Detailed Office 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/28/2025 has been entered.
This is a non-final Office Action on the merits. Claims 1-7 and 10-11 are currently pending and are addressed below.
Priority
Acknowledgment is made of applicant's claim of foreign priority for DE10-2022-203264.0 filed April 1, 2022.
Response to Arguments
Applicant’s amendments and/or arguments with respect to the rejection of claims 1-7 and 10 under 35 USC 102 and 103 as set forth in the office action of 07/15/2025 have been considered but are moot because the new ground(s) of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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 1-2, 7, and 10 rejected under 35 U.S.C. 103 as being unpatentable over Arditi (US 20190147331 A1) in view of Sorensen (US 20210293548 A1).
Regarding Claim 1, Arditi teaches a method for creating map data having lane-specific resolution (see at least Arditi [para.27 and para.74] The cameras may be used for, e.g., recognizing roads, lane markings, street signs, traffic lights, police, other vehicles, and any other visible objects of interest) Because the cameras and sensors that gather data for map data can recognize lane markings and lane dividers, the map data has lane-specific resolution.
comprising the following steps: receiving mapping data, transmitted by a vehicle, the received mapping data including a vehicle trajectory and at least one object feature, wherein the at least one object feature includes a distance between an object and the vehicle determined using at least one sensor and a direction of the object relative to the vehicle determined using the at least one sensor (see at least Arditi [para.20, para.45-48, and para.74] each training sample may be associated with an instance of data captured by a data-gathering vehicle at a particular location…(e.g., at coordinates (x, y), latitude/longitude positions, etc.)…the object classifier, based on the sensor data (e.g., camera or LiDAR data), may detect the existence of the objects (i.e., the box 620 and the pothole 630) in the road, as well as other objects such as buildings, road dividers, sidewalks, etc.…The map-updating operation may begin at step 750, where the system may send sensor data and associated data gathered at that particular location to a server…A LiDAR is an effective sensor for measuring distances to targets, and as such may be used to generate a three-dimensional (3D) model of the external environment of the autonomous vehicle 940. As an example and not by way of limitation, the 3D model may represent the external environment including objects such as other cars, curbs, debris, objects, and pedestrians up to a maximum range of the sensor arrangement (e.g., 50, 100, or 200 meters)
checking whether map data for local surroundings of the received mapping data are already present (see at least Arditi [para.47-48] the computing system may compare the map data associated with the location (e.g., x, y coordinates) with the object detected in step 720 to determine whether the detected objects exist in the map data. For example, for each detected object, the system may check whether that object exists in the map data)
based on the check indicating that no map data are present, creating map data from the received mapping data and storing the map data in a memory (see at least Arditi [para.47-48] if the comparison results in a determination that at least one detected object does not exist or is not known in the HD map (e.g., the confidence score in the object existing in the map is lower than a threshold), then the system may proceed with a map-updating operation)
based on the check indicating that map data are present: comparing the map data with the received mapping data (see at least Arditi [para.47-48] the server may also perform a comparison of the received data (and any objects detected therefrom) with a server-copy of the HD map to determine whether a mismatch exists... if the server determines that the current HD map does not include the detected object, the server may update the server-copy of the HD map as well as the local copies of the HD map on autonomous vehicles)
based on the comparison revealing that the mapping data differ from the map data:…(ii) storing the adapted map data in the memory (see at least Arditi [para.42 and para.83] Once the HD map is complete, it may be provided to any computing device that may benefit from or be interested in using the HD map. For example, at step 595, the system may transmit the HD map to autonomous vehicles so that they may use the HD map to drive and navigate…in particular embodiments, storage 1006 includes mass storage for data or instructions)
wherein the map data are further transmitted to a vehicle and at least one driving function in the vehicle is controlled based on the map data (see at least Arditi [para.42 and Claim 10] at step 595, the system may transmit the HD map to autonomous vehicles so that they may use the HD map to drive and navigate…transmitting the high-definition map to a plurality of autonomous vehicles, wherein the high-definition map is configured to be used by the plurality of autonomous vehicles for autonomous driving).
However, Arditi does not explicitly teach based on the comparison revealing that the received mapping data differ from the map data: (i) adapting the map data based on at least one weighting factor including a first weighting factor element of the vehicle trajectory and a second weighting factor element of the at least one object feature.
Sorensen, in the same field as the endeavor, teaches based on the comparison revealing that the received mapping data differ from the map data: (i) adapting the map data based on at least one weighting factor including a first weighting factor element of the vehicle trajectory and a second weighting factor element of the at least one object feature (see at least Sorensen [Abstract, para.74, 41, 70] An example system includes a sensor for obtaining information about an object in an environment and one or more processing devices configured to use the information in generating or updating a map of the environment…the robot implements localization by scanning its surroundings and comparing detected elements in a space to content on the stored map…Information obtained from sensors on and/or off of the robot may be used to adjust static scores of elements on the map. In some implementations, a remote control system may transmit, to the robot, updates to the map, an entire new map containing updates, or information identifying a selected route independent of the map…a route may have one or more weights stored on the map. Each segment of the route may be weighted or an overall weight may be generated for the route…a weight represents a level of difficulty associated with traversing the route…The weight may be based on a variety of factors, such as the length of the route, the incline of the route, and the material included on the surface of the route. The weight of each route may also be based on the static scores of objects located along the route, such as objects that may block the route…The weights may be adjusted over time as the static scores of objects change and/or as objects move into or out of the path of travel along the route) The system disclosed in Sorensen teaches comparing map data and mapping data when it scans its environment and compares it to stored maps. The system further adapts the map data when the segment weights are updated with newly gained information from the scans. The adaptation of the map data through segment weight updates are based on “one or more weights” which may include “a weight based on…the length of a route, the incline of a route” which is analogous to an element of the vehicle trajectory, and also a “weight based on the static scores of objects located along the route” which is analogous to an element of an object feature.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Arditi to contain a system that based on the comparison revealing that the received mapping data differ from the map data: (i) adapts the map data based on at least one weighting factor including a first weighting factor element of the vehicle trajectory and a second weighting factor element of the at least one object feature with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the mapping system and routing system by updating the map based on multiple factors as discussed in Sorensen (see at least Sorensen [para.69] The control system may be configured to perform a cost-benefit analysis to determine whether the benefits of attempting the shortest route through the tunnel outweigh the costs that will incur for replanning and rerouting if the shortest route is taken but blocked. If the benefits outweigh the costs, then the robot may be instructed to travel along the shortest route; otherwise, the robot will be instructed to travel along the longer route. In other words, if the benefits outweigh the costs, then the shortest route will be selected; otherwise, the longer route will be selected).
Regarding Claim 2, Arditi in view of Sorensen teaches all limitations of Claim 1 as set forth above. Arditi further teaches wherein the creation of map data from the received mapping data takes place in such a way that received mapping data of multiple vehicles are averaged and are statistically evaluated, the creation of the map data taking place only when a predefined statistical accuracy of the averaged received mapping data is present (see at least Arditi [para.44-47] inanimate objects may be detected based on a determination that data gathered at a particular location by different vehicles at different times within a time frame (e.g., 1 minute, 5 minutes, 30 minutes, etc.) consistently include the same new object, which may indicate that the object is inanimate and may continue to remain in the street. As such, the object may be considered as a hazard, which may warrant the HD map to be updated…the confidence score in the object existing in the map is higher than a threshold) Because multiple vehicles are checking for the same object to determine its detecting, the data of many vehicles is being averaged to determine if the object is likely to be detected and stationary on the street, and because the map is only updated if the object is determined to exist (based on a confidence score threshold) the creating of the map data only takes place when a predefined statistical accuracy of the averaged mapping data is present.
Regarding Claim 7, Arditi in view of Sorensen teaches all limitations of Claim 1 as set forth above. Arditi further teaches wherein the vehicle trajectory includes waypoints determined using satellite navigation (see at least Arditi [para.14 and para.20] The types of sensors used for gathering data may include… global positioning systems (GPS)…at the particular location (e.g., at coordinates (x, y), latitude/longitude positions, etc.), a data-gathering vehicle may have gathered data in the training sample using its camera, LiDAR, radar, sonar, and/or any other suitable sensors as described herein).
Regarding Claim 10, Arditi teaches a central processing unit, comprising: a data interface, a memory, and a processor (see at least Arditi [para.80-81] computer system 1000 includes a processor 1002, memory 1004, storage 1006, an input/output (I/O) interface 1008, a communication interface 1010, and a bus 1012…processor 1002 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers)
the central processing unit being configured to receive mapping data from a vehicle via the data interface, the received mapping data including a vehicle trajectory and at least one object feature, wherein the at least one object feature includes a distance between an object and the vehicle determined using at least one sensor and a direction of the object relative to the vehicle determined using the at least one sensor (see at least Arditi [para.20, 45-48, and 74] each training sample may be associated with an instance of data captured by a data-gathering vehicle at a particular location…(e.g., at coordinates (x, y), latitude/longitude positions, etc.)…the object classifier, based on the sensor data (e.g., camera or LiDAR data), may detect the existence of the objects (i.e., the box 620 and the pothole 630) in the road, as well as other objects such as buildings, road dividers, sidewalks, etc.…The map-updating operation may begin at step 750, where the system may send sensor data and associated data gathered at that particular location to a server…A LiDAR is an effective sensor for measuring distances to targets, and as such may be used to generate a three-dimensional (3D) model of the external environment of the autonomous vehicle 940. As an example and not by way of limitation, the 3D model may represent the external environment including objects such as other cars, curbs, debris, objects, and pedestrians up to a maximum range of the sensor arrangement (e.g., 50, 100, or 200 meters)
the processor being configured to check whether map data for local surroundings of the received mapping data are already present (see at least Arditi [para.47-48] the computing system may compare the map data associated with the location (e.g., x, y coordinates) with the object detected in step 720 to determine whether the detected objects exist in the map data. For example, for each detected object, the system may check whether that object exists in the map data)
in the event the check indicates that no map data are present, to create map data from the received mapping data and to store the map data in the memory (see at least Arditi [para.47-48] if the comparison results in a determination that at least one detected object does not exist or is not known in the HD map (e.g., the confidence score in the object existing in the map is lower than a threshold), then the system may proceed with a map-updating operation)
in the event the check indicates that map data are present, to compare the map data with the received mapping data (see at least Arditi [para.47-48] the server may also perform a comparison of the received data (and any objects detected therefrom) with a server-copy of the HD map to determine whether a mismatch exists... if the server determines that the current HD map does not include the detected object, the server may update the server-copy of the HD map as well as the local copies of the HD map on autonomous vehicles)
and to store the adapted map data in the memory (see at least Arditi [para.42 and para.83] Once the HD map is complete, it may be provided to any computing device that may benefit from or be interested in using the HD map. For example, at step 595, the system may transmit the HD map to autonomous vehicles so that they may use the HD map to drive and navigate…in particular embodiments, storage 1006 includes mass storage for data or instructions)
wherein the central processing unit is further configured to transmit the map data to a vehicle and at least one driving function in the vehicle is controlled based on the map data (see at least Arditi [para.42 and Claim 10] at step 595, the system may transmit the HD map to autonomous vehicles so that they may use the HD map to drive and navigate…transmitting the high-definition map to a plurality of autonomous vehicles, wherein the high-definition map is configured to be used by the plurality of autonomous vehicles for autonomous driving).
However, Arditi does not explicitly teach based on the comparison revealing that the received mapping data differ from the map data: (i) adapting the map data based on at least one weighting factor including a first weighting factor element of the vehicle trajectory and a second weighting factor element of the at least one object feature.
Sorensen, in the same field as the endeavor, teaches to compare the map data with the received mapping data and, in the event the comparison reveals that the received mapping data differ from the map data, to adapt the map data based on at least one weighting factor including a first weighting factor element of the vehicle trajectory and a second weighting factor element of the at least one object feature (see at least Sorensen [Abstract, para.74, 41, 70] An example system includes a sensor for obtaining information about an object in an environment and one or more processing devices configured to use the information in generating or updating a map of the environment…the robot implements localization by scanning its surroundings and comparing detected elements in a space to content on the stored map…Information obtained from sensors on and/or off of the robot may be used to adjust static scores of elements on the map. In some implementations, a remote control system may transmit, to the robot, updates to the map, an entire new map containing updates, or information identifying a selected route independent of the map…a route may have one or more weights stored on the map. Each segment of the route may be weighted or an overall weight may be generated for the route…a weight represents a level of difficulty associated with traversing the route…The weight may be based on a variety of factors, such as the length of the route, the incline of the route, and the material included on the surface of the route. The weight of each route may also be based on the static scores of objects located along the route, such as objects that may block the route…The weights may be adjusted over time as the static scores of objects change and/or as objects move into or out of the path of travel along the route) The system disclosed in Sorensen teaches comparing map data and mapping data when it scans its environment and compares it to stored maps. The system further adapts the map data when the segment weights are updated with newly gained information from the scans. The adaptation of the map data through segment weight updates are based on “one or more weights” which may include “a weight based on…the length of a route, the incline of a route” which is analogous to an element of the vehicle trajectory, and also a “weight based on the static scores of objects located along the route” which is analogous to an element of an object feature.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Arditi to contain to compare the map data with the received mapping data and, in the event the comparison reveals that the received mapping data differ from the map data, to adapt the map data based on at least one weighting factor including a first weighting factor element of the vehicle trajectory and a second weighting factor element of the at least one object feature with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the mapping system and routing system by updating the map based on multiple factors as discussed in Sorensen (see at least Sorensen [para.69] The control system may be configured to perform a cost-benefit analysis to determine whether the benefits of attempting the shortest route through the tunnel outweigh the costs that will incur for replanning and rerouting if the shortest route is taken but blocked. If the benefits outweigh the costs, then the robot may be instructed to travel along the shortest route; otherwise, the robot will be instructed to travel along the longer route. In other words, if the benefits outweigh the costs, then the shortest route will be selected; otherwise, the longer route will be selected).
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Arditi (US 20190147331 A1) in view of Sorensen (US 20210293548 A1) and Fan et al (CN 112683284 A). Hereafter referred to as Arditi, Sorensen, and Fan respectively.
Regarding Claim 3, Arditi in view of Sorensen teaches all limitations of Claim 1 as set forth above. However, Arditi does not explicitly teach wherein the adaptation of the map data takes place in such a way that in the case of a deviation of the received mapping data from the map data, a shift of a traffic lane by a predefined distance is recognized and the shift is taken into account when adapting the map data.
Fan, in the same field as the endeavor, teaches wherein the adaptation of the map data takes place in such a way that in the case of a deviation of the mapping data from the map data, a shift of a traffic lane by a predefined distance is recognized and the shift is taken into account when adapting the map data (see at least Fan [English Translation, page.4 para.2-3] if the number of the plurality of deviation lane line position corresponding to the target lane line occupying the target lane line corresponding to the plurality of target road element position information of the number ratio is greater than the preset proportion threshold value, then according to the target lane line corresponding to a plurality of deviation lane line position; determining the position of the lane line to be updated corresponding to the target lane line…using the to-be-updated lane line position corresponding to the target lane line to update the high precision map) An update to map data occurs when the lane line position is greater than a preset proportion threshold (a predefined distance).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Arditi to contain a system for only adapting map data when a shift of a predefined distance has occurred with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the automatic driving vehicle as discussed in Fan [English Translation, page.2 para.3] Because of road construction and so on, the road mark position and lane line position in the road is changed, so, in order to ensure the automatic safety of the vehicle, it needs to update the road mark position and lane line position in the high precision map).
Regarding Claim 4, Arditi in view of Sorensen and Fan teaches all limitations of Claim 3 as set forth above. However, the combination of Arditi and Fan does not explicitly teach wherein the predefined distance is at least 50 centimeters.
However, Fan teaches the use of using a predefined distance threshold as a means for determining whether or not to update map data as set forth above (see at least Fan [English Translation, page.4 para.2-3])
Therefore, the combination of Arditi and Fan disclose the claimed invention except for wherein the predefined distance is at least 50 centimeters. It would have been obvious to anyone of ordinary skill in the art before the effective filing date of the claimed invention to have included a predefined distance of at least 50 centimeters since it has been held to be within the general skill of a worker in the art to select a predefined distance of at least 50 centimeters based on its suitability for the intended use as a matter of design choice.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Arditi (US 20190147331 A1) in view of Sorensen (US 20210293548 A1) and Del Pero et al (US 20210407119 A1). Hereafter referred to as Arditi, Sorensen, and Del Pero respectively.
Regarding Claim 5, Arditi in view of Sorensen teaches all limitations of Claim 1 as set forth above. However, Arditi does not explicitly teach wherein the map data is created or adapted for a section of a map, the map data being linked at an edge of the section to an existing map.
Del Pero, in the same field as the endeavor, teaches wherein the map data is created or adapted for a section of a map, the map data being linked at an edge of the section to an existing map (see at least Del Pero [para.102 and para.108] example embodiments seek to generate a refined global map by generating a plurality of neighboring map segments for a large-scale global map 802, where each of the plurality neighboring map segments correspond to an area of the global map or an area to be updated in or added to the global map, and further generating a reconstruction of the area corresponding to each of the plurality of neighboring map segments 804, wherein each of the plurality of neighboring map segments substantially overlaps with one or more neighboring map segments).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Arditi to contain a system for adapting map data that is linked to the edge of an existing map with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the alignment of the new map data as discussed in Del Pero (see at least Del Pero [para.102 and para.108] in doing so, each map segment shares a number of data points with its neighboring map segments which can help alignment of the segments).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Arditi (US 20190147331 A1) in view of Sorensen (US 20210293548 A1) and Duan at el (US 20210331703 A1). Hereafter referred to as Arditi, Sorensen, and Duan respectively.
Regarding Claim 6, Arditi in view of Sorensen teaches all limitations of Claim 1 as set forth above. However Arditi does not explicitly teach wherein the second weighting factor element of the object feature, and the second weighting factor element being greater than the first weighting factor element.
Duan, in the same field as the endeavor, teaches wherein the second weighting factor element is greater than the first weighting factor element (see at least Duan [para.36-38 and para.28-29] The prediction component 324…can output predictions associated with one or more objects within the environment of the vehicle 302…the vehicle 102 can alter a planned trajectory to include regions of high consistency (e.g., confidence score(s) that meet or exceed respective threshold(s)) and avoid regions of low consistency (e.g., confidence score(s) below respective threshold(s)) Because Duan teaches a first weighting factor relating to the vehicle trajectory and a second weighting factor relating to the element of an object feature, and becuase the weighting factor of the element of an object feature can alter the trajectory of the vehicle, it is disclosed that the weighting factor of the object element is weighted higher than that of the weighting factor of the trajectory.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Arditi to contain a system for containing weights and wherein the second weight is the higher weight with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the vehicle by improving the accuracy of a stored map as discussed in Duan (see at least Duan [para.09] if a stored map is not accurate, an autonomous vehicle relying on such a stored map can make decisions that may not be safe).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Arditi (US 20190147331 A1) in view of Sorensen (US 20210293548 A1) and Jiralerspong et al (WO 2021131597 A1). Hereafter referred to as Arditi, Sorensen, and Jiralerspong respectively.
Regarding Claim 11, Arditi in view of Sorensen teaches all limitations of Claim 1 as set forth above. However, Arditi does not explicitly teach wherein a sum of the first weighting factor element and the second weighting factor element is equal to 100.
Jiralerspong, in the same field as the endeavor teaches wherein a sum of the first weighting factor element and the second weighting factor element is equal to 100 (see at least Jiralerspong [English Translation pg.9 para.7] The weights are added so that the total value of the weights of all the divided regions is 100%).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Arditi to contain a system for wherein a sum of the first weighting factor element and the second weighting factor element is equal to 100 with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the weighting system by implementing a commonly used method surrounding weights.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH A YANOSKA whose telephone number is (703)756-5891. The examiner can normally be reached M-F 9:00am to 5:00pm (Pacific Time).
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/JOSEPH ANDERSON YANOSKA/Examiner, Art Unit 3664
/RACHID BENDIDI/Supervisory Patent Examiner, Art Unit 3664