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 .
Claims 1-13 and 15-39 are presented for examination.
Response to Amendment
The prior objections to the claims and specification have been obviated by the amendments. Accordingly, these objections are withdrawn.
Claim Objections
Claim 33 is objected to because of the following informalities:
“claim 32 wherein” should read “claim 32, wherein”
Appropriate correction is required.
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.
Claims 1-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”).
Claim 1
Step 1: The claim recites a method, and is therefore directed to the statutory category of processes.
Step 2A Prong 1: The claim recites, inter alia:
“…generate information for picking up at least a first object among the plurality of objects”; This limitation encompasses mentally generating information for picking up at least a first object among the plurality of objects.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “obtaining, by at least one processor, data in relation to a plurality of objects to be picked up by the robot, the data including at least one of information in relation to a shape of each of the plurality of objects, or information obtained by processing the information in relation to the shape of each of the plurality of objects,” however, this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP § 2106.05(g)). The claim also recites “causing, by the at least one processor, a neural network to… [perform the judicial exception] by inputting the data in relation to the plurality of objects into the neural network.” However, this amounts to mere instructions to apply the judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)), given that it is merely reciting a processor and a neural network for performing the judicial exception of generating information for picking up an object using input data. The claim further recites that the method is “of picking up one or more objects by a robot” and “controlling, by the at least one processor and based on the information for picking up at least the first object, the robot to pick up at least the first object,” however these limitations amount to generally linking the judicial exception to the field of use of picking up objects by a robot (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The obtaining data limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP § 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2. As an ordered whole, the claim is directed to a mentally performable process of generating information for picking up at least a first object based on data in relation to a plurality of objects. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 1 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the information obtained by processing the information in relation to the shape of each of the plurality of objects includes at least one of position information of each of the plurality of objects, orientation information of each of the plurality of objects, or an image including the plurality of objects.” However, this limitation merely further limits the information obtaining step, and still amounts to the insignificant extra-solution activity of mere data gathering (MPEP § 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The obtaining data limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP § 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 1 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the information in relation to the shape of each of the plurality of objects includes at least one of an image including the plurality of objects, three-dimensional position information of each of the plurality of objects, or distance information from a measuring device to a surface of each of the plurality of objects.” However, this limitation merely further limits the information obtaining step, and still amounts to the insignificant extra-solution activity of mere data gathering (MPEP § 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The obtaining data limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP § 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“wherein the neural network is updated by reinforcement learning using a reward calculated based on information in relation to a picking operation of an object”; This limitation could encompass mentally updating a neural network by reinforcement learning using a reward calculated based on information in relation to a picking operation of an object, such as by mentally updating weights of the neural network.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 1.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“wherein the information in relation to the picking operation of the object includes at least one of success or failure of the picking operation of the object, a number of times of success of picking operations of objects, a time taken for picking up or transporting the object, a force acting on a hand unit picking up or transporting the object, an achievement level of a post-process after the picking operation of the object, a change in state of the object, or energy for picking up or transporting the object”; This limitation merely further limits the information used in updating the neural network by reinforcement learning, which is still mentally performable given at least one of these options for calculating a reward.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 1.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“wherein the information for picking up at least the first object includes information for moving another object different from the first object”; This limitation merely further limits the generated information for picking up at least the first object, and generating information is still mentally performable given this further limitation, as one can mentally generate information for moving another object different from the first object.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 1.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“wherein the neural network comprises a value function in the reinforcement learning”; This limitation merely further limits the step of updating the neural network by reinforcement learning, which is still mentally performable when the neural network comprises a value function in the reinforcement learning.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 1.
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“wherein the value function represents a value of control information of the robot picking up or transporting the first object”; This limitation merely further limits the step of updating the neural network by reinforcement learning, which is still mentally performable when the value function represents a value of control information of a robot picking up or transporting the object.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 1.
Claim 9
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“wherein the neural network is updated to minimize an error calculated based on a label for picking up an object and an output of the neural network”; This limitation could encompass mentally updating the neural network to minimize an error calculated based on a label for picking up an object and an output of the neural network, such as by mentally updating weight values.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 1.
Claim 10
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“wherein the neural network outputs at least one of position information of the first object or information in relation to a success rate of picking up the first object”; This limitation merely further limits the output of the neural network used to update the neural network, and updating the neural network is still mentally performable given at least one of these options.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 1.
Claim 11
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“determining…whether the information for picking up at least the first object is abnormal”; This limitation encompasses mentally determining whether the information for picking up the one of the plurality of objects is abnormal.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the determining is performed by the at least one processor. However, this limitation amounts to mere instructions to apply the judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The processor limitation amounts to mere instructions to apply the judicial exception on a generic computer (MPEP § 2106.05(f)) as stated above.
Claim 12
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“wherein the neural network is updated by using data obtained from simulations”; This limitation encompasses mentally updating the neural network using data obtained from simulations, such as by mentally updating weights.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 1.
Claim 13
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“wherein the information for picking up the first object includes at least one of robot control information, position information of a hand of the robot picking up or transporting the first object, orientation information of the hand, take-out direction information of the hand, position information of the first object, success rate information of picking up the first object, or control information of a measuring device”; This limitation merely further limits the “generate information” step, which is still mentally performable given at least one of the recited options.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 1.
Claim 15
Step 1: The claim recites a method, and therefore is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites:
“…generate information for picking up at least a first object among the plurality of objects”; This limitation encompasses mentally generating information for picking up at least a first object among the plurality of objects.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “obtaining, by at least one processor, data in relation to a plurality of objects to be picked up by the robot, the data including at least one of information in relation to a shape of each of the plurality of objects, or information obtained by processing the information in relation to the shape of each of the plurality of objects,” however, this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP § 2106.05(g)). The claim also recites “updating, by the at least one processor, a neural network to… [perform the judicial exception] by inputting the data in relation to the plurality of objects into the neural network.” However, this amounts to mere instructions to apply the judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)), given that it is merely reciting a processor and a neural network that is updated to perform the judicial exception of generating information for picking up an object using input data. The claim further recites that the information generated by the neural network is “used to control the robot to pick up at least the first object,” however this limitation amounts to generally linking the use of the judicial exception of generating information for picking up at least the first object to the field of use of picking up objects by a robot (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The obtaining data limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP § 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2. As an ordered whole, the claim is directed to a mentally performable process of generating information for picking up an object based on data in relation to a plurality of objects. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claims 16-27
Step 1: A process, as above.
Step 2A Prong 1: Claims 16-27 recite the same judicial exception as claims 2-13, respectively, except insofar as the claims inherit the abstract ideas from method claim 15 rather than method claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step mirrors that of claims 2-13, respectively, except insofar as the claims inherit the additional elements from method claim 15 rather than method claim 1.
Step 2B: The claims do not contain significantly more than the judicial exception. The analysis at this step mirrors that of claims 2-13, respectively, except insofar as the claims inherit the additional elements from method claim 15 rather than method claim 1.
Claim 28
Step 1: The claim recites a device comprising at least one memory and at least one processor, and therefore is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites:
“…generate information for picking up at least a first object among the plurality of objects”; This limitation encompasses mentally generating information for picking up at least a first object among the plurality of objects.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “obtain data in relation to a plurality of objects to be picked up by a robot, the data including at least one of information in relation to a shape of each of the plurality of objects, or information obtained by processing the information in relation to the shape of each of the plurality of objects,” however, this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP § 2106.05(g)). The claim also recites “update a neural network to [perform the judicial exception] from the neural network by inputting the data in relation to the plurality of objects into the neural network.” However, this amounts to mere instructions to apply the judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)), given that it is merely updating a generic neural network to perform the judicial exception of generating information for picking up an object using input data. The claim further recites that the information generated by the neural network is “used to control the robot to pick up at least the first object,” however this limitation amounts to generally linking the use of the judicial exception of generating information for picking up at least the first object to the field of use of picking up objects by a robot (MPEP 2106.05(h)). The claim further recites “A learning device, comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: [perform the method],” however this limitation amounts to mere instructions to apply the judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The obtain data limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP § 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2. As an ordered whole, the claim is directed to a mentally performable process of generating information for picking up an object based on data in relation to a plurality of objects. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 29
Step 1: A process, as claim 1 above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “the causing of the neural network to generate the information for picking up at least the first object includes inputting the data in relation to the plurality of objects and information in relation to a state of a hand of the robot into the neural network.” However, this limitation amounts to mere instructions to apply the judicial exception on a generic computer using a generic class of computer algorithms (MPEP § 2106.05(f)), as it merely recites inputting data into the generically recited neural network in order to perform the judicial exception.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.
Claim 30
Step 1: A process, as claim 1 above.
Step 2A Prong 1: The claim recites, inter alia:
“selecting… the neural network from a plurality of neural networks based on a type of a hand of the robot”; This limitation encompasses mentally choosing a neural network based on a type of a hand of the robot.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the selecting is performed “by the at least one processor,” however, this limitation amounts to mere instructions to apply the judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.
Claim 31
Step 1: The claim recites a system comprising at least one memory, a robot, and at least one processor, and is therefore directed to the statutory category of machines.
Step 2A Prong 1: The claim recites, inter alia:
“estimate information for picking up one of the plurality of objects…”; This limitation encompasses mentally estimating information for picking up one of the plurality of objects.
“…generate information for picking up at least a first object among the plurality of objects”; This limitation encompasses mentally generating information for picking up at least a first object among the plurality of objects.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “obtain data in relation to a plurality of objects to be picked up by the robot, the data including at least one of information in relation to a shape of each of the plurality of objects, or information obtained by processing the information in relation to the shape of each of the plurality of objects,” however, this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP § 2106.05(g)). The claim also recites that the estimation is performed “by inputting the data in relation to the plurality of objects into a neural network,” and “cause a neural network to… [perform the judicial exception] by inputting the data in relation to the plurality of objects into the neural network.” However, these limitations amount to mere instructions to apply the judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)), given that these limitations merely recite inputting data into a generic neural network in order to perform the judicial exception of estimating/generating information for picking up an object. The claim further recites “control, based on the information for picking up at least the first object, the robot to pick up at least the first object,” however this limitation amounts to generally linking the use of the judicial exception of generating information for picking up at least the first object to the field of use of picking up objects by a robot (MPEP 2106.05(h)). The claim further recites “A system, comprising: at least one memory; a robot, and at least one processor coupled to the at least one memory and configured to: [perform the method],” however this limitation amounts to mere instructions to apply the judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The obtain data limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP § 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2. As an ordered whole, the claim is directed to a mentally performable process of generating information for picking up an object. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claims 32-39
Step 1: A machine, as above.
Step 2A Prong 1: Claims 32-39 recite the same judicial exception as claims 4-6, 9-10, 12-13, and 29, respectively, except insofar as the claims inherit the abstract ideas from system claim 31 rather than method claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step mirrors that of claims 4-6, 9-10, 12-13, and 29, respectively, except insofar as the claims inherit the additional elements from system claim 31 rather than method claim 1.
Step 2B: The claims do not contain significantly more than the judicial exception. The analysis at this step mirrors that of claims 4-6, 9-10, 12-13, and 29, respectively, except insofar as the claims inherit the additional elements from system claim 31 rather than method claim 1.
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.
Claims 1-10, 12-13, 15-24, 26-29, and 31-39 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (Grasping Novel Objects with a Dexterous Robotic Hand through Neuroevolution) (hereinafter “Huang”) in view of Maeda et al. (View-based Programming with Reinforcement Learning for Robotic Manipulation) (“Maeda”).
Regarding claim 1, Huang discloses “A method of picking up one or more objects by a robot (Huang, Fig 4(b): “Mekahand Grasp an object”), the method comprising:
obtaining… data in relation to a plurality of objects to be picked up by the robot (Huang, Fig 5 depicts data in relation to a mug and a cube), the data including at least one of
information in relation to a shape of each of the plurality of objects (Huang, Fig. 5: “(a) The RGB pixel data of the scene from the camera within GraspIt!”), or
information obtained by processing the information in relation to the shape of each of the plurality of objects (Huang, Fig. 5: “(b) The 20×20 depth data array supplied to the neural network as input. The depth data is normalized to a floating point number between [0, 1]” and Huang V.A: “The raw depth data from the Kinect sensor is of high dimensionality, so for practical reasons the array is first down scaled. Thus, before the input data is supplied to an ANN, the 640×480 pixel array was sampled to form a reduced 20×20 array. This smaller array was converted to gray-scale intensity values, and then normalized between zero and one; an example is shown in Figure 5”); and
causing… a neural network to generate information for picking up at least a first object among the plurality of objects by inputting the data in relation to the plurality of objects into the neural network” (Huang, Fig. 5: “(b) The 20×20 depth data array supplied to the neural network as input” and Huang, Fig. 3: “Representation of the designed grasp controller network. The left side of the figure shows GraspIt! simulation environment; the right side of the figure shows a neural network receiving input consisting of depth data and the goal coordinate (a, b) on the GraspIt! visual input scene. The network has seven output nodes: hand position (X,Y,Z), rotation axis (x,y,z) and rotation angle (r)” and IV, paragraph 3: “Each ANN predicts where the object is and in what direction to grasp the object by outputting 3D hand positions and orientations”), and
controlling… based on the information for picking up at least the first object, the robot to pick up at least the first object (Huang, IV. B: “To reduce execution time, the following computational steps were parallelized, as shown in Figure 4.(b)… GraspIt! tasks accept ANNs and communicate with a GraspIt! process to send the output from a neural network for simulation in GraspIt!, and receive the resulting quality” and Fig 4(b): “Mekahand Grasp an object”).
Huang does not appear to explicitly disclose that the steps are performed by a processor.
However, Maeda discloses a processor (Maeda, VII: “We used a PC with Corei7870 CPU (at2.93GHz) and GeForce GTX 460 GPU”).
Maeda and the instant application both relate to object-manipulating robots using neural networks and reinforcement learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Huang with the teachings of Maeda such that the method steps are performed “by a processor,” and one would have been motivated to do so for the purpose of performing learning experiments in a virtual environment to autonomously achieve robot adaptability (see Maeda, VII. C).
Regarding claim 2, the rejection of claim 1 is incorporated. Huang as modified by Maeda further discloses “wherein the information obtained by processing the information in relation to the shape of each of the plurality of objects includes at least one of position information of each of the plurality of objects, orientation information of each of the plurality of objects, or an image including the plurality of objects” (Huang, Fig. 5: “(b) The 20×20 depth data array supplied to the neural network as input. The depth data is normalized to a floating point number between [0, 1]”; Examiner notes that the depth data array of the scene containing the cube and the mug in Fig. 5(b) corresponds to “position information of each of the plurality of objects”).
Regarding claim 3, the rejection of claim 1 is incorporated. Huang as modified by Maeda further discloses “wherein the information in relation to the shape of each of the plurality of objects includes at least one of an image including the plurality of objects, three-dimensional position information of each of the plurality of objects, or distance information from a measuring device to a surface of each of the plurality of objects” (Huang, Fig. 5: (a) The RGB pixel data of the scene from the camera within GraspIt!”; Examiner notes that the RGB pixel data of the scene containing the cube and the mug in Fig. 5(a) corresponds to “an image including the plurality of objects”).
Regarding claim 4, the rejection of claim 1 is incorporated. Huang as modified by Maeda further discloses “wherein the neural network is updated by reinforcement learning using a reward calculated based on information in relation to a picking operation of an object” (Maeda, VI-A: “In the end of an episode, the neural network is retrained by BPM using desired actor and critic outputs ((5) and (11)) obtained in the episode in addition to the teaching signals obtained in the demonstration in supervised learning. Nmax episodes are repeated in reinforcement learning to obtain an improved neural network that can achieve manipulation tasks in wider task conditions” and Maeda, VI-B: “Design of appropriate reward rt is necessary for successful reinforcement learning. In order to carry the object to the goal, the Euclidean distance between the object and the goal is useful to design the reward function”; Examiner notes that “distance between the object and the goal” corresponds to “information in relation to a picking operation of an object”).
Maeda and the instant application both relate to object-manipulating robots using neural networks and reinforcement learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Huang to include updating the neural network by reinforcement learning using a reward calculated based on information in relation to a picking operation of an object as disclosed by Maeda, and one would have been motivated to do so for the purpose of improving the performance of the object-manipulating robot by allowing the robot to adapt to a wider range of changes in task conditions (see Maeda, III-2).
Regarding claim 5, the rejection of claim 4 is incorporated. Huang as modified by Maeda further discloses “wherein the information in relation to the picking operation of the object includes at least one of success or failure of the picking operation of the object, a number of times of success of picking operations of objects, a time taken for picking up or transporting the object, a force acting on a hand unit of the robot picking up or transporting the object, an achievement level of a post-process after the picking operation of the object, a change in state of the object, or a change in energy for picking up or transporting the object” (Maeda, VI-B: “Design of appropriate reward rt is necessary for successful reinforcement learning. In order to carry the object to the goal, the Euclidean distance between the object and the goal is useful to design the reward function”; Examiner notes that “distance between the object and the goal” corresponds to “an-achievement level of a post-process after the picking operation of the object” because it reflects how far the object is from a goal location after being picked up and transported by the robot).
Maeda and the instant application both relate to object-manipulating robots using neural networks and reinforcement learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Huang such that the information in relation to the picking operation of the object includes an achievement level of a post-process after the picking operation of the object, as disclosed by Maeda, and one would have been motivated to do so for the purpose of improving the performance of the object-manipulating robot by allowing the robot to adapt to a wider range of changes in task conditions (see Maeda, III-2).
Regarding claim 6, the rejection of claim 4 is incorporated. Huang as modified by Maeda further discloses “wherein the information for picking up at least the first object includes information for moving another object different from the first object” (Huang, V. B: “Figure 7(c) shows training results for networks trained to grasp both a cube and a mug”; Examiner notes that a network trained to grasp both a cube and a mug must generate information for picking up both a mug (first object) and a cube (another object different from the first object)).
Regarding claim 7, the rejection of claim 4 is incorporated. Huang as modified by Maeda further discloses “wherein the neural network comprises a value function in the reinforcement learning” (Maeda, V: “The output of the neural network is [at,V(st)], where at =Δxt is the action of the robot and V(st) is the state value. Thus the neural network is composed of two parts: mapping from states to robot motions (“actor”) and mapping from states to state values (“critic”). The latter is added for actor-critic reinforcement learning, which is described in the next section”; Examiner notes that V(st) is a value function in the reinforcement learning, and is an output of the neural network).
Maeda and the instant application both relate to object-manipulating robots using neural networks and reinforcement learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Huang such that the neural network comprises a value function in the reinforcement learning, as disclosed by Maeda, and one would have been motivated to do so for the purpose of improving the performance of the object-manipulating robot by allowing the robot to adapt to a wider range of changes in task conditions (see Maeda, III-2).
Regarding claim 8, the rejection of claim 7 is incorporated. Huang as modified by Maeda further discloses “wherein the value function represents a value of control information of the robot picking up or transporting the first object” (Maeda, V: “The output of the neural network is [at,V(st)], where at =Δxt is the action of the robot and V(st) is the state value. Thus the neural network is composed of two parts: mapping from states to robot motions (“actor”) and mapping from states to state values (“critic”). The latter is added for actor-critic reinforcement learning, which is described in the next section” and Maeda, VII-B: “Programming of picking the object up and placing it to a goal position by the robot hand was tested”; Examiner notes that the value function represents the value of states which are mapped to robot motions, and the robot motions include picking up and transporting the object).
Maeda and the instant application both relate to object-manipulating robots using neural networks and reinforcement learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Huang such that the value function represents a value of control information of a robot picking up or transporting the first object, as disclosed by Maeda, and one would have been motivated to do so for the purpose of improving the performance of the object-manipulating robot by allowing the robot to adapt to a wider range of changes in task conditions (see Maeda, III-2).
Regarding claim 9, the rejection of claim 1 is incorporated. Huang as modified by Maeda further discloses “wherein the neural network is updated to minimize an error calculated based on a label for picking up an object and an output of the neural network” (Maeda, V: “We train the neural network with backpropagation with momentum (BPM) [9]. The training signals for the input of the neural network are state values (st) in the demonstration. The training signals for the actor part of the output of the neural network are robot motions (at) in the demonstration. The training signals for the critic part of the output are state values (V(st)), which are calculated as follows…The trained neural network (the actor part) can be used to control the robot hand to play back the demonstrations [1]”; Examiner notes that the training signals obtained from the demonstration of the pick-and-place operation (see Maeda, VII-B: “In view-based supervised learning, a human operator demonstrated pick-and-place with keyboard commands”) correspond to “a label for picking up an object”, and that backpropagation involves minimizing an error between the desired output (from the training signals) and the actual output of the neural network).
Maeda and the instant application both relate to object-manipulating robots using neural networks and reinforcement learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Huang to include updating the neural network to minimize an error calculated based on a label for picking up an object and an output of the neural network, as disclosed by Maeda, and one would have been motivated to do so for the purpose of ensuring that the neural network produces the desired output related to picking up an object, given an input image (see Maeda, III-1).
Regarding claim 10, the rejection of claim 9 is incorporated. Huang as modified by Maeda further discloses “wherein the neural network outputs at least one of position information of the first object or information in relation to a success rate of picking up the first object” (Huang, IV, paragraph 3: “Each ANN predicts where the object is and in what direction to grasp the object by outputting 3D hand positions and orientations”).
Regarding claim 12, the rejection of claim 1 is incorporated. Huang as modified by Maeda further discloses “wherein the neural network is updated by using data obtained from simulations” (Maeda, VII: “Using our proposed method, we performed learning experiments in the virtual environment presented in Section IV…” and Maeda, VII-B: “Next, view-based reinforcement learning was performed. In the reinforcement learning, the object was initially located at a shifted position from which the initial neural network was not able to carry it to the goal (Fig. 12). After Nmax episodes in reinforcement learning, an updated neural network was obtained. It can drive the virtual hand to the goal not only from the initial position of the object in the human demonstration but also from the shifted position (Fig. 13)”).
Maeda and the instant application both relate to object-manipulating robots using neural networks and reinforcement learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Huang to include updating the neural network by using data obtained from simulations, as disclosed by Maeda, and one would have been motivated to do so for the purpose of obtaining an improved neural network that can achieve manipulation tasks in wider task conditions (see Maeda, VI-A).
Regarding claim 13, the rejection of claim 1 is incorporated. Huang as modified by Maeda further discloses “wherein the information for picking up the first object includes at least one of robot control information, position information of a hand of the robot picking up or transporting the first object, orientation information of the hand, take-out direction information of the hand, position information of the first object, success rate information of picking up the first object, or control information of a measuring device” (Huang, IV, paragraph 3: “Each ANN predicts where the object is and in what direction to grasp the object by outputting 3D hand positions and orientations”).
Regarding claim 15, Huang discloses “A learning method, comprising:
obtaining… data in relation to a plurality of objects to be picked up by a robot (Huang, Fig 5 depicts data in relation to a mug and a cube), the data including at least one of
information in relation to a shape of each of the plurality of objects (Huang, Fig. 5: “(a) The RGB pixel data of the scene from the camera within GraspIt!”), or
information obtained by processing the information in relation to the shape of each of the plurality of objects (Huang, Fig. 5: “(b) The 20×20 depth data array supplied to the neural network as input. The depth data is normalized to a floating point number between [0, 1]” and Huang V.A: “The raw depth data from the Kinect sensor is of high dimensionality, so for practical reasons the array is first down scaled. Thus, before the input data is supplied to an ANN, the 640×480 pixel array was sampled to form a reduced 20×20 array. This smaller array was converted to gray-scale intensity values, and then normalized between zero and one; an example is shown in Figure 5”); and
updating… a neural network to generate information for picking up at least a first object among the plurality of objects by inputting the data in relation to the plurality of objects into the neural network (Huang, Fig. 5: “(b) The 20×20 depth data array supplied to the neural network as input” and Huang, Fig. 3: “Representation of the designed grasp controller network. The left side of the figure shows GraspIt! simulation environment; the right side of the figure shows a neural network receiving input consisting of depth data and the goal coordinate (a, b) on the GraspIt! visual input scene. The network has seven output nodes: hand position (X,Y,Z), rotation axis (x,y,z) and rotation angle (r). Note that NEAT can add internal hidden nodes as evolution progresses” and IV, paragraph 3: “Each ANN predicts where the object is and in what direction to grasp the object by outputting 3D hand positions and orientations”; Examiner notes that adding internal hidden nodes as evolution progresses corresponds to updating a neural network), wherein
the information for picking up at least the first object generated by the neural network is used to control the robot to pick up at least the first object (Huang, IV. B: “To reduce execution time, the following computational steps were parallelized, as shown in Figure 4.(b)… GraspIt! tasks accept ANNs and communicate with a GraspIt! process to send the output from a neural network for simulation in GraspIt!, and receive the resulting quality” and Fig 4(b) “Mekahand Grasp an object”).
Huang does not appear to explicitly disclose that the steps are performed by a processor.
However, Maeda discloses a processor (Maeda, VII: “We used a PC with Corei7870 CPU (at2.93GHz) and GeForce GTX 460 GPU”).
Maeda and the instant application both relate to object-manipulating robots using neural networks and reinforcement learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Huang with the teachings of Maeda such that the method steps are performed “by a processor,” and one would have been motivated to do so for the purpose of performing learning experiments in a virtual environment to autonomously achieve robot adaptability (see Maeda, VII. C).
Regarding claim 16, the rejection of claim 15 is incorporated. Claim 16 is a learning method claim corresponding to “method of picking up one or more objects” claim 2, and the remainder of the rejection follows the same rationale given for claim 2 above.
Regarding claim 17, the rejection of claim 15 is incorporated. Claim 17 is a learning method claim corresponding to “method of picking up one or more objects” claim 3, and the remainder of the rejection follows the same rationale given for claim 3 above.
Regarding claim 18, the rejection of claim 15 is incorporated. Claim 18 is a learning method claim corresponding to “method of picking up one or more objects” claim 4, and the remainder of the rejection follows the same rationale given for claim 4 above.
Regarding claim 19, the rejection of claim 18 is incorporated. Claim 19 is a learning method claim corresponding to “method of picking up one or more objects” claim 5, and the remainder of the rejection follows the same rationale given for claim 5 above.
Regarding claim 20, the rejection of claim 18 is incorporated. Claim 20 is a learning method claim corresponding to “method of picking up one or more objects” claim 6, and the remainder of the rejection follows the same rationale given for claim 6 above.
Regarding claim 21, the rejection of claim 18 is incorporated. Claim 21 is a learning method claim corresponding to “method of picking up one or more objects” claim 7, and the remainder of the rejection follows the same rationale given for claim 7 above.
Regarding claim 22, the rejection of claim 21 is incorporated. Claim 22 is a learning method claim corresponding to “method of picking up one or more objects” claim 8, and the remainder of the rejection follows the same rationale given for claim 8 above.
Regarding claim 23, the rejection of claim 15 is incorporated. Claim 23 is a learning method claim corresponding to “method of picking up one or more objects” claim 9, and the remainder of the rejection follows the same rationale given for claim 9 above.
Regarding claim 24, the rejection of claim 23 is incorporated. Claim 24 is a learning method claim corresponding to “method of picking up one or more objects” claim 10, and the remainder of the rejection follows the same rationale given for claim 10 above.
Regarding claim 26, the rejection of claim 15 is incorporated. Claim 26 is a learning method claim corresponding to “method of picking up one or more objects” claim 12, and the remainder of the rejection follows the same rationale given for claim 12 above.
Regarding claim 27, the rejection of claim 15 is incorporated. Claim 27 is a learning method claim corresponding to “method of picking up one or more objects” claim 13, and the remainder of the rejection follows the same rationale given for claim 13 above.
Regarding claim 28, Huang discloses “…obtain data in relation to a plurality of objects to be picked up by the robot (Huang, Fig 5 depicts data in relation to a mug and a cube), the data including at least one of
information in relation to a shape of each of the plurality of objects (Huang, Fig. 5: “(a) The RGB pixel data of the scene from the camera within GraspIt!”), or
information obtained by processing the information in relation to the shape of each of the plurality of objects (Huang, Fig. 5: “(b) The 20×20 depth data array supplied to the neural network as input. The depth data is normalized to a floating point number between [0, 1]” and Huang V.A: “The raw depth data from the Kinect sensor is of high dimensionality, so for practical reasons the array is first down scaled. Thus, before the input data is supplied to an ANN, the 640×480 pixel array was sampled to form a reduced 20×20 array. This smaller array was converted to gray-scale intensity values, and then normalized between zero and one; an example is shown in Figure 5”); and
update a neural network to generate information for picking up at least a first object among the plurality of objects from the neural network by inputting the data in relation to the plurality of objects into the neural network” (Huang, Fig. 5: “(b) The 20×20 depth data array supplied to the neural network as input” and Huang, Fig. 3: “Representation of the designed grasp controller network. The left side of the figure shows GraspIt! simulation environment; the right side of the figure shows a neural network receiving input consisting of depth data and the goal coordinate (a, b) on the GraspIt! visual input scene. The network has seven output nodes: hand position (X,Y,Z), rotation axis (x,y,z) and rotation angle (r). Note that NEAT can add internal hidden nodes as evolution progresses” and IV, paragraph 3: “Each ANN predicts where the object is and in what direction to grasp the object by outputting 3D hand positions and orientations”; Examiner notes that adding internal hidden nodes as evolution progresses corresponds to updating a neural network), wherein
the information for picking up at least the first object generated by the neural network is used to control the robot to pick up at least the first object (Huang, IV. B: “To reduce execution time, the following computational steps were parallelized, as shown in Figure 4.(b)… GraspIt! tasks accept ANNs and communicate with a GraspIt! process to send the output from a neural network for simulation in GraspIt!, and receive the resulting quality” and Fig 4(b) “Mekahand Grasp an object”).
Huang does not appear to explicitly disclose a learning device comprising at least one memory and at least one processor coupled to the at least one memory.
However, Maeda discloses “A learning device, comprising: at least one memory; and at least one processor coupled to the at least one memory” (Maeda, VII: “We used a PC with Corei7870 CPU (at2.93GHz) and GeForce GTX 460 GPU”).
Maeda and the instant application both relate to object-manipulating robots using neural networks and reinforcement learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Huang with the teachings of Maeda such that the method is performed by a learning device comprising “at least one memory; and at least one processor coupled to the at least one memory,” and one would have been motivated to do so for the purpose of performing learning experiments in a virtual environment to autonomously achieve robot adaptability (see Maeda, VII. C).
Regarding claim 29, the rejection of claim 1 is incorporated. Huang as modified by Maeda further discloses “wherein the causing of the neural network to generate the information for picking up at least the first object includes inputting the data in relation to the plurality of objects and information in relation to a state of a hand of the robot into the neural network” (Huang, Fig. 5: “(b) The 20×20 depth data array supplied to the neural network as input” and IV, paragraph 2: “Each ANN evaluated by NEAT receives input data denoting the current state of the robot in its environment”).
Regarding claim 31, Huang discloses “A system, comprising…
a robot (Huang, III. A: “GraspIt! [22], [23] facilitates simulating the Mekahand robot in representative grasping tasks and aids in measuring the quality of resulting grips”) … and configured to:
obtain data in relation to a plurality of objects to be picked up by the robot (Huang, Fig 5 depicts data in relation to a mug and a cube), the data including at least one of
information in relation to a shape of each of the plurality of objects (Huang, Fig. 5: “(a) The RGB pixel data of the scene from the camera within GraspIt!”), or
information obtained by processing the information in relation to the shape of each of the plurality of objects (Huang, Fig. 5: “(b) The 20×20 depth data array supplied to the neural network as input. The depth data is normalized to a floating point number between [0, 1]” and Huang V.A: “The raw depth data from the Kinect sensor is of high dimensionality, so for practical reasons the array is first down scaled. Thus, before the input data is supplied to an ANN, the 640×480 pixel array was sampled to form a reduced 20×20 array. This smaller array was converted to gray-scale intensity values, and then normalized between zero and one; an example is shown in Figure 5”); and
estimate information for picking up one of the plurality of objects by inputting the data in relation to the plurality of objects into a neural network (Huang, Fig. 5: “(b) The 20×20 depth data array supplied to the neural network as input” and Fig. 3: “Representation of the designed grasp controller network. The left side of the figure shows GraspIt! simulation environment; the right side of the figure shows a neural network receiving input consisting of depth data and the goal coordinate (a, b) on the GraspIt! visual input scene. The network has seven output nodes: hand position (X,Y,Z), rotation axis (x,y,z) and rotation angle (r)” and IV, paragraph 3: “Each ANN predicts where the object is and in what direction to grasp the object by outputting 3D hand positions and orientations”);
cause the neural network to generate information for picking up at least a first object among the plurality of objects by inputting the data in relation to the plurality of objects into the neural network (Huang, Fig. 5: “(b) The 20×20 depth data array supplied to the neural network as input” and Fig. 3: “Representation of the designed grasp controller network. The left side of the figure shows GraspIt! simulation environment; the right side of the figure shows a neural network receiving input consisting of depth data and the goal coordinate (a, b) on the GraspIt! visual input scene. The network has seven output nodes: hand position (X,Y,Z), rotation axis (x,y,z) and rotation angle (r)” and IV, paragraph 3: “Each ANN predicts where the object is and in what direction to grasp the object by outputting 3D hand positions and orientations”); and
control, based on the information for picking up at least the first object, the robot to pick up at least the first object (Huang, IV. B: “To reduce execution time, the following computational steps were parallelized, as shown in Figure 4.(b)… GraspIt! tasks accept ANNs and communicate with a GraspIt! process to send the output from a neural network for simulation in GraspIt!, and receive the resulting quality” and Fig 4(b) “Mekahand Grasp an object”).
Huang does not appear to explicitly disclose that the system comprises at least one memory and at least one processor coupled to the at least one memory.
However, Maeda discloses “A system, comprising: at least one memory… and at least one processor coupled to the at least one memory” (Maeda, VII: “We used a PC with Corei7870 CPU (at2.93GHz) and GeForce GTX 460 GPU”).
Maeda and the instant application both relate to object-manipulating robots using neural networks and reinforcement learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Huang with the teachings of Maeda such that the system comprises “at least one memory; a robot, and at least one processor coupled to the at least one memory,” and one would have been motivated to do so for the purpose of performing learning experiments in a virtual environment to autonomously achieve robot adaptability (see Maeda, VII. C).
Regarding claim 32, the rejection of claim 31 is incorporated. Claim 32 is a system claim corresponding to “method of picking up one or more objects” claim 4, and the remainder of the rejection follows the same rationale given for claim 4 above.
Regarding claim 33, the rejection of claim 32 is incorporated. Claim 33 is a system claim corresponding to “method of picking up one or more objects” claim 5, and the remainder of the rejection follows the same rationale given for claim 5 above.
Regarding claim 34, the rejection of claim 32 is incorporated. Claim 34 is a system claim corresponding to “method of picking up one or more objects” claim 6, and the remainder of the rejection follows the same rationale given for claim 6 above.
Regarding claim 35, the rejection of claim 31 is incorporated. Claim 35 is a system claim corresponding to “method of picking up one or more objects” claim 9, and the remainder of the rejection follows the same rationale given for claim 9 above.
Regarding claim 36, the rejection of claim 35 is incorporated. Claim 36 is a system claim corresponding to “method of picking up one or more objects” claim 10, and the remainder of the rejection follows the same rationale given for claim 10 above.
Regarding claim 37, the rejection of claim 31 is incorporated. Claim 37 is a system claim corresponding to “method of picking up one or more objects” claim 12, and the remainder of the rejection follows the same rationale given for claim 12 above.
Regarding claim 38, the rejection of claim 31 is incorporated. Claim 38 is a system claim corresponding to “method of picking up one or more objects” claim 13, and the remainder of the rejection follows the same rationale given for claim 13 above.
Regarding claim 39, the rejection of claim 31 is incorporated. Claim 39 is a system claim corresponding to “method of picking up one or more objects” claim 29, and the remainder of the rejection follows the same rationale given for claim 29 above.
Claims 11 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Maeda and further in view of Asada (US20120226382).
Regarding claim 11, the rejection of claim 1 is incorporated. Neither Huang nor Maeda appear to explicitly disclose the further limitations of the claim.
However, Asada discloses “determining, by… at least one processor, whether… information for picking up… at least… [a] first object is abnormal” (Asada, [0076-0078]: “A procedure of the processing to calculate the position of the robot in the data processing unit 63 having the configuration explained above is explained with reference to FIGS. 7 and 8… When the data processing unit 63 determines in step S11 that the position data is normal (YES in step S11), the data processing unit 63 sets the first flag to "0" and updates the first position to the position data (step S12). The data processing unit 63 shifts to the next step S14. On the other hand, when the data processing unit 63 determines in step S11 that the position data is abnormal (NO in step S11), the data processing unit 63 sets the first flag to "1" and does not update the first position (step S13)”; Examiner notes that “position data” corresponds to “information for picking up at least a first object” because it is position data of a robot that picks up and moves workpieces, see Asada, [0032]: “The workpiece W selected as the workpiece target is carried to a predetermined position on a workbench 15 located in a movable range of the robot 12. To accomplish this, the workpiece W is lifted to predetermined height by the robot 12”).
Asada and the instant application both relate to robots that manipulate workpieces and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Huang/Maeda to include determining, by the at least one processor, whether the information for picking up at least the first object is abnormal, as disclosed by Asada, and one would have been motivated to do so for the purpose of ensuring that the information for picking up the one of the plurality of objects is accurate and reliable, at that noisy data is not used (see Asada, [0015]).
Regarding claim 25, the rejection of claim 15 is incorporated. Claim 25 is a learning method claim corresponding to “method of picking up one or more objects” claim 11, and the remainder of the rejection follows the same rationale given for claim 11 above.
Claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Maeda, and further in view of Domae et al. (JP2013052490) (hereinafter “Domae”).
Regarding claim 30, the rejection of claim 1 is incorporated. Huang further discloses “selecting, by the at least one processor, the neural network from a plurality of neural networks based on… [fitness]” (Huang, V.A., paragraph 2: “The experiments are divided into two parts: training and testing. For training, NEAT runs for 100 generations of evolution (neuroevolution does not require correct instances of behavior but learns them through optimizing the fitness function); for testing, the best neural network generated from training is further tested in simulations over objects placed in different locations”).
Neither Huang nor Maeda appear to explicitly disclose that the selection is “based on a type of a hand of the robot.”
However, Domae discloses “selecting…[a candidate gripping position] from a plurality of…[ candidate gripping positions] based on a type of hand of the robot” (Domae, [0040]: “Furthermore, as an "alternative method for matching hands," for example, if the hand is of the suction type, locations with high matching between the model and the feature point cloud are selected as candidates, and if the feature surface is small or has holes within the feature surface, resulting in a low degree of agreement with the model, the matching score is lowered from the candidates… Furthermore, with clamping or expanding clamping types, interference with the surrounding environment can also be considered simultaneously. In other words, for example, in the case of a clamping type, if the measurement data is included in the area defined by the entry depth and the vertical and horizontal widths of the hand, this can be achieved by lowering the matching score. In the hand matching control unit, for each of the extracted features, the gripping position and posture with the highest matching score is calculated”).
Domae and the instant application both relate to picking up objects by a robot and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Huang/Maeda with the teachings of Domae such that the selecting of the neural network from a plurality of neural networks is based on a type of hand of the robot, and one would have been motivated to do so for the purpose of ensuring that the characteristics of different types of hands are taken into consideration when generating the information for picking up an object, such that grasping points resulting in a low degree of agreement with the hand type are not selected (see Domae, [0040]).
Response to Arguments
Applicant's arguments filed May 11, 2026 regarding the rejections under 35 U.S.C. 101 have been fully considered, but they are not persuasive. Applicant argues that claim 1 as amended is “directed to a method of picking up one or more objects and recites controlling the robot to pick up at least the first object, which is more than mentally generating information for picking up one of the plurality of objects, and amounts to more than insignificant extra-solution active of mere data gathering” (Remarks, page 17). Examiner respectfully disagrees. While not mere data gathering, the limitations of “a method of picking up one or more objects by a robot” and “controlling, by the at least one processor and based on the information for picking up at least the first object, the robot to pick up at least the first object” merely generally link the judicial exception of generating information for picking up at least a first object among the plurality of objects to the field of use of picking up objects by a robot, as analyzed in the 101 rejection of claim 1 above. Merely generally linking the use of a judicial exception to a particular technological environment or field of use does not integrate a judicial exception into a practical application (see MPEP 2106.04(d)(I)).
Applicant's arguments regarding the rejections under 35 U.S.C. 102 and 35 U.S.C. 103 have been fully considered but, except insofar as they have been rendered moot by the new ground of rejection, are not persuasive. Applicant argues that neither Domae nor Maeda teach or suggest the following limitations of claim 1: (i) inputting information relating to a plurality of objects into a neural network; and (ii) optimizing, within the neural network framework, the process of extracting features from information relating to a plurality of objects (Remarks, page 19). Regarding (i), the new ground of rejection relies on Huang to teach this limitation, thus rendering this argument moot. Regarding (ii), claim 1 does not recite any limitations regarding “optimizing, within the neural network framework, the process of extracting features from information relating to a plurality of objects.” Claim 1 merely recites inputting the data in relation to the plurality of objects into the neural network to generate a result, with no recitation of how the neural network generates said result. Thus, whether or not the applied art of record teaches such optimization of feature extraction from information relating to a plurality of objects within the neural network framework is irrelevant to the claim as currently drafted.
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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/G.A.D./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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