DETAILED ACTION
Claims 1-7 received on 11/10/2025 are considered in this office action. Claims 1-7 are pending for examination.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/10/2025 is being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 7 recite the limitation “the basis of”. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the Examiner will interpret the basis of as based on.
Claim 1 recites the limitation " the specified state". There is insufficient antecedent basis for this limitation in the claim. Furthermore, it is unclear whether the specified state is referring to an environmental state of the robot or a state of an article handled by the robot. For examination purposes, the Examiner will interpret basis of a difference between the specified state and the state represented by the command as basis of a difference between the specified environmental state of the robot and the state represented by the command.
Claims 2-7 are dependent on claim 1, and fail to cure the deficiencies thereof, thus are rejected on the same basis.
Regarding claim 5, the phrase "missing articles in the state of the robot for the state represented by the command" renders the claim indefinite because it is unclear. For examination purposes, the Examiner will interpret missing articles in the state of the robot for the state represented by the command as missing articles in the state of the robot with respect to the state represented by the command, as supported by para. [0032] of the specification, which is reproduced below.
[0032] Furthermore, the management server 11 orders replenishment of missing articles
in the state of the robot with respect to the state represented by the command, thereby preventing a lack of an article and increasing availability
Claim 6 is dependent on claim 5, and fails to cure the deficiencies thereof, thus are rejected on the same basis.
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)(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.
Claim 1 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cesic (US20220121837A1).
Regarding claim 1, Cesic teaches a system comprising a management server (FIG. 1 Central Communication System 130; para. [0028]: “these devices may be located outside of environment 100 (e.g., at a home, office, data center, cloud environment, etc.).”; para. [0079]: “In a networked deployment, the machine may operate in the capacity of a server machine or a client machine in a server-client network environment”), a robot (FIG. 1 Autonomous Mobile Robot 140), and a sensor that measures an environment including a space of an action range of the robot (para. [0048]: “The sensors may include one or more cameras, one or more depth sensors, one or more scan sensors (e.g., RFID), a location sensor (e.g., showing location of the robot within the facility and/or GPS coordinates), and so on”; para. [0051]: “Pose determination module 332 captures a red-green-blue (RGB) image of an object to be transported from a source to a destination (e.g., using a camera sensor of the robot when approaching the object).”; para. [0099]: “The central communication system receives sensory input from one or more of the selected robots”), wherein the management server comprises:
a reception unit configured to receive a mission (FIG. 1; para. [0075]: “An operator (e.g., operating operator device 110) may define a mission, where the objects are to be transported from the source area to a destination area”; para. [0086]: “a user may configure a mission by interacting with user interface 700. In the exemplary embodiment depicted in FIG. 7”; para. [0031]: “Central communication system 130 receives information from the fleet and/or operator device 110 and uses that information to make decisions about activity to be performed by the fleet.”, wherein mission inputted from user interface is received in Central communication system, which corresponds to management server, and thus indicates a reception unit), and
a command unit configured to generate a command on the basis of the mission and transmit the command to the robot, wherein the command includes a first command representing a state after execution of the mission (para. [0041]: “Robot instruction module 234 transmits instructions to the selected one or more robots to manipulate the object(s) in the source area (e.g., to ultimately transport the object(s) to the destination area). […] robot instruction module 234 includes detailed step-by-step instructions on how to transport the objects. In another embodiment, robot instruction module 234 transmits a general instruction to transmit one or more objects from the source area to the destination area, leaving the manner in which the objects will be manipulated and ultimately transmitted up to the robot to determine autonomously”; para. [0100]: “The central communication provides 1408 an instruction to each selected robot to transport the plurality of objects from the source area to the destination area (e.g., using robot instruction module 234).”, wherein robot transmitting one or more object to destination indicates a state after execution of the mission), and
the robot includes a state specification unit configured to specify an environmental state of the robot including a state of an article handled by the robot on the basis of measurement data of the sensor (FIG. 7; para. [0047]: “autonomous mobile robot 140 includes object identification module 331, pose determination module 332, object state determination module 333”; para. [0048]: “Object identification module 331 ingests information received from sensors of robot 140, and outputs information that identifies an object in proximity to the robot.”; para. [0049]: “Object identification module 331 determines, based on the probability exceeding a threshold for a given candidate object, that the candidate object is a detected object from the sensor information”; para. [0062]: “Robot instruction module 334, after having selected an object and having approached the object and manipulated it for transfer to the destination area, may instruct the robot to transport the selected object through the facility from the source area to a destination area”; para. [0061]: “may revise its assessment on which side is optimal for manipulation based on an interaction with the object”, wherein robot is aware that it approached the object, and lifting the object for transport to destination, thus indicating a state specification unit configured to specify an environmental state of the robot including a state of an article handled by the robot on the basis of measurement data of the sensor), and
an instruction unit configured to receive the command and drive the robot so that a state of the robot approaches a state represented by the command on the basis of a difference between the specified state and the state represented by the command (para. [0041]: “general instruction to transmit one or more objects from the source area to the destination area, leaving the manner in which the objects will be manipulated and ultimately transmitted up to the robot to determine autonomously”; para. [0100]: “Each robot is configured to autonomously select an object of the plurality of objects based on a position and location of the object within the source area, transport the selected object through the facility from the source area to a destination area along a route selected by the robot, and unload the selected object at a location within the destination area […] In an embodiment, the one or more robots unload objects at least a threshold distance from the destination boundary. After transporting the selected object to the destination, a robot may capture an image of the source area.””; para. [0060]: “approaching the object from a given side may result in a more efficient path to be taken from the source area to the destination area than approaching from a different side.”, wherein transporting to a “threshold distance” of a destination corresponds to command which is generated based on a difference between the specified state and the state represented by the command).
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 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 2-3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Cesic, in view of Hazan (US20240220924A1).
Regarding claim 2, Cesic teaches the system according to claim 1. Cesic further teaches wherein the instruction unit drives the robot so that a difference between the state of the robot and the state represented by the first command satisfies a predetermined requirement (para. [0100]: “In an embodiment, the one or more robots unload objects at least a threshold distance from the destination boundary. After transporting the selected object to the destination, a robot may capture an image of the source area.”, wherein “threshold distance” indicates a predetermined requirement), but fails to specifically teach when the first command has not been received, drives the robot so that a difference between the state of the robot and an ideal state of the robot satisfies a predetermined requirement.
However, Hazan teaches when the first command has not been received, drives the robot so that a difference between the state of the robot and an ideal state of the robot satisfies a predetermined requirement (FIG. 6A-6B; Abstract: “dummy transport order requests an allocation of an AGV vehicle, in a busy state, to a plant location until a set of corresponding anticipated conditions are satisfied or until a predefined timer expires”; para. [0070]: “cause the order generator 202 to send, to the fleet manager 201, dummy AGV transport orders in a plant location which is preferably chosen to be in the proximity of a set of working areas”; para. [003]: “an anticipated condition for dummy task dummy_task1 is the arrival, at the fleet manager 201, of a new order containing transport orders in a plant location”, wherein allocation is requested prior to “a new order” thus indicating the first command has not been received).
Cesic and Hazan are considered analogous art to the claimed invention because they are in the same field of endeavor of using AGVs to perform tasks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Cesic to incorporate the teachings of Hazan by including dummy task to allocate AGVs to a specified location. Doing so would allow an AGV to be allocated into a convenient position in a waiting mode and does not drive to a faraway location (Hazan, para. [0123]), thus enhancing efficiency in allocating efficiency based on look-ahead vision (Hazan, para. [0022]).
Regarding claim 3, Cesic in view of Hazan teaches the system according to claim 2. Hazan further teaches wherein the robot receives a second command representing the ideal state of the robot from the management server (FIG. 6A-6B; Abstract: “dummy transport order requests an allocation of an AGV vehicle, in a busy state, to a plant location until a set of corresponding anticipated conditions are satisfied or until a predefined timer expires”; para. [0070]: “cause the order generator 202 to send, to the fleet manager 201, dummy AGV transport orders in a plant location which is preferably chosen to be in the proximity of a set of working areas”, wherein allocation of an AGV to a plant location indicates second command).
Regarding claim 7, Cesic in view of Hazan teaches the system according to claim 2. Hazan further teaches wherein the management server includes an ideal state estimation unit that estimates, as the ideal state, a state in which a future mission is executable on the basis of a mission executed in the past (para. [0022]: “the AGV fleet managers 201 take their decisions based on currently received orders from the order generators 202, without seeing the full picture and without having a look-ahead vision of the plant,”; para. [0098]: “Phase2: analyzing 720 data via pattern recognition and generating 720 of anticipating logical rules”; para. [0112]: “data are analyzed 720 in order to recognize, via a pattern recognition algorithm, repetitive AGV order patterns with recurring AGV transport orders, herein called anticipated conditions, and corresponding recurring anticipating plant status conditions which precede such AGV order patterns”, wherein pattern indicate on the basis of a mission executed in the past).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Cesic, in view of ALI (US 20220187841 A1).
Regarding claim 4, Cesic teaches the system according to claim 1. Cesic further teaches wherein the robot includes a control quantity calculation unit for determining a drive quantity (para. [0041]: “Robot instruction module 234 transmits instructions to the selected one or more robots to manipulate the object(s) in the source area (e.g., to ultimately transport the object(s) to the destination area). […] robot instruction module 234 includes detailed step-by-step instructions on how to transport the objects. In another embodiment, robot instruction module 234 transmits a general instruction to transmit one or more objects from the source area to the destination area, leaving the manner in which the objects will be manipulated and ultimately transmitted up to the robot to determine autonomously”) but fails to specifically teach using a reinforcement learning algorithm that uses a reward based on a difference between a state of the robot and the state represented by the command.
However, in the same field of endeavor, ALI teaches wherein the robot includes a control quantity calculation unit for determining a drive quantity using a reinforcement learning algorithm that uses a reward based on a difference between a state of the robot and the state represented by the command (para. [0897]: “Mapping and localization algorithms may transmit and receive data from one another and transmit data to the path planning algorithm. Mapping, localization/re-localization, and path planning algorithms may transmit and receive data back and forth with the controller that commands the robot to start and stop by moving the wheels of the robot. Mapping, localization/re-localization, and path planning algorithms may also transmit and receive data back and forth with the trajectory measurement and observation algorithm. The trajectory measurement and observation algorithm uses a cost function minimize the difference between the controller command and the actual trajectory. The algorithm assigns a reward or penalty based on the difference between the controller command and the actual trajectory. This continuous process fine tunes the SLAM and control of the robot over time. At each time sequence, data from the controller, SLAM and path planning algorithms, and the reward system of trajectory measurement and observation algorithm are transmitted to the database for input into the Deep Q-Network for reinforcement learning. In embodiments, reinforcement learning algorithms may be used to fine tune perception, actuation, or another aspect. For example, reinforcement learning algorithms may be used to prevent or reduce bumping into an object.”; para. [1137]: “Based on the output, the processor actuates the robot to perform an action. Based on the observed outcome of the action, the processor assigns a reward. This information is provided back to the network such that the network may readjust and learn from the actions of the robot.”).
Cesic and ALI are considered analogous art to the claimed invention because they are in the same field of endeavor of using AGVs to perform tasks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified AGVs of Cesic to incorporate the teachings of ALI by controlling AGVs based on reward and penalty from reinforcement learning. Doing so would actuate the AGV such that it follows a target trajectory based on reward (ALI, para. [0897]), and allow the algorithm to further learn from its actions (ALI, para. [1137]).
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Cesic, in view of Jarvis (US 20180127211 A1).
Regarding claim 5, Cesic teaches the system according to claim 1, but fails to specifically teach wherein the management server includes an ordering unit that orders replenishment of missing articles in the state of the robot for the state represented by the command.
However, in the same field of endeavor, Jarvis teaches wherein the management server includes an ordering unit that orders replenishment of missing articles in the state of the robot for the state represented by the command (para. [0134]: “FIG. 5 is a data-flow diagram of an example method of replenishing a supply of items in a carton. At 502, a WMS 104 may receive input indicating a short pick (e.g., an inventory of items that is insufficient to fill an order, below a defined threshold, etc.). In some implementations, the determination that there is a short pick may be made automatically by a component of the system 100 (e.g., the WMS 104, the REX 132, or WES 102) determining that a level of inventory in the order fulfillment facility or in a particular modular storage unit 702 is below a defined threshold quantity or percentage or is insufficient to fill an outstanding order. In some implementations, a picker (e.g., at a pick-cell station 316) may provide an input indicating an insufficient or low quantity of items are in a modular storage unit 702.”; para. [0136]: “At 504, the WMS 104 may create wave or lean tasks. A wave task may be, for example, a request for an emergency replenishment, such as when an order is set to be filled, but the system (e.g., the WMS 104 or REX 132) determines that there are insufficient items in one or more modular storage units 702 to fill the order”).
Cesic and Jarvis are considered analogous art to the claimed invention because they are in the same field of endeavor of using AGVs to perform tasks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified management server of Cesic to incorporate the teachings of Jarvis by creating replenishment order in response to insufficient items. Doing so would allow prompt replenishment of insufficient items to fulfill order.
Regarding claim 6, Cesic in view of Jarvis teaches the system according to claim 5. Jarvis further teaches wherein the state specification unit collates data of an article received in response to the order measured by the sensor with content of the order placed by the ordering unit, and manages the article (FIG. 5; para. [0140]: “At 516, the WMS 516 may trigger a fill active message responsive to a scan of the product, for example, in the replenishment area 318.”; para. [0144]: “At 526, responsive to receiving a confirmatory input from the picker (e.g., via voice, input into a graphical interface, or pressing a button at the pick-cell station 316) that inventory has been added to a container at the pick-cell station 316 (e.g., from the putwall location), the REX 132 may signal a picking AGV 114 to retrieve the container from the pick-cell station 316 and store it in the high-density storage area 304”; para. [0145]: “At 528, 530, and 532, the picking AGV 114 may retrieve the container from the pick-cell station 316, navigate to a storage shelf, and place the container on the storage shelf, for example, as described in reference to 474, 476, and 479 above”; para. [0135]: “In implementations, where an input indicating an insufficient quantity of items is in the modular storage unit 702 is received during a pick at a pick-cell station 316, the picking system 108 or WMS 104 may provide instructions to the picker to leave the order (e.g., a carton corresponding to the order) in a cart coupled with the cart AGV 116 until the pick is fulfilled”, wherein paused order from para. [0135] will be completed after replenishment).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Elazary (US20160304281A1) teaches a system for fully autonomous order fulfillment and inventory management within a distribution site or warehouse, which also comprises restocking maneuver.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW S KIM whose telephone number is (571)272-7356. The examiner can normally be reached Mon - Fri 8AM - 5PM.
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/ANDREW SANG KIM/Examiner, Art Unit 3668
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