DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-7, 10-16, 18-21, 23, and 24 of US Application No. 18/352,578 are currently pending and have been examined. Applicant amended claims 1, 4-7, 11, 13-15, 19, 21, and 24. Applicant previously canceled claims 8, 9, 17, and 22.
Response to Arguments/Amendments
The previous objections to claims 1, 11, and 19 are withdrawn in consideration of amended claims 11 and 19.
The previous interpretation of claims 11 and 19 are withdrawn in consideration of amended claims 11 and 19.
The previous rejections of claims 11-16, 18-21, and 24 under 35 U.S.C. 101 are withdrawn in consideration of amended independent claims 11 and 19. Applicant amended claim 11 to recite “one or more processors to cause a first machine to navigate within an environment using a map of the environment”. Applicant amended claim 19 to recite a substantially similar limitation. Navigating a machine based on the map/updated map uses or applies the judicial exception in a meaningful way beyond generally linking the exception to a technological environment.
Applicant’s arguments regarding the previous rejections of claims 1-4, 6, 7, 10-16, 18-21, 23, and 24 under 35 U.S.C. 102, see REMARKS, filed 04 May 2026, have been fully considered but are not persuasive. Applicant amended independent claim 1 to recite “selecting, using at least a distance threshold between the first location of the second machine when obtaining the first sensor data and a second location of the second machine when obtaining the second sensor data, the second sensor data” and argues that the previously-cited prior art, Li, does not teach this limitation. Applicant similarly amended independent claims 11 and 19. The Examiner respectfully disagrees. Li discloses:
[0373] The frequency depends on activity of the vehicles. For instance, the frequency may depend on speed of the vehicles. Thereby, a higher frequency may be provided for a higher speed of the vehicles. For example, in a situation where the vehicle is in a traffic jam and the speed of the vehicle is very lower, for example, at a speed of less than 20 km/hour, 18 km/hour, 15 km/hour, 12 km/hour, or 10 km/hour, there may be redundant or overlapping in the data captured by the sensors about the surrounding environment, which is not changing fast due to low speed of the vehicle. In some instances, the frequency of data collection by the sensors may be less when the vehicle is traveling more slowly, since there is less likely to be significant change. Optionally, the frequency of providing the data to the cloud server can be lowered so that less redundant data would be transmitted to the cloud server for generating or updating the 3D map. When the frequency of data communication to the server is varied, the frequency of data collection may or may not be correspondingly varied. In some instances, data collection may also be varied to match the frequency of the data communication (e.g., less frequency of data communication may correspond to less frequency of data collection, and/or greater frequency of data communication may correspond to greater frequency of data collection). In other instances, data collection frequency may be independent of data communication frequency. If data communication frequency is lowered, there may be more data that has been collected that may be accumulated and transmitted. In some instances, when data communication frequency is decreased, some of the collected data may be discarded or not transmitted.
Data measuring or reporting at a particular frequency for a particular speed is the same as data measuring or reporting at a particular distance threshold. When the speed and frequency are maintained, the measuring and reporting will occur over a constant distance interval. Therefore, the Examiner maintains that Li teaches selecting, using at least a distance threshold . . ., the second sensor data (i.e., data measuring or reporting based on speed and frequency). Therefore, the previous rejections are maintained.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 6, 7, 10-16, 18-21, 23, and 24 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et al. (US 2020/0109954 A1, “Li”).
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]);
generating first data representative of one or more first locations of one or more first landmarks represented by the first sensor data and a first location of the second machine when obtaining the first sensor data (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, using at least a distance threshold between the first location of the second machine when the obtaining the first sensor data and a second location of the second machine when obtaining, the second sensor data (data from the plurality of vehicles are provided at a variable frequency – see at least ¶ [0371]-[0372]).
generating, based at least on the selecting the second sensor data, second data representative of one or more second locations of one or more second landmarks represented by the second sensor data and the second location of the second machine when obtaining the second sensor data (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]).
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 claims 11 and 19, Li discloses map generation systems and teaches:
one or more processing units (processors 103, 1013, 1023 – see at least Fig. 1) to:
generate, 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]);
select, based on at least one of a time threshold or a distance threshold having occurred from the obtaining of the first sensor data using the one or more sensors, 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]);
generate, 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
send, to a remote system, the first data and the second to cause a generation of a map, wherein the map is sent to one or more second machines to cause the one or more second machine[s] to navigate according to the map (data collected by processors on-board the vehicle may be transmitted to a remote server for generating a 3D map or updating an existing map – see at least ¶ [0337], [0341]; on-board processors may detect objects within the environment and determine positional information relating to the objects before being communicated to a remote server – see at least ¶ [0341]; the collected data may be transmitted for generating or updating a 3D map – see at least Fig. 9 and ¶ [0440]-[0441]; remote server may update the previously-stored map and share these changes to other vehicles for navigation or path planning – see at least ¶ [0450]).
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 first 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 first 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 first 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 first 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 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 24, Li further teaches:
wherein the second sensor data is selected based on at least one of: a distance between the second location and the first location being equal to or greater than the distance threshold; or a time period between when the second sensor data was obtained and the first sensor data was obtained being equal to or greater than the time threshold (data from the plurality of vehicles are provided at a variable frequency – see at least ¶ [0371]-[0372]).
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Li 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 map generation system of Li 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]).
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 AARON L TROOST whose telephone number is (571)270-5779. The examiner can normally be reached Mon-Fri 7:30am-4pm.
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/AARON L TROOST/Primary Examiner, Art Unit 3666
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