CTNF 19/072,884 CTNF 82641 DETAILED ACTION Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (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. 07-15 AIA Claim s 1-3, 9, 11, 15 and 20 are rejected under 35 U.S.C. 102( a)(1) / 102(a)(2 ) as being anticipated by Shin et al. (US 2021/0101283 A1) Regarding claim 1, Shin et al. disclose a robot control method, the method comprising: receiving sensor data and one or more goal specifications ( e.g., receiving (i) video signal from a camera (par. 58) and (ii) user’s requirement / intention information (par. 71-72 and 103) ); processing the sensor data, a robot size, and the one or more goal specifications using one or more trained encoders ( e.g., artificial neural network (par. 45) processing (i) video signals (par. 58) (ii) user’s requirement / intention information (par. 71-72 and 103) and (iii) robot’s radius range of operation (par. 110) ) to generate a plurality context tokens (e.g., the artificial neural network configured to generate output values / function values based on input signals (i), (ii) and (iii) (par. 45, 46 and 60), which covers a plurality of context tokens ); processing the plurality of context tokens using one or more trained decoders to generate a robot plan ( e.g., the artificial neural network processing generated output values / function values to determine certain operation for a robot to perform (par. 60 and 100) – for instance, travel route and / or travel plan (par. 101) ); and controlling a robot based on the robot plan ( e.g., controlling a driving unit such as a robot travels along the determined travel route and / or travel plan (par.101 and 103) ). Regarding claim 2, Shin et al. disclose a robot control method, wherein the sensor data includes at least one of a plurality of red-green-blue images with depth (RGB-D) inputs , a plurality of light detection and ranging (LiDAR) inputs ( e.g., sensor signals from Lidar (par. 64) ), or robot state data ( alternative limitation ). Regarding claim 3, Shin et al. disclose a robot control method, wherein the one or more goal specifications include at least one of a reference image ( e.g., restricted region / object to be monitored (par. 115) ), a look-at pose, and a target object mask (alternative limitation). Regarding claim 9. The method of claim 1, wherein processing the plurality of context tokens comprises determining, based on the plurality of context tokens and using a first trained decoder included in the one or more trained decoders, a camera tilt ( e.g., analyzing image information and change monitoring region of the camera 42 (par. 125 and 115) using artificial neural network (par. 95, 98-99), which covers determining camera tilt ). Regarding claim 11, Shin et al. disclose a robot’s memory configured to store application program (par. 75 and 82) and be executed by a processor (par. 75) to perform the steps of: receiving sensor data and one or more goal specifications ( e.g., receiving (i) video signal from a camera (par. 58) and (ii) user’s requirement / intention information (par. 71-72 and 103) ); processing the sensor data, a robot size, and the one or more goal specifications using one or more trained encoders ( e.g., artificial neural network (par. 45) processing (i) video signals (par. 58) (ii) user’s requirement / intention information (par. 71-72 and 103) and (iii) robot’s radius range of operation (par. 110) ) to generate a plurality context tokens (e.g., the artificial neural network configured to generate output values / function values based on input signals (i), (ii) and (iii) (par. 45, 46 and 60), which covers a plurality of context tokens ); processing the plurality of context tokens using one or more trained decoders to generate a robot plan ( e.g., the artificial neural network processing generated output values / function values to determine certain operation for a robot to perform (par. 60 and 100) – for instance, travel route and / or travel plan (par. 101) ); and controlling a robot based on the robot plan ( e.g., controlling a driving unit such as a robot travels along the determined travel route and / or travel plan (par.101 and 103) ). Regarding claim 15, Shin et al. disclose a robot’s memory wherein processing the plurality of context tokens comprises determining, based on the plurality of context tokens and using a first trained decoder included in the one or more trained decoders, a camera tilt ( e.g., analyzing image information and change monitoring region of the camera 42 (par. 125 and 115) using artificial neural network (par. 95, 98-99), which covers determining camera tilt ). Regarding claim 20, Shin et al. disclose a robot control system comprising: one or more memories storing instructions ( e.g., memory configured to store application program (par. 75 and 82) ), and one or more processors that are coupled to the one or more memories and, when executing the instructions ( e.g., a processor configured to execute stored application program with a memory (par. 75 and 82) ), are configured to: receive sensor data and one or more goal specifications ( e.g., receiving (i) video signal from a camera (par. 58) and (ii) user’s requirement / intention information (par. 71-72 and 103) ), process the sensor data, a robot size, and the one or more goal specifications using one or more trained encoders ( e.g., artificial neural network (par. 45) processing (i) video signals (par. 58) (ii) user’s requirement / intention information (par. 71-72 and 103) and (iii) robot’s radius range of operation (par. 110) ) to generate a plurality context tokens (e.g., the artificial neural network configured to generate output values / function values based on input signals (i), (ii) and (iii) (par. 45, 46 and 60), which covers a plurality of context tokens ), process the plurality of context tokens using one or more trained decoders to generate a robot plan ( e.g., the artificial neural network processes generated output values / function values to determine certain operation for a robot to perform (par. 60 and 100) – for instance, travel route and / or travel plan (par. 101) ), and control a robot based on the robot plan ( e.g., controlling a driving unit such as a robot travels along the determined travel route and / or travel plan (par.101 and 103) ) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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 of this title, 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. 07-21-aia AIA Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. (US 2021/0101283 A1) in view of Danielczuk et al. (US 2022/0152826 A1) Regarding claim 14, Shin et al. failed to specifically disclose wherein a first trained decoder included in the one or more trained decoders comprises at least one of a transformer decoder or a regression model. However, Danielczuk et al. teach the implementation of regression model within neural network algorithms to determine whether a robot’s path will result in collision (par. 84, 129 and 62). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA) to modify the artificial neural network for a robot system, taught by Shin et al., such that artificial neural network implements a regression model to determine whether a robot’s path will result in collision, in view of Danielczuk et al., with reasonable expectation of success, since doing so would have achieved the benefit of determining collision-free trajectories for robot arm, the gripper, and/or the object in situations in which the scene and/or the object can be changing as time elapses (par. 62) . 07-21-aia AIA Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. (US 2021/0101283 A1) in view of Sternitzke (US 2023/0039466 A1) Regarding claim 19, Shin et al. failed to specifically disclose wherein a first trained encoder included in the one or more trained encoder receives as input camera extrinsics derived from odometry of the robot. However, Sternitzke teaches a machine learning / neural network configured to process camera’s distance and orientation in comparison to the position and orientation of the robot 1 (e.g. derived from the odometry unit 31 ) (par. 56, 58 and Figure 1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA) to modify the artificial neural network for a robot system, taught by Shin et al., such that artificial neural network process camera’s distance and orientation in comparison to the position and orientation of the robot 1 , in view of Sternitzke, with reasonable expectation of success, since doing so would have achieved the benefit of efficiently calculate a path for a robot by optimizing various criteria to find an optimal path (par. 38) while minimize interference with the movements of people in its environment (par. 4) . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 4-8 and 10 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. 12-151-08 AIA 07-43 12-51-08 Claim s 12-13 and 16-18 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. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jorge O. Peche whose telephone number is (571)270-1339. The examiner can normally be reached Monday-Friday 8:30 AM - 5:30 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Khoi H. Tran can be reached at 571 272 6919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Jorge O Peche/Examiner, Art Unit 3656 Application/Control Number: 19/072,884 Page 2 Art Unit: 3656
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