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 .
DETAILED ACTION
This is a non-final Office Action on the merits in response to communications filed by Applicant on March 31, 2025. Claims 1-20 are currently pending and examined below.
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on is/are being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, the independent claims, 1, 11 and 20 are directed toward non-statutory subject matter, as shown below:
STEP 1: Does claim(s) fall within one of the statutory categories? Yes. The independent claim 1 is directed to a computer-implement method, claim 11 is directed to a non-transitory computer-readable medium and claim 20 is directed to a system.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim(s) is/are directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Claims 1, 11 and 20 recite a series of steps for training a robot control model such as generating a plurality of disassembly trajectories, and performing one or more operation of training a machine learning model. Under its broadest reasonable interpretation, the limitations as recited encompasses a person performing the steps of training a machine learning model. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” and “methods of organizing human activity” grouping of abstract ideas.
The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). As such, a person observing the traffic rule at an intersection could give directions to a driver based on the traffic light conditions. The mere nominal recitation that the transmitting is being executed does not take the limitation out of the mental process grouping. Thus, the claim recites a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
The claim recites additional elements of compute and to control a robot; however, the limitations are recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Accordingly, even in combination, these 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.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
The claim(s) does not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere performance of an action is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data (which is one example listed in applicant’s disclosure as a remedial action) is a well understood, routine, and conventional function.
CONCLUSION
Since claims 1, 11, 20 are: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, the claim(s) is directed towards non-statutory subject matter.
Claims 2-10, 12-19 are also rejected under 35 U.S.C. 101 as being dependent on the rejected claims and for failing to cure the deficiencies listed above.
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.
Claims 1-5, 7, 9-13, 15-20 are rejected under 35 U.S.C. 102(a)(1) and/or 102(a)(2) as being anticipated by Koga et al. US2023/0278213 (“Koga”).
Regarding claim(s) 1, 11, 20. Koga discloses a computer-implemented method for training a robot control model ([0028] The model trainer 116 is configured to train machine learning models. In some embodiments, the model trainer 116 trains (1) a grasp perception machine learning model 150 (“grasp perception model 150”) that predicts grasp proposals given a height map associated with one or more parts, and (2) a pose estimation machine learning model 152 (“pose estimation model 152”) that predicts a pose proposal given a height map associated with a part being grasped by a robot. Example architectures of the grasp perception model 150 and the pose estimation model 152, as well as techniques for training and utilizing the same, are discussed in greater detail below in conjunction with FIGS. 3-7 and 10-11.), the method comprising:
generating, using one or more simulations, a plurality of disassembly trajectories along which a first part is disassembled from a second part ([0045] Given the assembly order, the motion planning module 308 applies a motion planning technique to determine, for each part being grasped, movements of the robotic system that are required to assemble the part with one or more other parts according to the assembly order. Any technically feasible motion planning technique can be employed, including well-known motion planning techniques, in some embodiments. In some embodiments, the motion planning technique can be applied with grasp constraints to solve for (1) how the part needs to move, such as through one or more waypoints, in order to reach a target destination where the part is assembled with the one or more other parts, and (2) the grasp that is required to assemble the part with the one or more other parts.);
reversing the plurality of disassembly trajectories to generate a plurality of reversed disassembly trajectories ([0071] At step 1212, the robot control application 146 determines movements of the robotic system required to assemble the part with one or more other parts based on results of a motion planning technique with grasping constraints. Any technically feasible motion planning technique can be used in some embodiments, including well-known motion planning techniques. In some embodiments, the motion planning technique can be used to verify a disassembly sequence specified by a user and a reverse of the disassembly sequence that includes a set of waypoints for the part to reach a goal location in the assembly, as well as a required grasp for assembling the part with the one or more other parts, as described above in conjunction with FIG. 3. In some embodiments, the motion planning technique can be applied with a grasping constraint for each part that requires a robot and fingers to maintain a grasp of the part and the part, robot, and the fingers must also be collision-free throughout the path.); and
performing, based on the plurality of reversed disassembly trajectories, one or more operations to train an untrained machine learning model to generate a trained machine learning model, wherein the trained machine learning model is trained to control a robot to assemble the first part and the second part ([0044] The motion planning module 308 is responsible for determining movements of the robotic system required to assemble a part that is being grasped by the robotic system with one or more other parts. In some embodiments, an order in which parts are to be assembled together (also referred to herein as the “assembly order”) is specified by a user (e.g., via a GUI) or determined based on a user specification. For example, the assembly order could include covering a base part with another part, screwing the other part and the base part in place, etc. In some embodiments, the workflow used to specify the assembly order is reversed. In such cases, the user first designs an assembly with all parts put together. Then, the user specifies (e.g., via a GUI) how to disassemble the assembly piece by piece. For example, the user could click on screws of the assembly to make the screws disappear, click on a cover plate to make the cover plate disappear, etc. The assembly order can be obtained by reversing the disassembly order specified by the user. In some other embodiments, the assembly order of parts can be generated using appropriate automated planning techniques.).
Regarding claim(s) 2, 12. Koga discloses determining, based on the first part, the second part, and the robot, a grasp pose with which the robot can grasp the first part; and generating, based on the grasp pose, a randomized state of the first part, the second part, and the robot, wherein, during the one or more simulations, the robot grasps the first part using the grasp pose ([0050] As shown, the pose estimation model 152 is trained via backpropagation 708. In some embodiments, the model trainer 116 generates training data by simulating a CAD model that represents a part being grasped in various poses by a robot, and rendering, via one or more virtual depth sensors, depth images of the simulated CAD model. Then, the depth images are converted to height maps and input into the pose estimation model 152 during training. More specifically, in some embodiments, the model trainer 116 samples a heightmap of each part randomly positioned in front of a virtual depth sensor, with the distance between the parts and the virtual depth sensor being controlled in a fixed interval that ensures all parts are fully viewable by the virtual depth sensor. In some embodiments, an associated pose proposal label can be computed for each such sample as follows.).
Regarding claim(s) 3, 13. Koga discloses generating the randomized state comprises: generating, based on first geometry associated with the first part and second geometry associated with the second part, prepared geometry data for the first part and the second part to include in the randomized state; and generating, based on the prepared geometry data for the first part and the second part, the grasp pose ([0040] In some embodiments, height maps (e.g., height map 502), which each indicate the height from a plane behind one or more parts, can be generated from depth data indicating distance from a depth sensor, such as depth images captured by a depth camera that is mounted on a robot or elsewhere. In some embodiments, the height maps can be point clouds generated from depth data point clouds. In the example of FIG. 5, the height map 502 is associated with parts that are rectangular in shape, and the prediction 504 includes rectangular grasp proposals indicating stable and collision-free regions of the parts that a robot can grasp.).
Regarding claim(s) 4, 15. Koga discloses generating the randomized state comprises: generating a randomized robot gripper state of a gripper of the robot; generating a randomized first part state of the first part; generating, based on the randomized first part state and the randomized robot gripper state, a randomized second part state of the second part; and generating, based on the randomized first part state, the randomized second part state, and the randomized robot gripper state, the randomized state ([0050] As shown, the pose estimation model 152 is trained via backpropagation 708. In some embodiments, the model trainer 116 generates training data by simulating a CAD model that represents a part being grasped in various poses by a robot, and rendering, via one or more virtual depth sensors, depth images of the simulated CAD model. Then, the depth images are converted to height maps and input into the pose estimation model 152 during training. More specifically, in some embodiments, the model trainer 116 samples a heightmap of each part randomly positioned in front of a virtual depth sensor, with the distance between the parts and the virtual depth sensor being controlled in a fixed interval that ensures all parts are fully viewable by the virtual depth sensor. In some embodiments, an associated pose proposal label can be computed for each such sample as follows. The model trainer 116 first creates a pose proposal for each primary view direction for each part by taking the bounding box of the part and making each face the pose proposal for the associated view direction. For example, the bounding box face on the top side would be the pose proposal for the top view direction. The frame for each pose proposal is centered on the face, with the x and y axes aligned with the width and height directions and the z-axis along the face normal.).
Regarding claim(s) 5, 16. Koga discloses generating a grasp pose with which the robot grasps the first part, wherein generating the grasp pose comprises: determining one or more candidate grasp poses for the first part; and selecting the grasp pose from the one or more candidate grasp poses based on a physics-based evaluation ([0030] Once trained, the grasp perception model 150 and the pose estimation model 152 can be deployed to any suitable applications that control one or more physical robots to perform assembly tasks. Illustratively, a robot control application 146 that utilizes the grasp perception model 150 and the pose estimation model 152 is stored in a memory 144, and executes on a processor 142, of the computing device 140 to control a robotic system 160. Components of the computing device 140, including the memory 144 and the processor 142 may be similar to corresponding components of the machine learning server 110, described above. The robotic system 160 can include any technically feasible robot or robots, operating in any suitable environment (e.g., a manufacturing environment), for which the grasp perception model 150 and the pose estimation model 152 were trained. One or more sensors 162, such as depth camera(s), acquire sensor data in the environment that is used to control the robotic system 160.).
Regarding claim(s) 7. Koga discloses generating the grasp pose comprises rejecting at least one candidate grasp pose included in the one or more candidate grasp poses based on at least one of (i) an intersection of two or more geometries associated with the first part, the second part, and/or the robot, or (ii) a first geometry associated with a gripper of the robot being outside one or more bounds ([0057] At step 904, the model trainer 116 validates each robot grasp in the set of robot grasps. Validating a robot grasp ensures that a robot can perform the grasp. In some embodiments, validating a robot grasp includes checking whether the fingers of a robot intersect the part or any other objects in the environment during the grasp, in which case the grasp would not be valid.).
Regarding claim(s) 9 Koga discloses processing one or more observations and one or more geometries associated with the first part and the second part using the untrained machine learning model to generate one or more predicted robot actions; computing, based on the one or more predicted robot actions and one or more of the plurality of reversed disassembly trajectories, one or more loss values; and updating, based on the one or more loss values, one or more parameters of the untrained machine learning model ([0048] Given a set of pose proposals for a part (e.g., pose proposals 610, 612, 614, 616, 618, and 620), a pose estimation model (e.g., pose estimation model 152) can be trained to predict pose proposals based on sensor data (e.g., sensor data 302) associated with the part. In some embodiments, the pose estimation model outputs the pose proposal for the view direction of an input part heightmap. Each proposal can have a fixed offset from the part frame, and PCA can be used to obtain the center and 3D orientation of the proposal. Applying the offset, a pose estimate of the part can be inferred.).
Regarding claim(s) 10. Koga discloses receiving sensor data and one or more geometries associated with one or more additional parts; processing the sensor data and the one or more geometries using the trained machine learning model to generate one or more robot actions; and controlling the robot to move based on the one or more robot actions ([0048] Given a set of pose proposals for a part (e.g., pose proposals 610, 612, 614, 616, 618, and 620), a pose estimation model (e.g., pose estimation model 152) can be trained to predict pose proposals based on sensor data (e.g., sensor data 302) associated with the part. In some embodiments, the pose estimation model outputs the pose proposal for the view direction of an input part heightmap. Each proposal can have a fixed offset from the part frame, and PCA can be used to obtain the center and 3D orientation of the proposal. Applying the offset, a pose estimate of the part can be inferred.).
Regarding claim(s) 17. Koga discloses wherein performing one or more operations to train the machine learning model comprises: processing one or more observations and one or more geometries associated with the first part and the second part using the machine learning model to generate one or more predicted robot actions; computing, based on the one or more predicted robot actions and one or more of the plurality of reversed disassembly trajectories that are selected using at least one of a dynamic time warping or a signature transform, one or more loss values; and updating, based on the one or more loss values, one or more parameters of the machine learning model ([0052] determines movements of the robotic system required to assemble each part with one or more other parts based on results of a motion planning technique with grasping constraints; determines a re-grasping of each part that needs to be re-grasped in order to be assembled together with other part(s); and causes the robotic system to perform the determined movements and re-graspings, if any. In some embodiments, the foregoing steps are validated via simulation using a digital twin (e.g., digital twin 210) of a workcell that includes a robotic system before the robot control application 146 is deployed to control a physical robotic system. For example, in some embodiments, the steps can be specified using a “recipe” of tasks in a script that is either programmed by a user or automatically generated. The recipe can include instructions specifying, for each part, a type of the part, a general location in a workcell of the part, which sensor(s) are used to locate the part, fingers that will grasp the part, sensor(s) that will be used to determine a pose estimate when the part is grasped, a goal grasp to assemble the part with one or more other parts, and intermediate waypoints to assembly the part with the one or more other parts. The recipe can be executed using the digital twin to identify issues during the assembly process, such as the robotic system colliding with objects in the environment, and corrections can be made, either by the user or automatically, to the steps that the robot control application 146 performs before the robot control application 146 is deployed to control the physical robotic system. Pseudo-code for the script corresponding to an exemplar recipe is shown in Algorithm 1.).
Regarding claim(s) 18. Koga discloses further comprising: receiving sensor data and one or more geometries associated with one or more additional parts; processing the sensor data and the one or more geometries using the trained machine learning model to generate one or more robot actions; and controlling the robot to move based on the one or more robot actions ([0048] Given a set of pose proposals for a part (e.g., pose proposals 610, 612, 614, 616, 618, and 620), a pose estimation model (e.g., pose estimation model 152) can be trained to predict pose proposals based on sensor data (e.g., sensor data 302) associated with the part. In some embodiments, the pose estimation model outputs the pose proposal for the view direction of an input part heightmap. Each proposal can have a fixed offset from the part frame, and PCA can be used to obtain the center and 3D orientation of the proposal. Applying the offset, a pose estimate of the part can be inferred.).
Regarding claim(s) 19. Koga discloses wherein the instructions, when executed by the one or more processors, further cause the one or more processors to perform the step of determining a grasp pose with which the robot grasps the first part, wherein determining the grasp pose comprises: lifting a grasped part that a gripper of the robot grasps from the second part until a first convex hull of at least one portion of the grasped part no longer intersects a second convex hull of the second part; and moving the gripper of the robot to a random pose ([0045] the motion planning technique can be run multiple times with the entire assembly design in different random starting poses in the assembly area. If multiple grasps exist, the grasp with the x-axis of the fingers aligned with the motion with which the part is assembled with other parts can be used in some embodiments. Such a grasp is stable and can keep the part from twisting between the fingers during contact with the surfaces of the parts during assembly. The grasp associated with the resulting collision-free path is then the goal grasp.).
Allowable Subject Matter
Claims 6, 8, 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRUC M DO whose telephone number is (571)270-5962. The examiner can normally be reached on 9AM-6PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramón Mercado, Ph.D. can be reached on (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TRUC M DO/Primary Examiner, Art Unit 3658
