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 .
Status of Claims
This Office action is in response to the amendments filed on February 06, 2026. Claims 1-24 are currently pending, with Claims 1, 9-10, 17, and 23 being amended, and Claim 24 being newly added.
Response to Amendments
In response to Applicant’s amendments, filed May 19, 2025, the Examiner withdraws the previous claim interpretation, maintains the previous 35 U.S.C. 112 rejection, and withdraws the previous 35 U.S.C. 102 and 103 rejections.
Response to Arguments
Applicant’s arguments, filed February 06, 2026, pertaining to the previous 35 U.S.C. 102 and 103 rejections, of Claims 1-23 under Woolley, in view of Rubin, Golding, Ratnasingam, and Yamakawa, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection of Claims 1-24 is made in view of Woolley, in view of Wang, Rubin, Golding, Ratnasingam, and Yamakawa.
Applicant's arguments filed February 06, 2026, regarding the 35 U.S.C. 112 rejection, have been fully considered but they are not persuasive. Regarding applicant’s arguments pertaining the 35 U.S.C. 112(a) rejection, the Examiner is unpersuaded. The written description provides support for scoring a difficulty for a maneuver at a 70/100 (see at least Paragraph [0054] of the instant specification), and for a user may score a difficulty preference at a certain number (see Paragraph [0058] of the instant specification), but does not provide support that the user with a score 0f 70/100 is different than a user with a score of 50/100. The instant specification merely describes that a user can identify and set their preference levels, and that a route option may be provided in response to the difficulty level. As such, the Examiner is unpersuaded, and maintains the corresponding 35 U.S.C. 112 rejections.
The remaining arguments are essentially the same as those addresses above and/or below and are unpersuasive for essentially the same reasons. Therefore, the corresponding rejections are maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 23 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor, at the time the application was filed, had possession of the claimed invention. Claim 23 recites “receive a second query for a second route … identify a second difficulty preference corresponding to a second user of the vehicle as a second score along the scale …”. The Written Description (at Paragraphs [0066], [0076]) only state that “the query may include an identifier of the user …” and “the input device 612 may include any of various devices that enable the computing device 602 to receive control inputs from users …”, but does not provide support for 1) distinguishing between user inputs to determine which user is providing inputs, 2) distinguishing a preference level of the second user, 3) distinguishing that the preferences of each user have different difficulty preferences. The current claim language indicates that the first and second user are associated with the same vehicle, not a separate vehicle traversing the route, and this limitation is also further not supported in the Written Description.
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 1-4, 8-12, 16-19, 21, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2019/0078906 A1, to Woolley (hereinafter referred to as Woolley; previously of record); in view of U.S. Patent No. 9,576,490 B1, to Wang, et al (hereinafter referred to as Wang; newly of record).
As per Claim 1, Woolley discloses the features of a system for customized routing of vehicles based on lane-level difficulty (e.g. Paragraphs [0013], [0021]; where a navigation system (100) receives requestion for a navigation route from a user device (120) via a network (130) and provides the requested navigation route, and the navigation system (100) provides lane guidance to indicate particular lanes that are preferable at various points in the navigation route, based on an ordered or score references for lanes at a particular point (i.e. difficulty level of navigating a lane)), comprising:
non-transitory memory (e.g. Paragraph [0050]; where the computer program may be stored in a non-transitory tangible computer readable storage medium) configured to
maintain lane-level difficulty scores for a plurality of lanes of travel of roadway (e.g. Paragraph [0020]; where the routing module (104) can access the map data store (104) to identify possible routes and road segments that a vehicle can travel on to get from the start position to the destination, and the routing module (104) scores the possible routes and identifies a preferred route to reach the destination),
the lane-level difficulty scores being a determined numeric quantity along a numeric scale (e.g. Paragraphs [0031], [0037]; where the lane guidance module (106) generates a score for each of the one or more lanes at that road segment, and a first lane may score 15, a second lane may score 60, a third lane may score 75, etc. (i.e., a numeric scale); and where the lane scoring may indicate 100% for certain lanes that will continue along the route for a greater distance than another lane) that is indicative of how difficult it is for a driver to traverse the respective lane (e.g. Paragraphs [0022]-[0023], [0044]-[0045]; where the lane scoring for the road segment indicates which lanes are most desirable for traversing the road segment, such as by indicating the most preferred lane for continuing onto a subsequent road segment after changing on to the next segment; and where the routing module (104) can determine lane-by-lane traffic scoring, and the scores for each lane are scored relative to the highest-scoring lane),
the lane-level difficulty scores being computed based on traffic information compiled from a plurality of vehicles having traversed the roadway (e.g. Paragraphs [0021]-[0022]; where real-time and historical data of the traffic along a road segment is determined; and where the routing module (104) can also determine lane-by-lane traffic scoring based on traffic related to a lane, and the traffic module (108) can receive a plurality of GPS location points from individual user devices (120) based on their corresponding timestamps); and
one or more processors (e.g. Paragraph [0050]; where the system includes a computer program stored in a non-transitory computer readable storage medium for executing the instructions), configured to:
receive a query for a route from a vehicle (e.g. Paragraphs [0013]-[0014]; where the user device (120) may be any suitable computing system, such as an in-vehicle navigation system or car dashboard; and where the navigation system (100) receives requests for a navigation route from a user device (120)),
identify a difficulty preference corresponding to a user of the vehicle as a ‘…’ score along the numeric scale (e.g. Paragraphs [0020], [0023], [0032], [0038]; where the routing module (104) may send the navigation routes to the user device (120) for selection by the user, and which accesses the map data store (1100) to identify possible routes and road segments that a vehicle can travel, and the routing module (104) scores the possible routes and identifies a preferred route to reach the destination; and where the score interpolation may be configurable by the user to include or exclude various scoring features (i.e. user preferences for scoring), and the user may determine which lane is preferable to travel along),
compute the route to include only maneuvers that have lane-level difficulty scores at or below the difficulty preference (e.g. Paragraphs [0023], [0029], [0031], [0037]; Figures 5A-B; where the lane guidance module (106) determines lane information and describes preferred lanes of the route based on a score for each lane of a road segment of the route; where the scores may be based on a distance or time that a lane continues along a route, or scoring a higher travel that is faster or quicker or most continuous without conducting a lane change (i.e. difficulty preference); and where lanes that are preferable (i.e. below a threshold) are provided based on the lane parameters, traffic, etc.; ad where textual, graphical, or visual content may be displayed corresponding to the most preferred lane for the user to travel (i.e. includes maneuvers for the most preferred)), and
send the route to the vehicle, responsive to the query (e.g. Paragraphs [0013]-[0014], [0023], [0031]; where the user device (120) may be any suitable computing system, such as an in-vehicle navigation system or car dashboard; and where the navigation system (100) receives requests for a navigation route from a user device (120); and where the navigation system (100) provides lane guidance to the user device (120) to indicate particular lanes that are preferable at various points in the route).
Woolley fails to disclose every feature of identify a difficulty preference corresponding to a user of the vehicle as a numeric score along the numeric scale.
However, Wang, in a similar field of endeavor, teaches a personalized navigation route for a transportation device, where the controller (20) is programmed to obtain a respective path score (PS.sub.i) for each of the respective paths (i) based at least partially on a plurality of preference categories, which correlates to a personal preference of the driver (16), and the maximum score in each preference category may be set to 100, and other scores in the preference categories may be re-scaled linearly (e.g. Col. 5 lines 30-54).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the navigation lane guidance system of Woolley, with the feature of determining a numeric preference score in the system of Wang, in order to account for driver traits associated with a route (see at Col. 6 lines 14-16 and lines 46-60 of Wang).
As per Claim 9, Woolley discloses the features of method for customized routing of vehicles based on lane-level difficulty (e.g. Paragraphs [0013], [0021]; where a navigation system (100) receives requestion for a navigation route from a user device (120) via a network (130) and provides the requested navigation route, and the navigation system (100) provides lane guidance to indicate particular lanes that are preferable at various points in the navigation route, based on an ordered or score references for lanes at a particular point (i.e. difficulty level of navigating a lane)), comprising:
extracting data elements from traffic information indicative of performance of maneuvers by the vehicles (e.g. Paragraphs [0018], [0020]-[0021]; where the map data can specify the number of lanes for a road at various parts of the road, average speed of drivers on a road (i.e. performance of maneuvers));
determining raw difficulty scores for each of the maneuvers based on the data elements (e.g. Paragraph [0031]; where raw scores are determined for each lane),
identifying lanes of travel for the maneuvers (e.g. Paragraph [0031]; where the lane guidance module (105) generates a score for each of the one or more lanes at that road segment (i.e., identifies the number of lanes));
for each lane, generating a lane-level difficulty score based on the raw difficulty scores corresponding to the maneuvers using that lane (e.g. Paragraphs [0031], [0036]; where the raw scores of each lane are adjusted based on a change in lane or traffic conditions),
the lane-level difficulty scores being a determined numeric quantity along a numeric scale (e.g. Paragraphs [0031], [0037]; where the lane guidance module (106) generates a score for each of the one or more lanes at that road segment, and a first lane may score 15, a second lane may score 60, a third lane may score 75, etc. (i.e., a numeric scale); and where the lane scoring may indicate 100% for certain lanes that will continue along the route for a greater distance than another lane) that is indicative of how difficult it is for a driver to traverse the respective lane (e.g. Paragraphs [0022]-[0023], [0044]-[0045]; where the lane scoring for the road segment indicates which lanes are most desirable for traversing the road segment, such as by indicating the most preferred lane for continuing onto a subsequent road segment after changing on to the next segment; and where the routing module (104) can determine lane-by-lane traffic scoring, and the scores for each lane are scored relative to the highest-scoring lane); and
routing vehicles accounting for the lane-level difficulty scores to include only maneuvers that have lane-level difficulty scores at or below a difficulty preference specified as a ‘…’ score along the numeric scale (e.g. Paragraphs [0023], [0029], [0031], [0037]; Figures 5A-B;where the lane guidance module (106) determines lane information and describes preferred lanes of the route based on a score for each lane of a road segment of the route; where the scores may be based on a distance or time that a lane continues along a route, or scoring a higher travel that is faster or quicker or most continuous without conducting a lane change (i.e. difficulty preference); and where lanes that are preferable (i.e. below a threshold) are provided based on the lane parameters, traffic, etc.).
Woolley fails to disclose every feature of lane-level difficulty scores to include only maneuvers that have lane-level difficulty scores ‘..’ a numeric score along the numeric scale.
However, Wang, in a similar field of endeavor, teaches a personalized navigation route for a transportation device, where the controller (20) is programmed to obtain a respective path score (PS.sub.i) for each of the respective paths (i) based at least partially on a plurality of preference categories, which correlates to a personal preference of the driver (16), and the maximum score in each preference category may be set to 100, and other scores in the preference categories may be re-scaled linearly (e.g. Col. 5 lines 30-54).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the navigation lane guidance system of Woolley, with the feature of determining a numeric preference score in the system of Wang, in order to account for driver traits associated with a route (see at Col. 6 lines 14-16 and lines 46-60 of Wang).
As per Claim 17, Woolley discloses the features of non-transitory computer-readable medium comprising instructions for customized routing of vehicles based on lane-level difficulty (e.g. Paragraphs [0013], [0021], [0050]; where the system includes a computer program stored in a non-transitory computer readable storage medium; and where a navigation system (100) receives requestion for a navigation route from a user device (120) via a network (130) and provides the requested navigation route, and the navigation system (100) provides lane guidance to indicate particular lanes that are preferable at various points in the navigation route, based on an ordered or score references for lanes at a particular point; and the traffic module (108) provides traffic data relating to roads and individual lanes of traffic on a road (i.e. difficulty level of navigating a lane), that when executed by one or more processors, cause the one or more processors to perform operations including to:
extract data elements from traffic information indicative of performance of maneuvers by the vehicles (e.g. Paragraphs [0018], [0020]-[0021]; where the map data can specify the number of lanes for a road at various parts of the road, average speed of drivers on a road (i.e. performance of maneuvers));
determine raw difficulty scores for each of the maneuvers based on the data elements (e.g. Paragraph [0031]; where raw scores are determined for each lane),
identify lanes of travel for the maneuvers (e.g. Paragraph [0031]; where the lane guidance module (105) generates a score for each of the one or more lanes at that road segment (i.e., identifies the number of lanes));
for each lane, generating a lane-level difficulty score based on the raw difficulty scores corresponding to the maneuvers using that lane (e.g. Paragraphs [0031], [0036]; where the raw scores of each lane are adjusted based on a change in lane or traffic conditions),
the lane-level difficulty scores being a determined numeric quantity along a numeric scale (e.g. Paragraphs [0031], [0037]; where the lane guidance module (106) generates a score for each of the one or more lanes at that road segment, and a first lane may score 15, a second lane may score 60, a third lane may score 75, etc. (i.e., a numeric scale); and where the lane scoring may indicate 100% for certain lanes that will continue along the route for a greater distance than another lane) that is indicative of how difficult it is for a driver to traverse the respective lane (e.g. Paragraphs [0022]-[0023], [0044]-[0045]; where the lane scoring for the road segment indicates which lanes are most desirable for traversing the road segment, such as by indicating the most preferred lane for continuing onto a subsequent road segment after changing on to the next segment; and where the routing module (104) can determine lane-by-lane traffic scoring, and the scores for each lane are scored relative to the highest-scoring lane);
receive a query for a route from a vehicle (e.g. Paragraphs [0013]-[0014]; where the user device (120) may be any suitable computing system, such as an in-vehicle navigation system or car dashboard; and where the navigation system (100) receives requests for a navigation route from a user device (120)),
identify a difficulty preference corresponding to a user of the vehicle as a ‘…’ score along the numeric scale (e.g. Paragraphs [0020], [0023], [0032], [0038]; where the routing module (104) may send the navigation routes to the user device (120) for selection by the user, and which accesses the map data store (1100) to identify possible routes and road segments that a vehicle can travel, and the routing module (104) scores the possible routes and identifies a preferred route to reach the destination; and where the score interpolation may be configurable by the user to include or exclude various scoring features (i.e. user preferences for scoring), and the user may determine which lane is preferable to travel along),
compute the route to include only maneuvers that have lane-level difficulty scores at or below the difficulty preference (e.g. Paragraphs [0023], [0029], [0031], [0037]; Figures 5A-B;where the lane guidance module (106) determines lane information and describes preferred lanes of the route based on a score for each lane of a road segment of the route; where the scores may be based on a distance or time that a lane continues along a route, or scoring a higher travel that is faster or quicker or most continuous without conducting a lane change (i.e. difficulty preference); and where lanes that are preferable (i.e. below a threshold) are provided based on the lane parameters, traffic, etc.), and
send the route to the vehicle, responsive to the query (e.g. Paragraphs [0013]-[0014], [0023], [0031]; where the user device (120) may be any suitable computing system, such as an in-vehicle navigation system or car dashboard; and where the navigation system (100) receives requests for a navigation route from a user device (120); and where the navigation system (100) provides lane guidance to the user device (120) to indicate particular lanes that are preferable at various points in the route).
Woolley fails to disclose every feature of identify a difficulty preference corresponding to a user of the vehicle as a numeric score along the numeric scale.
However, Wang, in a similar field of endeavor, teaches a personalized navigation route for a transportation device, where the controller (20) is programmed to obtain a respective path score (PS.sub.i) for each of the respective paths (i) based at least partially on a plurality of preference categories, which correlates to a personal preference of the driver (16), and the maximum score in each preference category may be set to 100, and other scores in the preference categories may be re-scaled linearly (e.g. Col. 5 lines 30-54).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the navigation lane guidance system of Woolley, with the feature of determining a numeric preference score in the system of Wang, in order to account for driver traits associated with a route (see at Col. 6 lines 14-16 and lines 46-60 of Wang).
As per Claim 2, Woolley, in view of Wang, teaches the features of Claim 1, and Woolley further teaches the features of wherein the one or more processors are further configured to:
extract data elements from the traffic information indicative of performance of maneuvers by the vehicles by the vehicles (e.g. Paragraphs [0018], [0020]-[0021], [0029]; where the lane scoring module (106) may determine traffic and lane-related data related to other vehicles based on time of day or current day of the week, rush-hour, etc., and the traffic data may be based on data received from third party users or by analyzing the speed of users navigating routes planned by the navigation system; and where the map data can specify the number of lanes for a road at various parts of the road, average speed of drivers on a road (i.e. performance of maneuvers));
determine raw difficulty scores for each of the maneuvers based on the data elements(e.g. Paragraph [0031]; where raw scores are determined for each lane);
identify lanes of travel for the maneuvers (e.g. Paragraph [0031]; where the lane guidance module (105) generates a score for each of the one or more lanes at that road segment (i.e., identifies the number of lanes)); and
for each lane, generate the lane-level difficulty score based on the raw difficulty scores corresponding to the maneuvers using that lane (e.g. Paragraphs [0031], [0036]; where the raw scores of each lane are adjusted based on a change in lane or traffic conditions);
determine raw difficulty scores for each of the maneuvers based on the data elements (e.g. Paragraph [0031]; where raw scores are determined for each lane).
As per Claim 3, and similarly for Claims 11 and 18, Woolley, in view of Wang, teaches the features of Claims 2, 9, and 17, respectively, and Woolley further discloses the features of wherein the data elements include: speed data indicative of how fast the vehicles performed the maneuvers; speed change data indicative of how often the vehicles changed speed during the maneuvers; and/or wait time data indicative of how long the vehicles took to perform the maneuvers (e.g. Paragraphs [0018], [0021], [0030]; where the map data may determine the average speed of the drivers on the road and in the travel lane; and the traffic module (108) analyzes the speed of users (i.e. how fast) navigating the routes planned by the navigation system; and where the real-time speed of the current lane (i.e. observed speed changes, lane changes) may be used to inform other vehicles of the best traffic lane or lanes to be in).
As per Claim 4, and similarly for Claims 12 and 19, Woolley, in view of Wang, teaches the features of Claims 2, 9, 18, respectively, and Woolley further discloses the features of wherein to identify the lanes of travel for the maneuvers includes to infer the lane of travel through an intersection based on a direction of a turn performed by the vehicle (e.g. Paragraphs [0022], [0032]; where the routing module (104) can determine lane-by-lane scoring based on inferences of traffic related to a lane; and where the lane guidance information may indicate scores for lanes after a user turns left or right).
As per Claim 8, Woolley, in view of Wang, teaches the features of Claim 2, and Woolley further discloses the features of wherein the one or more processors are further configured to:
scale the raw difficulty scores to remove effects of ambient factors in determining the lane-level difficulty scores (e.g. Paragraph [0019], [0035]; Claim 3; where each lane score is adjusted to normalize the scores (i.e. scaled); where the routing module (104) may generate a navigation route that reflects expected conditions of the route (e.g., current day of the week or current time of day), such that a route generated for travel during rush-hour may differ from a route generated for travel late at night);
determine the lane-level difficulty scores using the raw difficulty scores as scaled (e.g. Paragraphs [0022], [0035]; where the scores for the lanes are scaled relative to the highest scoring lane and may indicate a percentile of the lane’s score compared to the highest scoring lane); and
compute the route using the lane-level difficulty scores scaled to current ambient factors (e.g. Paragraphs [0019]-[0020], [0035]; Claim 3; where the routing module (104) may generate one or more routes and send it to a user device; and where each lane score is adjusted to normalize the scores (i.e. scaled); where the routing module (104) may generate a navigation route that reflects expected conditions of the route (e.g., current day of the week or current time of day), such that a route generated for travel during rush-hour may differ from a route generated for travel late at night).
As per Claim 10, Woolley, in view of Wang, teaches the features of Claim 9, and Woolley further discloses the features of further comprising:
receiving a query for a route from a vehicle (e.g. Paragraphs [0013]-[0014]; where the user device (120) may be any suitable computing system, such as an in-vehicle navigation system or car dashboard; and where the navigation system (100) receives requests for a navigation route from a user device (120));
identifying the difficulty preference corresponding to a user of the vehicle as a ‘…’ score along the numeric scale (e.g. Paragraphs [0020], [0023], [0032], [0038]; where the routing module (104) may send the navigation routes to the user device (120) for selection by the user, and which accesses the map data store (1100) to identify possible routes and road segments that a vehicle can travel, and the routing module (104) scores the possible routes and identifies a preferred route to reach the destination; and where the score interpolation may be configurable by the user to include or exclude various scoring features (i.e. user preferences for scoring), and the user may determine which lane is preferable to travel along);
computing the route to include only maneuvers that have lane-level difficulty scores at or below the difficulty preference (e.g. Paragraphs [0023], [0029], [0031], [0037]; Figures 5A-B;where the lane guidance module (106) determines lane information and describes preferred lanes of the route based on a score for each lane of a road segment of the route; where the scores may be based on a distance or time that a lane continues along a route, or scoring a higher travel that is faster or quicker or most continuous without conducting a lane change (i.e. difficulty preference); and where lanes that are preferable (i.e. below a threshold) are provided based on the lane parameters, traffic, etc.); and
sending the route to the vehicle, responsive to the query (e.g. Paragraphs [0013]-[0014], [0023], [0031]; where the user device (120) may be any suitable computing system, such as an in-vehicle navigation system or car dashboard; and where the navigation system (100) receives requests for a navigation route from a user device (120); and where the navigation system (100) provides lane guidance to the user device (120) to indicate particular lanes that are preferable at various points in the route).
Woolley fails to disclose every feature of identifying the difficulty preference corresponding to a user of the vehicle as a numeric score along the numeric scale.
However, Wang, in a similar field of endeavor, teaches a personalized navigation route for a transportation device, where the controller (20) is programmed to obtain a respective path score (PS.sub.i) for each of the respective paths (i) based at least partially on a plurality of preference categories, which correlates to a personal preference of the driver (16), and the maximum score in each preference category may be set to 100, and other scores in the preference categories may be re-scaled linearly (e.g. Col. 5 lines 30-54).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the navigation lane guidance system of Woolley, with the feature of determining a numeric preference score in the system of Wang, in order to account for driver traits associated with a route (see at Col. 6 lines 14-16 and lines 46-60 of Wang).
As per Claim 16, Woolley, in view of Wang, teaches the features of Claim 9, and Woolley further discloses the features of further comprising:
scaling the raw difficulty scores to remove effects of ambient factors in determining the lane-level difficulty scores (e.g. Paragraph [0019], [0035]; Claim 3; where each lane score is adjusted to normalize the scores (i.e. scaled); where the routing module (104) may generate a navigation route that reflects expected conditions of the route (e.g., current day of the week or current time of day), such that a route generated for travel during rush-hour may differ from a route generated for travel late at night);
determining the lane-level difficulty scores using the raw difficulty scores as scaled (e.g. Paragraphs [0022], [0035]; where the scores for the lanes are scaled relative to the highest scoring lane and may indicate a percentile of the lane’s score compared to the highest scoring lane); and
performing the routing using the lane-level difficulty scores scaled to current ambient factors (e.g. Paragraphs [0019]-[0020], [0035]; Claim 3; where the routing module (104) may generate one or more routes and send it to a user device; and where each lane score is adjusted to normalize the scores (i.e. scaled); where the routing module (104) may generate a navigation route that reflects expected conditions of the route (e.g., current day of the week or current time of day), such that a route generated for travel during rush-hour may differ from a route generated for travel late at night).
As per Claim 21, Woolley, in view of Wang, teaches the features of Claim 2, and Woolley further discloses the features of wherein the data elements include at least two of: speed data indicative of how fast the vehicles performed the maneuvers; speed change data indicative of how often the vehicles changed speed during the maneuvers; and/or wait time data indicative of how long the vehicles took to perform the maneuvers (e.g. Paragraphs [0018], [0021], [0023], [0030]; where the map data may determine the average speed of the drivers on the road and in the travel lane; and the traffic module (108) analyzes the speed of users (i.e. how fast) navigating the routes planned by the navigation system; and where the real-time speed of the current lane (i.e. observed speed changes, lane changes) may be used to inform other vehicles of the best traffic lane or lanes to be in; and where the scores for individual lanes may be generated based on a time that a lane continues along the navigation route, which may be modified based on factors reflecting the desirability of a lane, such as expected traffic in the lane and the system may suggest a lane when real-time traffic in another lane is slower than the typical traffic-free speed of other vehicles on the road (i.e. wait time)).
As per Claim 24, Woolley, in view of Wang, teaches the features of Claim 1, and Woolley further discloses the features of wherein the numeric scale is a hundred point scale (e.g. Paragraphs [0031], [0037]; where the lane guidance module (106) generates a score for each of the one or more lanes at that road segment, and a first lane may score 15, a second lane may score 60, a third lane may score 75, etc. (i.e., a numeric scale); and where the lane scoring may indicate 100% for certain lanes that will continue along the route for a greater distance than another lane).
Wang, in a similar field of endeavor, more explicitly teaches the features of wherein the numeric scale is a hundred point scale. Wang teaches a personalized navigation route for a transportation device, where the controller (20) is programmed to obtain a respective path score (PS.sub.i) for each of the respective paths (i) based at least partially on a plurality of preference categories, which correlates to a personal preference of the driver (16), and the maximum score in each preference category may be set to 100, and other scores in the preference categories may be re-scaled linearly (e.g. Col. 5 lines 30-54).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the navigation lane guidance system of Woolley, with the feature of determining a numeric preference score in the system of Wang, in order to account for driver traits associated with a route (see at Col. 6 lines 14-16 and lines 46-60 of Wang).
Claims 5-6, 13-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Woolley in view of Wang, as applied to Claims 2, 9, and 19 above, and further in view of U.S. Patent Publication No. 2013/0282271 A1, to Rubin, et al (hereinafter referred to as Rubin; previously of record).
As per Claim 5, and similarly for Claim 13, Woolley, in view of Wang, teaches the features of Claims 2 and 9, respectively, but the combination of Woolley, in view of Wang, fails to teach every feature of wherein the one or more processors are further configured to: compute an average of the raw difficulty scores for each lane, resulting in the lane- level difficulty for each lane.
However, Rubin, in a similar field of endeavor, teaches a route guidance system, where the current traffic conditions are ranked as “light”, “moderate”, or “aggressive or challenging”, and for each traffic condition ranking, the sub-risk value is determined for the observed braking behavior; and where the average speed, and position of each lane is determined based on the behavior sub-risk associated with each lane (e.g. Paragraphs [0422], [0425], [0534]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the navigation lane guidance system of Woolley, in view of Wang, with the feature of averaging the data in the system of Rubin, in order to improve the ability of the system to distinguish traffic at different grade levels (see at least Paragraph [0501] of Rubin).
As per Claim 6, and similarly for Claims 14, Woolley, in view of Wang and Rubin, teaches the features of Claims 5 and 13, respectively, and Rubin further teaches the features of wherein the average is a weighted average using weights for each of the data elements.
Rubin teaches a route guidance system, where lane map weighting is used in proportion to the confidence level of the lane map data (e.g. Paragraphs [0474], [0482]-[0483], [0514]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the navigation lane guidance system of Woolley, in view of Wang, with the feature of averaging the data in the system of Rubin, in order to improve the ability of the system to distinguish traffic at different grade levels (see at least Paragraph [0501] of Rubin).
As per Claim 20, Woolley, in view of Wang, teaches the features of Claim 19, but the combination of Woolley, in view of Wang, fails to teach every feature of wherein the average is a weighted average using weights for each of the data elements.
However, Rubin, in a similar field of endeavor, teaches a route guidance system, where lane map weighting is used in proportion to the confidence level of the lane map data (e.g. Paragraphs [0474], [0482]-[0483], [0514]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the navigation lane guidance system of Woolley, in view of Wang, with the feature of averaging the data in the system of Rubin, in order to improve the ability of the system to distinguish traffic at different grade levels (see at least Paragraph [0501] of Rubin).
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Woolley, in view of Wang and Rubin, as applied to Claims 5 and 13 above, and further in view of U.S. Patent Publication No. 2010/0174479 A1, to Golding, et al (hereinafter referred to as Golding; previously of record).
As per Claim 7, and similarly for Claim 15, Woolley, in view of Wang and Rubin, teaches the features of Claims 5 and 13, respectively, but the combination of Woolley, in view of Wang and Rubin, fails to teach the features of wherein a highest subset of raw difficulty scores are utilized in computing the average.
However, Golding, in a similar field of endeavor, teaches a method for generating attribute models for use in an adaptive navigation system, where candidate routes are determined, desirability scores are calculated and sorted, and the top N scoring routes (which may be a subset of all the scoring data) are provided for user selection (e.g. Paragraph [0096]; Figure 4b).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the navigation lane guidance system of Woolley, in view of Wang and Rubin, with the features of using the highest set of values in the system of Golding, order to improve learning functions of the system and more accurately present routes (see at least Paragraphs [0066] and [0074] of Golding).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Woolley, in view of Wang, as applied to Claim 2 above, and further in view of U.S. Patent No. 9,672,734 B1 A1, to Ratnasingam (hereinafter referred to as Ratnasingam; previously of record).
As per Claim 22, Woolley, in view of Wang, teaches the features of Claim 2, but the combination of Woolley, in view of Wang, fails to teach every feature of wherein the data elements include: speed data indicative of how fast the vehicles performed the maneuvers; speed change data indicative of how often the vehicles changed speed during the maneuvers; and wait time data indicative of how long the vehicles took to perform the maneuvers.
However, Ratnasingam, in a similar field of endeavor, teaches a traffic-aware lane determination system for a vehicle, where navigation data is received from other vehicles on a road segment, including determining congestion among a plurality of vehicles, ranking a plurality of lanes, and providing an optimal lane for a host vehicle to travel in; where the first vehicle receives navigation data from other vehicles, where the navigation data of the other vehicles include how often each vehicle stops, average speed over a predetermined time or distance, speed variation over a predetermined time, time to perform a driving maneuver, etc. (e.g. Col. 11 lines 45-51; Col. 12 lines 45-51).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the navigation lane guidance system of Woolley, in view of Wang, with the feature of determining navigation characteristics of other vehicles, in the system of Ratnasingam, order to improve accuracy of navigation data (see at least Col. 43 lines 39-43 of Ratnasingam).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2019/0078906 A1, to Woolley (hereinafter referred to as Woolley; previously of record), as applied to Claim 2 above, and further in view of U.S. Patent Publication No. 2013/0311081 A1, to Yamakawa, et al (hereinafter referred to as Yamakawa; newly of record).
As per Claim 23, Woolley, in view of Wang, teaches the features of Claim 1, but Woolley fails to disclose every feature of wherein the route is from an origin location of the vehicle to a destination location, and one or more processors are further configured to: receive a second query for a second route from the origin location to the destination location, identify a second difficulty preference corresponding to a second user as a second numeric score along the numeric scale, compute the second route to include only maneuvers that have lane-level difficulty scores at or below the second difficulty preference, wherein the second difficulty preference differs from the first difficulty preference and the second route includes different maneuvers, and send the second route responsive to the second query.
However, Yamakawa, in a similar field of endeavor, teaches the features of wherein the route is from an origin location of the vehicle to a destination location, and one or more processors are further configured to: receive a second query for a second route from the origin location to the destination location, identify a second difficulty preference corresponding to a second user as a second ‘…’ score along the numeric scale, compute the second route to include only maneuvers that have lane-level difficulty scores at or below the second difficulty preference, wherein the second difficulty preference differs from the first difficulty preference and the second route includes different maneuvers, and send the second route responsive to the second query.
Yamakawa teaches a method for displaying turn-by-turn guidance on a navigation device, where the navigation device may have multiple users; and where the navigation device may maintain a database of frequent routes for each user, and the system may recognize a particular user (i.e. a first, second, etc. user); where the particular user may request driving directions to an indicated destination, based on the determination of a user’s familiarity with a route, or the difficulty in navigating the route, and the system may issue enhanced instructions for the identified user when it is determined that the use frequently misses a turn for a route, but other users do not; and where the system determines that a difficult turn is ahead and then determines whether one or more turns along the planned route exceeds a difficulty threshold, and issues a new route in response which does not exceed a predefined difficulty threshold (e.g. Paragraphs [0028], [0030], [0043]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the navigation lane guidance system of Woolley, in view of Wang, with the feature of distinguishing between users in the system of Yamakawa, in order to provided more frequent warnings to the user based on the user’s preferences (see at least Paragraph [0018] of Yamakawa).
Wang, in a similar field of endeavor, further teaches the features of identify a ‘..’. difficulty preference corresponding to a ‘…’ user as a second numeric score along the numeric scale
Wang teaches a personalized navigation route for a transportation device, where the controller (20) is programmed to obtain a respective path score (PS.sub.i) for each of the respective paths (i) based at least partially on a plurality of preference categories, which correlates to a personal preference of the driver (16), and the maximum score in each preference category may be set to 100, and other scores in the preference categories may be re-scaled linearly (e.g. Col. 5 lines 30-54).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the navigation lane guidance system of Woolley, with the feature of determining a numeric preference score in the system of Wang, in order to account for driver traits associated with a route (see at Col. 6 lines 14-16 and lines 46-60 of Wang).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Slusar, et al (U.S. 2024/0046365 A1), which teaches a method for generating polynomial risk maps for a plurality of road segments.
Wang, et al (U.S. 2012/0136567 A1), which teaches a method for planning vehicle routes based on safety factors.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MERRITT LEVY/Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663