areas, dangerous intersections, etc.) and/or subjective risks (e.g., blind left-hand turns, bridges, etc.) when generating a map and/or routes for presentation to the driver. Such information may be overlaid on a map and indicate such risks based on a particular driver, or a particular grouping of drivers. Drivers may be grouped by any combination of age, relative driving experience, a driver license type (e.g., passenger, commercial, etc.), a number of passengers within the vehicle, a preference of route types (e.g., a fastest route, a route avoiding major roadways, etc.) and/or the like.”).
Both Filimotov and Chintakindi are in the same field of system and method for rout stress index determination. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Filimotov vehicle and navigation system for a route with Chintakindi a highway, tunnel, bridge or normal street. No new functionality would arise from the combination and the combination would improve usability of Filimotov by adding a highway, tunnel, bridge or normal street to include in stress index will help with stress assessment of the routs. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 4 Filimotov in view of Chintakindi and Soni teaches the vehicle according to claim 3, Filimonov does not teach but Chintakindi teaches, wherein the indexed coefficients indicate the complexity of the attribute; (See Chintakindi column 33, line 22-49; “The route frustration index risks described above may be variables in a multivariate model of insurance losses, frequencies, severities, and/or pure premiums. Interactions of the variables would also be considered. The coefficient the model produces for each variable (along with the coefficient for any interaction terms) would be the value to apply to each subjective risk type. The user device and/or the personal navigation device 110 may initially provide the quickest/shortest route from a start location A to an end location B, and then determine the route frustration index value by determining either the sum product of the number of each route frustration index parameters and the value for that route frustration index or the overall product of the number of each route frustration index and the value for that route frustration index. (Traffic and weather conditions could either be included or excluded from the determination of the route frustration index value for comparison of routes. If not included, an adjustment may be made to the route risk value once the route has been traveled). The driver may be presented with an alternate route which is less risky than the initial route calculated. The user device and/or the personal navigation device 110b may display the difference in route frustration between the alternate routes and permit the driver to select the preferred route. In some embodiments in accordance with this disclosure, a driver/vehicle may be provided a monetary benefit (e.g., a credit towards a future insurance policy) for selecting a route having lesser route frustration index values.”).
Both Filimotov and Chintakindi are in the same field of system and method for rout stress index determination. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Filimotov vehicle and navigation system for a route with Chintakindi the attributes are assigned with respective coefficients to indicate the complexity. No new functionality would arise from the combination and the combination would improve usability of Filimotov by adding a the attributes are assigned with respective coefficients to indicate the complexity will help with stress assessment of the routs. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 5 Filimotov in view of Chintakindi and Soni teaches the vehicle according to claim 4, Filimotov does not explicitly teach but Chinatakindi teaches, wherein the stress of the link is determined as: ((∑lanes (lanek+changek) x environment) +highwayk+tunnelk+bridgek+normalk)x time
wherein coefficient k represents a first to k-th lane, coefficient lanek represents complexity of the k-th lane of the link, coefficient changek represents complexity of the lane associated with whether it is possible to change from one lane to another, and coefficient environment represents a road type that proportionally increases or decreases the complexity of the link, coefficients highwayk, tunnelk, bridgek, and normalk represent complexity of the link associated with presence or absence of a highway, tunnel, bridge or normal road, respectively; (See Chinatakindi column 31-32, line 34-18; “To calculate an aggregate route frustration index value (e.g., a frustration trend), an illustrative method may include calculating or estimating a base time corresponding to an actual expected for traveling each route segment along a route based on rated speed for the road and/or historical traffic information and a route distance. Once calculated, for every route segment base time and distance, a time weight (Tw) parameter and a distance weight (Dw) parameter may be calculated, such as by using
Tw.sub.seg=(SegBaseTime)/(RouteBaseTime); and
Dw.sub.seg=(SegDistance)/(RouteDistance),
Where:
SegBaseTime is an expected travel time associated with traveling a particular segment;
RouteBaseTime is an expected travel time associated with traveling a particular route;
SegDistance is distance of a particular route segment; and
RouteDistance is a total distance of the route including a plurality of segments.
A Segment RFI (e.g., RFI.sub.seg) may be calculated for each route segment and a Route RFI (e.g., RFI.sub.route) where the segment RFI, RFI.sub.seg, may be retrieved from an RFI model look-up table, such as RFI model 700 or RFI model 800, based at least on a vehicle speed, a rated speed, and a road type for a vehicle traveling or predicted to be traveling on the particular route. The RFI aggregate value for the route may be calculated as:
RFI.sub.route=Σ.sub.IRFI.sub.route[Segment.sub.i]*Dw[Segment.sub.i]
Returning to FIG. 5, at 535, an RFI value (e.g., a segment RFI, a route aggregate RFI, etc.) may be compared to a criterion, such as a threshold RFI value corresponding to a frustration level at which a driver may be at greater risk of performing risky driving events, and/or may be subject to risky driving events performed by another driver in proximity to the driver's vehicle. If the RFI value is not greater than the threshold, the travel route may be presented to the user for use in navigating between a start location and an end location at 540. If, at 535, the RFI value meets the criterion (greater than, greater than or equal to, etc.) the criterion, then the road frustration index analysis system 250 may be used to access the mapping information 270, the route frustration models, and/or additional information (e.g., weather information, traffic information, objective risk information, subjective risk information, etc.) when generating one or more alternate travel routes or route segments between the desired start location and end location of a trip at 550. The RFI values may be calculates for each of the newly generated routes, similarly to 530, and then one or more of the routes may be presented to the user at 560.”).
Both Filimotov and Chintakindi are in the same field of system and method for rout stress index determination. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Filimotov vehicle and navigation system for a route with Chintakindi equation to determine the stress of the link. No new functionality would arise from the combination and the combination would improve usability of Filimotov by adding an equation to determine the stress of the link will allow a better stress assessment of the routs. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 6 Filimove teaches, a computer implemented method for determining a stress index of a route, comprising; (See Filimonov paragraph 0022; “a computer readable medium comprising instructions which, when executed by a computing device, the above mentioned computer system or the above mentioned computing device, cause the computing device or system to carry out the above described method.”);
determining, based on a cartographic representation, at least one route from a first point to a second point; dividing, by a stress index determination system, the at least one route into one or more links and/or intersections; (See Filimonov paragraph 0031-0032; “…determining respective individual stress level indicators associated with each of the route properties (step 108) and assigning a total stress level indicator to the route properties based on the individual stress level indicators (step 110).
For example, certain (individual) stress level indicators may be determined for the number of lanes, intersections or traffic lights on a route…”).
Filimotov does not explicitly teach but Chintakindi teaches, wherein dividing the at least one route comprises identifying road segments having substantially constant road properties; (See Chinatakindi column 7, line 16-23; “The frustration index value may also be used to represent a pattern of risk across a plurality of route segments of a route, where a transition between a route segment having a low road frustration index value and a route segment having a higher road frustration index value may be made more gradual to avoid a quick transition between road frustration index levels.”); wherein the link is a road segment having substantially constant road properties along the road segment; determining, by the stress index determination system, the stress for the at least one or more links and/or intersections by accessing the indexed coefficients from the memory and performing a summation of the indexed coefficients for each link and intersection to generate a stress value; (See Chintakindi column 20, line 30-53; “ the road frustration index analysis engine 252 may analyze the frustration risk information entered or otherwise obtained about a particular driver. Using this frustration risk information, a frustration risk profile may be built for each driver where the frustration risk profile includes personalized frustration risk information that may be used to generate a road frustration index value for one or more road segments or types of road segments. The frustration risk profile may include information to generate weighting factors or other such information to customize a mathematical algorithm for use in generating a personalized road frustration index value associated with each of a plurality of road segments for a particular driver. For example, a total route road frustration index value may be customized for the particular road segments comprising the route and/or the individual traveling the route. For example, a road frustration index value may be calculated for a route as a sum of the road frustration index value values for the route segments that comprise the total route. Further, each route segment may be calculated as a weighted combination of road frustration risks that may be encountered as part of the particular route segment and may be customized using weighting factors (e.g., coefficient) customized for each driver.”); combining, by the stress index determination system, the stress of the one or more links and/or intersections to obtain an overall stress index of the one or more routes, wherein the overall stress index is obtained in real time; (See Chintakindi column 12, line 28-42; “one or more application programs 119, such as a road frustration index determination application, may be used by one or more computing devices (e.g., the computing device 101) within the system 100, including computer executable instructions for identifying a road frustration index being experienced by a driver (or owner, passenger, parent of the driver, etc.) of a vehicle in near-real time, predicting one or more road segments upon which the driver may experience some level of road frustration, generating a road frustration index value associated with the driver corresponding to a driving speed on a road segment having an associated road classification type, and generating one or more travel routes each predicted to have an associated road frustration index value based on information received from a plurality of drivers.”).
Both Filimotov and Chintakindi are in the same field of system and method for rout stress index determination. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Filimotov vehicle and navigation system for a route with Chintakindi navigation system comprises a processor and memory with instructions stored as well as stress index of the one or more routes in real-time. No new functionality would arise from the combination and the combination would improve usability of Filimotov by adding a navigation system comprises a processor and memory with instructions stored as well as real time updating the stress index. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Filimotov does not explicitly teach but Soni teaches, receiving cartographic data in a data format comprising a plurality of road segments, each road segment associated with indexed coefficients stored in memory, wherein the indexed coefficients are assigned using an attribute and type; (See Soni column 16, line 23-44; “The databases may also include other attributes of or about the roads such as, for example, geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and/or other navigation related attributes (e.g., one or more of the road segments is part of a highway or toll way, the location of stop signs and/or stoplights along the road segments), as well as points of interest (POIs), such as gasoline stations, hotels, restaurants, museums, stadiums, offices, automobile dealerships, auto repair shops, buildings, stores, parks, etc. The databases may also contain one or more node data record(s) which may be associated with attributes (e.g., about the intersections) such as, for example, geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and other navigation related attributes, as well as POIs such as, for example, gasoline stations, hotels, restaurants, museums, stadiums, offices, automobile dealerships, auto repair shops, buildings, stores, parks, etc. The geographic data may additionally or alternatively include other data records such as, for example, POI data records, topographical data records, cartographic data records, routing data, and maneuver data.”).
Both Filimotov and Soni are in the same field of system and method for rout stress index determination. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Filimotov vehicle and navigation system for a route with Soni cartographic data of each road segment associated with indexed coefficients. No new functionality would arise from the combination and the combination would improve usability of Filimotov by adding a cartographic data of each road segment associated with indexed coefficients will allow for more detailed data to determine the stress index of the roads. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 7 Filimotov in view of Chintakindi and Soni teaches the method according to claim 6, Filimotov does not teach but Soni teaches, wherein the substantially constant road properties comprise a constant number of lanes along the road segment, such that the route is divided into a new link each time the number of lanes changes, and wherein a road segment is further divided into sub-links when road markings along the road segment change; (See Soni column 13, line 16-30; “The images may be patches divided from the modified aerial image. In one example, the images are tiles cut from a grid overlaid on the entire aerial image. In another example, the images are defined according to sections of roadway. That is, in the road network a predetermined length of road defines a length of the image patches. Alternatively, the length of the image patch may be defined according to a length of road that extends until the road turns by a predetermined angle. Alternatively, the length of the image patch may be defined according to a length of road that extends until the road meets another road. Alternatively, the length of the image patch may be defined according to a length of road that extends until the road experiences a change in road attribute such as speed limit, functional classification, number of lanes or another feature.”).
Regarding claim 8 Filimotov in view of Chintakindi and Soni teaches the method according to claim 6, Filimotov further teaches, wherein the stress of the link is determined based on a complexity of the link; (See Filimonov paragraph 0013; “Implementing any number or combination of such data, a more precise determination of the total stress level value/indicator of the routes is enabled and a better suggestion of a route with less or the least stress associated to it may be possible. In particular, including road conditions enables avoiding routes with e.g. a large number of traffic lights or complex intersections, thereby reducing stress for the driver.”).
Regarding claim 9 Filimotov in view of Chintakindi and Soni teaches the method according to claim 8, Filimonov further teaches, wherein the complexity of the link is determined based on attributes of the link relating to a number of lanes of the link and whether it is possible to change from one lane to another; (See Filimonov paragraph 0013; “According to an embodiment, the one or more route properties comprise route or road conditions and/or sensor data associated with at least the respective part of the one or more routes. For example, the route properties may comprise a number of lanes, a number of traffic lights, a number and/or complexity of intersections and/or the current traffic situation along the one or more routes. The route properties may further comprise currently and/or previously determined sensor data associated with a user and/or the driver of the car. Said sensor data may indicate a stress level of the driver or similar data of other drivers of other vehicles on one of the one or more routes, respective parts of the routes or proximate locations. Implementing any number or combination of such data, a more precise determination of the total stress level value/indicator of the routes is enabled and a better suggestion of a route with less or the least stress associated to it may be possible. In particular, including road conditions enables avoiding routes with e.g. a large number of traffic lights or complex intersections, thereby reducing stress for the driver. Including traffic information allows for a real-time determination of stress related to a respective route…”).
With respect to dependent claims 10-12, please see the rejection above with respect to claims 3-5 which is commensurate in scope to claims 10-12, with claims 3-5 being drown to vehicle, and claims 10-12 being drawn to a corresponding method.
Regarding claim 13 Filimotov in view of Chintakindi and Soni teaches the method according to claim 12, Filimonov further teaches, wherein the coefficient environment depends on whether the link is a part of an urban road, country road or suburban road; (See Filimonov paragraph 0020; “…combining the sensor data with location data and/or data of the route properties and determining stressors of the individual driver from the combined data. Stressors may represent one or more route properties…”).
Regarding claim 14 Filimotov in view of Chintakindi and Soni teaches the method according to claim 13, Filimonov further teaches, wherein the intersection is a junction where more than two links converge, and the intersection comprises a crossing and a link at the intersection; (See Filimonov paragraph 0033; “In step 112, a suggested route from the one or more routes is indicated. The suggestion is based on a comparison of the assigned stress level indicators for each route. In particular, the route with the lowest stress level indication is suggested. For example, roads may be represented by a network of links. Each link is assigned a stress level value or indicator. For example, each type of lane number, location, traffic light number, intersection or other link is assigned a respective stress level indicator or value. The values might be weighted with personal preferences of the driver…”).
Regarding claim 15 Filimotov in view of Chintakindi and Soni teaches the method according to claim 14, Filimonov further teaches, wherein the stress of the intersection is determined depending on at least one of the following factors:
- time used to pass by the intersection;
- type of the intersection;
- type of turning at the intersection;
- whether there is traffic light at the intersection;
- the stress index of the link at the intersection;
(See Filimonov paragraph 0032-0033; “For example, certain (individual) stress level indicators may be determined for the number of lanes, intersections or traffic lights on a route. This may be based on scientific data and/or on a driver preference. An individual stress level indicator may then be assigned to each route property. For example, a high stress level indicator or value may be assigned to each complex intersection, while a lower stress level indicator/value may be assigned to each traffic light. When determining the total stress level indicator of a route, these predetermined individual indicators may be, for example, summed up for a certain route containing a number of each of or part of the individual route properties.
In step 112, a suggested route from the one or more routes is indicated. The suggestion is based on a comparison of the assigned stress level indicators for each route. In particular, the route with the lowest stress level indication is suggested. For example, roads may be represented by a network of links. Each link is assigned a stress level value or indicator. For example, each type of lane number, location, traffic light number, intersection or other link is assigned a respective stress level indicator or value. The values might be weighted with personal preferences of the driver. An algorithm then determines the total stress level for each of or part of the possible routes between the starting point and the destination point. For example, the total stress level of a route may be represented as a cumulative value of the stress level indicators of the links or route properties of the respective route.”).
Regarding claim 16 Filimotov in view of Chintakindi and Soni teaches the method according to claim 15, Filimonov does not explicitly teach but Chintakindi teaches, wherein the stress of the intersection is defined as: (nodetypek+turntypek+trafficlightk+∑link) x time wherein coefficient nodetypek represents complexity of the intersection associated with the type of the intersection or road ending; coefficeint turntypek represents complexity of the intersection associated with the type of directional movement allowed or expected at the crossing, coefficient trafficlightk represents complexity of the intersection associated with the presence of a traffic light at the crossing, coefficient time represents the driving time required to navigate the crossing, and coefficient time represent stress index of the link at this intersection in which a driver passes through; (See Chintakindi column 33, line 25-49; “The coefficient the model produces for each variable (along with the coefficient for any interaction terms) would be the value to apply to each subjective risk type. The user device and/or the personal navigation device 110 may initially provide the quickest/shortest route from a start location A to an end location B, and then determine the route frustration index value by determining either the sum product of the number of each route frustration index parameters and the value for that route frustration index or the overall product of the number of each route frustration index and the value for that route frustration index. (Traffic and weather conditions could either be included or excluded from the determination of the route frustration index value for comparison of routes. If not included, an adjustment may be made to the route risk value once the route has been traveled). The driver may be presented with an alternate route which is less risky than the initial route calculated. The user device and/or the personal navigation device 110b may display the difference in route frustration between the alternate routes and permit the driver to select the preferred route. In some embodiments in accordance with this disclosure, a driver/vehicle may be provided a monetary benefit (e.g., a credit towards a future insurance policy) for selecting a route having lesser route frustration index values.”; also see Chintakindi column 6, line 28-33; “based on a road type being traveled and/or a time of day of the trip. In some cases, other parameters may be used instead of, or in addition to, vehicle speed, such as a time duration during which a speed level was reduced, where the length of time may be analyzed to determine a level of frustration.”).
Both Filimotov and Chintakindi are in the same field of system and method for rout stress index determination. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Filimotov vehicle and navigation system for a route with Chintakindi equation to determine the stress of the link. No new functionality would arise from the combination and the combination would improve usability of Filimotov by adding an equation to determine the stress of the link will allow a better stress assessment of the routs. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 17 Filimotov in view of Chintakindi and Soni teaches the method according to claim 16, Filimonov also teaches, the method further comprising: comparing the stress index of the one or more routes and generating an indication to a user to suggest a route having the lowest stress index; (See Filimonov paragraph 0033; “In step 112, a suggested route from the one or more routes is indicated. The suggestion is based on a comparison of the assigned stress level indicators for each route. In particular, the route with the lowest stress level indication is suggested. For example, roads may be represented by a network of links. Each link is assigned a stress level value or indicator. For example, each type of lane number, location, traffic light number, intersection or other link is assigned a respective stress level indicator or value. The values might be weighted with personal preferences of the driver. An algorithm then determines the total stress level for each of or part of the possible routes between the starting point and the destination point. For example, the total stress level of a route may be represented as a cumulative value of the stress level indicators of the links or route properties of the respective route.”).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/L.K./ Examiner, Art Unit 3666
/SCOTT A BROWNE/ Supervisory Patent Examiner, Art Unit 3666