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 .
Specification
The disclosure is objected to because of the following informalities:
Typographical error at paragraph [0074] reading “There are three gyroscopes and three accelerometers are mounted orthogonal to each other.”, should read “There are three gyroscopes and three accelerometers that are mounted orthogonal to each other.”
Appropriate correction is required.
Claim Objections
Claim 16 is objected to because of the following informalities:
Typographical error at claim 16 reading “generating position of the autonomous unit”, should read “generating a position of the autonomous unit”.
Appropriate correction is required.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-8, 10, 13, 14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dryanovski et al. of US 2015/0193971 (hereinafter referred to as Dryanovski) in view of Nehmadi et al. of US 2018/0232947 A1 (hereinafter referred to as Nehmadi).
Regarding claim 1, Dryanovski discloses a method for building 3D maps from a surrounding scenery, including: receiving from a first source, at least one of one or more objects from first visual information capturing the surrounding scenery (see Dryanovski par. [0004] “The method may include receiving one or more outputs of the plurality of sensors … and the one or more outputs comprise a first set of data corresponding to one or more visual features of the environment”) and a position where the first visual information was captured (see Dryanovski par. [0067] “Combining the information provided within the images and the motion information, the computing device may determine a pose (e.g., position and orientation) of the computing device in an environment”); determining from the set of non-moving objects (see Dryanovski claim 8: ”comprising … identifying … data in the sparse mapping corresponding to one or more moving objects in the environment; and removing the data in the sparse mapping corresponding to the one or more moving objects”), a sparse 3D mapping of object feature points based at least in part upon the first visual information of the surrounding scenery (see Dryanovski par. [0024] “the computing device may develop a first level made up of sparse mapping data … the sparse mapping data may provide data points corresponding to estimates of visual features tracked in the environment as the computing device changes position”); and building a first 3D map of object feature points from the sparse 3D mapping of object feature points (see Dryanovski par. [0004] “The method may further include generating, based on correspondence in the one or more outputs of the plurality of sensors, a map of the environment comprising sparse mapping data indicative of the first set of data.”)
Dryanovski does not disclose classifying the at least one of one or more objects into a set of moving objects and a set of non-moving objects.
However, Nehmadi discloses classifying the at least one of one or more objects into a set of moving objects and a set of non-moving objects (see Nehmadi par. [0054] “create a list of stationary and moving points of interest; determine stationary or nearly stationary features … actively measure the distance from features, segments or objects of interest”)
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski with the feature classifier of Nehmadi because it is predictable that doing so would allow for the mapping system to have a classification feature that would determine the moving objects so as to remove them from or separate them in the 3D map and create a static map of the surrounding scenery, increasing reliability and accuracy.
Claim 14 is also rejected according to the same analysis as claim 1 above.
Regarding claim 2, Dryanovski discloses receiving from a second source, at least one of one or more objects from second visual information capturing the surrounding scenery (see Dryanovski par. [0004] “the one or more additional outputs comprise a second set of data corresponding to one or more visual features of the environment”) and a position where the second visual information was captured (see Dryanovski claim 8: ”comprising … identifying … data in the sparse mapping corresponding to one or more moving objects in the environment; and removing the data in the sparse mapping corresponding to the one or more moving objects”); generating a second 3D map using the second visual information from the second source (see Dryanovski par. [0089] “the computing device may generate multiple maps”); and merging the second 3D map with the first 3D map (see Dryanovski par. [0004] “the method may further comprise modifying the map of the environment to further comprise sparse mapping data indicative of the second set of data”) .
Regarding claim 3, Dryanovski discloses wherein the first 3D map and the second 3D map both cover common location and further comprising: updating the first 3D map using the second 3D map (see Dryanovski par. [0090] “In another implementation, the computing device may use newly received sparse mapping data to further refine a generated map. For example, the computing device may determine within information provided by sensor outputs that the environment has changed since the last time that sparse mapping information was captured there. In such a situation, the computing device may modify the map to include some received sparse mapping data by changing the map based on previously acquired sparse mapping data to reflect the information received in the latest outputs of that environment”).
Regarding claim 4, Dryanovski fails to disclose generating several hundred thousand images during one hour of operation by an autonomous unit.
However, Nehmadi discloses generating several hundred thousand images during one hour of operation by an autonomous unit (see Nehmadi par. [0060] “In another embodiment, the processor 270 can support input sensors at acquisition rates exceeding 60 frames per second, and provide a real-time updated high-density 3D map 276 plus high-density image 274 at an equivalent output rate.”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski with the image acquisition and output rate of Nehmadi because it is predictable that doing so would increase the output rate of Dryanovski’s mapping system so that it can output for real-time use, increasing speed of mapping and usefulness.
Regarding claim 5, Dryanovski fails to disclose substantially contemporaneously tracking a position of moving autonomous units against at least one 3D map.
However, Nehmadi discloses substantially contemporaneously tracking a position of moving autonomous units against at least one 3D map (see Nehmadi par. [0051] “In other embodiments, the sensor is controllable, and can be commanded to perform distance measurements on demand, such that the measurement list can be updated from scan to scan, and directed to specific directions to determine the distance of the vehicle from points of interest in the scene”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski with the contemporaneous tracking of Nehmadi because it is predictable that doing so would allow live real-time tracking of the mapping system so that it can be used instantaneously and for real-time scenarios, increasing usefulness.
Regarding claim 6, Dryanovski fails to disclose storing a time of day with at least one 3D map.
However, Nehmadi discloses storing a time of day with at least one 3D map (see Nehmadi par. [0123] “a frame may include a camera frame, a radar reading, and a LiDAR scan mapped to the time of the camera reading”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski with the time-of-day tracking of Nehmadi because it is predictable that doing so would allow the mapping system to store data related to certain periods of time such as day or night, allowing the system to conduct pattern recognition programming and therefore increase usefulness.
Regarding claim 7, Dryanovski fails to disclose storing a weather condition with at least one 3D map.
However, Nehmadi discloses storing a weather condition with at least one 3D map (see Nehmadi par. [0136] “Weather conditions may be estimated 1334 using global statistics on noise, contrast, intensity, and other statistics” According to Nehmadi Figures 12 and 13, these conditions are stored in the data that is used to generate the 3D Map.).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski with the weather condition storing of Nehmadi because it is predictable that doing so would allow the mapping system to store data related to certain conditions so that it can conduct pattern recognition programming and therefore increase usefulness.
Regarding claim 8, Dryanovski discloses wherein the position where the first visual information was captured includes a position of a first autonomous unit obtained using combinations of global positioning system, an inertial measurement sensor(s), and visual information of the surrounding scenery (see Dryanovski par. [0107] “the computing device may further also use information provided by the IMU unit and/or other sensors to further determine a location of the device” and par. [0035] “The sensor(s) 110 may include one or more sensors, or may represent one or more sensors included within the computing device 100. Example sensors include an accelerometer, gyroscope, pedometer, light sensors, microphone, camera(s), infrared flash, barometer, magnetometer, GPS, Wi-Fi, near field communication (NFC), Bluetooth, projector, depth sensor, temperature sensors, or other location and/or context-aware sensors.”).
Regarding claim 10, Dryanovski does not disclose extracting a first set of 2D features of an object from a first 360-degrees image in a keyrig selected from a subset of keyrigs; extracting a second set of 2D features of the object from a second 360-degrees image in the keyrig selected; receiving a position of an autonomous unit when the first 360-degrees image and the second 360-degrees image were captured including longitude and latitude as input; triangulating the first set of 2D features from the first 360-degrees image and the second set of 2D features from the 360-degrees second image to derive location for feature points of the object relative to the position of the autonomous unit; and generating for at least one feature point of the object, a global position, including longitude, latitude, and height and adding the global position and feature descriptors of the object to the sparse 3D mapping of object feature points.
However, Nehmadi discloses extracting a first set of 2D features of an object from a first 360-degrees image in a keyrig selected from a subset of keyrigs; extracting a second set of 2D features of the object from a second 360-degrees image in the keyrig selected (see Nehmadi par. [0061] “The apparatus of FIG. 2B contains one or more front facing high-density passive camera sensors 278, 280 … side passive camera sensors 292 and back passive camera sensors 294 configured for generating 360 degree imaging when combined with the front cameras 278, 280” indicating it can take multiple pictures providing a 360-degree view. See Nehmadi par. [0150] “Feature tracking may be also performed using rigid surfaces and invariant characteristics”); receiving a position of an autonomous unit when the first 360-degrees image and the second 360-degrees image were captured including longitude and latitude as input (see Nehmadi par. [0089] “Other non-image sensors, such as GPS and inertial measurement units (IMU) sensors, may be calibrated to the selected location within the vehicle and aligned using a common timestamp” and par. [0108] “The location of the vehicle may be tracked via GPS”. GPS is well known to include at least longitude and latitude in its position tracking.); triangulating the first set of 2D features from the first 360-degrees image and the second set of 2D features from the 360-degrees second image to derive location for feature points of the object relative to the position of the autonomous unit (see Nehmadi par. [0078] “ POI identification analysis may further include detecting the distance of features or objects using stereoscopy from motion process in the image. This involves using two images captures from the same sensor at two close points in time, or using two images generated at the same point in time from two different sensors. Comparing such images allows for the calculation of the distance of the object or feature.”); and generating for at least one feature point of the object, a global position, including longitude, latitude, and height and adding the global position and feature descriptors of the object to the sparse 3D mapping of object feature points (see Nehmadi par. [0139] “The depth of an object can be mapped for each camera direction 1422, so that each camera pixel gets an XYZ value associated with it” indicating XYZ spatially accounts for longitude, latitude, and height; and [0121] “The segmentation can employ optical flow, normal XYZ space, RGB color, and other parameters” indicating that the segmentation maps specific feature descriptors to the object).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski with the 360-degree imaging of Nehmadi because it is predictable that doing so would allow the user to employ segmentation on all sides of the autonomous unit at all times, allowing for enhanced navigation and safety if used on specific implementations such as a vehicle.
Regarding claim 13, Dryanovski fails to disclose wherein 3D maps are built using information sourced by one or more moving autonomous units that include at least a camera visual sensor and at least one selected from a global positioning system and an inertial measurement sensor.
However, Nehmadi discloses wherein 3D maps are built using information sourced by one or more moving autonomous units that include at least a camera visual sensor and at least one selected from a global positioning system and an inertial measurement sensor (see Nehmadi [0061] “configured for scanning around the vehicle” indicates technology intended to be used on a moving unit such as a vehicle; and Nehmadi Fig. 2B where the flowchart indicates GPS, IMU, and camera data being fed into a system that outputs a 3D Map).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski with GPS/IMU and imaging system of Nehmadi because it is predictable that building 3D maps using this information accounts for pose and scenery data, both of which are crucial for autonomous vehicle operation, therefore adding necessary functionality.
Regarding claim 20, Dryanovski discloses at least a camera visual sensor (see Dryanovski par. [0035] “The sensor(s) 110 may include one or more sensors, or may represent one or more sensors included within the computing device 100. Example sensors include an accelerometer, gyroscope, pedometer, light sensors, microphone, camera(s)”); and at least one selected from a global positioning system and an inertial measurement unit (see Dryanovski par. [0035] “Example sensors include an accelerometer, gyroscope, pedometer, light sensors, microphone, camera(s), infrared flash, barometer, magnetometer, GPS”); and a processor coupled to a memory storing instructions for performing actions (see Dryanovski par. [0062] for processor, CRM, and memory applications), including: capturing visual information of the surrounding scenery and a position where the visual information was captured (see Dryanovski par. [0004] “The method may include receiving one or more outputs of the plurality of sensors … and the one or more outputs comprise a first set of data corresponding to one or more visual features of the environment” and Dryanovski par. [0067] “Combining the information provided within the images and the motion information, the computing device may determine a pose (e.g., position and orientation) of the computing device in an environment”); and providing the visual information of the surrounding scenery and a position where the visual information was captured to a server for: determination from the set of non-moving objects, of a sparse 3D mapping of object feature points based at least in part upon the visual information of the surrounding scenery (see Dryanovski claim 8: ”comprising … identifying … data in the sparse mapping corresponding to one or more moving objects in the environment; and removing the data in the sparse mapping corresponding to the one or more moving objects” and Dryanovski par. [0024] “the computing device may develop a first level made up of sparse mapping data … the sparse mapping data may provide data points corresponding to estimates of visual features tracked in the environment as the computing device changes position”) building of a 3D map of object feature points from the sparse 3D mapping of object feature points (see Dryanovski par. [0004] “The method may further include generating, based on correspondence in the one or more outputs of the plurality of sensors, a map of the environment comprising sparse mapping data indicative of the first set of data.”).
Dryanovski fails to disclose classification of at least one of one or more objects from the visual information capturing the surrounding scenery into a set of moving objects and a set of non-moving objects.
However, Nehmadi discloses classification of at least one of one or more objects from the visual information capturing the surrounding scenery into a set of moving objects and a set of non-moving objects (see Nehmadi par. [0054] “create a list of stationary and moving points of interest; determine stationary or nearly stationary features … actively measure the distance from features, segments or objects of interest”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski with the feature classifier of Nehmadi because it is predictable that doing so would allow for the mapping system to have a classification feature that would determine the moving objects so as to remove them from or separate them in the 3D map and create a static map of the surrounding scenery, increasing reliability and accuracy.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dryanovski in view of Nehmadi in further view of Wallach et al. of US 6374155 (hereinafter referred to as Wallach).
Dryanovski fails to disclose wherein a position of the first autonomous unit is obtained using combinations further including visual information captured by a second autonomous unit.
However, Wallach discloses wherein a position of the first autonomous unit is obtained using combinations further including visual information (see Wallach col. 4 line 59 “A sensor is mounted on navigator 110” and col. 5 lines 1-2 “in one example implementation, sensor 202 is a camera that records optical images of the surrounding environment”) captured by a second autonomous unit (see Wallach Claim 22 (col. 15 lines 12-18) “A system of autonomous, mobile robots operating within an environment and comprising: one or more functional mobile robot(s) that are responsible for performing functional tasks; one or more navigator mobile robot(s) that localize themselves and the functional robot(s) within the environment”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski with the autonomous tracking of Wallach because it is predictable that doing so would allow multiple autonomous units to be mapped and tracked together, increasing range of its use and utility.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dryanovski in view of Nehmadi and further in view of Liu, W., Cheung, Y., Sabouri, P., Arai, T.J., Sawant, A. and Ruan, D. (2015), A continuous surface reconstruction method on point cloud captured from a 3D surface photogrammetry system. Med. Phys., 42: 6564-6571. https://doi.org/10.1118/1.4933196 (hereinafter referred to as Liu).
Dryanovski and Nehmadi do not disclose including determining accuracy by a difference between a location of an object depiction on at least the first 3D map and an actual location in space of an object corresponding to the object depiction.
However, Liu discloses further including determining accuracy by a difference between a location of an object depiction on at least the first 3D map and an actual location in space of an object corresponding to the object depiction (see Liu pg. 4 “we compared the reconstructed surfaces under different testing configurations against the reference surface reconstructed from phantom without any patches, and quantitatively evaluated the reconstruction accuracy with respect to root mean squared error (RMSE)”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski and the feature classifier of Nehmadi with the accuracy determination of Liu because it is predictable that doing so would allow the mapping system of Dryanovski to check its accuracy in order to ensure safe operation and accurate segmentation and positional traction, therefore increasing safety.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dryanovski in view of Nehmadi and Liu, and further in view of Chen of CN 105227846 A (hereinafter referred to as Chen).
Dryanovski, Nehmadi, and Liu fail to disclose further including at least one 3D map with an accuracy within 10 centimeters.
However, Chen discloses further including at least one 3D map with an accuracy within 10 centimeters (see Chen pg. 3 “the generated three-dimensional map resolving can reach centimetre level”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski, the feature classifier of Nehmadi, and the accuracy determination of Liu with the centimeter level precision of Chen because it is predictable that doing so would allow the system to have extremely accurate mapping for smaller mapping environments where accurate mapping is necessary, such as indoor or tight operational environments, therefore increasing usefulness.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dryanovski in view of Nehmadi, and further in view of Maekawa of US 10061321 (hereinafter referred to as Maekawa).
Dryanovski fails to disclose two or more mobile autonomous units, including a first autonomous unit and a second autonomous unit, each having a mobile platform and disposed thereon: a visual sensor comprising cameras providing capturing images including at least two frames, thereby providing a 360-degrees view about a centerline of the mobile platform; and at least one of: multi-axis inertial measuring unit (IMU) sensor capable of providing measurement of at least acceleration using one or more accelerometers; and a global positioning system (GPS) receiver.
However, Nehmadi discloses a visual sensor comprising cameras providing capturing images including at least two frames, thereby providing a 360-degrees view about a centerline of the mobile platform; and at least one of: multi-axis inertial measuring unit (IMU) sensor capable of providing measurement of at least acceleration using one or more accelerometers; and a global positioning system (GPS) receiver; (see Nehmadi Fig. 2B and par. [0061] “The apparatus of FIG. 2B contains one or more front facing high-density passive camera sensors 278, 280; … side passive camera sensors 292 and back passive camera sensors 294 configured for generating 360 degree imaging when combined with the front cameras 278, 280”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski with the 360-degree imaging of Nehmadi because it is predictable that doing so would allow the imaging and mapping to be conducted through a 360-degree view, therefore increasing the unit’s ability to scan around itself and detect relevant objects and scenery.
Nehmadi fails to disclose two or more mobile autonomous units, including a first autonomous unit and a second autonomous unit, each having a mobile platform.
However, Maekawa discloses two or more mobile autonomous units, including a first autonomous unit and a second autonomous unit, each having a mobile platform (see Maekawa Maekawa col. 1 lines 39-44, “an autonomous mobile apparatus including one or more memories and circuitry which, in operation, performs operations including determining a first control method or a second control method as a control method for controlling the autonomous mobile apparatus on the basis of information regarding another autonomous mobile apparatus”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system of Dryanovski and the 360-degree imaging of Nehmadi with the multiple autonomous units of Maekawa because it is predictable that doing so would allow the imaging and mapping to be conducted through multiple units, and allow the system to operate over a larger area, therefore covering more ground and increasing efficiency.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dryanovski in view of Nehmadi and Maekawa, and further in view of Woodman, Oliver J. “An Introduction to Inertial Navigation.” University of Cambridge, Computer Laboratory, UCAM-CL-TR-696, Aug. 2007, https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-696.pdf (hereinafter referred to as Woodman).
Nehmadi and Maekawa fail to disclose wherein the multi-axis inertial measuring unit (IMU) sensor further includes one or more gyroscopes for reporting a rotational rate, and wherein a position for an autonomous unit is generated using combinations of global positioning system (GPS) receiver, the multi-axis inertial measurement unit (IMU), and visual information of the surrounding scenery by the first autonomous unit during travel from a starting point to an end point further includes: generating a position of the autonomous unit using the rotational rate from the one or more gyroscopes.
However, Dryanovski discloses wherein the multi-axis inertial measuring unit (IMU) sensor further includes one or more gyroscopes for reporting a rotational rate, and wherein a position for an autonomous unit is generated using combinations of global positioning system (GPS) receiver, the multi-axis inertial measurement unit (IMU), and visual information of the surrounding scenery by the first autonomous unit during travel from a starting point to an end point (see Dryanovski par. [0107] “the computing device may further also use information provided by the IMU unit and/or other sensors to further determine a location of the device” and par. [0035] “The sensor(s) 110 may include one or more sensors, or may represent one or more sensors included within the computing device 100. Example sensors include an accelerometer, gyroscope, pedometer, light sensors, microphone, camera(s), infrared flash, barometer, magnetometer, GPS, Wi-Fi, near field communication (NFC), Bluetooth, projector, depth sensor, temperature sensors, or other location and/or context-aware sensors.”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the mapping system and position detecting of Dryanovski with the 360-degree imaging of Nehmadi and the multiple autonomous units of Maekawa because it is predictable that doing so would allow the position and speed of the autonomous unit to be detected, therefore implementing the systems main functionality into hardware components.
Dryanovski fails to disclose generating a position of the autonomous unit using the rotational rate from the one or more gyroscope.
However, Woodman discloses generating a position of the autonomous unit using the rotational rate from the one or more gyroscopes (see Woodman pg. 21 “The orientation, or attitude … is tracked by ‘integrating’ the angular velocity signal … obtained from the system’s rate-gyroscopes” where angular velocity signal is commonly understood to be rotational rate. Also see Woodman pg. 7 “To keep track of orientation the signals from the rate gyroscopes are ‘integrated’, as described in Section 6. To track position the three accelerometer signals are resolved into global coordinates using the known orientation, as determined by the integration of the gyro signals. The global acceleration signals are then integrated as in the stable platform algorithm. This procedure is shown in Figure 4” and Woodman Figure 4, which shows that gyroscope rotational rate is used in a process to determine position).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the system of Dryanovski, the 360-degree imaging of Nehmadi and the multiple autonomous units of Maekawa with the gyroscopic rate detection of Woodman because it is predictable that doing so would ensure the gyroscopes would detect orientation and position effectively, which is the standard intended use of gyroscopes.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dryanovski in view of Nehmadi, Maekawa, Woodman, and further in view of Zhang, Jing, Wanshou Jiang, and San Jiang. “Automated Mounting Bias Calibration for Airborne Lidar System” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 1-4, 2012, pp. 263-268, https://isprs-annals.copernicus.org/articles/I-4/263/2012/isprsannals-I-4-263-2012.pdf (hereinafter referred to as Zhang).
Dryanovski, Nehmadi, and Maekawa fail to disclose wherein the multi-axis inertial measuring unit (IMU) sensor further including three gyroscopes and three accelerometers mounted substantially orthogonal to each other, and coupled to a memory that stores instructions for performing: determining misalignment between the IMU and the mobile platform by performing a sighting estimation to determine an offset between an IMU measurement frame and a sensor frame, wherein the offset determined between the IMU measurement frame and the sensor frame is a transformation stored by the autonomous unit.
However, Woodman discloses wherein the multi-axis inertial measuring unit (IMU) sensor further including three gyroscopes and three accelerometers mounted substantially orthogonal to each other (see Woodman pg. 5 “Inertial measurement units (IMUs) typically contain three orthogonal rate-gyroscopes and three orthogonal accelerometers, measuring angular velocity and linear acceleration respectively.”)
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the system of Dryanovski, the 360-degree imaging of Nehmadi and the multiple autonomous units of Maekawa with the gyroscopes and accelerometers of Woodman because it is predictable that doing so would give the system hardware and also give it an exact configuration to detect movement and location of the autonomous unit.
Woodman fails to disclose determining misalignment between the IMU and the mobile platform by performing a sighting estimation to determine an offset between an IMU measurement frame and a sensor frame, wherein the offset determined between the IMU measurement frame and the sensor frame is a transformation stored by the autonomous unit.
Zhang discloses determining misalignment between the IMU and the mobile platform by performing a sighting estimation to determine an offset between an IMU measurement frame and a sensor frame, wherein the offset determined between the IMU measurement frame and the sensor frame is a transformation stored by the autonomous unit (see Zhang pg. 2 “we choose bore-sighting angles and lever-arm offsets … as system calibration parameters”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the system of Dryanovski, the 360-degree imaging of Nehmadi, the multiple autonomous units of Maekawa and the gyroscopes and accelerometers of Woodman with the bore-sighting of Zhang because it is predictable that doing so would allow the system to calibrate its IMU, as known and used on common IMU technologies.
Claim(s) 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dryanovski, in view of Nehmadi, Maekawa, Woodman, and Zhang, and further in view of Li, Ming, and Aristotelis I. Mourikis. “Online Temporal Calibration for Camera and IMU.” The International Journal of Robotics Research, vol. 33(7), 2014, pp. 947-964, doi:10.1177/0278364913515286 (hereinafter referred to as Li).
Regarding claim 18, Dryanovski, Nehmadi, Maekawa, Woodman, and Zhang do not disclose correcting misalignment correction using the offset determined between the IMU measurement frame and the sensor frame.
However, Li discloses correcting misalignment correction using the offset determined between the IMU measurement frame and the sensor frame (see Li pg. 6 “In order to perform online spatial and temporal calibration, we here additionally include td and the camera-to-IMU transformation in the EKF state vector”).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the system of Dryanovski, the 360-degree imaging of Nehmadi, the multiple autonomous units of Maekawa, the gyroscopes and accelerometers of Woodman, and the bore-sighting of Zhang with the misalignment correction of Li because it is predictable that doing so would allow the system to correct its misalignments and adjust the sensors rather than just detect misalignments, therefore enhancing functionality.
Regarding claim 19, Dryanovski, Nehmadi, Maekawa, Woodman, and Zhang do not disclose performing continuous estimation and correction during system operation to minimize effect of the offset determined between the IMU measurement frame and the sensor frame.
However, Li discloses performing continuous estimation and correction during system operation to minimize effect of the offset determined between the IMU measurement frame and the sensor frame (see Li pg. 1 “The first contribution of this work is an online approach for estimating this time offset, by treating it as an additional state variable to be estimated along with all other variables of interest (inertial measurement unit (IMU) pose and velocity, biases, camera-to-IMU transformation, feature positions).
It would have been obvious for a person having ordinary skill in the art before the effective filing date to combine the system of Dryanovski, the 360-degree imaging of Nehmadi, the multiple autonomous units of Maekawa, the gyroscopes and accelerometers of Woodman, and the bore-sighting of Zhang with the misalignment correction of Li because it is predictable that doing so would allow the system to correct its misalignments during system operation rather than after, allowing it to be run more efficiently and with more accuracy.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claim 1-12, 14, 15, and 17-20 is/are rejected under 35 U.S.C. 101 as claiming the same invention as that of corresponding claims 1-12, 14, 15, and 17-20 of prior U.S. Patent No. 11900536. This is a statutory double patenting rejection.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 13 rejected on the ground of nonstatutory double patenting as being unpatentable over corresponding claim 13 of U.S. Patent No. 11900536.
The patented claims are narrower than the pending claims.
Claim 16 is rejected on the ground of nonstatutory double patenting as being unpatentable over corresponding claim 16 of U.S. Patent No. 11900536.
The patented claims are narrower than the pending claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIO A. RODIN whose telephone number is (571)272-8003. The examiner can normally be reached M-F 8:00-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer can be reached at 571-272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARIO ANTHONY RODIN/Examiner, Art Unit 2675
/ANDREW M MOYER/Supervisory Patent Examiner, Art Unit 2675
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