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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 26-32, 34-37, 40-45 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu et al (US 20200097006, hereinafter Liu)
Regarding Claim 26, Liu teaches:
26. (New) A device for robot position error correction (see at least "As used herein, the term “robotic device” refers to one of various types of robotic vehicles, robotic appliances, robots, etc. including an onboard processing device configured to provide some autonomous or semi-autonomous capabilities" in par. 0021 ) , the device comprising:
a first interface to a first sensor of a robot, the first sensor configured to provide measurements of an environment of the robot (see at least "In some embodiments, the robotic device 102 may be equipped with at least one spatial measurement device 103, such as an imaging sensor (e.g., a camera)." in par. 0026 and “While shown together, the pre-processor(s) 404 may represent multiple pre-processors that each correspond to a different sensor or group of sensors. For example, some pre-processors may be configured to receive and jointly process raw measurements from multiple sensors, or may be configured to receive and process raw measurements from a single sensor 402.” In par. 0072) ;
a second interface to a second sensor of the robot (see at least "In some embodiments, the robotic device 102 may be equipped with any of a number of additional sensors useful for SLAM and navigation, such as wheel/rotary encoders, a global navigation satellite system (GNSS) receiver (e.g., a Global Positioning System (GPS) receiver), an inertial measurement unit (IMU) or components thereof (e.g., accelerometer, gyroscope, magnetometer, etc.), an orientation sensor, and a monocular image sensor." in par. 0026 and “While shown together, the pre-processor(s) 404 may represent multiple pre-processors that each correspond to a different sensor or group of sensors. For example, some pre-processors may be configured to receive and jointly process raw measurements from multiple sensors, or may be configured to receive and process raw measurements from a single sensor 402.” In par. 0072) ; and
processing circuitry that, when in operation, is configured to (see at least "The control unit 210 that may house various circuits and devices used to power and control the operation of the robotic device 200. The control unit 210 may include a processor 220, a power module 230, sensors 240, one or more payload-securing units 244, an output module 250, an input module 260, and a radio module 270." in par. 0039 ) :
establish a position of the robot based on measurements, received via the first interface, of the environment of the robot from the first sensor (see at least "SLAM algorithms account for a variety of parameters, such as sensors, map representation, device dynamics, environmental dynamics, and the integration of sensor measurements and the robot's control system over time. Mapping the spatial information of the environment generally requires employing spatial sensors (e.g., spatial measurement device(s) 206) to which SLAM algorithms may be applied. For example, landmarks may be extracted from images obtained by one or multiple cameras, which may be any of a variety of configurations (e.g., monocular, stereo vision, multiple camera, etc.)." in par. 0059 and “Localization of the robotic device may be performed by obtaining sensory information regarding the position and orientation (i.e., pose) of the device within the generated map.” In par. 0060) ;
obtain, via the second interface, an error value based on data from the second sensor of the robot (see at least "An adverse event may be detected in various embodiments by comparing the processed measurements from the corresponding pre-processor 404 with a current estimated pose for the robotic device. In various embodiments, feedback from a localization module 408 for SLAM may provide the current estimated pose to each semantic information extractor(s) 406. If the difference between a pose indicated by the processed measurement(s) from the pre-processor 404 and the current estimated pose is greater than a pre-determined threshold, the semantic information extractor 406 may record an adverse event for the related one or more associated sensor(s) 402." in par. 0077) ;
select a weight for the error value based on a context of the robot (see at least "The semantic information extractor 406 may periodically seek to identify patterns in the adverse event occurrence across timeslots and/or poses for each associated sensor 402, such as when each time period ends. In various embodiments, an identified pattern for an associated sensor 402 may represent a temporal and/or spatial pattern of inaccuracy for that sensor. Any such identified inaccuracy patterns may be provided to a weighting module 412, which may receive the processed measurement(s) from the corresponding pre-processor 404. In various embodiments, the weighting module 412 may adjust the weight given to data (i.e., processed measurement(s)) from the related associated sensor(s) 402 based on the identified inaccuracy pattern. For example, such adjustment may involve lowering the weighting factor of data from a sensor 402 for a specific time duration, or when the robotic device is located in a particular spatial region. That is, the degree and manner of use of the data from a particular sensor 402 may depend in part on the reliability of that sensor, as inferred using any corresponding identified inaccuracy pattern." in par. 0078) ;
create a weighted error value by combining the weight and the error value (see at least "After adjusting weighting factor(s) based on identified inaccuracy patterns derived from semantic information, the processed measurements may be provided to the localization module 408. As discussed, the localization module 408 may generate a current estimated pose for the robotic device as part of SLAM processes." in par. 0079);
combine the position and the weighted error value to create a corrected position (see at least "For example, localization (i.e., current pose estimation) may be performed in real time on one thread, while the mapping thread runs processes in the background to minimize the differences between the tracked point locations and where the points are expected to be given the pose estimate (i.e., reprojection errors). Upon completion, the mapping thread updates the information used to track the set of points, and in turn the localization thread adds new observations to expand the map." in par. 0033) ; and
select an operation for the robot based on the corrected position. (see at least "The path planning module 416 may use the received map(s) and the current estimated pose information to select, create, or update a navigation path for the robotic device." in par. 0080)
Regarding Claim 27, Liu teaches:
27. (New) The device of claim 26,
wherein the first sensor includes an optical sensor. (see at least " In some embodiments, landmarks may be extracted from images taken by any of a number of image sensors (e.g., cameras, optical readers, etc.). In some embodiments, any of a number of other devices capable of detecting a landmark in its vicinity may also be implemented as a spatial measurement device." in par. 0037 )
Regarding Claim 28, Liu teaches:
28. (New) The device of claim 27, wherein the optical sensor is a passive sensor. (see at least " In some embodiments, landmarks may be extracted from images taken by any of a number of image sensors (e.g., cameras, optical readers, etc.). In some embodiments, any of a number of other devices capable of detecting a landmark in its vicinity may also be implemented as a spatial measurement device." in par. 0037 )
Regarding Claim 29, Liu teaches:
29. (New) The device of claim 28, wherein the passive sensor is at least one of a visual light spectrum camera, an infrared camera, or an ultraviolet camera. (see at least " The processor 220 may further receive additional information from one or more sensors 240 (e.g., a camera, which may be a monocular camera) and/or other sensors. In some embodiments, the sensor(s) 240 may include one or more optical sensors capable of detecting infrared, ultraviolet, and/or other wavelengths of light." in par. 0043 )
Regarding Claim 30, Liu teaches:
30. (New) The device of claim 27,
wherein the optical sensor is an active sensor with an emitter. (see at least " In some embodiments, the spatial measurement device(s) 206 may include, for example, systems configured as raw range scan sensors, or feature-based systems configured to recognize landmarks from scans or images. For example, a laser-based scanner (e.g., Light Detection and Ranging (LiDAR))" in par. 0037)
Regarding Claim 31, Liu teaches:
31. (New) The device of claim 30,
wherein the active sensor is at least one of lidar or radar. (see at least " In some embodiments, the spatial measurement device(s) 206 may include, for example, systems configured as raw range scan sensors, or feature-based systems configured to recognize landmarks from scans or images. For example, a laser-based scanner (e.g., Light Detection and Ranging (LiDAR))" in par. 0037)
Regarding Claim 32, Liu teaches:
32. (New) The device of claim 26,
wherein the first sensor is a sonic sensor. (see at least " In some embodiments, the spatial measurement device(s) 206 may include, for example, … sonar-based system may be used to extract landmarks from scans." in par. 0037)
Regarding Claim 34, Liu teaches:
34. (New) The device of claim 26,
wherein, to select the weight for the error value based on the context of the robot, the processing circuitry is configured obtain the context from a map of the environment (see at least " For example, such adjustment may involve lowering the weighting factor of data from a sensor 402 for a specific time duration, or when the robotic device is located in a particular spatial region. That is, the degree and manner of use of the data from a particular sensor 402 may depend in part on the reliability of that sensor, as inferred using any corresponding identified inaccuracy pattern." in par. 0078 and “In block 518, the processor may adjust a weighting factor for one or more sensors at any corresponding spatial region or time of low performance. For example, using an identified pattern of inaccuracy for a sensor, the processor may apply a weighting factor to downscale the value of measurements taken by that sensor during particular time(s), and/or when the robotic device is within the particular spatial region(s).” in par. 0090) .
Regarding Claim 35, Liu teaches:
35. (New) The device of claim 34, wherein the map includes an indication of second sensor performance at the position. (see at least " For example, such adjustment may involve lowering the weighting factor of data from a sensor 402 for a specific time duration, or when the robotic device is located in a particular spatial region. That is, the degree and manner of use of the data from a particular sensor 402 may depend in part on the reliability of that sensor, as inferred using any corresponding identified inaccuracy pattern." in par. 0078 and “In block 518, the processor may adjust a weighting factor for one or more sensors at any corresponding spatial region or time of low performance. For example, using an identified pattern of inaccuracy for a sensor, the processor may apply a weighting factor to downscale the value of measurements taken by that sensor during particular time(s), and/or when the robotic device is within the particular spatial region(s).” in par. 0090)
Regarding Claim 36, Liu teaches:
36. (New) The device of claim 35, wherein the weight is selected to reduce correction of the position towards the error value when the indication of the second sensor performance is less than a value. (see at least " For example, such adjustment may involve lowering the weighting factor of data from a sensor 402 for a specific time duration, or when the robotic device is located in a particular spatial region. That is, the degree and manner of use of the data from a particular sensor 402 may depend in part on the reliability of that sensor, as inferred using any corresponding identified inaccuracy pattern." in par. 0078 and “In block 518, the processor may adjust a weighting factor for one or more sensors at any corresponding spatial region or time of low performance. For example, using an identified pattern of inaccuracy for a sensor, the processor may apply a weighting factor to downscale the value of measurements taken by that sensor during particular time(s), and/or when the robotic device is within the particular spatial region(s).” in par. 0090)
Regarding Claim 37, Liu teaches:
37. (New) The device of claim 36,
wherein the second sensor is a satellite positioning sensor, an inertial sensor, or a historical record of sensor data. (see at least " the robotic device 102 may be equipped with any of a number of additional sensors useful for SLAM and navigation, such as wheel/rotary encoders, a global navigation satellite system (GNSS) receiver (e.g., a Global Positioning System (GPS) receiver), an inertial measurement unit (IMU) or components thereof (e.g., accelerometer, gyroscope, magnetometer, etc.), an orientation sensor, and a monocular image sensor." in par. 0026)
Regarding Claim 40, Liu also teaches:
At least on non-transitory machine readable medium (see at least " If implemented in software, the functions may be stored as one or more instructions or code on a non-transitory computer-readable storage medium or non-transitory processor-readable storage medium." in par. 0103) for implementing the device of Claim 26 (see Claim 26 analysis for rejection of the device).
Regarding Claim 41, Liu also teaches:
At least on non-transitory machine readable medium for implementing the device of Claim 27 (see Claim 27 analysis for rejection of the device)
Regarding Claim 42, Liu also teaches:
At least on non-transitory machine readable medium for implementing the device of Claim 28 (see Claim 28 analysis for rejection of the device)
Regarding Claim 43, Liu also teaches:
At least on non-transitory machine readable medium for implementing the device of Claim 30 (see Claim 30 analysis for rejection of the device)
Regarding Claim 44, Liu also teaches:
At least on non-transitory machine readable medium for implementing the device of Claim 34 (see Claim 34 analysis for rejection of the device)
Regarding Claim 45, Liu also teaches:
At least on non-transitory machine readable medium for implementing the device of Claim 35 (see Claim 35 analysis for rejection of the device)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US 20200097006, hereinafter Liu) in view of Damsgaard et al (US 20240111041, hereinafter Damsgaard).
Regarding Claim 33, Liu teaches:
33. (New) The device of claim 32,
Liu does not appear to explicitly teach all of the following, but Damsgaard does teach wherein the sonic sensor is a hypersonic sensor. (see at least "Additionally or alternatively, user-location device 102 can be configured to locate an audio-output device 104 by causing the audio-output device 104 to emit a sound outside the audible-frequency range for humans, such as a hypersonic signal. In such cases, user-location device 102 includes an integrated microphone array configured to “hear” the hypersonic signal. User-location device 102 can then perform standard ranging and positioning techniques (e.g., Time-of-Flight (ToF), Angle of Arrival (AoA), etc.) to locate the relevant audio-output device 104. Additionally or alternatively, user-location device 102 and audio-output device 104 may be in wired and/or wireless data communication with one another, either directly or via a local data network, such that the two devices may exchange sufficient metadata for user-location device 102 to locate audio-output device 104. " in par. 0023)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device taught by Liu to incorporate the teachings of Damsgaard wherein a hypersonic sensor is used for localization. The motivation to incorporate the teachings of Damsgaard would be to avoid a frequency range the user can hear, which would be unpleasant for users.
Claim(s) 38-39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US 20200097006, hereinafter Liu) in view of Amorim de Faria Cardote et al (US 20170195953, hereinafter Amorim de Faria Cardote).
Regarding Claim 38, Liu teaches:
38. (New) The device of claim 37,
Liu does not appear to explicitly teach all of the following, but Amorim de Faria Cardote does teach:
wherein the second sensor is the satellite positioning sensor, wherein the position on the map corresponds to a tunnel that will result in accuracy of the satellite positioning sensor being below a threshold. (see at least "For example, such a request may be a request to decrease the update rate (e.g., sampling rate) of a GNSS/GPS sensor/receiver from a medium value (e.g., 5 Hz or 8 Hz) to a lowest value (e.g., 1 Hz) when the vehicle is going, or is about to go through an area where received GNSS/GPS signal is known to be of poor quality (e.g., in a tunnel, a parking garage, an area with many tall buildings, etc. where signal strength, multipath, distortion, noise, etc., affect receiver operation)… Extending these examples, in accordance with aspects of the present disclosure, the output data rate of sensors other than a GNSS/GPS receiver (e.g., an accelerometer, a gyroscope, vehicle wheel rotation sensors) may be increased at the same time the GNSS/GPS update rate is decreased (e.g., in response to or as a part of a decision to decrease the update rate), in an attempt to maintain sufficiently accurate estimate of geographic position despite the reduced rate of position updated from the GNSS/GPS receiver." in par. 0173)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device taught by Liu to incorporate the teachings of Amorim de Faria Cardote wherein the sampling rate of the GPS sensor is reduced and the sampling rate of other sensors is increased when the vehicle is in a tunnel, in order to arrive at arrive at performing the same control with the influence weights taught by Liu. The motivation to incorporate the teachings of Amorim de Faria Cardote would be to increase the influence of reliable sensors and decrease the influence of unreliable sensors, which maintains accuracy of position estimation (see par. 0173).
Regarding Claim 39, Liu teaches:
39. The device of claim 37,
Liu does not appear to explicitly teach all of the following, but Amorim de Faria does teach:
wherein the second sensor is the inertial sensor, wherein the position on the map corresponds to a rough surface that will result in accuracy of the inertial positioning sensor being below a threshold. (see at least "As another example, in accordance with aspects of the present disclosure, such a request may be a request to reduce the sample rate of, for example, an accelerometer, gyroscope, or other sensor affected by vibration when on, or approaching, a known bumpy road, in order to avoid capturing output samples from those sensors, which may vary wildly and be of no use. " in par. 0173)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device taught by Liu to incorporate the teachings of Amorim de Faria Cardote wherein the sampling rate of the inertial sensors are reduced and the sampling rate of other sensors is increased when the vehicle is on a known bumpy road, in order to arrive at arrive at performing the same control with the influence weights taught by Liu. The motivation to incorporate the teachings of Amorim de Faria Cardote would be to increase the influence of reliable sensors and decrease the influence of unreliable sensors, which maintains accuracy of position estimation (see par. 0173).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN M KATZ whose telephone number is (571)272-2776. The examiner can normally be reached Mon-Thurs. 8:00-6:00.
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/DYLAN M KATZ/Primary Examiner, Art Unit 3657
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