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 .
Response to Amendment
After applicant’s amendments filed on 06/11/2025, the claim and specification objections have been withdrawn.
Applicant's arguments, see Applicant remarks, filed on 06/11/2025 regarding 35 U.S.C. 103 rejections have been fully considered but they are not persuasive.
On page 12, applicant’s remark states that the primary reference Verma’s location detection sensor does not include orientation of the vehicle.
While Verma discloses a position determining mechanism such as GNSS, and a gyroscope that could be used for some sort of orientation, it fails to specifically disclose the limitation where the location comprises a first and second position and orientation of the AV. However, new reference Hammond teaches a location determination mechanism of an autonomous vehicle that includes position and orientation data.(Hammond, paragraph 42, the first component 112 can process data received from the one or more sensor(s) 140 to determine at least a first pose (e.g., location and orientation) of the autonomous vehicle 124 at a particular time) (Hammond, paragraph 83, the system may compare the first pose to the second pose to determine one or more differences between the first pose and the second pose. The one or more differences can include location difference(s) and/or orientation difference(s)).
On page 12 and 13, applicant’ remark asserts that Verma is concerned with cell phones, tablets and such, and that there is no need to know the orientation of the device.
Cell phones, tablets, and such devices often can determine the orientation of a device because it allows them to rotate the screen or put the screen in a sleep mode, none the less Hammond teaches the new feature and its use. As disclosed above, new reference Hammond possess an orientation sensor, and discusses the use of orientation data to determine differences between two poses(orientations). This difference can help determine whether the vehicle has moved during data recording. (Hammond, paragraph 83, the system may compare the first pose to the second pose to determine one or more differences between the first pose and the second pose. The one or more differences can include location difference(s) and/or orientation difference(s))
On page 13, applicant further argued the examiner has not provided a reason for the matching and mismatching of position of the device.
However, as disclosed in the present application, one of the reason to know the position and orientation of the vehicle and evaluate the match or mismatch is to determine if the vehicle has moved during the power cycle. Similarly, Verma on paragraph 58 discloses determining whether the device has moved by comparing two position fixes. (Verma paragraph 58, determining whether the device was moving or stationary depended upon determining and comparing successive position fixes. If successive position fixes were from the same location, it was inferred that the device was stationary. If successive position fixes were from different locations, it was inferred that the device was in motion).
On page 13, applicant further argued that no reason was provided for incorporating Charan.
The examiner previously discussed in the rational of the non-final rejection that the reason for incorporating Charan is for its image comparison teaching. Image data is specifically important in Autonomous Vehicles because of its detailed visual information compared to other non-image sensor data. Additionally, by comparing two image data, we are able to determine if the vehicle has moved or not.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
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 1,2,6,7,10,11,15,16,19, and 20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Combination of Verma (US 20100149030 A1) (hereinafter Verma) in view of Adachi (US 20190154842 A1) (hereinafter Adachi) in further view of Charan (US 20220137219 A1) (hereinafter Charan) in further view of Hammond (US 20190138000 A1) (hereinafter Hammond).
Claim 1 Verma discloses A system(((FIG. 9 depicts an exemplary position determining system) comprising: a memory(Also coupled with bus 102 is a non-volatile read only memory (ROM) 103 for storing information and instructions of a more permanent nature, and a random-access memory (RAM) 104 for storing the digital information and instructions of a more volatile nature(paragraph 40)); and
one or more processors coupled to the memory, the one or more processors(paragraph 40 line 3, component 100 comprises a processor 101 coupled with an address/data bus. Line 7, Also coupled with bus 102 is a non-volatile read only memory (ROM) 103 for storing information) being configured to:
determine a first position of a device (page 2 paragraph 33, Embodiments of the present invention may be used to monitor the position of an electronic device (e.g., vehicles (paragraph 4)) (Verma does not teach this determining of position for autonomous vehicle) prior to a power cycle, wherein the power cycle begins when a computing system of the electronic device is one of powered off and offline and ends when the computing system of the electronic device is one of powered on and online(paragraph 53 details a deactivating and/or reactivating electronic device in full power-down event and/or re-energizing thereafter, device deactivated, as where wireless transmissions from device is disabled while within geo zone, and reactivating device after deactivation comprise restoring wireless transmission capability to device. In Paragraph 212, upon booting up, device 901 determines woke up and Wakeup logic 1700, then discusses provides control over power shutdowns for device 901. Page 9 paragraph 101 In one embodiment, initiating component 100 functions (e.g., is operated as, etc.) a state machine, which is persistent over power cycles. Such persistence allows initiating component 100, upon "waking" from a programmed sleep period, for instance, to know (e.g., be aware of, etc.) its current state, and thus take a step (e.g., action, etc.) appropriate for performance upon such waking, etc.),
receive, after the power cycle, sensor data from one or more sensors of the electronic device(On Page 4, paragraph 44 Verma discloses a position determining component 110, comprising a GNSS receiver 111 and a GNSS antenna 112, is also coupled with bus 102.Verma then teaches taking action after power cycle at Page 9 paragraph 101. Initiating component 100 functions (e.g., is operated as, etc.) a state machine, which is persistent over power cycles. Such persistence allows initiating component 100, upon "waking" from a programmed sleep period, for instance, to know (e.g., be aware of, etc.) its current state, and thus take a step (e.g., action, etc.) appropriate for performance upon such waking, etc. Verma does not teach the receiving of image data from sensors), the sensor data comprising image data; and
in response to a determination that the electronic device has completed the power cycle(Page 9 paragraph 101, initiating component 100 functions (e.g., is operated as, etc.) a state machine, which is persistent over power cycles. Such persistence allows initiating component 100, upon "waking" from a programmed sleep period, for instance, to know (e.g., be aware of, etc.) its current state, and thus take a step (e.g., action, etc.) appropriate for performance upon such waking, etc.),
Verma specifically fails to disclose a system being configured to: determine a first position of an autonomous vehicle (AV);
Vehicle position and wherein the first position comprises a first location and a first orientation of the AV;
receive, after the power cycle, sensor data from one or more sensors of the AV, the sensor data comprising image data;
determine, based on the image data from one or more sensors of the AV, whether a second position of the AV after the power cycle matches the first position of the AV prior to the power cycle, wherein the second position comprises a second location and a second orientation of the AV.
However, Adachi which is in the same analogous art and function for autonomous vehicle discloses the above unaddressed limitations. Adachi teaches about processors being configured to: determine a first position of an autonomous vehicle (AV) (the instructions when executed by a processor, cause the processor to perform steps comprising: initializing, by a processor of an autonomous vehicle configured to receive global navigation satellite system (GNSS) signals, a location of the autonomous vehicle and RTK corrections based on the location of the autonomous vehicle and raw GNSS signals from the location (page 11 paragraph 104). The location history record stores history of location from the point-in-time, when the car was turned off/stopped (page 4 paragraph 49).
receive, sensor data from one or more sensors of the AV, the sensor data comprising image data (The GNSS data processing module 290 receives 1030 sensor data from sensors mounted on vehicle, for example, lidar scans from lidar or camera images from cameras mounted on the vehicle (Adachi page 9 paragraph 90).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teaching of Verma’s full power-down event and/or re-energizing mechanism as the power cycle, its location comparison method as a matching mechanism, and Adachi’s teachings regarding autonomous vehicle position determination using different sensors. Similar to the claim, Verma also teaches waking up mechanism where the device takes action appropriate for performance upon such waking. For the matching or mismatch of position pre and after power cycle, Verma details a way of comparing coordinates of a geographic location of a device with a coordinate of pre-defined zones or locations. After the comparison of the locations, it will prompt the device to perform a specific action. The comparison implies the device’s coordinates must match the pre-defined zones’ location to proceed to the next step, however, if there is a mismatch while comparing the coordinates, it implies it will take a different step. For our case, the two positions would be the position before power cycle as the pre-defined location and after power cycle which will be the current location of the device. By incorporating the teaching of Verma’s position comparison and Adachi’s specificity towards retrieving location of autonomous vehicle, we are able to determine whether there is a match or mismatch of location of an AV before and after the power cycle.
The combination of Verma and Adachi specifically fail to disclose:
Vehicle position and wherein the first position comprises a first location and a first orientation of the AV; determine, based on the image data from one or more sensors of the AV, whether a second position of the AV after the power cycle matches the first position of the AV prior to the power cycle, wherein the second position comprises a second location and a second orientation of the AV.
However, Charan, which is an analogous art for autonomous vehicles, discloses the comparison of Lidar scan data (point cloud image) with a pre-identified information of geographic locations. It teaches techniques that include identifying, based on a comparison of the first LiDAR data point to at least one lidar data point of the plurality of lidar return points, and based on the scan lines determine a location of the autonomous vehicle. Determine, based on the image data from one or more sensors of the AV, whether a second position of the AV after the power cycle matches the first position of the AV(On Page 8 paragraph 91 the results of a lidar scan (e.g., LiDAR scan 800) are used by an autonomous vehicle for a localization process. That is, in an embodiment, the results of the LiDAR scan 800 are compared to pre-identified information of geographic locations. Based on this comparison, the autonomous vehicle is able to identify where the autonomous vehicle is currently located.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of both Verma and Adachi and combine it with Charan’s image comparison mechanism. Verma teaches the comparison of two positional information of a device retrieved from different sensors to determine if the two positions match or mismatch. Adachi teaches the determination of location of an AV through different sensors including image sensors such as camera and LiDAR. As Verma discussed it above, after the determination of the match/mismatch of device position, the system prompts the device to take a specific action. However, Verma fails to disclose image sensors to capture position of device. Adachi addresses this by its use of different location determining sensors. But Adachi fails to disclose the comparison of image data. However, Charan addresses this limitation by its teaching of Lidar comparison mechanism that compares image data retrieved by lidar sensors and comparing them with different pre-identified information of geographic locations, thus allowing an autonomous vehicle to identify its current location. It is an obvious modification to make to Verma’s coordinate comparison mechanism and include the teaching of Charan’s lidar data point (similar to coordinates) comparison so that it compares the images instead of non-image sensors used by Verma.
While the combination of Verma, Adachi, and Charan teach about determining location of an autonomous vehicle, and comparing location before and after power cycle, it specifically fails to disclose:Vehicle position and wherein the first position comprises a first location and a first orientation of the AV; whether a second position of the AV matches the first position of the AV, wherein the second position comprises a second location and a second orientation of the AV.
However, Hammond, which is an analogous art that teaches about redundant pose generation system, discloses position of a vehicle comprising position and orientation of a vehicle. Vehicle position and wherein the first position comprises a first location and a first orientation of the AV(Hammond, paragraph 42, the first component 112 can process data received from the one or more sensor(s) 140 to determine at least a first pose (e.g., location and orientation) of the autonomous vehicle 124 at a particular time); whether a second position of the AV matches the first position of the AV, wherein the second position comprises a second location and a second orientation of the AV(Hammond, paragraph 83, the system may compare the first pose to the second pose to determine one or more differences between the first pose and the second pose. The one or more differences can include location difference(s) and/or orientation difference(s)).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Verma, Adachi, and Charan with Hammond’s vehicle orientation comparison mechanism. While Verma teaches the determining and comparing of locations, it fails to include the orientation in its location determining mechanism. However, Hammond teaches the inclusion of pose(orientation ) of an autonomous vehicle. Furthermore, Hammond discloses a comparison system where is detects differences between two orientations(pose). In terms of the current application, the first pose can be assumed to be the sensor data before the power cycle and the second pose being the data after the power cycle. By using Hammond’s differentiating mechanism, we are able to determine if the two sensor data match or mismatch. Comparing of orientation data is helpful in determining if the vehicle has moved during its power cycle. A mismatch or orientation might indicate unrecorded movement of the vehicle or unfunctional sensor or hardware. The mismatch indicates the necessity of the sensor to gather new accurate data regarding orientation.
Regarding claim 2, the system of claim 1, wherein determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle comprises: (the combination of Verma, Adachi, Charan, and Hammond discloses the system of claim 1) determining, based on the image data, a respective position of the AV after the power cycle relative to one or more reference points within a scene (Adachi discloses similar reference points to determine position of an AV. (In an embodiment, the system determines location of the autonomous vehicle by determining one or more features describing the surrounding of the autonomous vehicle based on the sensor data and matching the features with features in the HD map data. (Page 2 paragraph 15). The surrounding features are described as landmark features such as road signs and traffic lights. Paragraph 14 further details the sensor data may be retrieved by Lidar sensor (3D image). In an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle.)
wherein the second position of the AV comprises the respective position of the AV relative to the one or more reference points within the scene (Adachi discloses similar reference points to determine position of an AV. (In an embodiment, the system determines location of the autonomous vehicle by determining one or more features describing the surrounding of the autonomous vehicle based on the sensor data and matching the features with features in the HD map data. (Page 2 paragraph 15). The surrounding features are described as landmark features such as road signs and traffic lights. Paragraph 14 further details the sensor data may be retrieved by Lidar sensors (3D image). In an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle), the one or more reference points within the scene being depicted in the image data (On Paragraph 14 Adachi further details the sensor data may be retrieved by Lidar sensor (3D image). In an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle); and
comparing the first position of the AV prior to the power cycle with the second position of the AV after the power cycle, (Verma on Page 2 Paragraph 34 In one embodiment, the geographic location is compared with the coordinates of pre-defined zones. Based upon the zone in which the electronic device is present, e.g., spatially or spatially and temporally, the controller generates a command for causing the electronic device to perform a specific action. Deactivating and/or reactivating electronic device in full power-down event and/or re-energizing thereafter is discussed by Verma (paragraph 53). The first position is before the deactivation and the second position after the reactivation.) wherein the first position of the AV comprises an additional respective position of the AV relative to the one or more reference points within the scene.( Adachi Page 2 paragraph 15, in an embodiment, the system determines location of the autonomous vehicle by determining one or more features describing the surrounding of the autonomous vehicle based on the sensor data and matching the features with features in the HD map data. Then Adachi on paragraph 14 further discloses in an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle. Page 6 paragraph 61, Adachi further discloses stop lines, yield lines, spatial and etc. as features to map to geographical regions)
Regarding claim 6, the system of claim 1, wherein determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle (the combination of Verma, Adachi, Charan, and Hammond discloses the system of claim 1) comprises:
determining, based on at least one of odometer data (The claim did not disclose a specific odometry tool, so it is assumed as a visual odometer. Adachi discloses an embodiment, where enhanced GNSS position estimates are fused with other sensor data, like inertial measurements unit data or visual odometry data, to create a new position estimate that is used to initialize the localization algorithms. (Page 2 paragraph 12)) the vehicle location determination module 940 uses image-based odometry to track features in successive images to estimate distance travelled and uses the estimate of distance travelled from the previous location to determine current location of the vehicle. (Adachi Page 9 paragraph 85)
and wheel encoder data, whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle (Adachi on Page 9 paragraph 85 discloses
the vehicle location determination module 940 uses image-based odometry to track features in successive images to estimate distance travelled and uses the estimate of distance travelled from the previous location to determine current location of the vehicle. The combination of Verma, Adachi, Charan, and Hammond do not specifically disclose the comparison of two odometer sets. However, as discussed above in claim 1, the combination of Verma, Adachi, Charan, and Hammond disclose the comparison of two Lidar image data, and the determination of match/mismatch between Lidar data before and after power cycle.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Verma, Adachi, Charan, and Hammond so that it compares two sets of odometer data sets rather than LiDAR data. With simple modification of LiDAR data comparison system, we are able to achieve the determination match/mismatch of odometer data. Furthermore, Adachi’s visual odometer teaching is able determine the distance travelled between two images (previous location and current location). For our case, it can be implemented so that the previous location can be registered as the image before the power cycle and the current image as image after the power cycle. If there is difference in distance estimate, it would imply there is a mismatch between odometer reading of pre power cycle and post power cycle.)
Regarding claim 7, the system of claim 6, wherein determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle (the combination of Verma, Adachi, Charan, and Hammond discloses the system in claim 1)comprises:
receiving a first set of odometer data captured by an odometer of the AV prior to the power cycle((Adachi on Page 9 paragraph 85 discloses the vehicle location determination module 940 uses image-based odometry to track features in successive images to estimate distance travelled and uses the estimate of distance travelled from the previous location to determine current location of the vehicle. Previous location would be the odometer data before the power cycle i.e., first set of odometer data);
receiving a second set of odometer data captured by the odometer of the AV after the power cycle (Adachi Page 9 paragraph 85, a vehicle location determination module 940 uses image-based odometry to track features in successive images to estimate distance travelled and uses the estimate of distance travelled from the previous location to determine current location of the vehicle. Current location would be the odometer data after the power cycle i.e., second set of odometer data. The second set of odometers will be initiated and detected after the "waking" from a programmed sleep period as discussed by Verma. Verma discussed the device being aware of its current state, and thus taking a step (i.e., detect second set of odometers) appropriate for performance upon such waking);
comparing the first set of odometer data captured by the odometer of the AV prior to the power cycle and the second set of odometer data captured by the odometer of the AV after the power cycle; (the combination of Verma, Adachi, Charan, and Hammond discloses this limitation in claim 6), furthermore(Adachi Page 9 paragraph 85, a vehicle location determination module 940 uses image-based odometry to track features in successive images to estimate distance travelled and uses the estimate of distance travelled from the previous location to determine current location of the vehicle) and based on the comparing of the first set of odometer data captured by the odometer of the AV prior to the power cycle and the second set of odometer data captured by the odometer of the AV after the power cycle, determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle. (The combination of Verma, Adachi, Charan, and Hammond discloses this limitation in claim 6. Furthermore, Adachi on Page 9 paragraph 85 discloses the vehicle location determination module 940 uses image-based odometry to track features in successive images to estimate distance travelled and uses the estimate of distance travelled from the previous location to determine current location of the vehicle. The visual odometer determines the distance travelled between two images data of locations (previous location and current location). For our case, it can be implemented so that the previous location can be registered as the image before the power cycle and the current image as image after the power cycle. If there is difference in distance estimate, it would imply there is a mismatch between odometer reading of pre power and post power cycle.)
Regarding Claim 10, A method(FIG. 9 depicts an exemplary position determining system) comprising:
determining a first position of a device (page 2 paragraph 33, Embodiments of the present invention may be used to monitor the position of an electronic device (e.g., vehicles (paragraph 4)) (Verma does not teach this determining of position for autonomous vehicle) prior to a power cycle, wherein the power cycle begins when a computing system of the electronic device is one of powered off and offline and ends when the computing system electronic device is one of powered on and online(paragraph 53 details a deactivating and/or reactivating electronic device in full power-down event and/or re-energizing thereafter, device deactivated, as where wireless transmissions from device is disabled while within geo zone, and reactivating device after deactivation comprise restoring wireless transmission capability to device. In Paragraph 212, upon booting up, device 901 determines woke up and Wakeup logic 1700, then discusses provides control over power shutdowns for device 901. Page 9 paragraph 101 In one embodiment, initiating component 100 functions (e.g., is operated as, etc.) a state machine, which is persistent over power cycles. Such persistence allows initiating component 100, upon "waking" from a programmed sleep period, for instance, to know (e.g., be aware of, etc.) its current state, and thus take a step (e.g., action, etc.) appropriate for performance upon such waking, etc.);
receiving, after the power cycle, sensor data from one or more sensors of the electronic device; (On Page 4, paragraph 44 Verma discloses a position determining component 110, comprising a GNSS receiver 111 and a GNSS antenna 112, is also coupled with bus 102.Verma then teaches taking action after power cycle at Page 9 paragraph 101. Initiating component 100 functions (e.g., is operated as, etc.) a state machine, which is persistent over power cycles. Such persistence allows initiating component 100, upon "waking" from a programmed sleep period, for instance, to know (e.g., be aware of, etc.) its current state, and thus take a step (e.g., action, etc.) appropriate for performance upon such waking, etc. Verma does not teach the receiving of image data from sensors)and
in response to a determination that the electronic device has completed the power cycle (Page 9 paragraph 101, initiating component 100 functions (e.g., is operated as, etc.) a state machine, which is persistent over power cycles. Such persistence allows initiating component 100, upon "waking" from a programmed sleep period, for instance, to know (e.g., be aware of, etc.) its current state, and thus take a step (e.g., action, etc.) appropriate for performance upon such waking, etc.)
Verma specifically fails to disclose being configured to: determine a first position of an autonomous vehicle (AV);
receive, after the power cycle, sensor data from one or more sensors of the AV,
determining, based on the sensor data from one or more sensors of the AV, whether a second position of the AV after the power cycle matches the first position of the AV prior to the power cycle.
However, Adachi which is in the same analogous art and function for autonomous vehicle discloses the above unaddressed limitations. Adachi teaches about processors being configured to: determine a first position of an autonomous vehicle (AV) (the instructions when executed by a processor, cause the processor to perform steps comprising: initializing, by a processor of an autonomous vehicle configured to receive global navigation satellite system (GNSS) signals, a location of the autonomous vehicle and RTK corrections based on the location of the autonomous vehicle and raw GNSS signals from the location (page 11 paragraph 104). The location history record stores history of location from the point-in-time, when the car was turned off/stopped (page 4 paragraph 49)receive, sensor data from one or more sensors of the AV, (The GNSS data processing module 290 receives 1030 sensor data from sensors mounted on vehicle, for example, lidar scans from lidar or camera images from cameras mounted on the vehicle (Adachi page 9 paragraph 90).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teaching of Verma’s full power-down event and/or re-energizing mechanism as the power cycle, its location comparison method as a matching mechanism, and Adachi’s teachings regarding autonomous vehicle position determination using different sensors. Similar to the claim, Verma also teaches waking up mechanism where the device takes action appropriate for performance upon such waking. For the matching or mismatch of position pre and after power cycle, Verma details a way of comparing coordinates of a geographic location of a device with a coordinate of pre-defined zones or locations. After the comparison of the locations, it will prompt the device to perform a specific action. The comparison implies the device’s coordinates must match the pre-defined zones’ location to proceed to the next step, however, if there is a mismatch while comparing the coordinates, it implies it will take a different step. For our case, the two positions would be the position before power cycle as the pre-defined location and after power cycle which will be the current location of the device. By incorporating the teaching of Verma’s position comparison and Adachi’s specificity towards retrieving location of autonomous vehicle, we are able to determine whether there is a match or mismatch of location of an AV before and after the power cycle
The combination of Verma and Adachi specifically fail to disclose:
determining, based on sensor data from one or more sensors of the AV, whether a second position of the AV after the power cycle matches the first position of the AV prior to the power cycle. However, Charan, which is an analogous art for autonomous vehicles, discloses the comparison of Lidar scan data (point cloud image) with a pre-identified information of geographic locations. It teaches techniques that include identifying, based on a comparison of the first LiDAR data point to at least one lidar data point of the plurality of lidar return points, and based on the scan lines determine a location of the autonomous vehicle. Determine, based on the sensor data from one or more sensors of the AV, whether a second position of the AV matches the first position of the AV. (On Page 8 paragraph 91 the results of a lidar scan (e.g., LiDAR scan 800) are used by an autonomous vehicle for a localization process. That is, in an embodiment, the results of the LiDAR scan 800 are compared to pre-identified information of geographic locations. Based on this comparison, the autonomous vehicle is able to identify where the autonomous vehicle is currently located.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of both Verma and Adachi and combine it with Charan’s LiDAR data comparison mechanism. Verma teaches the comparison of two positional information of a device retrieved from different sensors to determine if the two positions match or mismatch. Adachi teaches the determination of location of an AV through different sensors including image sensors such as camera and LiDAR. As Verma discussed it above, after the determination of the match/mismatch of device position, the system prompts the device to take a specific action. Charan teaches Lidar data comparison mechanism that compares LiDAR data retrieved by lidar sensors and comparing them with different pre-identified information of geographic locations, thus allowing an autonomous vehicle to identify its current location. It is an obvious modification to make to Verma’s coordinate comparison mechanism and include the teaching of Charan’s lidar data point (similar to coordinates) comparison so that it compares the LiDAR data.
Regarding claim 11, The method of claim 10, wherein determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle comprises: (the combination of Verma, Adachi, Hammond and Charan discloses the system of claim 10)
determining, based on the sensor data, a respective position of the AV after the power cycle relative to one or more reference points within a scene (Adachi discloses similar reference points to determine position of an AV. (In an embodiment, the system determines location of the autonomous vehicle by determining one or more features describing the surrounding of the autonomous vehicle based on the sensor data and matching the features with features in the HD map data. (Page 2 paragraph 15). The surrounding features are described as landmark features such as road signs and traffic lights. Paragraph 14 further details the sensor data may be retrieved by Lidar sensor (3D image). In an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle.), wherein the second position of the AV comprises the respective position of the AV relative to the one or more reference points within the scene, (Adachi discloses similar reference points to determine position of an AV. (In an embodiment, the system determines location of the autonomous vehicle by determining one or more features describing the surrounding of the autonomous vehicle based on the sensor data and matching the features with features in the HD map data. (Page 2 paragraph 15). The surrounding features are described as landmark features such as road signs and traffic lights. Paragraph 14 further details the sensor data may be retrieved by Lidar sensors (3D image). In an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle), the one or more reference points within the scene being depicted in the sensor data; In Paragraph 14 Adachi further details the sensor data may be retrieved by Lidar sensor (3D image). In an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle) and
comparing the first position of the AV prior to the power cycle with the second position of the AV after the power cycle, (Verma on Page 2 Paragraph 34 In one embodiment, the geographic location is compared with the coordinates of pre-defined zones. Based upon the zone in which the electronic device is present, e.g., spatially or spatially and temporally, the controller generates a command for causing the electronic device to perform a specific action. Deactivating and/or reactivating electronic device in full power-down event and/or re-energizing thereafter is discussed by Verma (paragraph 53). The first position is before the deactivation and the second position after the reactivation.) wherein the first position of the AV comprises an additional respective position of the AV relative to the one or more reference points within the scene.( Adachi Page 2 paragraph 15, in an embodiment, the system determines location of the autonomous vehicle by determining one or more features describing the surrounding of the autonomous vehicle based on the sensor data and matching the features with features in the HD map data. Then Adachi on paragraph 14 further discloses in an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle. Page 6 paragraph 61, Adachi further discloses stop lines, yield lines, spatial and etc. as features to map to geographical regions)
Regarding Claim 15, The method of claim 10, wherein determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle comprises: (the combination of Verma, Adachi, Hammond and Charan discloses the system of claim 10)
determining, based on at least one of odometer data (The claim did not disclose a specific odometry tool, so it is assumed as a visual odometer. Adachi discloses an embodiment, where enhanced GNSS position estimates are fused with other sensor data, like inertial measurements unit data or visual odometry data, to create a new position estimate that is used to initialize the localization algorithms. (Page 2 paragraph 12))and wheel encoder data, whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle (Adachi on Page 9 paragraph 85 discloses
the vehicle location determination module 940 uses image-based odometry to track features in successive images to estimate distance travelled and uses the estimate of distance travelled from the previous location to determine current location of the vehicle.
The combination of Verma, Adachi and Charan do not specifically disclose the comparison of two odometer sets. However, as discussed above in claim 10, the combination of Verma, Adachi and Charan disclose the comparison of two Lidar image data, and the determination of match/mismatch between Lidar data before and after power cycle.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Verma, Adachi, and Charan so that it compares two sets of odometer data sets rather than LiDAR data. With simple modification of LiDAR data comparison system, we are able to achieve the determination match/mismatch of odometer data. Furthermore, Adachi’s visual odometer teaching is able determine the distance travelled between two images (previous location and current location). For our case, it can be implemented so that the previous location can be registered as the image before the power cycle and the current image as image after the power cycle. If there is difference in distance estimate, it would imply there is a mismatch between odometer reading of pre power cycle and post power cycle.)
Regarding Claim 16, The method of claim 15, wherein determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle (the combination of Verma, Adachi, Hammond, and Charan discloses the system in claim 10)comprises:
receiving a first set of odometer data captured by an odometer of the AV prior to the power cycle((Adachi on Page 9 paragraph 85 discloses the vehicle location determination module 940 uses image-based odometry to track features in successive images to estimate distance travelled and uses the estimate of distance travelled from the previous location to determine current location of the vehicle. Previous location would be the odometer data before the power cycle i.e., first set of odometer data);
receiving a second set of odometer data captured by the odometer of the AV after the power cycle; (Adachi Page 9 paragraph 85, a vehicle location determination module 940 uses image-based odometry to track features in successive images to estimate distance travelled and uses the estimate of distance travelled from the previous location to determine current location of the vehicle. Current location would be the odometer data after the power cycle i.e., second set of odometer data. The second set of odometers will be initiated and detected after the "waking" from a programmed sleep period as discussed by Verma. Verma discussed the device being aware of its current state, and thus taking a step (i.e., detect second set of odometers) appropriate for performance upon such waking);
comparing the first set of odometer data captured by the odometer of the AV prior to the power cycle and the second set of odometer data captured by the odometer of the AV after the power cycle; (the combination of Verma, Adachi, Hammond, and Charan discloses this limitation in claim 15), furthermore(Adachi Page 9 paragraph 85, a vehicle location determination module 940 uses image-based odometry to track features in successive images to estimate distance travelled and uses the estimate of distance travelled from the previous location to determine current location of the vehicle)and
based on the comparing of the first set of odometer data captured by the odometer of the AV prior to the power cycle and the second set of odometer data captured by the odometer of the AV after the power cycle, determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle. (The combination of Verma, Adachi, Hammond, and Charan discloses this limitation in claim 15. Furthermore, Adachi on Page 9 paragraph 85 discloses the vehicle location determination module 940 uses image-based odometry to track features in successive images to estimate distance travelled and uses the estimate of distance travelled from the previous location to determine current location of the vehicle. The visual odometer determines the distance travelled between two images data of locations (previous location and current location). For our case, it can be implemented so that the previous location can be registered as the image before the power cycle and the current image as image after the power cycle. If there is difference in distance estimate, it would imply there is a mismatch between odometer reading of pre power and post power cycle.)
Regarding claim 19, A non-transitory computer-readable medium having stored thereon instructions which, when executed by one or more processors, cause the one or more processors (FIG. 9 depicts an exemplary position determining system. Also coupled with bus 102 is a non-volatile read only memory (ROM) 103 for storing information and instructions of a more permanent nature, and a random-access memory (RAM) 104 for storing the digital information and instructions of a more volatile nature (paragraph 40). paragraph 40 line 3, component 100 comprises a processor 101 coupled with an address/data bus. Line 7, Also coupled with bus 102 is a non-volatile read only memory (ROM) 103 for storing information) to: determine a first position of an a device (page 2 paragraph 33, Embodiments of the present invention may be used to monitor the position of an electronic device (e.g., vehicles (paragraph 4)) (Verma does not teach this determining of position for autonomous vehicle)prior to a power cycle, wherein the power cycle begins when a computing system of the electronic device is one of powered off and offline and ends when the computing system of the electronic device is one of powered on and offline(paragraph 53 details a deactivating and/or reactivating electronic device in full power-down event and/or re-energizing thereafter, device deactivated, as where wireless transmissions from device is disabled while within geo zone, and reactivating device after deactivation comprise restoring wireless transmission capability to device. In Paragraph 212, upon booting up, device 901 determines woke up and Wakeup logic 1700, then discusses provides control over power shutdowns for device 901. Page 9 paragraph 101 In one embodiment, initiating component 100 functions (e.g., is operated as, etc.) a state machine, which is persistent over power cycles. Such persistence allows initiating component 100, upon "waking" from a programmed sleep period, for instance, to know (e.g., be aware of, etc.) its current state, and thus take a step (e.g., action, etc.) appropriate for performance upon such waking, etc.);
receive, after the power cycle, sensor data from one or more sensors of the electronic device; (On Page 4, paragraph 44 Verma discloses a position determining component 110, comprising a GNSS receiver 111 and a GNSS antenna 112, is also coupled with bus 102.Verma then teaches taking action after power cycle at Page 9 paragraph 101. Initiating component 100 functions (e.g., is operated as, etc.) a state machine, which is persistent over power cycles. Such persistence allows initiating component 100, upon "waking" from a programmed sleep period, for instance, to know (e.g., be aware of, etc.) its current state, and thus take a step (e.g., action, etc.) appropriate for performance upon such waking, etc. Verma does not teach the receiving of image data from sensors);
and
in response to a determination that the electronic device has completed the power cycle (Page 9 paragraph 101, initiating component 100 functions (e.g., is operated as, etc.) a state machine, which is persistent over power cycles. Such persistence allows initiating component 100, upon "waking" from a programmed sleep period, for instance, to know (e.g., be aware of, etc.) its current state, and thus take a step (e.g., action, etc.) appropriate for performance upon such waking, etc.) Verma specifically fails to disclose being configured to: determine a first position of an autonomous vehicle (AV);
receive, after the power cycle, sensor data from one or more sensors of the AV, the sensor data comprising Light Detection and Ranging (LIDAR) data;
determine, based on the LiDAR data from one or more sensors of the AV, whether a second position of the AV after the power cycle matches the first position of the AV prior to the power cycle
However, Adachi which is in the same analogous art and function for autonomous vehicle discloses the above unaddressed limitations. Adachi teaches about processors being configured to: determine a first position of an autonomous vehicle (AV) (the instructions when executed by a processor, cause the processor to perform steps comprising: initializing, by a processor of an autonomous vehicle configured to receive global navigation satellite system (GNSS) signals, a location of the autonomous vehicle and RTK corrections based on the location of the autonomous vehicle and raw GNSS signals from the location (page 11 paragraph 104). The location history record stores history of location from the point-in-time, when the car was turned off/stopped (page 4 paragraph 49)receive, sensor data from one or more sensors of the AV, the sensor data comprising Light Detection and Ranging (LIDAR) data (The GNSS data processing module 290 receives 1030 sensor data from sensors mounted on vehicle, for example, lidar scans from lidar or camera images from cameras mounted on the vehicle (Adachi page 9 paragraph 90).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teaching of Verma’s full power-down event and/or re-energizing mechanism as the power cycle, its location comparison method as a matching mechanism, and Adachi’s teachings regarding autonomous vehicle position determination using different sensors. Similar to the claim, Verma also teaches waking up mechanism where the device takes action appropriate for performance upon such waking. For the matching or mismatch of position pre and after power cycle, Verma details a way of comparing coordinates of a geographic location of a device with a coordinate of pre-defined zones or locations. After the comparison of the locations, it will prompt the device to perform a specific action. The comparison implies the device’s coordinates must match the pre-defined zones’ location to proceed to the next step, however, if there is a mismatch while comparing the coordinates, it implies it will take a different step. For our case, the two positions would be the position before power cycle as the pre-defined location and after power cycle which will be the current location of the device. By incorporating the teaching of Verma’s position comparison and Adachi’s specificity towards retrieving location of autonomous vehicle, we are able to determine whether there is a match or mismatch of location of an AV before and after the power cycle.
The combination of Verma and Adachi specifically fail to disclose:
determine, based on the LiDAR data from one or more sensors of the AV, whether a second position of the AV after the power cycle matches the first position of the AV prior to the power cycle.
However, Charan, which is an analogous art for autonomous vehicles, discloses the comparison of Lidar scan data (point cloud image) with a pre-identified information of geographic locations. It teaches techniques that include identifying, based on a comparison of the first LiDAR data point to at least one lidar data point of the plurality of lidar return points, and based on the scan lines determine a location of the autonomous vehicle. Determine, based on the LIDAR data from one or more sensors of the AV, whether a second position of the AV matches the first position of the AV. (On Page 8 paragraph 91 the results of a lidar scan (e.g., LiDAR scan 800) are used by an autonomous vehicle for a localization process. That is, in an embodiment, the results of the LiDAR scan 800 are compared to pre-identified information of geographic locations. Based on this comparison, the autonomous vehicle is able to identify where the autonomous vehicle is currently located.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of both Verma and Adachi and combine it with Charan’s LiDAR data comparison mechanism. Verma teaches the comparison of two positional information of a device retrieved from different sensors to determine if the two positions match or mismatch. Adachi teaches the determination of location of an AV through different sensors including image sensors such as camera and LiDAR. As Verma discussed it above, after the determination of the match/mismatch of device position, the system prompts the device to take a specific action. However, Verma fails to disclose LiDAR sensors to capture position of device. Adachi addresses this by its use of different location determining sensors. But Adachi fails to disclose the comparison of LiDAR data. However, Charan addresses this limitation by its teaching of Lidar comparison mechanism that compares image data retrieved by lidar sensors and comparing them with different pre-identified information of geographic locations, thus allowing an autonomous vehicle to identify its current location. It is an obvious modification to make to Verma’s coordinate comparison mechanism and include the teaching of Charan’s lidar data point (similar to coordinates) comparison so that it compares the LiDAR data instead of non-image sensors used by Verma.
Regarding Claim 20, The non-transitory computer-readable medium of claim 19, wherein determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle (the combination of Verma, Adachi, Hammond, and Charan discloses the system of claim 19) comprises:
determining, based on the LIDAR data, a respective position of the AV after the power cycle relative to one or more reference points within a scene, (Adachi discloses similar reference points to determine position of an AV. (In an embodiment, the system determines location of the autonomous vehicle by determining one or more features describing the surrounding of the autonomous vehicle based on the sensor data and matching the features with features in the HD map data. (Page 2 paragraph 15). The surrounding features are described as landmark features such as road signs and traffic lights. Paragraph 14 further details the sensor data may be retrieved by Lidar sensor (3D image). In an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle.) wherein the second position of the AV comprises the respective position of the AV relative to the one or more reference points within the scene, (Adachi discloses similar reference points to determine position of an AV. (In an embodiment, the system determines location of the autonomous vehicle by determining one or more features describing the surrounding of the autonomous vehicle based on the sensor data and matching the features with features in the HD map data. (Page 2 paragraph 15). The surrounding features are described as landmark features such as road signs and traffic lights. Paragraph 14 further details the sensor data may be retrieved by Lidar sensors (3D image). In an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle) the one or more reference points within the scene being depicted in the LIDAR data (In Paragraph 14 Adachi further details the sensor data may be retrieved by Lidar sensor (3D image). In an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle); and
comparing the first position of the AV prior to the power cycle with the second position of the AV after the power cycle, (Verma on Page 2 Paragraph 34 In one embodiment, the geographic location is compared with the coordinates of pre-defined zones. Based upon the zone in which the electronic device is present, e.g., spatially or spatially and temporally, the controller generates a command for causing the electronic device to perform a specific action. Deactivating and/or reactivating electronic device in full power-down event and/or re-energizing thereafter is discussed by Verma (paragraph 53). The first position is before the deactivation and the second position after the reactivation.) wherein the first position of the AV comprises an additional respective position of the AV relative to the one or more reference points within the scene. (Adachi Page 2 paragraph 15, in an embodiment, the system determines location of the autonomous vehicle by determining one or more features describing the surrounding of the autonomous vehicle based on the sensor data and matching the features with features in the HD map data. Then Adachi on paragraph 14 further discloses in an embodiment, the system determines location of the autonomous vehicle by determining a point cloud based on sensor data, for example, lidar data. The system determines a point cloud of a geographical region surrounding the autonomous vehicle based on the HD map data. The system aligns the two-point clouds to determine the location of the vehicle)
Claims 3,4,5,12,13, and 14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Combination of Verma (US 20100149030 A1) (hereinafter Verma) in view of Adachi (US 20190154842 A1) (hereinafter Adachi) in further view of Charan (US 20220137219 A1) (hereinafter Charan) in further view of Hammond(US 10831188 B2) (hereinafter Hammond) in further view of Zheng (US 20210263166 A1) (hereinafter Zheng)
Regarding claim 3, the system of claim 1, wherein the one or more processors are configured to:
in response to determining the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle(the combination of Verma, Adachi, Charan, and Hammond discloses the system of claim 1) The combination of Verma, Adachi, Charan, and Hammond does not specifically disclose control an operation of the AV based on the first position of the AV. However, Zheng, which is an analogous art that teaches about relative location determination for autonomous vehicles, discloses the controlling of operation of an AV. As disclosed in claim 4, controlling an operation of the AV is navigating an AV and planning a route of an AV. Control an operation of the AV based on the first position of the AV (Zheng on page 10 paragraph 64 discusses vehicle navigation and planning. The output from block 314 may be provided to prediction and planning block 318, which determines detected objects and vehicles and their associated trajectory via block 320 and determines vehicle maneuver and path planning in block 322, the outputs of which are utilized in block 326 vehicle maneuver execution either directly or via V2X inter-vehicle negotiation block. Zheng on page 4 paragraph 27 further discloses, in an embodiment, the shared GNSS information, such as that contained in the above-mentioned data elements, may be and utilized for relative positioning between vehicles and/or for vehicle control and maneuvering.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Verma, Adachi, Charan, and Hammond and incorporate Zheng’s teaching of controlling of operation of an AV based on the first position. Zheng teaching about vehicle maneuver and path planning of an AV. As discussed above by Verma, the first position is the position before the power the cycle. It would have been an obvious modification to add the operation of an autonomous vehicle taught by Zheng to add the controlling and maneuvering feature. Regarding claim 4, the system of claim 3, wherein controlling the operation of the AV based on the first position of the AV (The combination of Verma, Adachi, Charan, Hammond, and Zheng disclose this limitation in claim 3. page 10 paragraph 64 and page 4 paragraph 27) prior to the power cycle comprises at least one of navigating the AV and planning a route of the AV (The combination of Verma, Adachi, Charan, and Zheng disclose this limitation in claim 3)
Regarding claim 5, the system of claim 1, wherein the one or more processors are configured to:
in response to determining the second position and the second orientation of the AV after the power cycle does not match the first position and the first orientation of the AV prior to the power cycle (the combination of Verma, Adachi, Charan, and Hammond disclose the system of claim 1)
The combination of Verma, Adachi, Charan, and Hammond do not specifically disclose determine a location and orientation of the AV after the power cycle based on the sensor data;
wherein the sensor data further comprises at least one of a signal from a Global Positioning System (GPS), a signal from a Radio Detection and Ranging (RADAR) sensor, a signal from a Light Detection and Ranging (LiDAR) sensor, one or more measurements from an Inertial Measurement Unit (IMU), a signal from an acoustic sensor, and a signal from a time of flight sensor.
However, Zheng, which is an analogous art that teaches about relative location determination for autonomous vehicles, discloses determine a location of the AV after the power cycle based on the sensor data (Zheng on page 2 paragraph 28 discloses gyro sensors which are used to determine the angle of a device, in addition to location determining technologies such as SONAR, RADAR and/or LIDAR. A GNSS-based absolute location may be verified and/or corrected using other technologies such as dead reckoning information from distance sensors (wheel ticks, etc.), accelerometer and gyro measurements, camera information, SONAR, RADAR and/or LIDAR or other both absolute and relative positioning technologies, possibly used in conjunction with points of reference such as landmarks or roadside units). wherein the sensor data further comprises at least one of a signal from a Global Positioning System (GPS), a signal from a Radio Detection and Ranging (RADAR) sensor, a signal from a Light Detection and Ranging (LiDAR) sensor (Zheng Paragraph 28 A GNSS-based absolute location may be verified and/or corrected using other technologies such as dead reckoning information from distance sensors (wheel ticks, etc.), accelerometer and gyro measurements, camera information, SONAR, RADAR and/or LIDAR or other both absolute and relative positioning technologies, possibly used in conjunction with points of reference such as landmarks or roadside units), one or more measurements from an Inertial Measurement Unit (IMU), a signal from an acoustic sensor, and a signal from a time of flight sensor.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Verma, Adachi, Charan, Hammond and incorporate Zheng’s teachings. For determine a location and orientation of the AV after the power cycle based on the sensor data, it is a simple addition of a sensor for determining the location of an AV. And the performing of the localization after the power cycle is discussed by Verma’s taking action after “waking” up mechanism. Regarding wherein sensor data further comprises at least one signal from different sensors, Zheng discloses different types of sensors that determine the location of an AV. It also discloses LiDAR sensors that can be used for determining relative location of AVs.
Regarding Claim 12, The method of claim 10, further comprising: in response to determining the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle (the combination of Verma, Adachi, and Charan discloses the system of claim 10),
The combination of Verma, Adachi and Charan do not specifically disclose control an operation of the AV based on the first position of the AV. However, Zheng, which is an analogous art that teaches about relative location determination for autonomous vehicles, discloses the controlling of operation of an AV. As disclosed in claim 4, controlling an operation of the AV is navigating an AV and planning a route of an AV. Control an operation of the AV based on the first position of the AV (Zheng on page 10 paragraph 64 discusses vehicle navigation and planning. The output from block 314 may be provided to prediction and planning block 318, which determines detected objects and vehicles and their associated trajectory via block 320 and determines vehicle maneuver and path planning in block 322, the outputs of which are utilized in block 326 vehicle maneuver execution either directly or via V2X inter-vehicle negotiation block. Zheng on page 4 paragraph 27 further discloses, in an embodiment, the shared GNSS information, such as that contained in the above-mentioned data elements, may be and utilized for relative positioning between vehicles and/or for vehicle control and maneuvering.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Verma, Adachi, Hammond, and Charan by incorporated Zheng’s teaching of controlling of operation of an AV based on the first position. Zheng teaching about vehicle maneuver and path planning of an AV. As discussed above by Verma, the first position is the position before the power the cycle. It would have been an obvious modification to add the operation of an autonomous vehicle taught by Zheng to add the controlling and maneuvering feature. Regarding claim 13, The method of claim 12, wherein controlling the operation of the AV based on the first position of the AV The combination of Verma, Adachi, Charan, Hammond, and Zheng disclose this limitation in claim 12. page 10 paragraph 64 and page 4 paragraph 27 prior to the power cycle comprises at least one of navigating the AV and planning a route of the AV (The combination of Verma, Adachi, Hammond, Charan, and Zheng disclose this limitation in claim 12)
Regarding claim 14, The method of claim 10, further comprising: in response to determining the second position of the AV after the power cycle does not match the first position of the AV prior to the power cycle (the combination of Verma, Adachi, Hammond, and Charan discloses the system of claim 10),
The combination of Verma, Adachi and Charan does not specifically disclose determining a location and orientation of the AV after the power cycle based on the sensor data, wherein the sensor data further comprises at least one of a signal from a Global Positioning System (GPS), a signal from a Radio Detection and Ranging (RADAR) sensor, a signal from a Light Detection and Ranging (LiDAR) sensor, one or more measurements from an Inertial Measurement Unit (IMU), a signal from an acoustic sensor, and a signal from a time of flight sensor.
However, Zheng, which is an analogous art that teaches about relative location determination for autonomous vehicles, discloses determining a location and orientation of the AV after the power cycle based on the sensor data (Zheng on page 2 paragraph 28 discloses gyro sensors which are used to determine the angle(orientation) of a device, in addition to location determining technologies such as SONAR, RADAR and/or LIDAR. A GNSS-based absolute location may be verified and/or corrected using other technologies such as dead reckoning information from distance sensors (wheel ticks, etc.), accelerometer and gyro measurements, camera information, SONAR, RADAR and/or LIDAR or other both absolute and relative positioning technologies, possibly used in conjunction with points of reference such as landmarks or roadside units). wherein the sensor data further comprises at least one of a signal from a Global Positioning System (GPS), a signal from a Radio Detection and Ranging (RADAR) sensor, a signal from a Light Detection and Ranging (LiDAR) sensor (Zheng Paragraph 28 A GNSS-based absolute location may be verified and/or corrected using other technologies such as dead reckoning information from distance sensors (wheel ticks, etc.), accelerometer and gyro measurements, camera information, SONAR, RADAR and/or LIDAR or other both absolute and relative positioning technologies, possibly used in conjunction with points of reference such as landmarks or roadside units), one or more measurements from an Inertial Measurement Unit (IMU), a signal from an acoustic sensor, and a signal from a time of flight sensor.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Verma, Adachi, Hammond, and Charan, and incorporated Zheng’s teachings. For determining a location and orientation of the AV after the power cycle based on the sensor data, it is a simple addition of a sensor for determining the location of an AV as disclosed by Hammond on paragraph 42. And the performing of the localization after the power cycle is discussed by Verma’s taking action after “waking” up mechanism. Regarding wherein sensor data further comprises at least one signal from different sensors, Zheng discloses different types of sensors that determine the location of an AV. It also discloses LiDAR sensors that can be used for determining relative location of AVs.
Claims 8,9,17, and 18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Combination of Verma (US 20100149030 A1) in view of Adachi (US 20190154842 A1) (hereinafter Adachi) in further view of Charan (US 20220137219 A1) (hereinafter Charan) in further view of Hammond(US 10831188 B2) (hereinafter Hammond) in further view of Zheng (US 20210263166 A1) (hereinafter Zheng) in further view of Patel (US 20190242743 A1) (hereinafter Patel). Regarding claim 8, the system of claim 6, wherein determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle(the combination of Verma, Adachi, Charan, Hammond, and Zheng discloses the system of claim 6)
comprises:
receiving a first set of LiDAR image data of the AV prior to the power cycle;( The combination of Verma, Adachi, Charan, Hammond, and Zheng discloses the retrieval of LiDAR data before power cycle, but fails to disclose a wheel encoder to capture wheel encoder data)
receiving a second set of LiDAR image data of the AV after the power cycle; (The combination of Verma, Adachi, Charan, Hammond, and Zheng discloses the retrieval of LiDAR data after power cycle, but fails to disclose a wheel encoder to capture wheel encoder data )
comparing the first set of LiDAR image data of the AV prior to the power cycle and the second set of LiDAR image data of the AV after the power cycle(The combination of Verma, Adachi, Hammond and Charan discloses the comparison of LiDAR data before power cycle, but does not teach the comparison of wheel encoder data); and
based on the comparing of the first set of LiDAR image data of the AV prior to the power cycle and the second set of LiDAR image data of the AV after the power cycle, determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle.( The combination of Verma, Adachi, Charan, Hammond, and Zheng discloses the determination of a match/mismatch of LiDAR data before and after power cycle, but it does not teach a determination of match or mismatch of wheel encoder data).
The combination of Verma, Adachi, Charan, Hammond, and Zheng specifically fail to disclose receiving a first and second set of wheel encoder data captured by a wheel encoder and performing a comparison;
However Patel which is an analogous art that teaches about vehicle localization precision enhancement methods, discloses receiving a first set of wheel encoder data captured by a wheel encoder of the AV ( On Page 4 Paragraph 36 Patel discloses the movement module 272 configures the processor to receive movement data from one or more wheel encoders to determine the position of the robotic vehicle ); receiving a second set of wheel encoder data captured by the wheel encoder of the AV after the power cycle(Patel on page 4 paragraph 36 discloses the movement module 272 configures the processor to receive movement data from one or more wheel encoders to determine the position of the robotic vehicle. comparing the first set of wheel encoder data captured by the wheel encoder of the AV and the second set of wheel encoder data captured by the wheel encoder of the AV, (Patel on page 7 paragraph 63 discloses that information is then used to determine along what angle the vehicle was moving by comparing the wheel encoder readings to this distance measurement relative to the ground)
and based on the comparing of the first set of wheel encoder data captured by the wheel encoder of the AV, determining whether the second position of the AV matches the first position of the AV (Patel on page 7 paragraph 63 discloses that information is then used to determine along what angle the vehicle was moving by comparing the wheel encoder readings to this distance measurement relative to the ground.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Verma, Adachi, Charan, Hammond, and Zheng to incorporate the teaching of Patel that allows the retrieval of wheel encoder data. Patel details the use of wheel encoder to determine the position of the robotic vehicle. By incorporating wheel encoder, it is possible to retrieve two sets of wheel encoder data, that is, before and after the power cycle as disclosed by Verma. After the wheel encoder data retrieval, it is possible to compare the two wheel encode data using Patel’s comparison method. Even though Patel compares the wheel encoder data with the value of distance value between a sensor and ground. It is an obvious modification to make the comparison between two sets of values of a wheel encoder rather than comparison between a wheel encoder value and another distance measurement. Regarding claim 9, the system of claim 1, wherein the one or more processors(the combination of Verma, Adachi, Charan, Hammond, Zheng, and Patel disclose the system of claim 1) are configured to: detect a first wireless signal from a wireless access point (Zheng on page 4 paragraph 39 discloses an embodiment, vehicle 100, for example, a car, truck, motorcycle and/or other motorized vehicle, may transmit radio signals to, and receive radio signals from a wireless communication network 470, in an embodiment, via wide area network (WAN) base station (BTS) and/or wireless access point)
at a location associated with the AV prior to the power cycle (In Page 2 Paragraph 34 Verma discloses comparing coordinates of pre-defined zones (location associated with our AV in our case) based upon the zone in which the electronic device is present, e.g., spatially or spatially and temporally, the controller generates a command for causing the electronic device to perform a specific action);
detect a second wireless signal from the wireless access point after the power cycle (Zheng Page 4 paragraph 39 discloses an embodiment, vehicle 100, for example, a car, truck, motorcycle and/or other motorized vehicle, may transmit radio signals to, and receive radio signals from a wireless communication network 470, in an embodiment, via wide area network (WAN) base station (BTS) and/or wireless access point); The second signal will be detected after the "waking" from a programmed sleep period as disclosed by Verma’s teaching. Verma discussed the device being aware of its current state, and thus taking a step (e.g., detect a second wireless signal) appropriate for performance upon such waking, and
based on the detecting the second wireless signal from the wireless access point after the power cycle, determine, after the power cycle, the AV is within a region comprising the location associated with the AV prior to the power cycle. (As discussed above by Verma reactivating feature, the second wireless signal is the signal detected immediately after the power cycle. Verma’s power cycle feature, "waking" from a programmed sleep period discussed the device being aware of its current state, and thus taking a step (e.g., detect a second wireless signal) appropriate for performance upon such waking. After detecting the second signal, on page 2 paragraph 34 Verma further discloses the geographic (the second signal) location is compared with the coordinates of pre-defined zones (location associated with our AV in our case). Based upon the zone in which the electronic device is present, e.g., spatially or spatially and temporally, the controller generates a command for causing the electronic device to perform a specific action. Zheng then discloses on page 15 paragraph 92, entering a region of influence may cause vehicles to share location and GNSS measurement data with the Roadside Unit 1406 for that region of influence). Verma’s location comparison system, that is geographic location comparison of coordinates with pre-defined zones. Combined with Zheng’s wireless signal detection system, a vehicle may transmit radio signals to, and receive radio signals from a wireless communication network in an embodiment, via wide area network (WAN) base station (BTS) and/or wireless access point. These teachings disclose the detection and comparison of wireless signals, and determine if the AV is within the region comprising the location associated with the AV (pre-defined zones) prior to the power cycle.
Regarding claim 17,The method of claim 15, wherein determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle (the combination of Verma, Adachi, Charan, Hammond and Zheng discloses the system of claim 15) comprises:
receiving a first set of LiDAR image data of the AV prior to the power cycle;( The combination of Verma, Adachi, Hammond and Charan discloses the retrieval of LiDAR data before power cycle, but fails to disclose a wheel encoder to capture wheel encoder data)
receiving a second set of LiDAR image data of the AV after the power cycle; (The combination of Verma, Adachi, and Charan discloses the retrieval of LiDAR data after power cycle, but fails to disclose a wheel encoder to capture wheel encoder data )
comparing the first set of LiDAR image data of the AV prior to the power cycle and the second set of LiDAR image data of the AV after the power cycle(The combination of Verma, Adachi, Hammond, and Charan discloses the comparison of LiDAR data before power cycle, but does not teach the comparison of wheel encoder data); and
based on the comparing of the first set of LiDAR image data of the AV prior to the power cycle and the second set of LiDAR image data of the AV after the power cycle, determining whether the second position of the AV after the power cycle matches the first position of the AV prior to the power cycle.( The combination of Verma, Adachi, Charan, Hammond and Zheng discloses the determination of a match/mismatch of LiDAR data before and after power cycle, but it does not teach a determination of match or mismatch of wheel encoder data).
The combination of Verma, Adachi, Charan, Hammond, and Zheng specifically fail to disclose receiving a first and second set of wheel encoder data captured by a wheel encoder and performing a comparison;
However Patel which is an analogous art that teaches about vehicle localization precision enhancement methods, discloses receiving a first set of wheel encoder data captured by a wheel encoder of the AV ( On Page 4 Paragraph 36 Patel discloses the movement module 272 configures the processor to receive movement data from one or more wheel encoders to determine the position of the robotic vehicle ); receiving a second set of wheel encoder data captured by the wheel encoder of the AV after the power cycle(Patel on page 4 paragraph 36 discloses the movement module 272 configures the processor to receive movement data from one or more wheel encoders to determine the position of the robotic vehicle. comparing the first set of wheel encoder data captured by the wheel encoder of the AV and the second set of wheel encoder data captured by the wheel encoder of the AV, (Patel on page 7 paragraph 63 discloses that information is then used to determine along what angle the vehicle was moving by comparing the wheel encoder readings to this distance measurement relative to the ground)
and based on the comparing of the first set of wheel encoder data captured by the wheel encoder of the AV, determining whether the second position of the AV matches the first position of the AV (Patel on page 7 paragraph 63 discloses that information is then used to determine along what angle the vehicle was moving by comparing the wheel encoder readings to this distance measurement relative to the ground.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Verma, Adachi, Zheng, Hammond, and Charan to incorporate the teaching of Patel that allows the retrieval of wheel encoder data. Patel details the use of wheel encoder to determine the position of the robotic vehicle. By incorporating wheel encoder, it is possible to retrieve two sets of wheel encoder data, that is, before and after the power cycle as disclosed by Verma. After the wheel encoder data retrieval, it is possible to compare the two wheel encode data using Patel’s comparison method. Even though Patel compares the wheel encoder data with the value of distance value between a sensor and ground. It is an obvious modification to make the comparison between two sets of values of a wheel encoder rather than comparison between a wheel encoder value and another distance measurement.
Regarding claim 18,The method of claim 10, further comprising:
detecting a first wireless signal from a wireless access point (Zheng on page 4 paragraph 39 discloses an embodiment, vehicle 100, for example, a car, truck, motorcycle and/or other motorized vehicle, may transmit radio signals to, and receive radio signals from a wireless communication network 470, in an embodiment, via wide area network (WAN) base station (BTS) and/or wireless access point) at a location associated with the AV prior to the power cycle(In Page 2 Paragraph 34 Verma discloses comparing coordinates of pre-defined zones (location associated with our AV in our case) based upon the zone in which the electronic device is present, e.g., spatially or spatially and temporally, the controller generates a command for causing the electronic device to perform a specific action); detecting a second wireless signal from the wireless access point after the power cycle(Zheng Page 4 paragraph 39 discloses an embodiment, vehicle 100, for example, a car, truck, motorcycle and/or other motorized vehicle, may transmit radio signals to, and receive radio signals from a wireless communication network 470, in an embodiment, via wide area network (WAN) base station (BTS) and/or wireless access point);
The second signal will be detected after the "waking" from a programmed sleep period as disclosed by Verma’s teaching. Verma discussed the device being aware of its current state, and thus taking a step (e.g., detect a second wireless signal) appropriate for performance upon such waking); and based on the detecting the second wireless signal from the wireless access point after the power cycle, determining, after the power cycle, the AV is within a region comprising the location associated with the AV prior to the power cycle(As discussed above by Verma reactivating feature, the second wireless signal is the signal detected immediately after the power cycle. Verma’s power cycle feature, "waking" from a programmed sleep period discussed the device being aware of its current state, and thus taking a step (e.g., detect a second wireless signal) appropriate for performance upon such waking. After detecting the second signal, on page 2 paragraph 34 Verma further discloses the geographic (the second signal) location is compared with the coordinates of pre-defined zones (location associated with our AV in our case). Based upon the zone in which the electronic device is present, e.g., spatially or spatially and temporally, the controller generates a command for causing the electronic device to perform a specific action. Zheng then discloses on page 15 paragraph 92, entering a region of influence may cause vehicles to share location and GNSS measurement data with the Roadside Unit 1406 for that region of influence). Verma’s location comparison system, that is geographic location comparison of coordinates with pre-defined zones. Combined with Zheng’s wireless signal detection system, a vehicle may transmit radio signals to, and receive radio signals from a wireless communication network in an embodiment, via wide area network (WAN) base station (BTS) and/or wireless access point. These teachings disclose the detection and comparison of wireless signals, and determine if the AV is within the region comprising the location associated with the AV (pre-defined zones) prior to the power cycle.)
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Di, US 20220283298 A1 which teaches vehicle localization enhancement via multi-sensor fusion. It details different sensors that can be used determine the location of a vehicle. It teaches about wheel encoders sensors for location determination. In addition, it discloses embodiments of the invention that may be used to monitor the position of an electronic device and to generate commands for causing the device to automatically perform a designated action based upon its geographic location, or its geo-temporal location
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.
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/BESUFEKAD LEMMA TESSEMA/Examiner, Art Unit 3665
/HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665
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