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
Status of Claims
Claims 1-8 of U.S. Application No. 18/966020 filed on 12/02/2024 have been examined.
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.
Claim 1-20 rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huawei et al. [US 2021/0174209 A1], hereinafter referred to as Huawei.
As to Claim 1, Huawei discloses a discloses a computer implemented method for maximizing warehouse operations using different combinations of robotic vehicles and human workers, for accomplishing tasks in the warehouse, comprising: a processor, and a non-transitory, computer readable medium communicably coupled to the processor and storing instructions that, when executed by the processor, cause the processor to perform operations comprising: continuously calculating tasks to be performed in said warehouse (In this case, skills used to complete the tasks and neural networks specific for the tasks need to be configured on the intelligent terminals, to implement a function of completing specific tasks by using the intelligent terminals. Specifically, the intelligent products may be applied to a movable intelligent terminal, para [0095]); calculating a combination of robotic vehicles and human workers to accomplish said tasks in a least amount of time (More specifically, types of reinforcement learning algorithms used when the first neural network is trained may include a proximal policy optimization (PPO) algorithm, a trust region policy optimization (TRPO) algorithm, a temporal-difference learning (TD) algorithm, or another on-policy reinforcement learning algorithm, para [0138]); actuating said robotic vehicles to perform said tasks in combination with said human workers (In a process in which the server controls the intelligent device to execute the first task, the server obtains data for executing the first task by the intelligent device, and updates the parameter of the first neural network by using a third reinforcement learning algorithm, para [0143]).
Regarding claim 2, Huawei discloses including operation of MHE components in said
calculating of a combination of robotic vehicles and human workers to accomplish said tasks in a least amount of time; and actuating changes in said operation of said MHE components to perform said tasks in combination with said human workers in said least amount of time (The server updates the parameter of the first neural network based on the data by using the third reinforcement learning algorithm. Concepts of the intelligent device, the preset duration, and the execution status are all described in detail in the foregoing descriptions, and details are not described herein again, para [0144]).
Regarding claim 3, Huawei discloses the steps of: establishing operative electronic communication with each of said robotic vehicles; establishing operative electronic communication with each said MHE component; actuating said robotic vehicles by electronically signaling said robotic vehicles to work in combination with said human workers and said MHE components to accomplish said tasks; and actuating each said MHE component by electronically signaling said MHE component to work in combination with said human workers and said robotic vehicles to accomplish said tasks (The infrastructure provides computing power support for the artificial intelligence system, achieves communication with the outside world, and achieves support through a basic platform, para [0083]).
Regarding claim 4, Huawei discloses the steps of: storing an electronic map of said warehouse (The environment status information includes status information of the intelligent device and information about a surrounding environment of the intelligent device in the simulated environment corresponding to the first task, and may specifically include map information surrounding the intelligent device, destination information of the intelligent device, movement information of a neighboring intelligent device, movement information or another type of environment information of a current intelligent device, and the like, para [0016]); running a plurality of electronic simulations using different operation combinations including different numbers and types of said robotic vehicles and different numbers of said human workers to virtually accomplish said tasks in a virtual time period by virtual movement employment of said chosen robotic vehicles and said human workers to perform said tasks, within said electronic map; ascertaining a determined one of said plurality of electronic simulations having a smallest said virtual time period; and actuating said robotic vehicles, said human workers employed in said determined one of said plurality of electronic simulations to perform said tasks (The data obtaining device 220 is configured to obtain the environment status information. Specifically, a simulator maybe configured on the server 210. The data obtaining device 220 collects status information of a current surrounding environment existing in the simulator when the intelligent device 230 executes the first task, para [0099], Fig.2).
Regarding claim 5, Huawei discloses the steps of: storing an electronic map of said warehouse (The environment status information includes status information of the intelligent device and information about a surrounding environment of the intelligent device in the simulated environment corresponding to the first task, and may specifically include map information surrounding the intelligent device, destination information of the intelligent device, movement information of a neighboring intelligent device, movement information or another type of environment information of a current intelligent device, and the like, para [0016]); running a plurality of electronic simulations using different operation combinations including different numbers and types of said robotic vehicles and different numbers of said human workers to virtually accomplish said tasks in a virtual time period by virtual movement employment of said chosen robotic vehicles and said human workers to perform said tasks, within said electronic map (The data obtaining device 220 is configured to obtain the environment status information. Specifically, a simulator may be configured on the server 210. The data obtaining device 220 collects status information of a current surrounding environment existing in the simulator when the intelligent device 230 executes the first task, para [0099], Fig. 2); ascertaining a determined one of said plurality of electronic simulations having a smallest said virtual time period (In this implementation, the parameter of the second neural network is initialized by using the parameter of the first neural network that has been obtained through training, so that a capability learned by the first neural network can be directly inherited, thereby shortening a time for training he second neural network, and improving efficiency of training the second neural network, para [0014]); and actuating said robotic vehicles, said human workers, and different said changes in said operation of said MHE components employed in said determined one of said plurality of electronic simulations to perform said tasks (In some implementations, the processed output vector can be used as an activation input of the operational circuit 2303, for example, can be used at a subsequent layer in the neural network, para [0354], Fig. 23).
Regarding claim 6, Huawei discloses the steps of: storing an electronic map of said warehouse (The environment status information includes status information of the intelligent device and information about a surrounding environment of the intelligent device in the simulated environment corresponding to the first task, and may specifically include map information surrounding the intelligent device, destination information of the intelligent device, movement information of a neighboring intelligent device, movement information or another type of environment information of a current intelligent device, and the like, para [0016]); running a plurality of electronic simulations using different
operation combinations including different numbers and types of said robotic vehicles and different numbers of said human workers to virtually accomplish said tasks in a virtual time period by virtual movement employment of said chosen robotic vehicles and said human workers to perform said tasks, within said electronic map (The data obtaining device 220 is configured to obtain the environment status information. Specifically, a simulator may be configured on the server 210. The data obtaining device 220 collects status information of a current surrounding environment existing in the simulator when the intelligent device 230 executes the first task, para [0099], Fig.2); ascertaining a determined one of said plurality of electronic simulations having a smallest said virtual time period (In this implementation, the parameter of the second neural network is initialized by using the parameter of the first neural network that has been obtained through training, so that a capability learned by the first neural network can be directly inherited, thereby shortening a time for training the second neural network, and improving efficiency of training the second neural network, para[0014]); and actuating said robotic vehicles, said human workers, and different said changes in said operation of said MHE components employed in said determined one of said plurality of electronic simulations to perform said tasks (In some implementations, the processed output vector can be used as an activation input of the operational circuit 2303, for example, can be used at a subsequent layer in the neural network, para [0354], Fig.23).
Regarding claim 7, Huawei discloses a computer-implemented method for maximizing warehouse operations using different combinations of robotic vehicles and human workers for
accomplishing tasks in the warehouse, comprising: a processor, and a non-transitory, computer readable medium communicably coupled to the processor and storing instructions that, when executed by the processor, cause the processor to perform operations comprising: determining an optimized score of operation of a warehouse using an electronic map thereof and simulating routes of robotic workers and human workers performing a number of tasks in said electronic map within a determined period of time (The environment status information includes status information of the intelligent device and information about a surrounding environment of the intelligent device in the simulated environment corresponding to the first task, and may specifically include map information surrounding the intelligent device, destination information of the intelligent device, movement information of a neighboring intelligent device, movement information or another type of environment information of a current intelligent device, and the like, para [0016]); running a plurality of simulations of current operations of said warehouse where each simulation includes changes to a current number of robotic workers and a current number of human workers to discern a simulation score for each simulation (The data obtaining device 220 is configured to obtain the environment status information. Specifically, a simulator may be configured on the server 210. The data obtaining device 220 collects status information of a current surrounding environment existing in the simulator when the intelligent device 230 executes the first task, para [0099], Fig. 2); and changing said current number of robotic workers and said current number of human workers to that used in a said simulation having a simulation score closest to that of said optimized score (In this way, the server can iteratively update a policy of the new skill and a parameter of the new skill in a timely manner based on operation behavior information of the intelligent device, and this helps improve accuracy of a training process, para[0010]).
Regarding claim 8, Huawei discloses including a preferred operation of MHE components in said calculation of said optimized score; and including a current MHE operation in said a plurality of simulations of current operations of said warehouse; changing said current number of robotic workers and said current number of human workers and said current MHE operation, to that used in a said simulation having a simulation score closest to that of said optimized score (For another example, in a simulated scenario of carrying goods by a warehouse robot (an example of the intelligent device), a scenario in which a plurality of warehouse robots execute carrying tasks in a warehouse may be demonstrated through the simulator. Other scenarios are not described one by one herein, para [0135]).
Conclusion
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/YAZAN A SOOFI/Primary Examiner, Art Unit 3668