DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 15 September 2026 has been entered.
Status of Claims
Claims 1-7, 10-16, 18-20, 23, 25, and 26 of US Application No. 18/352,578 are currently pending and have been examined. Applicant amended claims 1, 11, 13, 15, and 19 and canceled claims 21 and 24. Applicant previously canceled claims 8, 9, 17, and 22.
Response to Arguments/Amendments
The previous rejections of claims 1-7, 10-16, 18-20, 23 under 35 U.S.C. 102 are withdrawn in consideration of amended independent claims 1, 11, and 19.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6, 7, 10-16, 18-20, 23, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2020/0109954 A1, “Li”) in view of Jiang et al. (US 2022/0189302 A1, “Jiang”).
Regarding claim 1, Li discloses map generation systems and teaches:
causing a first machine to navigate within an environment using a map corresponding to the environment (remote server may update the previously-stored map and share these changes to other vehicles for navigation or path planning – see at least ¶ [0450]), wherein the map is generated or updated, at least, by:
obtaining, while a second machine is navigating along a path of the environment, a first sensor data using one or more sensors of the second machine and second sensor data using the one or more sensors of the second machine (sensors 1012 that may sense information relating to the environment outside the vehicle – see at least Fig. 1 and ¶ [0322]; one or more sensors carried by the vehicle may include proximity sensors, such as radar and lidar – see at least ¶ [0336]; data can be received from different types – see at least ¶ [0336]; first set of sensors may include a camera, a radar unit, and a lidar unit – see at least ¶ [0397]);
selecting at least a first frame associated with the first sensor data (data collection frequency – see at least ¶ [0373]; i.e., first instance of data collected at the frequency = first sensor data);
generating first data representative of one or more first locations of one or more first landmarks represented by the first sensor frame and a first location of the second machine when obtaining the first sensor frame (sensors may detect the presence of objects within the environment and sensor data may include position information with regard to the identified objects within the environment – see at least ¶ [0334]);
[ ];
[ ];
selecting, [ ], a second frame associated with the second sensor data from a plurality of frames associated with the second sensor data (data from the plurality of vehicles are provided at a variable frequency – see at least ¶ [0371]-[0372]; data collection frequency – see at least ¶ [0373]; i.e., second instance of data collected at the frequency = second sensor data).
generating second data representative of one or more second locations of one or more second landmarks represented by the second frame and a second location of the second machine when obtaining the second frame (sensors may detect the presence of objects within the environment and sensor data may include position information with regard to the identified objects within the environment – see at least ¶ [0334]); and
sending, to a remote system, the first data and the second data for generating the map corresponding to the environment (data package 414 may be sent to one or more servers 415, 416, 417 – see at least Fig. 4 and ¶ [0382]; map generated based on data collected by various types of sensors on-board a vehicle – see at least ¶ [0003]; data associated with the map may be transmitted to one or more processors off-board the vehicle for map generation – see at least ¶ [0006], [0105]).
Li fails to teach determining a distance traveled by the second machine relative to the first location; determining that the distance traveled by the second machine satisfies a distance threshold; selecting, in response to the distance traveled by the second machine satisfying the distance threshold, a second frame.
However, Jiang discloses vehicular localization systems and teaches:
determining a distance traveled by the second machine relative to the first location; determining that the distance traveled by the second machine satisfies a distance threshold; selecting, in response to the distance traveled by the second machine satisfying the distance threshold, a second frame (a sequence of data is collected by a vehicle as it traverses a route – see at least ¶ [0022]; sequence of data may be obtained in a distance domain, e.g., as a function of distance traveled, or in a time domain, e.g., as a function of driving time – see at least ¶ [0023]; data may be collected at regular distance intervals – see at least ¶ [0029]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map generation system of Li to provide for determining selecting a frame based on distance traveled, as taught by Jiang, with a reasonable expectation of success because it would provide a sequence of periodically collected data points that may be used to localize the vehicle on a road segment (Jiang at ¶ [0029] and abstract).
Regarding claim 2, Li further teaches:
wherein at least one of: a first landmark of the one or more first landmarks is a same landmark as a second landmark of the one or more second landmarks; or a third landmark of the one or more first landmarks is a different landmark as compared to a fourth landmark of the one or more second landmarks (there may be redundant or overlapping in the data captured by the sensors about the surrounding environment – see at least ¶ [0373]).
Regarding claim 3, Li further teaches:
the generating the first data is based at least on encoding the one or more first locations of the one or more first landmarks; and the generating the second data is based at least on encoding the one or more second locations of the one or more second landmarks (label information may be associated with the objects in the map – see at least ¶ [0042]; label information may uniquely identify or represent the object, e.g., position/geo-spatial coordinates – see at least ¶ [0334]).
Regarding claim 4, Li further teaches:
determining, based at least on the one or more first locations of the one or more first landmarks, one or more first three-dimensional (3D) locations of the one or more first landmarks; and determining, based at least on the one or more second locations of the one or more second landmarks, one or more second 3D locations of the one or more second landmarks, wherein the first data is representative of the one or more first 3D locations of the one or more first landmarks and the second data is representative of the one or more second 3D locations of the one or more second landmarks (label information may be associated with the objects in the map – see at least ¶ [0042]; label information may uniquely identify or represent the object, e.g., position/geo-spatial coordinates – see at least ¶ [0334]).
Regarding claim 6, Li further teaches:
obtaining motion data representative of a motion of the second machine, the motion including at least one of acceleration of the second machine, a velocity of the second machine, or a direction of travel associated with the second machine between the first sensor data being obtained and the second sensor data being obtained (internal sensors may be useful for collecting data of the vehicle, including, velocity, acceleration, – see at least ¶ [0331]); and determining the second location based on the at least one of the acceleration, the velocity, or the direction of travel (position information may include detection and/or measurement of movement of the vehicle – see at least ¶ [0331]).
Regarding claim 7, Li further teaches:
wherein the map is further generated by:
generating, based at least on the first data, a first layer of the map using the one or more first locations of the one or more first landmarks; generating, based at least on the second data, a second layer of the map using the one or more second locations of the one or more second landmarks (the remote server 905 may generate a 3D map based on the lidar data and camera data – see at least ¶ [0440]; i.e., data from each different sensor is a separate layer added to the overall 3D map).
Regarding claim 10, Li further teaches:
wherein at least one of the one or more first landmarks or the one or more second landmarks include: a lane divider; a road boundary; a sign; a pole; a wait condition; a vertical structure; a road user; a static object; or a dynamic object (three-dimensional map may include traffic signs, traffic lights, billboards, roads, lane lines, structures – see at least ¶ [0019], [0370]; objects in the map may comprise dynamic objects and/or static objects – see at least ¶ [0410]; sign posts, moving cars, pedestrians, barricades – see at least ¶ [0410]).
Regarding claim 11, Li discloses map generation systems and teaches:
one or more processors to cause a first machine to navigate within an environment using as map of the environment, wherein the map is updated, at least by (processors 103, 1013, 1023 – see at least Fig. 1; (remote server may update the previously-stored map and share these changes to other vehicles for navigation or path planning – see at least ¶ [0450])):
generating, based at least on first sensor data obtained using one or more sensors of a first machine while located at a first location, first data representative of the first location of the first machine and one or more first locations of one or more first landmarks as represented by the first sensor data (sensors 1012 that may sense information relating to the environment outside the vehicle – see at least Fig. 1 and ¶ [0322]; one or more sensors carried by the vehicle may include proximity sensors, such as radar and lidar – see at least ¶ [0336]; data can be received from different types – see at least ¶ [0336]; first set of sensors may include a camera, a radar unit, and a lidar unit – see at least ¶ [0397]; sensors may detect the presence of objects within the environment and sensor data may include position information with regard to the identified objects within the environment – see at least ¶ [0334]);
[ ];
[ ];
selecting, [ ], second sensor data obtained using the one or more sensors of the first machine while located at a second location (data from the plurality of vehicles are provided at a variable frequency – see at least ¶ [0371]-[0372]; data collection frequency – see at least ¶ [0373]; i.e., second instance of data collected at the frequency = second sensor data);
generating, based at least on the second sensor data obtained using the one or more sensors of the first machine, second data representative of the second location of the first machine [and] one or more second locations of one or more second landmarks as represented by the second sensor data (sensors 1012 that may sense information relating to the environment outside the vehicle – see at least Fig. 1 and ¶ [0322]; one or more sensors carried by the vehicle may include proximity sensors, such as radar and lidar – see at least ¶ [0336]; data can be received from different types – see at least ¶ [0336]; first set of sensors may include a camera, a radar unit, and a lidar unit – see at least ¶ [0397]; sensors may detect the presence of objects within the environment and sensor data may include position information with regard to the identified objects within the environment – see at least ¶ [0334]); and
sending, to a remote system, the first data and the second data for updating the map (data package 414 may be sent to one or more servers 415, 416, 417 – see at least Fig. 4 and ¶ [0382]; map generated based on data collected by various types of sensors on-board a vehicle – see at least ¶ [0003]; data associated with the map may be transmitted to one or more processors off-board the vehicle for map generation – see at least ¶ [0006], [0105]).
Li fails to teach determining a distance traveled by the second machine relative to the first location; determining that the distance traveled by the second machine satisfies a distance threshold; selecting, in response to the distance traveled by the second machine satisfying the distance threshold, a second frame.
However, Jiang discloses vehicular localization systems and teaches:
determining a distance traveled by the second machine relative to the first location; determining that the distance traveled by the second machine satisfies a distance threshold; selecting, in response to the distance traveled by the second machine satisfying the distance threshold, a second frame (a sequence of data is collected by a vehicle as it traverses a route – see at least ¶ [0022]; sequence of data may be obtained in a distance domain, e.g., as a function of distance traveled, or in a time domain, e.g., as a function of driving time – see at least ¶ [0023]; data may be collected at regular distance intervals – see at least ¶ [0029]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map generation system of Li to provide for determining selecting a frame based on distance traveled, as taught by Jiang, with a reasonable expectation of success because it would provide a sequence of periodically collected data points that may be used to localize the vehicle on a road segment (Jiang at ¶ [0029] and abstract).
Regarding claim 12, Li further teaches:
wherein the first data is generated based at least on encoding the first location of the first machine and the one or more first locations of the one or more first landmarks; and the second data is generated based at least on encoding the second location of the first machine and the one or more second locations of the one or more second landmarks (label information may be associated with the objects in the map – see at least ¶ [0042]; label information may uniquely identify or represent the object, e.g., position/geo-spatial coordinates – see at least ¶ [0334]; sensors may sense information relating to the vehicle itself, thereby obtaining location and orientation information of the vehicle – see at least ¶ [0322]; positional information of the object may be relative to the vehicle – see at least ¶ [0334]).
Regarding claim 13, Li further teaches:
wherein the one or more processors are further to: determine, based at least on the one or more first locations of the one or more first landmarks, one or more first three-dimensional (3D) locations of the one or more first landmarks (label information may be associated with the objects in the map – see at least ¶ [0042]; label information may uniquely identify or represent the object, e.g., position/geo-spatial coordinates – see at least ¶ [0334]) and determine, based at least on the one or more second locations of the one or more second landmarks, one or more second 3D locations of the one or more second landmarks (label information may be associated with the objects in the map – see at least ¶ [0042]; label information may uniquely identify or represent the object, e.g., position/geo-spatial coordinates – see at least ¶ [0334]), wherein the first data is representative of the first location of the second machine and the one or more first 3D location of the one or more first landmarks and the second data is representative of the second location of the second machine and the one or more second 3D locations of the one or more second landmarks (label information may be associated with the objects in the map – see at least ¶ [0042]; label information may uniquely identify or represent the object, e.g., position/geo-spatial coordinates – see at least ¶ [0334]; sensors may sense information relating to the vehicle itself, thereby obtaining location and orientation information of the vehicle – see at least ¶ [0322]; positional information of the object may be relative to the vehicle – see at least ¶ [0334]).
Regarding claim 14, Li further teaches:
wherein the one or more processors are further to: determine, based at least on the first type of sensor data, at least one of one or more poses associated with the one or more first landmarks or one or more geometries associated with the one or more first landmarks, wherein the first data is further representative of the at least one of the one or more poses associated with the one or more first landmarks or the one or more geometries associated with the one or more first landmarks (objects detected by external sensors may be labeled, where the label information includes attitude information relative to orthogonal translation axes – see at least ¶ [0334]).
Regarding claim 15, Li further teaches:
determine, based at least on a motion of the second machine, at least one of a translation or a rotation of the first machine relative to the first location (position information may include spatial location and attitude or pose relative to axes of rotation – see at least ¶ [0331]); and
determine the second location of the second machine based on at least one of the rotation or the translation with respect to the first location (position information may include spatial location and attitude or pose relative to axes of rotation – see at least ¶ [0331]).
Regarding claim 16, Li further teaches:
the map stores the one or more first locations of the one or more first landmarks and the one or more second locations of the one or more second landmarks (the remote server 905 may generate a 3D map based on the lidar data and camera data – see at least ¶ [0440]).
Regarding claim 18, Li further teaches:
wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (external device may be a remote server 103 – see at least ¶ [0337]; processors off-board the vehicle for map generation may be located at a remote server, for example a cloud server with cloud computing infrastructure – see at least ¶ [0006]).
Regarding claim 19, Li discloses map generation systems and teaches:
one or more processors comprising processing circuitry to cause a first machine to navigate within an environment using a map corresponding to the environment (remote server may update the previously-stored map and share these changes to other vehicles for navigation or path planning – see at least ¶ [0450]), wherein the map is generated or updated, at least, by:
obtaining sensor data using one or more sensors of a second machine, the sensor data representative of at least a first frame associated with a first location of the second machine within the environment and a second frame associated with a second location of the second machine within the environment (sensors 1012 that may sense information relating to the environment outside the vehicle – see at least Fig. 1 and ¶ [0322]; one or more sensors carried by the vehicle may include proximity sensors, such as radar and lidar – see at least ¶ [0336]; data can be received from different types – see at least ¶ [0336]; first set of sensors may include a camera, a radar unit, and a lidar unit – see at least ¶ [0397]);
selecting the first frame represented by the sensor data (data collection frequency – see at least ¶ [0373]; i.e., first instance of data collected at the frequency = first sensor data);
[ ];
[ ];
selecting, [ ], the second frame represented by the sensor data (data from the plurality of vehicles are provided at a variable frequency – see at least ¶ [0371]-[0372]; data collection frequency – see at least ¶ [0373]; i.e., second instance of data collected at the frequency = second sensor data).
generating, based at least on the first frame represented by the sensor data, first data representative of one or more first locations of one or more first landmarks and the first location of the second machine (sensors may detect the presence of objects within the environment and sensor data may include position information with regard to the identified objects within the environment – see at least ¶ [0334]);
generating, based at least on the second frame represented by the sensor data, second data representative of one or more second locations of one or more second landmarks and the second location of the second machine (sensors may detect the presence of objects within the environment and sensor data may include position information with regard to the identified objects within the environment – see at least ¶ [0334]); and
sending, to a remote system, the first data and the second data for updating the map (data package 414 may be sent to one or more servers 415, 416, 417 – see at least Fig. 4 and ¶ [0382]; map generated based on data collected by various types of sensors on-board a vehicle – see at least ¶ [0003]; data associated with the map may be transmitted to one or more processors off-board the vehicle for map generation – see at least ¶ [0006], [0105]).
Li fails to teach determining, based at least on the selecting the first frame, a distance traveled by the second machine relative to the first location; determining that the distance traveled by the second machine is equal to or greater than a distance threshold; selecting, in response to the distance traveled being equal to or greater than the distance threshold, the second frame represented by the sensor data.
However, Jiang discloses vehicular localization systems and teaches:
determining, based at least on the selecting the first frame, a distance traveled by the second machine relative to the first location; determining that the distance traveled by the second machine is equal to or greater than a distance threshold; selecting, in response to the distance traveled being equal to or greater than the distance threshold, the second frame represented by the sensor data (a sequence of data is collected by a vehicle as it traverses a route – see at least ¶ [0022]; sequence of data may be obtained in a distance domain, e.g., as a function of distance traveled, or in a time domain, e.g., as a function of driving time – see at least ¶ [0023]; data may be collected at regular distance intervals – see at least ¶ [0029]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map generation system of Li to provide for determining selecting a frame based on distance traveled, as taught by Jiang, with a reasonable expectation of success because it would provide a sequence of periodically collected data points that may be used to localize the vehicle on a road segment (Jiang at ¶ [0029] and abstract).
Regarding claim 20, Li further teaches:
wherein the one or more processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (external device may be a remote server 103 – see at least ¶ [0337]; processors off-board the vehicle for map generation may be located at a remote server, for example a cloud server with cloud computing infrastructure – see at least ¶ [0006]).
Regarding claim 23, Li further teaches:
determining, based at least on the first location of the second machine and motion data associated with the second machine, the second location of the second machine that is defined relative to the first location of the second machine, wherein the second data further defines the second location of the second machine relative to the first location of the second machine (internal sensors may be useful for collecting data of the vehicle, including, velocity, acceleration, – see at least ¶ [0331]; position information may include detection and/or measurement of movement of the vehicle – see at least ¶ [0331]).
Regarding claim 25, Jiang further teaches:
wherein the map is further generated or updated by: determining, based at least on the selecting the second frame, a second distance traveled by the second machine relative to the second location; determining that the second distance traveled by the second machine is equal to or greater than the distance threshold; selecting, in response to the second distance traveled being equal to or greater than the distance threshold, a third frame represented by the sensor data (a sequence of data is collected by a vehicle as it traverses a route – see at least ¶ [0022]; sequence of data may be obtained in a distance domain, e.g., as a function of distance traveled, or in a time domain, e.g., as a function of driving time – see at least ¶ [0023]; data may be collected at regular distance intervals – see at least ¶ [0029]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map generation system of Li to provide for determining selecting a frame based on distance traveled, as taught by Jiang, with a reasonable expectation of success because it would provide a sequence of periodically collected data points that may be used to localize the vehicle on a road segment (Jiang at ¶ [0029] and abstract).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Jiang, as applied to claim 1 above, and further in view of Englard et al. (US 2019/0113927 A1, “Englard”).
Regarding claim 5, Li fails to teach but Englard discloses controlling a vehicle using cost maps and teaches:
determining, based at least on processing the first sensor data using one or more first neural networks, the one or more first locations of the one or more first objects; and determining, based at least on processing the second sensor data using one or more second neural networks, the one or more second locations of the one or more second objects (object tracking may be performed utilizing a neural network or other machine learning model – see at least ¶ [0091]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined map generation system of Li and Jiang to provide for determining locations of objects using a known technique, such as neural networks, as taught by Englard, with a reasonable expectation of success because the neural network may track object locations over time (Englard at ¶ [0091], Li at ¶ [0334]).
Allowable Subject Matter
Claim 26 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/AARON L TROOST/Primary Examiner, Art Unit 3666
