DETAILED ACTION
The following is a Non-Final Office Action in response to communications filed on 05 March 2026.
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 .
Response to Arguments/Amendments
Applicant’s arguments with respect to claim(s) 1-3,5, and 7-20 under 35 USC 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim(s) 1,3,5,7 and 8-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Napoli (US 2022/0414566) in view of Ghoting et al. (US 2012/0173136)
Regarding Claim 1, Napoli (US 2022/0414566) a system, comprising:
an input device that provides an instruction set regarding at least one destination of interest to at least one processor (Para. [0063-0065]… Robots may provide a user interface in the form of a command line or graphical user interface to receive input from an operator or provide output to an operator… communications between the system and robots may be related to commands, activity, status, etc. with respect to shipping, receiving, transporting, or storing materials);
a navigation system that navigates a vehicle in response to the instruction set (Para. [0064]… Robots may use sensors and other technology to navigate the warehouse and perform the robot’s specific functions or tasks); and
a controller that initiates a material flow task to be executed by the vehicle (Para. [0069]… the ALIDA system 210 comprises one or more applications, programs, libraries, services, tasks, script and other types of executable instructions to intelligent, automatically and autonomously manages people 202, robots 204 and MHEs 206 to perform work assignments for jobs, or portions thereof as part of one or more workflows) wherein;
the controller receives at least one instruction of the instruction set during the execution
of the material flow task by the vehicle (Para. [0069]), and
the material flow task and includes a combination of known and unknown information about a route, destination, and robotic action (Para. [0083]… machine learning model 260 may be trained on known input-output pairs such that the machine learning model 260 can learn how to predict known outputs given known inputs. Once the machine learning model 260 has learned how to predict known input-output pairs, the machine learning model 260 can operate on unknown inputs to predict an output)
Napoli fails to teach wherein, at initiation of the material flow task, the unknown information comprises at least one of a destination location corresponding to the at least one destination of interest or a travel path for navigating the vehicle, and wherein the destination location or travel path is not provided to the controller at initiation of the material flow task, and
the controller receiving at least one instruction of the instruction set during the execution of the material flow task by the vehicle to resolve the unknown information by providing the destination location or the travel path while the vehicle is being navigated by the navigation system.
However, Ghoting teaches wherein, at initiation of the task, the unknown information comprises at least one of a destination location corresponding to the at least one destination of interest or a travel path for navigating the vehicle (Para. [0017]…If a driver of the vehicle does not enter information of the destination location to the computing system but the vehicle is moving, the computing system determines that the destination location is unknown and may be inferred by the computing system), and wherein the destination location or travel path is not provided to the controller at initiation of the task (Ghoting, Para. [0017])
the controller receiving at least one instruction of the instruction set during the execution of the task by the vehicle to resolve the unknown information by providing the destination location or the travel path while the vehicle is being navigated by the navigation system (Ghoting, Para. [0018]… Upon determining that the destination location of the vehicle is unknown, at step 105, the computing system is configured to learn the destination location or the destination region. In one embodiment, the computing system is configured to infer the destination location or the destination region based on the current location of the vehicle, a current driving direction of the vehicle, prior destination regions of the vehicle, prior destination locations of the vehicle, a current time of day, and/or a current day of week.)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the methods for autonomous labor intelligent dynamic assignment as taught by Napoli with the advanced routing of vehicle fleets as taught by Ghoting to obtain a traffic route for a vehicle ( e.g., taxi) to maximize a profit in operating the vehicle (Ghoting, Para. [0005]).
Regarding claim 2, Napoli in view of Ghoting, teach the system of claim 1, wherein the vehicle is an autonomous mobile robot (AMR) (Napoli, Robots 204).
Regarding claim 3, Napoli in view of Ghoting teach the system of claim 1, wherein the instruction set is output to the AMR, and a machine or human operator of the AMR controls the navigation system according to the instruction set (Napoli, Para. [0063]… Robots may have been programmed to be controllable, by a human operator or via command or instructions from another system.)
Regarding claim 5, Napoli in view of Ghoting teach the system of claim 4, wherein the input device communicates with a plurality of different input sources (Napoli, Fig. 2A), and wherein the controller parses and prioritizes multiple delayed inputs of the different input sources to the task plan (Napoli, Para. [0123]… Priority may identify a level of importance to have a MHE working on a particular work assignment 235. Priority may identify a level of importance to have a MHE working with a particular robot or person. Priority may identify a level of importance to have a MHE working on a certain job or with certain customers)
Regarding claim 7, Napoli in view of Ghoting, teach the system of claim 6, further comprising a user interface that receives data for addressing the unknown information while the vehicle is moving along a route for performing the material flow task (Napoli, Para. [0096]… The monitor may be designed, configured and/or constructed to be configured to interface to each of the devices of the people, robots and MHEs based on configuration and other data and information on each of the people, robots and MHEs stored in the database.)
Regarding Claim 8, Napoli in view of Ghoting teach a method, comprising: starting a movement by the vehicle (Napoli, Para. [0116]… Routing may identify a route for the robot to follow or traverse through the warehouse in performing any one or more work assignments. The route may indicate a sequence of locations for the robot to follow or traverse), the vehicle includes a plan including a combination of known and unknown features of a path to at least one destination of interest (Ghoting, Para. [0018]…the computing system deduces a possible destination location or destination region of the vehicle, e.g., by correlating the information of the current location of the vehicle with known map artifacts ( e.g. known residential areas, travel hubs, business locations or city centers, bridges or tunnels or toll booths etc.); wherein, at the start of movement, at least one of a destination location corresponding to the at least one destination of interest or a travel path for navigating the vehicle is unknown and absent from the plan (Ghoting, Para. [0017]…the computing system is configured to evaluate whether a destination location (i.e., a specific place) of the vehicle is known or, if not, whether a destination region (e.g., a particular neighborhood or part of a city) can be inferred. For example, if a taxi has a passenger, the passenger tells his/her destination location to a driver of the taxi, and the driver enters information of the destination location to the computing system, e.g., via an input/output interface, then the computing system determines that the destination location is known. If a driver of the vehicle does not enter information of the destination location to the computing system but the vehicle is moving, the computing system determines that the destination location is unknown and may be inferred by the computing system); providing instructions to the vehicle regarding information about the unknown features after the start of movement of the vehicle (Ghoting, Para. [0017]…If a driver of the vehicle does not enter information of the destination location to the computing system but the vehicle is moving, the computing system determines that the destination location is unknown and may be inferred by the computing system), wherein providing instructions comprise provides the destination location or the travel path; (Ghoting, Para. [0018]… upon determining that the destination location of the vehicle is unknown, at step 105, the computing system is configured to learn the destination location or the destination region. In one embodiment, the computing system is configured to infer the destination location or the destination region based on the current location of the vehicle, a current driving direction of the vehicle, prior destination regions of the vehicle, prior destination locations of the vehicle, a current time of day, and/or a current day of week…) and navigating the vehicle to the at least one destination of interest (Napoli, Fig. 3, Steps 310-312).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the methods for autonomous labor intelligent dynamic assignment as taught by Napoli with the advanced routing of vehicle fleets as taught by Ghoting to obtain a traffic route for a vehicle ( e.g., taxi) to maximize a profit in operating the vehicle (Ghoting, Para. [0005]).
Regarding claim 9, Napoli in view of Ghoting the method of claim 8, wherein the vehicle is an autonomous mobile robot (AMR), (Napoli, Robots 204).
Regarding claim 10, Napoli in view of Ghoting teach the method of claim 9, wherein the instruction set is output to the AMR, and a machine or human operator of the AMR controls the navigation system according to the instruction set (Napoli, Para. [0063]).
Regarding claim 11, Napoli in view of Ghoting the method of claim 8, wherein a task plan includes data about the material flow task (Napoli, Para. [0058]…the system 210 may
include a work distributor/manager 240 responsible for assigning work assignments 235 to each of the plurality of people 202a-n, robots 204a-n, and MHE 206a-n. The system 210 may include a database 280 for storing and retrieving any data and information for the operations and performance of the system described herein.) and includes a combination of known and unknown information about a route, destination, and robotic action of the material flow task (Ghoting, Para. [0017])
Regarding claim 12, Napoli in view of Ghoting teach the method of claim 11, wherein the input device communicates with a plurality of different input sources, and wherein the controller parses and prioritizes multiple delayed inputs of the different input sources to the task plan (Napoli, Para. [0123]… Priorities may identify a level of importance for the
MHE to work on certain task, functions, work assignments, jobs or work flows or with certain robots, MHEs, materials, products or services on in certain locations in the warehouse. Priority may identify a level of importance to have a MHE working on a particular work assignment 235)
Regarding claim 13, Napoli in view of Ghoting the method of claim 11, wherein the unknown data is resolved by the at least one instruction during the execution of the material flow task (Ghoting, Para. [0017-0018]).
Regarding claim 14, Napoli in view of Ghoting teach the method of claim 8, further comprising receiving by a user interface data for addressing the unknown information while the vehicle is moving along a route for performing the material flow task (Napoli, Para, [0096]… The monitor may be designed, configured and/or constructed to interface to the MHEs using the protocols and APis supported by the type and configuration of the specific
MHE)
Regarding claims 15-20, please refer to the rejection of claims 8-13 which are commensurate in scope.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JODI M JONES whose telephone number is (571)272-0107. The examiner can normally be reached M-F 8:30am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Antonucci can be reached at (313) 446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JODI JONES/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
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