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 .
Response to the Applicant’s arguments
The previous rejection is withdrawn. Applicant’s amendments are entered. Applicant’s remarks are also entered into the record. A new search was made necessitated by the applicant’s amendments.
A new reference was found. A new rejection is made herein.
Applicant’s arguments are now moot in view of the new rejection of the claims.
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Claim 1 is amended to recite and Matsumoto teaches “….(Currently Amended) A lane-level data updating method, applied to a lane-level
data updating apparatus, comprising: an updating operation on pre-stored lane-level data
to provide data support for intelligent driving”. (see paragraph 41-45 where the device includes a map system 1 including probe vehicle that can record the lane markings on the road; see paragraph 62 and 89-98 where the map and lane data can be updated by the probe vehicles in real time) (see FIG. 4 where the vehicle in FIG. 4 can provide a high frequency mode of recording the vehicle in the lane and update the lane and upload this to the server at a high or low frequency rate and paragraph 90-91)
It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the teachings of MATSUMOTO et al. with the disclosure of the primary reference since MATSUMOTO teaches that data samples from a number of different probe vehicle can provide an identification of the lanes and markings and edges of the road at a high frequency rate to a map server. The feature data includes lane marking data and landmark data. The lane marking data includes a lane marking ID for each lane marking and a group of coordinate points representing an installation portion. The lane marking data includes pattern information such as broken lines, solid lines, and road tacks. The lane marking data is associated with lane information (e.g., lane ID or link ID at the lane level). The landmark data represents the position and type of each landmark. The shape and position of each object are represented by a group of coordinate points. POI data is data indicative of the position and the type of the feature which affects vehicle travel plans such as branch points for exiting the main highway, junctions, speed limit change points, lane change points, traffic jams, construction sections, intersections, tunnels, toll gates, etc. POI data includes type and location information. This can provide information that the lanes can narrow or expand and provide a high definition map for autonomous driving. Some lanes can be occluded and blocked and this can be provided to autonomous vehicles for increased safety. See paragraph 90-95 and 294-7.
The 101 rejection is not overcome by adding a server, which is a general purpose computer. There is no autonomous vehicle nor sensor claimed in claim 1. It is an abstract idea. A person can tell another person that the vehicles lanes are blocked and to avoid that highway. This is fresh data that provides “reliable data support for intelligent driving”. This is data aggregation of lane information that is updated. This is an abstract idea. There are no elements other than a general purpose computer and the claim 1 and the other independent claims are not statutory.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1 and 14 and 20 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of United States Patent Application Pub. No.: US20230204784A1 to DI ZENG et al. assigned to NVIDIA and in view of United States Patent Application Pub. No.: US20210180981A1 to Matsumoto that was filed in 2019.
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In regard to claim 1, and 14 and 20, DI ZENG discloses “1. A lane-level data updating method, comprising:
obtaining at least one kind of road association data corresponding to a target area, (see Fig. 10 where the lidar can be facing down and capture the road ground plane in the front and the back of the vehicle; see claims 14-17)
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wherein the road association data comprises a road condition image, (see paragraph 26 where the vehicle can use an HD map with the road condition data)
a driving track, (see paragraph 82-84 and FIG. 8a-b and paragraph 81-88 where a track of the vehicle to maintain the lane is shown or for an offramp)
and a standard map associated with the target area; (see paragraph 31-35 where the autonomous vehicle can include an HD map that can be supplemented and provided to the cloud and to other vehicles)
determining, based on the at least one kind of road association data and a preset parameter associated with the road association data, at least one area to be updated in which a lane change exists in the target area; (see paragraph 92-97 where the LIDAR of the vehicle can capture the road condition and the ground plane in the front and the rear of the vehicle to show that the vehicle can be sloping down and the localization module can be compensated for the sloping down orientation of the vehicle)
obtaining, for each of the areas to be updated, a plurality of driving tracks corresponding to the area to be updated and a current road condition image; and (see paragraph 43 where the server can update the map with the current HD mapping and see 92-97 where the LIDAR of the vehicle can capture the road condition and the ground plane in the front and the rear of the vehicle to show that the vehicle can be sloping down and the localization module can compensated for the sloping down orientation of the vehicle)
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determining, based on the plurality of driving tracks and the current road condition image, (see FIG. 2 where the localization api, the map api and 3d map api and route api and all can be updated locally via blocks 285-275 and update the server via system interface 280) (see paragraph 92-97 where the LIDAR of the vehicle can capture the road condition and the ground plane in the front and the rear of the vehicle to show that the vehicle can be sloping down and the localization module can be compensated for the sloping down orientation of the vehicle)
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target lane-level data corresponding to the area to be updated, and performing,
based on the target lane-level data, an updating operation on pre-stored lane-level data”. (see FIG. 2 where the localization api, the map api and 3d map api and route api and all can be updated locally via blocks 285-275 and update the server via system interface 280)
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Claim 1 is amended to recite and Matsumoto teaches “….(Currently Amended) A lane-level data updating method, applied to a lane-level
data updating apparatus, comprising: an updating operation on pre-stored lane-level data
to provide data support for intelligent driving”. (see paragraph 41-45 where the device includes a map system 1 including probe vehicle that can record the lane markings on the road; see paragraph 62 and 89-98 where the map and lane data can be updated by the probe vehicles in real time) (see FIG. 4 where the vehicle in FIG. 4 can provide a high frequency mode of recording the vehicle in the lane and update the lane and upload this to the server at a high or low frequency rate and paragraph 90-91)
It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the teachings of MATSUMOTO et al. with the disclosure of the primary reference since MATSUMOTO teaches that data samples from a number of different probe vehicle can provide an identification of the lanes and markings and edges of the road at a high frequency rate to a map server. The feature data includes lane marking data and landmark data. The lane marking data includes a lane marking ID for each lane marking and a group of coordinate points representing an installation portion. The lane marking data includes pattern information such as broken lines, solid lines, and road tacks. The lane marking data is associated with lane information (e.g., lane ID or link ID at the lane level). The landmark data represents the position and type of each landmark. The shape and position of each object are represented by a group of coordinate points. POI data is data indicative of the position and the type of the feature which affects vehicle travel plans such as branch points for exiting the main highway, junctions, speed limit change points, lane change points, traffic jams, construction sections, intersections, tunnels, toll gates, etc. POI data includes type and location information. This can provide information that the lanes can narrow or expand and provide a high definition map for autonomous driving. Some lanes can be occluded and blocked and this can be provided to autonomous vehicles for increased safety. See paragraph 90-95 and 294-7.
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 2-4 and 15-18 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of United States Patent Application Pub. No.: US20230204784A1 to DI ZENG et al. assigned to NVIDIA filed in 2109 and in view of United States Patent Application Pub. No.: US20140149030A1 to Chapman that was filed in 2007 and in view of Matsumoto.
In regard to claim 2 and 15, the primary reference is silent but Chapman teaches “...2. The method according to claim 1, wherein the preset parameter associated with
the road association data comprises a weight parameter;
wherein the determining, based on the at least one kind of road association data (see paragraph 52)
and the preset parameter associated with the road association data, at least one area to
be updated in which a lane change exists in the target area comprises: (see paragraph 57 where the road traffic condition can be provided where there is a lane group that has an HOV lane and multiple lanes and a lane adjoining a road)
determining, for each road association data, based on the road association data, a current road characteristic of the target area;
determining, based on the road characteristic and pre-stored historical standard data corresponding to the target area, changing content corresponding to the target area; determining, based on a preset mapping relationship table, confidence information corresponding to the changing content, wherein the mapping relationship table comprises a mapping relationship between a plurality of changing contents and
confidence; performing, based on the confidence information corresponding to respective changing content and the weight parameter corresponding to the road association data, a weighted calculation to obtain a changing score corresponding to the target area; and determining, based on the changing score and a preset score threshold, at least one area to be updated in which a lane change exists in the target area”. (see FIG. 4 where the road sample data for a time period is provided and then each road segment data sample for each road segment is provided in blocks 405-415 and then a non-data samples of interest are provided and then these are filtered out based on the source and then these are used for a later use in blocks 400-490)
It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the teachings of CHAPMAN with the disclosure of the primary reference since CHAPMAN teaches that data samples from a number of different probe vehicle can provide hints about the traffic. This includes traffic speed of the vehicles. This can provide hints that the vehicles are opening up to an HOV lane or are constricted and in heavy traffic based on the data samples.
In regard to claim 3, and 16, the primary reference is silent but Chapman teaches “..3. The method according to claim 2, wherein the determining, based on the changing score and a preset score threshold, at least one area to be updated in which a lane change exists in the target area comprises: if the changing score is greater than the score threshold, determining a target changing content in the changing content that meets a preset filtering condition and taking an area corresponding to the target changing content as the area to be updated; or (optionally) if the changing score is less than the score threshold, determining that an area to be updated in which a lane changes does not exist currently in the target area” . (see paragraph 52-57 where the road traffic condition can be provided where there is a lane group that has an HOV lane and multiple lanes and a lane adjoining a road) (see FIG. 4 where the road sample data for a time period is provided and then each road segment data sample for each road segment is provided in blocks 405-415 and then a non-data samples of interest are provided and then these are filtered out based on the source and then these are used for a later use in blocks 400-490)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the teachings of CHAPMAN with the disclosure of the primary reference since CHAPMAN teaches that data samples from a number of different probe vehicle can provide hints about the traffic. This includes traffic speed of the vehicles. This can provide hints that the vehicles are opening up to an HOV lane or are constricted and in heavy traffic based on the data samples.
In regard to claim 4 and 17, the primary reference is silent but Chapman teaches “..4. The method according to claim 2, wherein the road association data comprises the road condition image: wherein the determining, based on the road association data, a current road characteristic of the target area comprises: determining lane description information corresponding to each lane in the road condition image, wherein the lane description information comprises one or more of the number of lanes in the road condition image, position information between each
lane and a socialized vehicle used for data collection, and obstacle information corresponding to each lane; and determining, based on the lane description information, whether accuracy of the
lane description information corresponding to any one of the target lanes not satisfying a preset condition exists in the target area;
if the accuracy of the lane description information corresponding to any one of the target lanes not satisfying the preset condition exists in the target area, continuing to obtain the road condition image, determining the lane description information, of which
the accuracy satisfies a preset condition, corresponding to the target lane, until each lane in the target area satisfies the preset condition, and determining the road characteristic based on the lane description information, of which the accuracy satisfies a preset condition, corresponding to each lane, wherein the road characteristic
comprises one or more of the number of lanes, a lane direction, a lane position, and a lane obstruction”. (see paragraph 52-57 where the road traffic condition can be provided where there is a lane group that has an HOV lane and multiple lanes and a lane adjoining a road) (see FIG. 4 where the road sample data for a time period is provided and then each road segment data sample for each road segment is provided in blocks 405-415 and then a non-data samples of interest are provided and then these are filtered out based on the source and then these are used for a later use in blocks 400-490)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the teachings of CHAPMAN with the disclosure of the primary reference since CHAPMAN teaches that data samples from a number of different probe vehicle can provide hints about the traffic. This includes traffic speed of the vehicles. This can provide hints that the vehicles are opening up to an HOV lane or are constricted and in heavy traffic based on the data samples.
Claims 5-9 and 18-19 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of United States Patent Application Pub. No.: US20230204784A1 to DI ZENG et al. assigned to NVIDIA filed in 2109 and in view of United States Patent Application Pub. No.: US 2014/0179030 A1 to Chapman that was filed in 2007 and in view of United States Patent Application Pub. No.: US20240124017A1 to Han filed in 2020 and assigned to NVIDIA and Matsumoto.
In regard to claim 5 and 18, the primary reference is silent but Han teaches “...5. The method according to claim 4, wherein the historical standard data comprises historical lane-level data; and wherein the determining, based on the road characteristic and the pre-stored historical standard data corresponding to the target area, changing content
corresponding to the target area comprises:
determining standard lane-level data matching the target area in the historical lane- level data; and performing a comparison operation between the road characteristic and the standard lane-level data and determining the changing content based on a comparison result.” (see paragraph 86-96 and the abstract where the vehicle can obtain a cloud based information for the lanes to see the condition and the merging of the lanes for autonomous functionality and see paragraph 152-154 where the different maps can be compared to determine the faint lane lines in the maps to provide that the vehicle stays in the lane)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teaches of HAN with the disclosure of DI ZENG with a reasonable expectation of success since HAN teaches that a vehicle can capture data while moving and also use a neural network HD Map. The vehicle can understand the current road and then understand and collect data about the connecting roads and add this data to the map. The vehicle can move from a 5 lane road and to a four way stop and then provide information about each of the connecting roads to the HD map. For example, it may understand that one road has very faint lane lines and that this can be recorded. The grade also can be recorded and a narrow road also can be recorded that is connected to the current road. These can provide features to the HD map for increased safe operation of the autonomous vehicle. See paragraph 120-7.
In regard to claim 6 and 19, Han teaches “6. The method according to claim 2, wherein the road association data comprises a driving track within a preset time range; and
wherein the determining, based on the road association data, a current road
characteristic of the target area comprises:
performing a track aggregation operation on driving tracks to obtain an aggregated track corresponding to the target area;
identifying a drivable width and/or a driving flux and/or a driving direction
corresponding to the aggregated track; and
determining the drivable width and/or the driving flux and/or th-e driving direction
as the current road characteristic of the target area”. (see paragraph 219-224 where the vehicle can use the different HP maps and captured map data in the vehicle to determine the lane edges and lane lines to keep the vehicle operating in the lanes within the width of the lanes and in the correct driving direction and see paragraph 86-96 and the abstract where the vehicle can obtain a cloud based information for the lanes to see the condition and the merging of the lanes for autonomous functionality and see paragraph 152-154 where the different maps can be compared to determine the faint lane lines in the maps to provide that the vehicle stays in the lane)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teaches of HAN with the disclosure of DI ZENG with a reasonable expectation of success since HAN teaches that a vehicle can capture data while moving and also use a neural network HD Map. The vehicle can understand the current road and then understand and collect data about the connecting roads and add this data to the map. The vehicle can move from a 5 lane road and to a four way stop and then provide information about each of the connecting roads to the HD map. For example, it may understand that one road has very faint lane lines and that this can be recorded. The grade also can be recorded and a narrow road also can be recorded that is connected to the current road. These can provide features to the HD map for increased safe operation of the autonomous vehicle. See paragraph 120-7.
Han teaches “...7. The method according to claim 6, wherein the historical standard data comprises
a historical driving track corresponding to the target area; and
wherein the determining, based on the road characteristic and the pre-stored historical standard data corresponding to the target area, changing content corresponding to the target area comprises:
performing a comparison operation between the drivable width and/or driving
direction and a historical drivable width and/or historical driving direction corresponding to the historical driving track, and determining the changing content based on a comparison result; and/or (OPTIONALLY) wherein the determining, based on the road characteristic and the pre-stored historical standard data corresponding to the target area, changing content
corresponding to the target area comprises:
determining a collection time period corresponding to the driving track; and performing a comparison operation between the driving flux and a historical driving flux of a same collection time period in history and determining the changing content based on a comparison result, wherein the historical driving flux is determined
after performing a set operation on the historical driving track”. (see paragraph 86-96 and 106 where the lidar map can be compared to the landmark server map and the hd map on the server to determine the more accurate map and see paragraph 219-224 where the vehicle can use the different HP maps and captured map data in the vehicle to determine the lane edges and lane lines to keep the vehicle operating in the lanes within the width of the lanes and in the correct driving direction and see paragraph 86-96 and the abstract where the vehicle can obtain a cloud based information for the lanes to see the condition and the merging of the lanes for autonomous functionality and see paragraph 152-154 where the different maps can be compared to determine the faint lane lines in the maps to provide that the vehicle stays in the lane)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teaches of HAN with the disclosure of DI ZENG with a reasonable expectation of success since HAN teaches that a vehicle can capture data while moving and also use a neural network HD Map. The vehicle can understand the current road and then understand and collect data about the connecting roads and add this data to the map. The vehicle can move from a 5 lane road and to a four way stop and then provide information about each of the connecting roads to the HD map. For example, it may understand that one road has very faint lane lines and that this can be recorded. The grade also can be recorded and a narrow road also can be recorded that is connected to the current road. These can provide features to the HD map for increased safe operation of the autonomous vehicle. See paragraph 120-7.
Han teaches “...8. The method according to claim 2, wherein the road association data comprises a standard map; and
wherein the determining, based on the road association data, a current road characteristic of the target area comprising:
identifying driving direction indication information and a topological relationship in the standard map; and
determining the driving direction indication information and the topological relationship as the road characteristics”. (see paragraph 222 where the grade of the road is provided at a four way stop to determine which road is higher or lower and then in paragraph 222 a different direction that the vehicle can be driving can be obtained as a one way or two way street and see paragraph 86-96 and 106 where the lidar map can be compared to the landmark server map and the hd map on the server to determine the more accurate map and see paragraph 219-224 where the vehicle can use the different HP maps and captured map data in the vehicle to determine the lane edges and lane lines to keep the vehicle operating in the lanes within the width of the lanes and in the correct driving direction and see paragraph 86-96 and the abstract where the vehicle can obtain a cloud based information for the lanes to see the condition and the merging of the lanes for autonomous functionality and see paragraph 152-154 where the different maps can be compared to determine the faint lane lines in the maps to provide that the vehicle stays in the lane)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teaches of HAN with the disclosure of DI ZENG with a reasonable expectation of success since HAN teaches that a vehicle can capture data while moving and also use a neural network HD Map. The vehicle can understand the current road and then understand and collect data about the connecting roads and add this data to the map. The vehicle can move from a 5 lane road and to a four way stop and then provide information about each of the connecting roads to the HD map. For example, it may understand that one road has very faint lane lines and that this can be recorded. The grade also can be recorded and a narrow road also can be recorded that is connected to the current road. These can provide features to the HD map for increased safe operation of the autonomous vehicle. See paragraph 120-7.
Han teaches “...9. The method according to claim 8, wherein the historical standard data comprises
a historical standard map; and wherein the determining, based on the road characteristic and the pre-stored
historical standard data corresponding to the target area, changing content corresponding to the target area comprises:
performing a comparison operation between the standard map and the historical
standard map to obtain a comparison result; and
determining a different portion between the standard map and the historical standard map in the comparison result as the changing content. (see paragraph 86-96 where one map may have centimeter level accuracy and may be favored or one map may not be able to discern the lane lines and another map can be used and paragraph 106 where the lidar map can be compared to the landmark server map and the hd map on the server to determine the more accurate map and see paragraph 219-224 where the vehicle can use the different HP maps and captured map data in the vehicle to determine the lane edges and lane lines to keep the vehicle operating in the lanes within the width of the lanes and in the correct driving direction and see paragraph 86-96 and the abstract where the vehicle can obtain a cloud based information for the lanes to see the condition and the merging of the lanes for autonomous functionality and see paragraph 152-154 where the different maps can be compared to determine the faint lane lines in the maps to provide that the vehicle stays in the lane)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teaches of HAN with the disclosure of DI ZENG with a reasonable expectation of success since HAN teaches that a vehicle can capture data while moving and also use a neural network HD Map. The vehicle can understand the current road and then understand and collect data about the connecting roads and add this data to the map. The vehicle can move from a 5 lane road and to a four way stop and then provide information about each of the connecting roads to the HD map. For example, it may understand that one road has very faint lane lines and that this can be recorded. The grade also can be recorded and a narrow road also can be recorded that is connected to the current road. These can provide features to the HD map for increased safe operation of the autonomous vehicle. See paragraph 120-7.
Claim 10 is rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of United States Patent Application Pub. No.: US20230204784A1 to DI ZENG et al. assigned to NVIDIA filed in 2109 and in view of Han and Matsumoto.
Han teaches “...10. The method according to claim 1, wherein the preset parameter associated with the road association data comprises a priority parameter; and wherein the determining, based on the at least one kind of road association data and the preset parameter associated with the road association data, at least one area to be updated in which a lane change exists in the target arca, comprising: determining, based on each road association data in turn according to the priority parameter, whether a lane change occurs in the target area; if it is determined based on the road association data that the lane change occurs in the target area, determining the area in which the lane change occurs as the area to be updated”. (see paragraph 204 where at a four way stop a priority of the vehicles can be determined and see paragraph 70 where when the vehicle travels on the road a priority update for the road they are traveling on for the most recent and best map data can occur and see paragraph 86-96 and 106 where the lidar map can be compared to the landmark server map and the hd map on the server to determine the more accurate map and see paragraph 219-224 where the vehicle can use the different HP maps and captured map data in the vehicle to determine the lane edges and lane lines to keep the vehicle operating in the lanes within the width of the lanes and in the correct driving direction and see paragraph 86-96 and the abstract where the vehicle can obtain a cloud based information for the lanes to see the condition and the merging of the lanes for autonomous functionality and see paragraph 152-154 where the different maps can be compared to determine the faint lane lines in the maps to provide that the vehicle stays in the lane)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teaches of HAN with the disclosure of DI ZENG with a reasonable expectation of success since HAN teaches that a vehicle can capture data while moving and also use a neural network HD Map. The vehicle can understand the current road and then understand and collect data about the connecting roads and add this data to the map. The vehicle can move from a 5 lane road and to a four way stop and then provide information about each of the connecting roads to the HD map. For example, it may understand that one road has very faint lane lines and that this can be recorded. The grade also can be recorded and a narrow road also can be recorded that is connected to the current road. These can provide features to the HD map for increased safe operation of the autonomous vehicle. See paragraph 120-7.
Claim 11-13 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of United States Patent Application Pub. No.: US20230204784A1 to DI ZENG et al. assigned to NVIDIA filed in 2109 and in view of United States Patent Application Pub. No.: US20230205219A1 to Blythe filed in 2019 and assigned to NVIDIA and Matsumoto.
The primary reference is silent but BLYTHE teaches “...11. The method according to claim 1, wherein the determining, based on the plurality of driving tracks and the current road condition image, target lane-level data
corresponding to the arca to be updated, comprises:
performing an aggregation operation on the plurality of driving tracks to obtain an aggregated track image, and determining, based on the aggregated track image, a passable area corresponding to the area to be updated; determining a passable width corresponding to the passable area, and determining,
based on the current road condition image, the number of lanes corresponding to the area to be updated;
determining, based on the passable width and the number of lanes, a lane width corresponding to each lane corresponding to the arca to be updated; and determining the passable area and the lane width corresponding to the passable area as target lane level data corresponding to the area to be updated. (see paragraph 124-128 and FIG. 6a-7 where the vehicle can be moving on a single lane and then open up to a multiple lane and can pass the vehicle ahead of the vehicle now being based on the more space and the server data provided to the vehicle and a lane width can be shown with a vehicle occupying the left lane but there is enough room on the other lane to pass via a width observation in FIG. 6a-7 and in FIG. 7 the road is narrowing and there is not enough room and the vehicle cannot pass anymore up ahead)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teaches of BLYTHE with the disclosure of DI ZENG with a reasonable expectation of success since BLYTHE teaches that a vehicle width can be determine from the lane edges to the lane edges for a road successively. The machine learning may do this by placing multiple anchor points in the pixel image and then monitoring the width in a number of different iterations. See Fig. 7 and paragraph 122-136. This can provide the autonomous vehicle a high definition map where the vehicle via machine learning can understand that there is a narrowing from two lanes to one lane and the vehicle can no longer pass and instead must merge or if there is a one lane road and a five lane road is ahead and the vehicle can move into the other lane and move safely around another vehicle. See paragraph 120-130 and FIG. 6a to 8b.
Blythe teaches “...12. The method according to claim 11, wherein the determining, based on the aggregated track image, a passable area corresponding to the area to be updated,
comprises: calculating an image gradient corresponding to the aggregated track image; and
performing, based on the image gradient, a cropping operation on an area of the aggregated track image that satisfies a preset cropping condition, to obtain the passable area. (see paragraph 129-137 where the width of the lane via ground truth anchor points can be provided from the lane edge to the lane edge of the two lane and then one lane road is provided to give an indication of the lane width and if the vehicle can pass or not and see paragraph 124-128 and FIG. 6a-7 where the vehicle can be moving on a single lane and then open up to a multiple lane and can pass the vehicle ahead of the vehicle now being based on the more space and the server data provided to the vehicle and a lane width can be shown with a vehicle occupying the left lane but there is enough room on the other lane to pass via a width observation in FIG. 6a-7 and in FIG. 7 the road is narrowing and there is not enough room and the vehicle cannot pass anymore up ahead)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teaches of BLYTHE with the disclosure of DI ZENG with a reasonable expectation of success since BLYTHE teaches that a vehicle width can be determine from the lane edges to the lane edges for a road successively. The machine learning may do this by placing multiple anchor points in the pixel image and then monitoring the width in a number of different iterations. See Fig. 7 and paragraph 122-136. This can provide the autonomous vehicle a high definition map where the vehicle via machine learning can understand that there is a narrowing from two lanes to one lane and the vehicle can no longer pass and instead must merge or if there is a one lane road and a five lane road is ahead and the vehicle can move into the other lane and move safely around another vehicle. See paragraph 120-130 and FIG. 6a to 8b.
Blythe teaches “...13. The method according to claim 11, wherein determining, based on the passable width and the number of lanes, a lane width corresponding to each lane corresponding
to the arca to be updated, comprises:
dividing, according to the number of lanes, the passable width equally, and
determining the lane width corresponding to each of the lanes. (see paragraph 129-137 where the width of the lane via ground truth anchor points can be provided from the lane edge to the lane edge of the two lane and then one lane road is provided to give an indication of the lane width and if the vehicle can pass or not and see paragraph 124-128 and FIG. 6a-7 where the vehicle can be moving on a single lane and then open up to a multiple lane and can pass the vehicle ahead of the vehicle now being based on the more space and the server data provided to the vehicle and a lane width can be shown with a vehicle occupying the left lane but there is enough room on the other lane to pass via a width observation in FIG. 6a-7 and in FIG. 7 the road is narrowing and there is not enough room and the vehicle cannot pass anymore up ahead)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teaches of BLYTHE with the disclosure of DI ZENG with a reasonable expectation of success since BLYTHE teaches that a vehicle width can be determine from the lane edges to the lane edges for a road successively. The machine learning may do this by placing multiple anchor points in the pixel image and then monitoring the width in a number of different iterations. See Fig. 7 and paragraph 122-136. This can provide the autonomous vehicle a high definition map where the vehicle via machine learning can understand that there is a narrowing from two lanes to one lane and the vehicle can no longer pass and instead must merge or if there is a one lane road and a five lane road is ahead and the vehicle can move into the other lane and move safely around another vehicle. See paragraph 120-130 and FIG. 6a to 8b.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. sec. 101 as being directed to an abstract idea.
Step 1: Are the claims are directed to an article, machine, manufacturing an article or a process. Claims 1 and 14 are directed to a lane level updating apparatus computer and are statutory. Claim 20 is directed to a non transitory device.
This is a statutory class of subject matter.
Step 2: The claims recited an abstract idea. Abstract ideas can be grouped as a mathematical concept, a mental process, and certain methods of organizing human activity can be an abstract idea. The claims recite determining a lane parameter and then updating a map. This is an abstract idea. A human can determine that a lane is incorrect and then provide an alert.
Step 2a: Do the claims have an integration into a practical application, or an additional element or combination that imposes a meaningful limit of the judicial exception such that the claim is more than a drafting effort to monopolize the exception. These can include improvements to a technology, reducing or transforming an article to a different state or thing (MPEP 2106.05c), applying or using the judicial exception in some meaningful way beyond linking the use to a particular technological environment such that the claim is more than a drafting effort to monopolize the exception (see MPEP 2106.05€ and Vanda memorandum). Also limitations that are not indicative include adding the words apply it, or more mere instructions to implement the abstract idea on a computer, adding insignificant extra solution activity or a general linking or a field of use or a technological environment. The claims do not recite any practical application.
Step 2b is the combination of the steps unconventional? Limitations of an inventive concept include improvements to the functioning of a computer, applying using a machine with the judicial exception, reducing the article to a different thing, applying the judicial exception in a meaningful way.
Improvements can pertain to improvement in the functioning of the computer itself or a computer functionality.
Well understood, routine and conventional changes are excluded under step 2a. See MPEP 2106.04(a).
Improvements can be found in McRo where animation tasks were held to be improvements v. Affinity labs. The feature that leads to the improvement must be in the claim.
A claim limitation can integrate a judicial exception by implementing the exception with a particular machine or manufacture that is integral to the claim. See MPEP 2106.05(b). A generic computer that is specifically programmed does not automatically overcome the exception and it must integrate the judicial exception.
A claim limitation can integrate a judicial exception by using or applying the judicial exception beyond general linking the use to a particular technological environment such that the claim as a whole is more than a drafting effort designed to monopolize the invention.
(For Example 37 of the USPTO Guidance: A method rearranging icons on a GUI by tracking the icons are selected or the amount of memory allocated to the icon, and then ranking the icons, and then placing those icons that are the most used next to or closest to the start icon of the computer based on the amount of use. Step 1 does this claim fall into a statutory category? Yes, the claim recites a method and a series of steps and is a process. Step 2A prong 1: is there a judicial exception recited and the specific limitations and if they are within the groupings of abstract ideas within the claim. Yes, the claims are directed to an abstract idea, of a method of organizing human activity or a mental process or a process of a concept in the human mind. The nominal recitation of a processor does not take it out of the mental process grouping. Step 2A prong 2. Is the judicial exception or combination provided claimed in a manner that provides meaningful limits on the judicial exception that is more than an attempt to draft around the judicial exception. Are they integrated into a practical application of the improvement. Yes, as a whole the mental process is integrated into a practical application of the mental process. Therefore, the claim is eligible versus performing Step 2B as there is no inventive concept recited).
For example, Example 38, organizing patient records, Step 1: the claim is directed to a process and a series of steps; Step 2A it recites an abstract idea of organizing activity. Step 2A prong two: Is there any additional element or combination of elements that recite more than the judicial exception or is more than an attempt to draft around the judicial exception. The claims recite 1. Storing, 2. Remote access, 3. Converting by a content server, automatically generating a message, and transmitting data. This combination of additional elements integrate the abstract idea into a practical application and the combination of elements recite an improvement over the prior art systems by allowing remote systems to share information in real time in a standardized format. Step 2A is no and the claim is eligible).
For example, example 38 claim 2 step 2a prong one the claims recite 1. Storing providing access and messaging. This is a method of organizing human activity. The claims recite performance of claim limitations using generic computer components but does not preclude the claim limitation from being in the certain methods of organizing human activity; this claim 2 recites an abstract idea; Step 2a, prong 2; are there any additional elements that apply on or rely on the judicial exception in a manner that provides meaningful limitations? The claims recite storing information on a memory in a network based storage devices. The claim as a whole does not integrate the abstract idea into a practical application as they do not impose meaningful limits on the abstract idea as the components are at a high level of generality. Step 2B are there elements or combination of elements that are more than the abstract of idea. The claims recite networked memory. This is implementing the abstract idea on a generic computer. Step 2B is no the claim does not provide the inventive concept and is not significantly more than the abstract idea and the claim is not eligible).
The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1-20 is/are directed to the general idea of tracking a position of two objects. The method is directed to tracking a position of a user and then tracking a position of a vehicle and then determining when they are within a predetermined distance and then selecting a driver for the user based on the location data. This is a mere abstract idea that is being applied on a general purpose computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only elements found are general purpose computers which is not significantly more than the abstract idea. The claim is directed to a movement vector and a second movement vector and then a matching of the coordinates to select one vehicle from another. This is an abstract idea. This may be performed mathematically or using a pen and paper and using the x, y, z, coordinates of the user and then a number of x, y, z coordinates of a number of vehicles and then seeing which is the closest based on a movement of each. Then a selection may be made based on the distance and time and speed.
The next step is to determine what elements are significantly more than the abstract idea. The only element found is a general purpose computer that displays a map. The arrival time data is input into is part of a general purpose computer in claim 8, and 15 and in claim 1 there is not even a general purpose computer being claimed (it is in the premable). See Alice and Electric Power Group v Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016) that recites “the claims in this case fall into a familiar class of claims “directed to” a patent-ineligible concept. The focus of the asserted claims, as illustrated by claim 12 quoted above, is on collecting information, analyzing it, and displaying certain results of the collection and analysis. We need not define the outer limits of “abstract idea,” or at this stage exclude the possibility that any particular inventive means are to be found somewhere in the claims, to conclude that these claims focus on an abstract idea— and hence require stage-two analysis under § 101.
Information as such is an intangible. See Microsoft Corp. v. AT & T Corp., 550 U.S. 437, 451 n.12 (2007); Bayer AG v. Housey Pharm., Inc., 340 F.3d 1367, 1372 (Fed. Cir. 2003). Accordingly, we have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas. See, e.g., Internet Patents, 790 F.3d at 1349; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347 (Fed. Cir. 2014); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011). In a similar vein, we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category. See, e.g., TLI Commc’ns, 823 F.3d at 613; Digitech, 758 F.3d at 1351; SmartGene, Inc. v. Advanced Biological Labs., SA, 555 F. App’x 950, 955 (Fed. Cir. 2014); Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011); SiRF Tech., Inc. v. Int’l Trade Comm’n, 601 F.3d 1319, 1333 (Fed. Cir. 2010); see also Mayo, 132 S. Ct. at 1301; Parker v. Flook, 437 U.S. 584, 589–90 (1978); Gottschalk v. Benson, 409 U.S. 63, 67 (1972). And we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis. See, e.g., Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014)”.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN PAUL CASS whose telephone number is (571)270-1934. The examiner can normally be reached Monday to Friday 7 am to 7 pm; Saturday 10 am to 12 noon.
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/JEAN PAUL CASS/Primary Examiner, Art Unit 3666
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