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 1-8 filed on 1/27/2025 have been reviewed and considered by this office action.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. IN2024 4100 6642, filed on 1/31/2024.
Information Disclosure Statement
The information disclosure statement filed on 1/27/2025 has been reviewed and considered by this office action.
Drawings
The drawings filed on 1/27/2025 have been reviewed and are considered acceptable.
Specification
The specification filed on 1/27/2025 has been reviewed and is considered acceptable.
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 (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 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.
Claims 1-4 and 7-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tremblay et al. (US PGPUB 20190228495).
Regarding Claims 1 and 8; Tremblay teaches; A control unit for performing a task related to a component, said control unit being configured to: (Tremblay; at least paragraph [0016]; disclose a system and method for teaching a robot/semi-autonomous device for performing a task)
monitor and identify at least one incomplete function related to said component in an operating mode; (Tremblay; at least paragraphs [0048]-[0050]; disclose wherein the system includes using the robot to perform a close-loop task trained from observation of a user performing the task, wherein the user can monitor and identify incorrect operations/mistakes made by the robot during performance of the task)
correct said at least one incomplete function manually and store said data of said at least one incomplete function in a memory of said control unit; (Tremblay; at least paragraphs [0048]-[0050]; disclose wherein the system and method includes allowing a user to manually correct the performance of the robot and store the corrected instructions for use by the robot to perform the corrected task)
build and train an intelligence module with said stored data; and (Tremblay; at least paragraphs [0016]-[0017]; training a set of neural networks to help learn and perform the desired task)
perform said task related to said component by using said trained intelligence module in real-time environment. (Tremblay; at least paragraph [0016]; providing the trained task to a robot such that it may execute the learned task).
Regarding Claim 2; Tremblay teaches; The control unit as claimed in claim 1, wherein said at least one incomplete function is a part of said task related to said component. (Tremblay; at least paragraphs [0026]-[0027]).
Regarding Claim 3; Tremblay teaches; The control unit as claimed in claim 1, wherein said intelligence module is chosen from a module comprising an artificial intelligence module, a deep learning module, and a machine learning module. (Tremblay; at least paragraph [0029]).
Regarding Claim 4; Tremblay teaches; The control unit as claimed in claim 1, wherein said at least one incomplete function involves multiple physical parameters comprising force, circumference, and distance. (Tremblay; at least paragraph [0029]).
Regarding Claim 7; Tremblay teaches; The control unit as claimed in claim 1, wherein said corrected data manually is referred as real data for training the intelligence module. (Tremblay; at least paragraph [0026]).
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.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Tremblay et al. (US PGPUB 20190228495) in view of Greco et al. (US PGPUB 20220301266).
Regarding Claim 5; Tremblay appears to be silent on; The control unit as claimed in claim 1, wherein said component is a screw and said at least one task is a tightening task and said at least one function related to said component is a distance that the screw needs to be tightened.
However, Greco teaches; The control unit as claimed in claim 1, wherein said component is a screw and said at least one task is a tightening task and said at least one function related to said component is a distance that the screw needs to be tightened. (Greco; at least paragraphs [0072]-[0079]; disclose a task performance and inspection system, wherein the component being monitored is a tightening task for a screw, wherein the system can detect that the screw was not completely tightened).
Tremblay and Greco are analogous art because they are from the same field of endeavor or similar problem solving area of, task inspection, correction, and control systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have incorporated the known method of monitoring a task of tightening a screw as taught by Greco with the task correction and control system as taught by Tremblay in order to provide a method for ensuring correct performance of a task as taught by Greco (paragraph [0081]).
Regarding Claim 6; the combination of Tremblay and Greco teach; The control unit as claimed in claim 1, wherein said trained intelligence module recommends correction of parameters based on said data, during any one of said tasks comprising pressing, clinching, and crimping in a manufacturing process. The control unit as claimed in claim 1, wherein said trained intelligence module recommends correction of parameters based on said data, during any one of said tasks comprising pressing, clinching, and crimping in a manufacturing process. (Tremblay; at least paragraphs [0020] and [0026]; which discloses the system and method includes training a robot on a plurality of task including various manufacturing and object manipulation tasks and wherein after performance of the robot, the data can be fed to a training module and corrected/updated parameters can be fed back to the robot in order for the robot to correctly perform a given task).
The combination of Tremblay and Greco do not expressly disclose, “…recommending correction parameters…comprising pressing, clinching, and crimping…”.
At the time the invention was made, it would have been an obvious matter of design choice to a person of ordinary skill in the art to have modified the references of Tremblay and Greco to include the specific parameters of pressing, clinching, or crimping as correction parameters as the reference of Tremblay provides several examples of tasks trained by the robot in which all of these parameters would be considered and further because Applicant has not disclosed that the specific parameters provides an advantage, is used for a particular purpose, or solves a stated problem. One of ordinary skill in the art, furthermore, would have expected Applicant’s invention to perform equally well with accounting for the specific parameters listed because the reference of Tremblay relies on accounting for specific parameters for monitoring and correcting in order for a robot to complete a given task.
Therefore, it would have been an obvious matter of design choice to modify the references of Tremblay and Greco to obtain the invention as specified in claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Holcomb et al. (US PGPUB 20250093860): disclose a task assistant system and method for monitoring and validating that a task is completed correctly, and if not, running sensor data to determine what is incorrect in the task, and providing corrective actions in order to fix the task so that it is completed successfully.
Tremblay et al. (US PGPUB 20210390653): disclose a system and method for teaching a robot to perform a plurality of tasks wherein the system can observe incorrect behavior, apply the data to a learning module, and provide corrective parameters such that the robot can correctly complete the task.
Vogelsong et al. (US Patent 10,926,408): disclose a robot control system and method in which a task is completed a plurality of times using different parameters, a success or failure of the task is provided to a reinforcement learning system, and all correct performances are given positive weights while incorrect performances are given negative weights such that the system can converge on a set of parameters in which the robot performs the task correctly.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER W CARTER whose telephone number is (469)295-9262. The examiner can normally be reached 9-6:30.
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/CHRISTOPHER W CARTER/Examiner, Art Unit 2117
