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 .
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.
Joint Inventors
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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 2 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim recites: “A reinforcement learning method, comprising: learning, using a neural network, one or more policies to control a robot to grasp a RF- tagged object, the RF-tagged object located in an environment which includes other objects, the learning including fusing vision information and an RF signal of the RF-tagged object; wherein fusing the vision information and the RF signal is performed based on RF-determined location information.”
These limitations, as drafted, are simple processes that, under their broadest reasonable interpretation, cover performance of the mind, but for the recitation of ‘learning, using a neural network, one or more policies to control a robot to grasp a RF- tagged object’ and ‘fusing vision information and an RF signal of the RF-tagged object’. That is, other than reciting the bolded limitations above, nothing in the claim elements preclude the steps from being performed in the mind or with pen and paper.
This judicial exception is not integrated into a practical application. The ‘learning, using a neural network, one or more policies to control a robot to grasp a RF- tagged object’ and ‘fusing vision information and an RF signal of the RF-tagged object’ steps is/are recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity (see MPEP 2106.05(g)). Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. In particular, the generation of data described in the steps above are merely automated determination and data processing steps, implemented without any meaningful limitations to the performance of the abstract idea, and acting as a generic computer operating in its ordinary capacity. The generation of control data is not a control step.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional step(s) of ‘learning, using a neural network, one or more policies to control a robot to grasp a RF- tagged object’ and ‘fusing vision information and an RF signal of the RF-tagged object’ is/are mere data gathering and is/are a well-understood, routine, and conventional function (see MPEP 2106.05(d) and see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93), and thus is/are no more than insignificant extra-solution activity (see MPEP 2106.05(g) and see OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). Thus, the limitations do not provide an inventive concept, and the claim contains ineligible subject matter.
Claims 3-10 recite limitations that are no more that the abstract idea recited in claim 2.
Regarding claim 3: The claim recites a further limitation of the neural network used to generate data, which is/are mere data gathering and is/are a well-understood, routine, and conventional function, and thus is/are no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Regarding claim 4: The claim recites a further limitation of the data fusion, which is/are mere data gathering and is/are a well-understood, routine, and conventional function, and thus is/are no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Regarding claim 5: The method of claim 3 wherein the RF-determined location information is used to apply a binary mask around the location of the RF-tagged object in a camera image corresponding to the vision information.
Regarding claim 6: The claim recites a further limitation of the control data generation, which is/are mere data gathering and is/are a well-understood, routine, and conventional function, and thus is/are no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Regarding claim 7: The claim recites a further limitation of the control data generation, which is/are mere data gathering and is/are a well-understood, routine, and conventional function, and thus is/are no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Regarding claim 8: The claim recites a further limitation of the control data generation, which is/are mere data gathering and is/are a well-understood, routine, and conventional function, and thus is/are no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Regarding claim 9: The claim recites a further limitation of the control data generation, which is/are mere data gathering and is/are a well-understood, routine, and conventional function, and thus is/are no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Regarding claim 10: The claim recites a further limitation of the control data generation, which is/are mere data gathering and is/are a well-understood, routine, and conventional function, and thus is/are no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Regarding claim 11: The claim recites a control step caused by the generation of the control data, and therefore contains a practical application and is NOT rejected.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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) 11 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Girardin et al. (, referred to as Girardin).
Regarding claim 11: A control system, comprising: a storage area configured to store instructions; and a controller configured execute the instructions to: a) determine a location of a tagged target object in an area of interest based on a radio frequency (RF) signal, ([page 48, lines 10-15] computing device 2510 may also be configured to receive one or more inputs from one or more sensors or other devices 2560 that are indicative of a wheeled mobility device being present on the vehicle. Those sensors or devices 2560 may include the floor pressure sensor 1300, the IR beams 1540, 1640, a WMD-mounted or occupant-retained RFID tag, WMD-mounted or occupant-retained QR or bar code, and/or a camera and image 15 recognition software.) b) determine a trajectory to grasp the tagged target object ([page 11, lines 10-12] the securement length can be set so that the gripping zone is located approximately on or adjacent the ideal point of contact when the securement member is moved to mobility device secured position. [page 11, lines 15-16] a set of squeeze-force criteria and/or securement length criteria can be developed to optimize securement for each application.) based on a combination of the RF frequency signal and visual information from a vision sensor; and ([page 38, lines 18-21] Various intelligent sensor technologies, such as optical sensors, video analytics, or RFID tags, alone or in combination with artificial intelligence may be used to identify the model of wheeled mobility device and/or locate the wheel) generate a control signal to perform a robot grasping operation which includes picking up the tagged target object along a line-of-sight relative to a robot in a state where the tagged target object is not partially or fully occluded. ([page 47, lines 9-12] the computing device 2510 may issue instructions, in the form of signals, to various components in the securement system such as lighting, audible alarms and ancillaries, as well as motor controls (collectively 2580), according to logical algorithm included with the computer program product. [page 4, lines 1-5] Those sensors may communicate information such as presence of obstructions, range from object, forces, etc. The sensors could operate via one or a combination of mechanical switching, current-sensing, visible light, IR, RF, sonar, magnetics, inertial sensing, resistance, hall-effect, induction, or capacitance, or other known sensor technologies.)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2-11 are rejected under 35 U.S.C. 103 as being unpatentable over Girardin et al. (CA3131314A1, referred to as Girardin) in view of Sibley et al. (US20220117211, referred to as Sibley).
Regarding claim 2: Girardin discloses: A reinforcement learning method, comprising: [learning, using a neural network], one or more policies to control a robot to grasp a RF- tagged object, ([page 38, lines 18-21] Various intelligent sensor technologies, such as optical sensors, video analytics, or RFID tags, alone or in combination with artificial intelligence may be used to identify the model of wheeled mobility device and/or locate the wheel) the RF-tagged object located in an environment which includes other objects, the learning including fusing vision information and an RF signal of the RF-tagged object; wherein fusing the vision information and the RF signal is performed based on RF- determined location information. ([page 48, lines 10-15] computing device 2510 may also be configured to receive one or more inputs from one or more sensors or other devices 2560 that are indicative of a wheeled mobility device being present on the vehicle. Those sensors or devices 2560 may include the floor pressure sensor 1300, the IR beams 1540, 1640, a WMD-mounted or occupant-retained RFID tag, WMD-mounted or occupant-retained QR or bar code, and/or a camera and image 15 recognition software.)
Girardin does not disclose: learning, using a neural network
Girardin does not explicitly disclose the following limitations, however Sibley, from an analogous field of endeavor, further teaches: learning, using a neural network ([0075] The systems, robots, computer software and systems, applications using computer vision and automation, or a combination thereof, can be implemented using data science and data analysis, including machine learning, deep learning including convolutional neural nets (“CNNs”), deep neural nets (“DNNs”), and other disciplines of computer-based artificial intelligence, as well as computer-vision techniques used to compare and correspond features or portions of one or more images, including 2D and 3D images, to facilitate detection, identification, classification, and treatment of individual agricultural objects, perform and implement visualization, mapping, pose of an agricultural object or of the robotic system, and/or navigation applications using simultaneous localization and mapping (SLAM) systems and algorithms, visual odometry systems and algorithms, including stereo visual odometry, or a combination thereof, receive and fuse sensor data with sensing technologies to provide perception, navigation, mapping, visualization, mobility, tracking, targeting, with sensing devices including cameras, depth sensing cameras or other depth sensors, black and white cameras, color cameras including RGB cameras, RGB-D cameras, infrared cameras, line scan cameras, area scan cameras, rolling shutter and global shutter cameras, optoelectric sensors, photooptic sensors, light detection and ranging sensors (LiDar) including spinning LiDar, flash LiDar, static LiDar, etc., lasers, radar sensors, sonar sensors, radio sensors, ultrasonic sensors and rangefinders, other range sensors,)
Girardin and Sibley are analogous art to the claimed invention since they are from the similar field of visual-input based path-planning for sensor fusion for robotic systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the localization grasping method of Girardin to enable the deep neural net of Sibley for the purpose of using a known optimization technique to improve accuracy over time.
The motivation for modification would have been to provide neural network learning to the optimization process disclosed in Girardin.
Regarding claim 3: The combination of Girardin and Sibley teaches: The method of claim 2,
Sibley further teaches: wherein the neural network includes a deep convolutional neural network. ([0075] The systems, robots, computer software and systems, applications using computer vision and automation, or a combination thereof, can be implemented using data science and data analysis, including machine learning, deep learning including convolutional neural nets (“CNNs”), deep neural nets (“DNNs”), and other disciplines of computer-based artificial intelligence)
As previously stated, Girardin and Sibley are analogous art to the claimed invention since they are from the similar field of visual-input based path-planning for sensor fusion for robotic systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the localization grasping method of Girardin to enable the deep neural net of Sibley for the purpose of using a known optimization technique to improve accuracy over time.
The motivation for modification would have been to provide neural network learning to the optimization process disclosed in Girardin.
Regarding claim 4: The combination of Girardin and Sibley teaches: The method of claim 3,
Girardin further discloses: wherein the fusing is performed by using the RF-tagged object as an attention mechanism and an RF kernel. ([page 38, lines 18-21] Various intelligent sensor technologies, such as optical sensors, video analytics, or RFID tags, alone or in combination with artificial intelligence may be used to identify the model of wheeled mobility device and/or locate the wheel.)
Regarding claim 5: The combination of Girardin and Sibley teaches: The method of claim 3,
Girardin further discloses: wherein the RF-determined location information is used to apply a binary mask around the location of the RF-tagged object in a camera image corresponding to the vision information. ([page 38, lines 18-21] Various intelligent sensor technologies, such as optical sensors, video analytics, or RFID tags, alone or in combination with artificial intelligence may be used to identify the model of wheeled mobility device and/or locate the wheel. [page 10-11, lines 18-] The WMD station on the vehicle can be monitored via cameras or other sensors that are linked to intelligent feature-recognition software. The WMD securement device can autonomously process the situation and react with the appropriate function that provides the best rider experience and trip safety. Such functions could include recognizing the presence and location of a WMD and occupant, recognizing the type of WMD, identifying ideal points of contact for securement (typically drive wheels), and avoiding sensitive surfaces and items such as fenders, accessory bags and occupants' limbs.)
Regarding claim 6: The combination of Girardin and Sibley teaches: The method of claim 2,
Girardin further discloses: wherein, learning the one or more policies includes implementing a spatio-temporal reward function, the spatio-temporal reward function implemented as an inverse of a distance from a location of the RF-tagged object, wherein the spatio-temporal reward function is maximized for earlier success corresponding to a temporal part of the spatio-temporal reward function. ([page 10-11, lines 18-] The WMD station on the vehicle can be monitored via cameras or other sensors that are linked to intelligent feature-recognition software. The WMD securement device can autonomously process the situation and react with the appropriate function that provides the best rider experience and trip safety. Such functions could include recognizing the presence and location of a WMD and occupant, recognizing the type of WMD, identifying ideal points of contact for securement (typically drive wheels), and avoiding sensitive surfaces and items such as fenders, accessory bags and occupants' limbs. [page 11, lines 14-16] Once WMD type is identified, a set of squeeze-force criteria and/or securement length criteria can be developed to optimize securement for each application.))
Regarding claim 7: The combination of Girardin and Sibley teaches: The method of claim 2,
Girardin further discloses: wherein the one or more policies correspond to directly grasping the RF-tagged object. ([page 11, lines 10-12] the securement length can be set so that the gripping zone is located approximately on or adjacent the ideal point of contact when the securement member is moved to mobility device secured position. [page 11, lines 15-16] a set of squeeze-force criteria and/or securement length criteria can be developed to optimize securement for each application. [page 54, lines 1-2] the computing device 2510 will send a signal to the motor controller (included within components 2580) for the gripping members.)
Regarding claim 8: The combination of Girardin and Sibley teaches: The method of claim 2,
Girardin further discloses: wherein the one or more policies include performing decluttering prior to controlling the robot to grasp the RF-tagged object. ([page 51, lines 10-13] in response to detecting that a WMD has entered the vehicle, the computing system could trigger seats to retract to a stored position and, optionally provide visual, audible, or vibratory alerts for the seated passengers that that the seats are moving.)
Regarding claim 9: The combination of Girardin and Sibley teaches: The method of claim 8,
Girardin further discloses: wherein the decluttering includes extracting one or more occluded objects from a pile. ([page 51, lines 10-13] in response to detecting that a WMD has entered the vehicle, the computing system could trigger seats to retract to a stored position and, optionally provide visual, audible, or vibratory alerts for the seated passengers that that the seats are moving. [page 52, lines 6-11] The computing system could then sense whether the WMD is positioned properly in the securement area. The computing system could then sense whether the WMD is stationary. The computing system could then sense whether the paths of the securement members are clear. Assuming one or more or all of these criteria are met, the computing system could then activate the securement system to move the securement members into place, without requiring any input from the vehicle attendant of WMD passenger.)
Regarding claim 10: The combination of Girardin and Sibley teaches: The method of claim 8,
Girardin further discloses: wherein the decluttering includes moving one or more occluded objects or moving one or more distractor items to a side location. ([page 51, lines 10-13] in response to detecting that a WMD has entered the vehicle, the computing system could trigger seats to retract to a stored position and, optionally provide visual, audible, or vibratory alerts for the seated passengers that that the seats are moving. [page 52, lines 6-11] The computing system could then sense whether the WMD is positioned properly in the securement area. The computing system could then sense whether the WMD is stationary. The computing system could then sense whether the paths of the securement members are clear. Assuming one or more or all of these criteria are met, the computing system could then activate the securement system to move the securement members into place, without requiring any input from the vehicle attendant of WMD passenger.)
Conclusion
The prior art made of record, and not relied upon, considered pertinent to applicant' s disclosure or directed to the state of art is listed on the enclosed PTO-892.
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/ATTICUS A CAMERON/ /JASON HOLLOWAY/ Primary Examiner, Art Unit 3658 Examiner, Art Unit 3658A
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