DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice on Prior Art Rejections
2. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of Claims
3. This Office Action is in response to the applicant's arguments/remarks filed March 6, 2026. Claims 1, 10, 11, 18-19 are amended. Claims 1-20 are presently pending and are presented for examination.
Response to Arguments/Remarks
4. Nonstatutory Double Patenting. Applicant's arguments/amendments filed March 6, 2026 regarding the Nonstatutory Double Patenting have been fully considered. Applicant's arguments/remarks are not persuasive. Applicant’s amendments fail to provide any distinction of both applications and the current claims are still obvious over the earlier claims. Therefore, the claims are not patentably distinct. Accordingly, the Nonstatutory Double Patenting rejection is maintained.
5. 35 USC § 101 rejection. Applicant's arguments/amendments filed March 6, 2026 regarding the 35 USC § 101 rejection have been fully considered. Applicant's arguments/remarks are not persuasive. The claim limitations, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Updating a map so a person can travel can be performed as part of human activities. Control operation of a vehicle after an update needs to be positively recited as to demonstrate that the system performs a practical application. Accordingly, the 35 USC § 101 rejection is maintained.
6. 35 USC § 103 rejection. Applicant's arguments/amendments filed March 6, 2026 regarding the 35 USC § 103 rejection have been fully considered. but are moot because the new ground 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. The applicant’s arguments are only directed to the new added amendments and not the prior rejection of record. Based on the new features of the claims presented in the amendments, further search and/or consideration was required to examine the amended claims, so a new 35 USC § 103 ground(s) of rejection is made further in view of Wheeler et al, US 2018 / 0188045, presented in this Final Office Action.
Nonstatutory Double Patenting
7. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,174,034. Although the claims at issue are not identical, they are not patentably distinct from each other because they disclosed the same subject matter.
Claims 2-10 depend from claim 1 and therefore include the same limitation as claim 1 so they are rejected for the same reason.
Claim 11 and 18 contain similar limitations as claim 1 so they are rejected for similar reasons.
Claims 12-17, 19-20 depend from claims 11 and 18 respectively, and therefore include the same limitations as claims 11 and 18, so they are rejected for the same reasons.
18/828,696 (Current Application)
Patent No 12,174,034
Claim 1: A method, comprising: performing one or more control operations associated with movement of a machine based at least on a current version of a map, the current version of the map having been updated from a prior version based at least on one or more update operations, the one or more update operations including:
Claim 1: A method, comprising: performing one or more movement control operations by a machine based at least on map data that has been updated according to one or more update operations, the one or more update operations including:
determining, based at least on a comparison between map data corresponding to the prior version of the map and sensor data obtained using one or more sensors of one or more machines, one or more differences between the map data and the sensor data with respect to one or more features of an environment;
determining, based at least on an identified discrepancy between information corresponding to a demarcated region of a navigable surface indicated in map data and information corresponding to the demarcated region as indicated by sensor data obtained using one or more sensors of a machine, that a change has occurred with respect to the demarcated region, the discrepancy being identified based at least on a comparison between the map data and the sensor data;
And updating, based at least on the one or more differences between the map data and the sensor data, at least a portion of the prior version of the map that corresponds to the one or more features to obtain the current version of the map that is used for the performing of the one or more control operations.
causing an update to at least a portion of the map data that corresponds to the demarcated region of the navigable surface based at least on the determining that the change has occurred with respect to the demarcated region;
and storing the map data as updated for retrieval by the machine.
Judicial Exception Claim Rejections - 35 USC § 101
8. 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.
9. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites “A method, comprising: performing one or more control operations associated with movement of a machine based at least on a current version of a map, the current version of the map having been updated from a prior version based at least on one or more update operations, the one or more update operations including: determining, based at least on a comparison between map data corresponding to the prior version of the map and sensor data obtained using one or more sensors of one or more machines, one or more differences with respect to one or more features of an environment; and updating, based at least on the one or more differences, at least a portion of the prior version of the map that corresponds to the one or more features.”.
The limitations of claim 1 presented above, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more sensors” nothing in the claims elements precludes the steps from practically being performed as part of human activities. For example, “determining, based at least on a comparison between map data corresponding to the prior version of the map and sensor data obtained using one or more sensors of one or more machines, one or more differences with respect to one or more features of an environment” in the context of this claim encompasses the user the user manually or mentally observing changes in the environment. Similarly, the limitation of “updating, based at least on the one or more differences, at least a portion of the prior version of the map that corresponds to the one or more features”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind where a person is mentally able to determine a change in the region. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim does not recite any additional elements that integrate the abstract idea into a practical application. The claim limitation of “storing the updated map data for retrieval by one or more machines” is not a practical application but a mere process of data manipulation and transfer. Accordingly, the claim lack of additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements that integrate the abstract idea into a practical application. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. The independent claims 2-10 are also rejected for their dependency upon claim 1. Further, claims 11-20 are also rejected because they amount no more than the same mere instructions of the method of claim 1 in a system which does not impose any meaningful limits on practicing the abstract idea.
Claim Rejections - 35 USC § 103
10. 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 of this title, 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.
11. Claims 1-20 are rejected under 35 U.S.C 103 as being unpatentable over Ratnasingam et al, US 9,672,734, in view of Jiang et al, US 2020/0124423, further in view of Wheeler et al, US 2018 / 0188045, hereinafter referred to as Ratnasingam, Jiang, and Wheeler, respectively.
Regarding claim 1, Ratnasingam discloses a method, comprising:
performing one or more control operations associated with movement of a machine based at least on a current version of a map, the current version of the map having been updated from a prior version based at least on one or more update operations (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”), (See at least Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”), the one or more update operations including:
determining, based at least on a comparison between map data corresponding to the prior version of the map and sensor data obtained using one or more sensors of one or more machines, one or more differences between the map data and the sensor data with respect to one or more features of an environment (See at least fig 1-20, Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”), (See at least fig 1-20, Col 35, lines 1-15, “The data provided by these external systems may include weather conditions, frosty road surface, flood, road condition, road/lane closure, accident, and other useful data. Further, the traffic condition may also be given in part by a user (non-limiting examples: driver, passenger, and road user). By way of example only, a user may provide data about a closed lane, stopped vehicle, traffic condition and other appropriate data”); and
updating, based at least on the one or more differences between the map data and the sensor data, at least a portion of the prior version of the map that corresponds to the one or more features to obtain the current version of the map that is used for the performing of the one or more control operations (See at least fig 1-20, Col 11, lines 40-51, “the method determines at least one of whether a change of lane is required and a lane to avoid in a multilane road segment to minimize travel time for the first vehicle. In a preferred embodiment, in the fine mode, the method further receives map data of a current road segment that includes the traffic restrictions at a current time”), (See at least fig 1-20, Col 22, lines 39-60, “Future lane change requirements may be obtained from traffic conditions ahead, history of traffic in each lane in the next road segment, lane closures ahead, lane merge, road work, and any other traffic restrictions in the recommended lane in the next road segment or in a future road segment.”).
Ratnasingam fails to explicitly discloses causing updating, based at least on the one or more differences, at least a portion of the prior version of the map.
However, Jiang teaches updating, based at least on the one or more differences, at least a portion of the prior version of the map (See at least fig 1-9, ¶ 17, “A map segment of a navigation map is then updated based on the lane configuration of one or more lanes within the road segment”), (See at least fig 1-9, ¶ 32, “Algorithms 124 can then be utilized by map update module 125 to update a standard navigation map based on the analysis of the trajectory information to generate a higher definition map that is sufficient for autonomous driving”).
Therefore, it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to modify the method of Ratnasingam and include updating, based at least on the one or more differences, at least a portion of the prior version of the map as taught by Jiang because it would allow the method to update the driving path dynamically while the autonomous vehicle is in operation (Jiang ¶ 44).
Ratnasingam fails to explicitly discloses one or more differences between the map data and the sensor data.
However, Wheeler teaches one or more differences between the map data and the sensor data (See at least fig 1-16, ¶ 23, 35, 40, 52, 53, 54, 55, 56, 82, 98, 104, 119, 1125, 132, 6, “The autonomous vehicles detect map discrepancies based on differences in the surroundings observed using sensor data compared to the high definition map and send messages describing these map discrepancies to the online system . The online system updates existing landmark maps to improve the accuracy of the landmark maps (LMaps ) , and to thereby improve passenger and pedestrian safety”).
Therefore, it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to modify the method of Ratnasingam and include one or more differences between the map data and the sensor data as taught by Wheeler because it would allow the autonomous vehicles to safely navigate to their destinations without human input or with limited human input (Wheeler ¶ 26)
Regarding claim 2, Ratnasingam discloses the method of claim 1, wherein the updating of at least the portion of the prior version of the map is based at least on particular sensor data that includes at least a portion of the sensor data used to determine the one or more differences (See at least Col 32, lines 55-63, “Data about surrounding conditions may be obtained from sensors mounted on the vehicles, environment and other appropriate sources”), (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”), (See at least Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”), (See at least Col 35, lines 1-15, “The data provided by these external systems may include weather conditions, frosty road surface, flood, road condition, road/lane closure, accident, and other useful data. Further, the traffic condition may also be given in part by a user (non-limiting examples: driver, passenger, and road user). By way of example only, a user may provide data about a closed lane, stopped vehicle, traffic condition and other appropriate data”).
Regarding claim 3, Ratnasingam discloses the method of claim 2, wherein the particular sensor data corresponds to a plurality of machines (See at least Col 18, lines 3-17, “method requires at least the current lane of the first vehicle, the current lane of the obstructing vehicle, location of the obstructing vehicle relative to the first vehicle, traffic information in each drivable lane and road map data. In exemplary embodiments, the system may use one or more other data for ranking the drivable lanes in the current road segment”), (See at least Col 34, lines 1-15, “To determine an optimum lane for the first vehicle to minimize travel time the system may require at least the location of the first vehicle, location and lane of the other obstructing vehicle, and traffic condition in other drivable lanes. To determine the rank order of drivable lanes for the first vehicle in the multilane road segment, the system may require a location and lane of the other obstructing vehicle, and traffic condition in other drivable lanes”).
Regarding claim 4, Ratnasingam discloses the method of claim 2, wherein a number of machines corresponding to the particular sensor data is based at least on an amount of traffic in an area that includes the one or more features (See at least Col 11, lines 40-51, “the method determines at least one of whether a change of lane is required and a lane to avoid in a multilane road segment to minimize travel time for the first vehicle. In a preferred embodiment, in the fine mode, the method further receives map data of a current road segment that includes the traffic restrictions at a current time…determines at least one of an optimum driving lane in the current road segment to minimize travel time and a rank ( or score) of drivable lanes in a multilane road segment according to increasing order of travel time for the first vehicle”), (See at least Col 34, lines 1-15, “To determine an optimum lane for the first vehicle to minimize travel time the system may require at least the location of the first vehicle, location and lane of the other obstructing vehicle, and traffic condition in other drivable lanes. To determine the rank order of drivable lanes for the first vehicle in the multilane road segment, the system may require a location and lane of the other obstructing vehicle, and traffic condition in other drivable lanes”).
Regarding claim 5, Ratnasingam discloses the method of claim 2, wherein the particular sensor data includes additional data that is different from the sensor data initially used to identify the one or more differences (See at least fig 1-20, Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”), (See at least fig 1-20, Col 35, lines 1-15, “The data provided by these external systems may include weather conditions, frosty road surface, flood, road condition, road/lane closure, accident, and other useful data. Further, the traffic condition may also be given in part by a user (non-limiting examples: driver, passenger, and road user). By way of example only, a user may provide data about a closed lane, stopped vehicle, traffic condition and other appropriate data”).
Regarding claim 6, Ratnasingam discloses the method of claim 5, wherein the additional data is requested from one or more selected machines (See at least Col 32, lines 55-63, “Data about surrounding conditions may be obtained from sensors mounted on the vehicles, environment and other appropriate sources”), (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”).
Regarding claim 7, Ratnasingam discloses the method of claim 6, wherein the one or more selected machines are selected based at least on: a number of machines present in an area that includes the one or more features; a number of machines available to provide the additional data; an amount of data provided by the selected machines over a particular amount of time; or a target number of machines for providing the additional data (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”), (See at least fig 1-20, Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”), (See at least fig 1-20, Col 35, lines 1-15, “The data provided by these external systems may include weather conditions, frosty road surface, flood, road condition, road/lane closure, accident, and other useful data. Further, the traffic condition may also be given in part by a user (non-limiting examples: driver, passenger, and road user). By way of example only, a user may provide data about a closed lane, stopped vehicle, traffic condition and other appropriate data”).
Regarding claim 8, Ratnasingam discloses the method of claim 1, wherein the updating of at least the portion of the prior version of the map is based at least on the one or more differences being identified based at least on sensor data corresponding to a threshold number of machines (See at least Col 18, lines 3-17, “method requires at least the current lane of the first vehicle, the current lane of the obstructing vehicle, location of the obstructing vehicle relative to the first vehicle, traffic information in each drivable lane and road map data. In exemplary embodiments, the system may use one or more other data for ranking the drivable lanes in the current road segment”), (See at least Col 34, lines 1-15, “To determine an optimum lane for the first vehicle to minimize travel time the system may require at least the location of the first vehicle, location and lane of the other obstructing vehicle, and traffic condition in other drivable lanes. To determine the rank order of drivable lanes for the first vehicle in the multilane road segment, the system may require a location and lane of the other obstructing vehicle, and traffic condition in other drivable lanes”).
Regarding claim 9, Ratnasingam discloses the method of claim 1, wherein the one or more features correspond to a demarcated region of a driving surface (See at least Col 18, lines 45-60, “updated road map according to current time or by some other means such as using a sensing device (non-limiting examples: a video camera, a device for capturing waves in the wavelength range of UV, IR, visible or any other appropriate range of wavelengths) to detect the lane marking(s) on the road.”), (See at least Col 32, lines 55-63, “Data about surrounding conditions may be obtained from sensors mounted on the vehicles, environment and other appropriate sources”), (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”).
Regarding claim 10, Ratnasingam discloses the method of claim 1, wherein the determining of the one or more differences is performed using one or more of: at least one machine of the one or more machines; or a map system used to generate at least the portion of the prior version of the map (See at least Col 18, lines 45-60, “updated road map according to current time or by some other means such as using a sensing device (non-limiting examples: a video camera, a device for capturing waves in the wavelength range of UV, IR, visible or any other appropriate range of wavelengths) to detect the lane marking(s) on the road.”), (See at least Col 32, lines 55-63, “Data about surrounding conditions may be obtained from sensors mounted on the vehicles, environment and other appropriate sources”), (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”).
Regarding claim 11, Ratnasingam discloses at least one processor comprising:
processing circuitry to cause performance of one or more movement control operations of a machine based at least on an updated map that has been updated according to one or more update operations (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”), (See at least Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”), the one or more update operations including:
updating, to obtain the updated map that is used for performance of the one or more movement control operations, at least a portion of map data that corresponds to one or more features of an environment, the updating of the map data being based at least on determination that a change has occurred with respect to the one or more features, the change being identified based at least on a comparison between map data corresponding to the one or more features and sensor data corresponding to the one or more features (See at least fig 1-20, Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”), (See at least fig 1-20, Col 35, lines 1-15, “The data provided by these external systems may include weather conditions, frosty road surface, flood, road condition, road/lane closure, accident, and other useful data. Further, the traffic condition may also be given in part by a user (non-limiting examples: driver, passenger, and road user). By way of example only, a user may provide data about a closed lane, stopped vehicle, traffic condition and other appropriate data”), (See at least fig 1-20, Col 11, lines 40-51, “the method determines at least one of whether a change of lane is required and a lane to avoid in a multilane road segment to minimize travel time for the first vehicle. In a preferred embodiment, in the fine mode, the method further receives map data of a current road segment that includes the traffic restrictions at a current time”), (See at least fig 1-20, Col 22, lines 39-60, “Future lane change requirements may be obtained from traffic conditions ahead, history of traffic in each lane in the next road segment, lane closures ahead, lane merge, road work, and any other traffic restrictions in the recommended lane in the next road segment or in a future road segment.”).
Ratnasingam fails to explicitly discloses updating of the map data being based at least on determination that a change has occurred with respect to the one or more features.
However, Jiang teaches updating of the map data being based at least on determination that a change has occurred with respect to the one or more features (See at least fig 1-9, ¶ 17, “A map segment of a navigation map is then updated based on the lane configuration of one or more lanes within the road segment”), (See at least fig 1-9, ¶ 32, “Algorithms 124 can then be utilized by map update module 125 to update a standard navigation map based on the analysis of the trajectory information to generate a higher definition map that is sufficient for autonomous driving”).
Therefore, it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to modify the system of Ratnasingam and include updating of the map data being based at least on determination that a change has occurred with respect to the one or more features as taught by Jiang because it would allow the method to update the driving path dynamically while the autonomous vehicle is in operation (Jiang ¶ 44)
Regarding claim 12, Ratnasingam discloses the at least one processor of claim 11, wherein the updating of the map data is based at least on particular sensor data that includes at least a portion of the sensor data used to determine the change (See at least Col 32, lines 55-63, “Data about surrounding conditions may be obtained from sensors mounted on the vehicles, environment and other appropriate sources”), (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”), (See at least Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”), (See at least Col 35, lines 1-15, “The data provided by these external systems may include weather conditions, frosty road surface, flood, road condition, road/lane closure, accident, and other useful data. Further, the traffic condition may also be given in part by a user (non-limiting examples: driver, passenger, and road user). By way of example only, a user may provide data about a closed lane, stopped vehicle, traffic condition and other appropriate data”).
Regarding claim 13, Ratnasingam discloses the at least one processor of claim 12, wherein the particular sensor data is obtained using one or more sensors corresponding to one or more machines (See at least Col 18, lines 3-17, “method requires at least the current lane of the first vehicle, the current lane of the obstructing vehicle, location of the obstructing vehicle relative to the first vehicle, traffic information in each drivable lane and road map data. In exemplary embodiments, the system may use one or more other data for ranking the drivable lanes in the current road segment”), (See at least Col 34, lines 1-15, “To determine an optimum lane for the first vehicle to minimize travel time the system may require at least the location of the first vehicle, location and lane of the other obstructing vehicle, and traffic condition in other drivable lanes. To determine the rank order of drivable lanes for the first vehicle in the multilane road segment, the system may require a location and lane of the other obstructing vehicle, and traffic condition in other drivable lanes”).
Regarding claim 14, Ratnasingam discloses the at least one processor of claim 13, wherein a number of machines corresponding to the particular sensor data is based at least on an amount of traffic in an area that includes the one or more features (See at least Col 11, lines 40-51, “the method determines at least one of whether a change of lane is required and a lane to avoid in a multilane road segment to minimize travel time for the first vehicle. In a preferred embodiment, in the fine mode, the method further receives map data of a current road segment that includes the traffic restrictions at a current time…determines at least one of an optimum driving lane in the current road segment to minimize travel time and a rank ( or score) of drivable lanes in a multilane road segment according to increasing order of travel time for the first vehicle”), (See at least Col 34, lines 1-15, “To determine an optimum lane for the first vehicle to minimize travel time the system may require at least the location of the first vehicle, location and lane of the other obstructing vehicle, and traffic condition in other drivable lanes. To determine the rank order of drivable lanes for the first vehicle in the multilane road segment, the system may require a location and lane of the other obstructing vehicle, and traffic condition in other drivable lanes”).
Regarding claim 15, Ratnasingam discloses the at least one processor of claim 12, wherein the particular sensor data includes additional data that is different from the sensor data initially used to identify the change (See at least fig 1-20, Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”), (See at least fig 1-20, Col 35, lines 1-15, “The data provided by these external systems may include weather conditions, frosty road surface, flood, road condition, road/lane closure, accident, and other useful data. Further, the traffic condition may also be given in part by a user (non-limiting examples: driver, passenger, and road user). By way of example only, a user may provide data about a closed lane, stopped vehicle, traffic condition and other appropriate data”).
Regarding claim 16, Ratnasingam discloses the at least one processor of claim 15, wherein the additional data is requested from one or more selected machines (See at least Col 32, lines 55-63, “Data about surrounding conditions may be obtained from sensors mounted on the vehicles, environment and other appropriate sources”), (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”).
Regarding claim 17, Ratnasingam discloses the at least one processor of claim 16, wherein the one or more selected machines are selected based at least on: a number of machines present in an area that includes the one or more features; a number of machines available to provide the additional data; an amount of data provided by the selected machines over a particular amount of time; or a target number of machines for providing the additional data (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”), (See at least fig 1-20, Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”), (See at least fig 1-20, Col 35, lines 1-15, “The data provided by these external systems may include weather conditions, frosty road surface, flood, road condition, road/lane closure, accident, and other useful data. Further, the traffic condition may also be given in part by a user (non-limiting examples: driver, passenger, and road user). By way of example only, a user may provide data about a closed lane, stopped vehicle, traffic condition and other appropriate data”).
Regarding claim 18, Ratnasingam discloses a cloud-based map system comprising:
at least one data store for storing one or more versions of one or more maps (See at least fig 1-20, Col 4, lines 20-30, “the methods described herein further comprise storing the vehicle data of at least the second vehicle to generate a traffic profile, the traffic profile being based on a time of day”), (See at least fig 1-20, Col 14, lines 35-45, “a storage system such as a database or some other means to store the updated digitized road map data, navigation data of vehicles and other related data as appropriate”);
at least one communication interface for at least one of receiving or transmitting data between the cloud-based map system and one or more machines (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”); and
at least one processor to: perform one or more update operations to at least one map of the one or more maps based at least on one or more identified differences between one or more features in an environment, the one or more differences identified based at least on a comparison of sensor data obtained using one or more sensors of at least a first machine of the one or more machines and map data corresponding the at least one map (See at least Col 32, lines 55-63, “Data about surrounding conditions may be obtained from sensors mounted on the vehicles, environment and other appropriate sources”), (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”), (See at least Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”); and
send, using the at least one communication interface, map data corresponding to an updated version of the at least one map to at least a second machine of the one or more machines, the second machine using the updated version of the at least one map to perform one or more navigation, localization, or control operations for maneuvering the second machine within the environment (See at least fig 1-20, Col 11, lines 40-51, “the method determines at least one of whether a change of lane is required and a lane to avoid in a multilane road segment to minimize travel time for the first vehicle. In a preferred embodiment, in the fine mode, the method further receives map data of a current road segment that includes the traffic restrictions at a current time”), (See at least fig 1-20, Col 22, lines 39-60, “Future lane change requirements may be obtained from traffic conditions ahead, history of traffic in each lane in the next road segment, lane closures ahead, lane merge, road work, and any other traffic restrictions in the recommended lane in the next road segment or in a future road segment.”), (See at least fig 1-20, Col 4, lines 20-30, “the methods described herein further comprise storing the vehicle data of at least the second vehicle to generate a traffic profile, the traffic profile being based on a time of day”), (See at least fig 1-20, Col 14, lines 35-45, “a storage system such as a database or some other means to store the updated digitized road map data, navigation data of vehicles and other related data as appropriate”).
Ratnasingam fails to explicitly discloses causing an updated version of the at least one map.
However, Jiang teaches an updated version of the at least one map (See at least fig 1-9, ¶ 17, “A map segment of a navigation map is then updated based on the lane configuration of one or more lanes within the road segment”), (See at least fig 1-9, ¶ 32, “Algorithms 124 can then be utilized by map update module 125 to update a standard navigation map based on the analysis of the trajectory information to generate a higher definition map that is sufficient for autonomous driving”).
Therefore, it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to modify the system of Ratnasingam and include an updated version of the at least one map as taught by Jiang because it would allow the system to update the driving path dynamically while the autonomous vehicle is in operation (Jiang ¶ 44)
Regarding claim 19, Ratnasingam discloses the cloud-based map system of claim 18, wherein communication between the cloud-based map system and the one or more machines is performed using one or more application programming interfaces (APIs) (See at least Col 32, lines 55-63, “Data about surrounding conditions may be obtained from sensors mounted on the vehicles, environment and other appropriate sources”), (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”), (See at least Col 30, lines 7-20, “the system may check the road map data, navigation route of the first vehicle, accidents, lane closure, broken down vehicles and other appropriate data when identifying a vehicle that causes traffic congestion or a bottleneck.”), (See at least Col 35, lines 1-15, “The data provided by these external systems may include weather conditions, frosty road surface, flood, road condition, road/lane closure, accident, and other useful data. Further, the traffic condition may also be given in part by a user (non-limiting examples: driver, passenger, and road user). By way of example only, a user may provide data about a closed lane, stopped vehicle, traffic condition and other appropriate data”).
Regarding claim 20, Ratnasingam discloses the cloud-based map system of claim 18, wherein the one or more differences are identified at least one of: locally on at least the first machine of the one or more machines prior to transmission of data indicating the differences to the cloud-based map system; or in the cloud using the cloud-based map system after receiving the sensor data associated with the one or more features from at least the first machine of the one or more machines (See at least Col 18, lines 45-60, “updated road map according to current time or by some other means such as using a sensing device (non-limiting examples: a video camera, a device for capturing waves in the wavelength range of UV, IR, visible or any other appropriate range of wavelengths) to detect the lane marking(s) on the road.”), (See at least Col 32, lines 55-63, “Data about surrounding conditions may be obtained from sensors mounted on the vehicles, environment and other appropriate sources”), (See at least Col 35, lines 1-15, “a computer system that drives an autonomous vehicle may provide data including type of vehicle, condition of the vehicle, speed, acceleration, current lane, navigation route, presence of neighboring vehicles detected by some means (including video camera, LIDAR, depth camera, communication signal or by some other means), density of vehicles, state of the driving system, and other relevant data. In addition, external environmental data or surrounding data may be provided”), (See at least Col 21, lines 1-20, “determining the lane information to minimize travel time or use an appropriate metric to indicate the confidence level on the estimated lane information. Confidence level may be presented to the driver in appropriate output such as color coded, percentage or any other appropriate method. If the moving direction in the current road segment assumed by the system is not correct, the driver may input the correct direction of movement by some means (such as voice, gesture, haptic, touch input or any other appropriate input method).”).
Conclusion
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 LUIS MARTINEZ whose email is luis.martinezborrero@uspto.gov and telephone number is (571)272-4577. The examiner can normally be reached on Monday-Friday 8:30AM-5:00PM EST.
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/LUIS A MARTINEZ BORRERO/Primary Examiner, Art Unit 3665
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