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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/19/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
This office action is in reply to filing by applicant on 03/19/2024.
Claims 1 – 20 are currently pending and have been examined.
This action is made non-final.
.
Examiner Note Regarding 35 USC 101 “Alice” Type Analysis
The invention utilizes friction and other environmental sensors on a vehicle to gauge road conditions. While the above might be argued as an abstract idea, the claims then go on to map localized portions of its acquired traction data to a coordinate map of the area, thereby achieving a practical application by highlighting friction issue areas on the map.
Claim Rejections – 35 USC 103
In the event the determination of the status of the application as subject to AIA 35 USC 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.
The following is a quotation of 35 USC 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 USC 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 – 20 are rejected pursuant to 35 USC 103 as being unpatentable over Yang (US20210072147A1) in view of Liu (JP2023505426A, an English copy of which is attached hereto).
Regarding mirrored claims 1, 9, and 18: (mapping claim 9).
Yang discloses:
associating environment perception data of a first vehicle with data indicative of a friction condition, wherein the environment perception data represent at least a portion of a roadway, (In another instance, the sensor data includes data indicative of characteristics of the environment, such as one or more temperature sensors, radar sensors, etc.”, [038]) and (“According to some examples, sensors of the vehicle generate sensor data while the vehicle performs the road friction probing maneuver. The vehicle and/or computing system may estimate the road friction within the road segment based at least in part on the sensor data. The computing system may update a road friction map indicative of road friction in different road segments, and may output or transmit all or a portion of the road friction map to one or more vehicles. The vehicle may adjust operations of the vehicle based on the road friction map.”, [005]) and (“According to some examples, sensors of the vehicle generate sensor data while the vehicle performs the road friction probing maneuver. The vehicle and/or computing system may estimate the road friction within the road segment based at least in part on the sensor data. The computing system may update a road friction map indicative of road friction in different road segments, and may output or transmit all or a portion of the road friction map to one or more vehicles. The vehicle may adjust operations of the vehicle based on the road friction map. In one example, the vehicle may adjust its speed and/or following distance based on the road friction map”, [038]); environmental and traction sensor data vis a vis a road traveled upon by a vehicle are associated
a second sensor associated with the first vehicle and configured to detect data indicative of a friction condition at a first position on the roadway; and (“the data indicative of the road friction may include sensor data generated by sensors of vehicle 101A. In one instance, the sensor data includes data indicative of characteristics of vehicle 101A, such as motion data generated by one or more motion sensors (e.g., an inertial measuring unit (IMU)), wheel speed data generated by one or more wheel speed sensors, among others. In another instance, the sensor data includes data indicative of characteristics of the environment, such as one or more temperature sensors, radar sensors, etc.”, [038]);
associate the at least one corresponding element with the data indicative of a friction condition relating to the first position. (“In one instance, vehicle data 152 includes historical probing maneuver data indicating when each of vehicles 101 has previously performed a road friction probing maneuver. In another instance, vehicle data 152 includes probing maneuver preference data for each of vehicles 101 and/or the respective drivers of vehicles 101. The probing maneuver preference data may include data indicating, for each of vehicles 101, a threshold amount of time between probing maneuvers (e.g., once per week, once per month, etc.); days, times, or locations at which probing maneuvers are permitted and/or prohibited;”, [032]); location and friction data are associated;
the system comprising: a first sensor configured to be mounted on a first vehicle and configured to provide the environment perception data; (“Responsive to performing a road friction probing maneuver, vehicle 101A may transmit data indicative of the road friction to computing system 120. In some instances, the data indicative of the road friction may include sensor data generated by sensors of vehicle 101A. In one instance, the sensor data includes data indicative of characteristics of vehicle 101A, such as motion data generated by one or more motion sensors (e.g., an inertial measuring unit (IMU)), wheel speed data generated by one or more wheel speed sensors, among others. In another instance, the sensor data includes data indicative of characteristics of the environment, such as one or more temperature sensors, radar sensors, etc.”, [038]);
a position sensor configured to detect a position of the first vehicle on the roadway; (“Vehicle selector module 232 may cause one or more vehicles 101 to perform a road friction probing maneuver in response to selecting one or more vehicles 101 for performing the road friction probing maneuvers. In some instances, vehicle selector module 232 transmits a command to each of vehicles 101A and 101B to cause vehicles 101A and 101B to perform the road friction probing maneuvers within road segment 104A or within subsegments of road segment 104A. The command may indicate the location of segment 104A (or the location of a subsegment). For example, each command may indicate GPS coordinates indicating the boundaries of the segment, or GPS coordinates at the center of the segment.”, [058]);
Yang does not expressly disclose, but Liu teaches:
a data processing apparatus, comprising a processor, communicatively connected to the first sensor, the position sensor, and the second sensor, wherein the data processing apparatus is configured to: transform the first position in a coordinate system centered on the first sensor using coordinate transformation based on the position of the first vehicle; Examiner broadly interprets this limitation to include that a processer is involved in determining the position coordinates of the vehicle on a map using the acquired vehicle sensor data, … (“Each of the lidar sensors can return data corresponding to detected surfaces in the environment. The data may be represented as points (eg, data points) having coordinates (eg, Cartesian coordinates, polar coordinates, etc.) corresponding to at least a portion of the detected surface.”, [paragraph 5 below “Description”]) and (“Determining a map of the environment using at least a portion of the collected sensor data includes determining a link between first sensor data and second sensor data of the collected sensor data. the link may indicate that the first sensor data and the second sensor data are associated with the same portion of the environment”, [paragraph 6 below “Description’]).
map the first position onto at least one corresponding element of the environment perception data; and Examiner broadly interprets this limitation to include using vehicle sensor data to generate position information on a map that is associated with a roadway (see Specification “12.[0014]), … (“Robots and other devices … may collect sensor data to determine what objects are in the environment surrounding the device and where the device is in the environment relative to those objects. In some examples, sensor data may be collected to generate a map of the environment and the path of the device through the environment.”, [paragraph 2, below “Description’).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Yang to incorporate the teachings of Liu because Yang would be more efficient and versatile in map generation if it utilized local environmental things to help correlate information, as was done in Liu. (“Generating a map associated with the environment may include collecting sensor data received from one or more vehicles and generating a set of links that align the sensor data, see Liu abstract, published 2/9/2023).
Regarding claim 2:
The combination of Yang and Liu disclose the limitations of claim 1:
Liu further teaches:
wherein transforming the first position in a coordinate system centered on the first sensor comprises transforming the first position in a coordinate system centered on a position sensor of the first vehicle using coordinate transformation based on the position data describing the position of the first vehicle. Examiner broadly interprets this limitation to include that a processer is involved in determining the position coordinates of the vehicle on a map using the acquired vehicle sensor data, … (“Each of the lidar sensors can return data corresponding to detected surfaces in the environment. The data may be represented as points (eg, data points) having coordinates (eg, Cartesian coordinates, polar coordinates, etc.) corresponding to at least a portion of the detected surface.”, [paragraph 5 below “Description”]) and (“Determining a map of the environment using at least a portion of the collected sensor data includes determining a link between first sensor data and second sensor data of the collected sensor data. the link may indicate that the first sensor data and the second sensor data are associated with the same portion of the environment”, [paragraph 6 below “Description’]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Yang to incorporate the teachings of Liu because Yang would be more efficient and versatile in map generation if it utilized local environmental things to help correlate information, as was done in Liu. (“Generating a map associated with the environment may include collecting sensor data received from one or more vehicles and generating a set of links that align the sensor data, see Liu abstract, published 2/9/2023).
Regarding claims 3 and 13:
The combination of Yang and Liu disclose the limitations of claims 1 and 9, respectively:
Liu further teaches:
receiving, by the system, position data describing a position on the roadway of a second vehicle carrying the second sensor, the second vehicle being associated with the first vehicle; and transforming, by the system, the position of the second vehicle in a coordinate system centered on a position sensor of the first vehicle using coordinate transformation based on the position data describing the position of the second vehicle and the position data describing the position of the first vehicle. (“The log data may additionally or alternatively include pose 314 associated with sensor data 316 , both of which represent the first pose 314 as the first vehicle or second vehicle traverses environment 300 . or by a second vehicle. Sensor data 312 and/or sensor data 316 may represent lidar data, depth camera data, and/or the like. In some examples, pose 310 and/or pose 314 may be part of a trajectory generated by the vehicle.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Yang to incorporate the teachings of Liu because Yang would be more efficient and versatile in map generation if it utilized local environmental things to help correlate information, as was done in Liu. (“Generating a map associated with the environment may include collecting sensor data received from one or more vehicles and generating a set of links that align the sensor data, see Liu abstract, published 2/9/2023).
Regarding claims 4 and 14:
The combination of Yang and Liu disclose the limitations of claims 3 and 13, respectively:
Liu further teaches:
time synchronizing, by the system, the position data describing the position of the first vehicle or the position data describing the position of the second vehicle with the data indicative of a friction condition. (“For example, the collection of sensor data may include first sensor data received from a vehicle associated with a portion of the environment and second sensor data associated with the same portion received from the vehicle at a subsequent time. , or second sensor data received from another vehicle. The first sensor data and the second sensor data may be associated with the same, similar, or different sensor poses. In other words, at the time the first sensor data is captured, the vehicle's sensor(s) are positioned and oriented in a first pose (i.e., pose may include position and/or orientation). and a second sensor data is captured while the sensor(s) of the vehicle (or another vehicle) are positioned in the same, similar, or different poses. In other words, parts of the environment may be visible from the same and different poses. In other words, at the time the first sensor data is captured, the vehicle's sensor(s) are positioned and oriented in a first pose (i.e., pose may include position and/or orientation). and a second sensor data is captured while the sensor(s) of the vehicle (or another vehicle) are positioned in the same, similar, or different poses.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Yang to incorporate the teachings of Liu because Yang would be more efficient and versatile in map generation if it utilized local environmental things to help correlate information, as was done in Liu. (“Generating a map associated with the environment may include collecting sensor data received from one or more vehicles and generating a set of links that align the sensor data, see Liu abstract, published 2/9/2023).
Regarding claims 5 and 15:
The combination of Yang and Liu disclose the limitations of claims 1 and 9, respectively:
Liu further teaches:
receiving, by the system, environment perception data from two distinct first sensors respectively; and expressing, by the system, the environment perception data of one of the two distinct first sensors in a coordinate system centered on another of the two distinct first sensors. (“Each of the lidar sensors can return data corresponding to detected surfaces in the environment. The data may be represented as points (eg, data points) having coordinates (eg, Cartesian coordinates, polar coordinates, etc.) corresponding to at least a portion of the detected surface.”, [paragraph 5 below “Description”]) and (“Determining a map of the environment using at least a portion of the collected sensor data includes determining a link between first sensor data and second sensor data of the collected sensor data. the link may indicate that the first sensor data and the second sensor data are associated with the same portion of the environment”, [paragraph 6 below “Description’]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Yang to incorporate the teachings of Liu because Yang would be more efficient and versatile in map generation if it utilized local environmental things to help correlate information, as was done in Liu. (“Generating a map associated with the environment may include collecting sensor data received from one or more vehicles and generating a set of links that align the sensor data, see Liu abstract, published 2/9/2023).
Regarding claims 6 and 16:
The combination of Yang and Liu disclose the limitations of claims 1 and 9, respectively:
Liu further teaches:
marking, by the system, a subset of the environment perception data corresponding to a portion of the roadway which has been covered by the second sensor. (“For example, the collection of sensor data may include first sensor data received from a vehicle associated with a portion of the environment and second sensor data associated with the same portion received from the vehicle at a subsequent time. , or second sensor data received from another vehicle. The first sensor data and the second sensor data may be associated with the same, similar, or different sensor poses. In other words, at the time the first sensor data is captured, the vehicle's sensor(s) are positioned and oriented in a first pose (i.e., pose may include position and/or orientation). and a second sensor data is captured while the sensor(s) of the vehicle (or another vehicle) are positioned in the same, similar, or different poses. In other words, parts of the environment may be visible from the same and different poses. In other words, at the time the first sensor data is captured, the vehicle's sensor(s) are positioned and oriented in a first pose (i.e., pose may include position and/or orientation). and a second sensor data is captured while the sensor(s) of the vehicle (or another vehicle) are positioned in the same, similar, or different poses. In other words, parts of the environment may be visible from the same and different poses. Thus, the second sensor data may be associated with the same portion of the environment as the first sensor data, but the second sensor data is at least a portion of the sensor data associated with an alternative or additional portion of that portion.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Yang to incorporate the teachings of Liu because Yang would be more efficient and versatile in map generation if it utilized local environmental things to help correlate information, as was done in Liu. (“Generating a map associated with the environment may include collecting sensor data received from one or more vehicles and generating a set of links that align the sensor data, see Liu abstract, published 2/9/2023).
Regarding claims 7, 10, and 19:
The combination of Yang and Liu disclose the limitations of claims 1, 9, and 18, respectively:
Liu further teaches:
wherein the second sensor comprises at least one of an infrared optical camera or a sensor wheel. (“In some examples, sensor 206 may represent sensor 104, lidar sensor, radar sensor, ultrasonic transducer, sonar sensor, position sensor (eg, global positioning system (GPS), compass, etc.), inertial sensor (eg, inertial measurement units (IMU), accelerometers, magnetometers, gyroscopes, etc.), image sensors (e.g., red-green-blue (RGB), infrared (IR), intensity, depth, time-of-flight cameras, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Yang to incorporate the teachings of Liu because Yang would be more efficient and versatile in map generation if it utilized local environmental things to help correlate information, as was done in Liu. (“Generating a map associated with the environment may include collecting sensor data received from one or more vehicles and generating a set of links that align the sensor data, see Liu abstract, published 2/9/2023).
Regarding claims 8, 11, and 20:
The combination of Yang and Liu disclose the limitations of claims 1, 9, and 18, respectively:
Liu further teaches:
wherein the second sensor is configured to be mounted on the first vehicle, connected to the first vehicle, or mounted on a second vehicle. (“Autonomous vehicle controller 147 may be further configured to determine a local pose (e.g., local position) of an autonomous vehicle 109 and to detect external objects relative to the vehicle. For example, consider that bidirectional autonomous vehicle 130 is traveling in the direction 119 in road network 110. A localizer (not shown) of autonomous vehicle controller 147 can determine a local pose at the geographic location 111. As such, the localizer may use acquired sensor data, such as sensor data associated with surfaces of buildings 115 and 117, which can be compared against reference data, such as map data (e.g., 3D map data, including reflectance data) to determine a local pose.”, [col. 6: 38 – 49]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Yang to incorporate the teachings of Liu because Yang would be more efficient and versatile in map generation if it utilized local environmental things to help correlate information, as was done in Liu. (“Generating a map associated with the environment may include collecting sensor data received from one or more vehicles and generating a set of links that align the sensor data, see Liu abstract, published 2/9/2023).
Regarding claim 12:
The combination of Yang and Liu disclose the limitations of claim 9:
Liu further teaches:
wherein the data processing apparatus is further configured to: transform the first position in a coordinate system centered on a position sensor of the first vehicle using coordinate transformation based on the position of the first vehicle. Examiner broadly interprets this limitation to include that a processer is involved in determining the position coordinates of the vehicle on a map using the acquired vehicle sensor data, … (“Each of the lidar sensors can return data corresponding to detected surfaces in the environment. The data may be represented as points (eg, data points) having coordinates (eg, Cartesian coordinates, polar coordinates, etc.) corresponding to at least a portion of the detected surface.”, [paragraph 5 below “Description”]) and (“Determining a map of the environment using at least a portion of the collected sensor data includes determining a link between first sensor data and second sensor data of the collected sensor data. the link may indicate that the first sensor data and the second sensor data are associated with the same portion of the environment”, [paragraph 6 below “Description’]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Yang to incorporate the teachings of Liu because Yang would be more efficient and versatile in map generation if it utilized local environmental things to help correlate information, as was done in Liu. (“Generating a map associated with the environment may include collecting sensor data received from one or more vehicles and generating a set of links that align the sensor data, see Liu abstract, published 2/9/2023).
Regarding claim 17:
The combination of Yang and Liu disclose the limitations of claim 9:
Liu further teaches:
wherein the environment perception data comprises training data or ground truth data for a machine learning unit or an artificial intelligence unit, wherein the machine learning unit or the artificial intelligence unit is configured to estimate a friction coefficient from environment perception data. (“Memory 220 and/or memory 224 may additionally or alternatively store collision avoidance systems, ride management systems, and the like. Although positioning component 226, perception component 228, planning component 230, map 234, and/or system controller 236 are illustrated as being stored in memory 220, any of these components may be processor-executable. may include instructions, machine learning models (e.g., neural networks), and/or hardware, and all or a portion of any of these components may be stored in memory 224 or stored in computing device 214.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Yang to incorporate the teachings of Liu because Yang would be more efficient and versatile in map generation if it utilized local environmental things to help correlate information, as was done in Liu. (“Generating a map associated with the environment may include collecting sensor data received from one or more vehicles and generating a set of links that align the sensor data, see Liu abstract, published 2/9/2023).
CONCLUSION
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form 892.
Levinson (US11106218B2) - Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide map data for autonomous vehicles. In particular, a method may include accessing subsets of multiple types of sensor data, aligning subsets of sensor data relative to a global coordinate system based on the multiple types of sensor data to form aligned sensor data, and generating datasets of three-dimensional map data. The method further includes detecting a change in data relative to at least two datasets of the three-dimensional map data and applying the change in data to form updated three-dimensional map data. The change in data may be representative of a state change of an environment at which the sensor data is sensed. The state change of the environment may be related to the presence or absences of an object located therein.
Harris (US20180188729A1) – Systems and methods relating to controlling a driving system operatively coupled to a vehicle are disclosed. A location is identified using one or more sensors included with the vehicle. An input of the driving system is identified using the location. A desired output of the driving system is determined using the input.
Lockwood (US10564638B1) - A driverless vehicle may include a processor, a sensor, a network interface, and a memory having stored thereon processor-executable instructions. The driverless vehicle may be configured to obtain a stream of sensor signals including sensor data related to operation of the driverless vehicle from the sensor and/or the network interface. The driverless vehicle may be configured to determine a confidence level associated with operation of the driverless vehicle from the sensor data, and store the confidence level and at least a portion of the sensor data. The driverless vehicle may also be configured to transmit via the network interface a request for teleoperator assistance, and the request may include the portion of the sensor data and the confidence level.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW COBB whose telephone number is (571) 272-3850. The examiner can normally be reached 9 - 5, M - F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan, can be reached at (571) 270-7016. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MATTHEW COBB/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661