DETAILED ACTION
Claims 1-15 are pending.
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 .
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to Chinese Patent Application No. 202310084915.4 filed on 2/7/2023.
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.
Claim(s) 1-15 is/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 pre-AIA the applicant regards as the invention.
With regard to claim 1, this claim recites ‘the equipment’ and ‘the equipment transport task’ for which there is no antecedent basis.
In addition, claim 1 recites ‘so as to execute a test task for the equipment’ and it is not clear if the test task is executed or not.
With regard to claim 4, this claim recites ‘input externally’ and it is not clear what the input is external to.
In addition, claim 6 recites ‘so as to determine whether the equipment type of the equipment is the equipment type corresponds to the current test task’ and it is not clear if this determination is made or not.
With regard to claim 9, this claim recites ‘the equipment’ and ‘the equipment transport task’ for which there is no antecedent basis.
In addition, claim 9 recites ‘so as to execute a test task for the equipment’ and it is not clear if the test task is executed or not.
With regard to claim 12, this claim recites ‘input externally’ and it is not clear what the input is external to.
In addition, claim 14 recites ‘so as to determine whether the equipment type of the equipment is the equipment type corresponds to the current test task’ and it is not clear if this determination is made or not.
The dependent claims are also rejected under 35 U.S.C. § 112 as they inherit all of the characteristics of the claim from which they depend and none of the dependent claims provide a cure for the indefiniteness of the parent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 8 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a non-statutory subject matter.
Claim 8 is directed to a computer-readable storage medium, storing a computer program, i.e. software. “Software per se” is non-statutory under 35 USC 101 because it is merely a set of instructions. See MPEP 2106.03. The examiner suggests considering amending ‘computer-readable storage medium’ to ‘non-transitory computer-readable storage medium’.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim(s) 1-2, 4, 7-10, 12 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rana et al. U.S. Publication Patent No. 20210232989 (hereinafter Rana) in view of Xu et al. U.S. Patent Publication No. 20230289494 (hereinafter Xu).
Regarding claim 1, Rana teaches a forming-machine equipment testing method, applied to a forming-machine equipment testing system [0008-0011 — By employing the present invention already to complete simulation and virtual testing scenarios throughout the whole product lifecycle… fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network… At least one mobile vehicle is configured as a mobile measurement vehicle comprising at least one spatial measurement sensor unit; 0060, Fig. 6 — the invention also relates to a method for manufacturing a work piece within a manufacturing environment by a system comprising multiple autonomously moving mobile vehicles; 0080, Fig. 1 — a manufacturing environment 1; 0111, Fig. 2 — a static part scenario, in which larger work pieces 2 are manufactured, e.g. airplane cabins or wings], wherein the forming-machine equipment testing system comprises a scheduling host computer [0067 — A device or system according to the present invention comprises computation units, microcontrollers, microcomputers, DSPs, TPUs or programmable or hardwired digital logics, wherefore the present invention can involve a computer program product with program code being stored on a machine readable medium; 0083-0085, Fig. 5 — other equipment in the manufacturing environment 1 can comprise such an edge computation unit 33, like the shown entities 3,3t,9,5a,5m,5a,6,13,35,10, etc… a cloud computing system 8… an edge computation system can also comprise intermediary computer systems 7, e.g. at the edge of a station of a production line, of a production line or of a factory, which processes data in close proximity to the corresponding data source; 0139, Fig. 5 — At the shown manufacturing environment 1, cars are manufactured as work pieces which is done according to the invention by orchestrating a plurality of mobile vehicles 5, which are cooperating to establish the required tasks to manufacture the car 2, and which mobile vehicles 5 are equipped with the here symbolized edge-client computation units 33, which are configured to run edge computation agents], and the scheduling host computer stores a task start time of equipment [0010 — fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network; 0148 — the vehicle is scheduled to autonomously navigate to the production machine at its next availability timeslot] and types of equipment [0044, 0086-0087, Figs. 1-2 and 5 — Mobile vehicles according to the invention which are equipped with sensor units 15, like e.g. laser scanner units 15s or laser tracker units 15t… the mobile vehicles can be equipped with one or more measurement tools 15, actuator tools 16 and/or machining tools 17, either fixedly or in an exchangeable manner. The lower level task can then comprise the detailed information on how to execute the upper level task at a specific work piece for a specific step in the manufacturing and measuring chain, e.g. where, in which order and how]; the method comprising:
determining, by the scheduling host computer, an equipment transport task for the equipment after a task start time of equipment is reached [0148 — the vehicle is scheduled to autonomously navigate to the production machine at its next availability timeslot];
determining, by the scheduling host computer, whether the equipment meets the equipment transport task [0149-0154, Fig. 6 —Box 42 symbolizes an automatic deployment of a subset of mobile vehicles at the manufacturing environment to the work piece, which is done automatically according to a capability information provided by a local computation system at the mobile vehicle. Therein, the subset comprises at least one mobile measurement vehicle having a spatial measurement sensor unit.]; and
in response to the equipment meeting the equipment transport task, instructing, by the scheduling host computer, an intelligent transport device to transport the equipment to a corresponding target location, so as to execute a test task for the equipment [0149-0154, Fig. 6 —Box 42 symbolizes an automatic deployment of a subset of mobile vehicles at the manufacturing environment to the work piece, which is done automatically according to a capability information provided by a local computation system at the mobile vehicle. Therein, the subset comprises at least one mobile measurement vehicle having a spatial measurement sensor unit.; 0086, Figs. 1-2 and 5 — Mobile vehicles according to the invention which are equipped with sensor units 15, like e.g. laser scanner units 15s or laser tracker units 15t for accurate spatial measurement].
But Rana fails to clearly specify a task start time corresponding to each type of equipment.
However, Xu that a computer stores a task start time corresponding to each type of equipment [0052-0055 — AGV (automated guided vehicles) system 22 includes an adapter 221, a scheduling manager 222, and a plurality of AGV controllers. The adapter 221 receives a transport order from the order generator 22, and converts the transport order into a plurality of AGV transport tasks and sends the AGV transport tasks to the scheduling manager 222 according to types of materials transported by different AGVs and maximum carrying capacities — scheduling/start time is based on different AGV carrying capacities (types) — Quantities and types of… the virtual AGVs in the emulator 21 may vary according to different factory environments.].
Rana and Xu are analogous art. They relate to manufacturing systems, particularly involving automated vehicles.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Rana, by incorporating the above limitations, as taught by Xu.
One of ordinary skill in the art would have been motivated to do this modification so that equipment is appropriately scheduled based on the individual capacity/capability of the equipment, as suggested by Xu [0052].
Regarding claim 2, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above.
Further, Rana teaches the forming-machine equipment testing system further comprises a location host computer [0008-0011 — By employing the present invention already to complete simulation and virtual testing scenarios throughout the whole product lifecycle… fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network… At least one mobile vehicle is configured as a mobile measurement vehicle comprising at least one spatial measurement sensor unit; 0060, Fig. 6 — the invention also relates to a method for manufacturing a work piece within a manufacturing environment by a system comprising multiple autonomously moving mobile vehicles; 0080, Fig. 1 — a manufacturing environment 1; 0111, Fig. 2 — a static part scenario, in which larger work pieces 2 are manufactured, e.g. airplane cabins or wings], wherein the forming-machine equipment testing system comprises a scheduling host computer [0067 — A device or system according to the present invention comprises computation units, microcontrollers, microcomputers, DSPs, TPUs or programmable or hardwired digital logics, wherefore the present invention can involve a computer program product with program code being stored on a machine readable medium; 0083-0085, Fig. 5 — other equipment in the manufacturing environment 1 can comprise such an edge computation unit 33, like the shown entities 3,3t,9,5a,5m,5a,6,13,35,10, etc… a cloud computing system 8… an edge computation system can also comprise intermediary computer systems 7, e.g. at the edge of a station of a production line, of a production line or of a factory, which processes data in close proximity to the corresponding data source; 0139, Fig. 5 — At the shown manufacturing environment 1, cars are manufactured as work pieces which is done according to the invention by orchestrating a plurality of mobile vehicles 5, which are cooperating to establish the required tasks to manufacture the car 2, and which mobile vehicles 5 are equipped with the here symbolized edge-client computation units 33, which are configured to run edge computation agents]; the method further comprising:
after the intelligent transport device transports the equipment to the corresponding target location, determining, by the location host computer, a target test task corresponding to the equipment and delivering the target test task to the equipment [0010 — The mobile vehicle therein at least comprises a spatial localization system for deriving a location of the mobile vehicle in the manufacturing environment... The mobile vehicle also comprises a communication interface providing a data link to at least one other mobile vehicle and/or to a fog- and/or cloud-computation and storage system (input externally), in particular configured for cloud analytics which can comprise an analysis by big data processing. The fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network. The data link can therein e.g. be established directly in-between the mobile vehicles for direct interaction of the vehicles and/or swarm intelligence, indirectly via a common gateway, hub or control system, or exclusively or optionally by central fog- and/or cloud computation unit… An edge computation unit comprised at the mobile vehicle is configured for a local data analysis at the mobile vehicle by intelligent, deployable edge analytics software agents. The edge computation unit at the mobile vehicle can comprises a central processing unit, a memory and program code configured for a local data analysis at the mobile vehicle, in particular of data derived by the mobile vehicle and of data derived from the task information and from the manufacturing environment…This local data analysis can preferably be established by intelligent, deployable edge analytics software agents, comprising at least one program-code-module configured to be transferred by the manufacturing environment network and to be executes by the edge client, preferably in real time by a stream analytics agent at the edge computation unit which is configured to compute real time analysis on an online stream of data at the manufacturing environment network. Such edge analytics software agents can e.g. also be deployable via the communication interface from the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system. The edge computation unit can therein at least partially operate in real time and/or by a stream analytics agent, and can be configured to interact with the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system via the communication interface. The invention can comprise an automatic deployment (delivery) of a workflow information for the processing of the work-piece for the at least one mobile vehicle, which workflow comprises at least one current task (target test task input). In particular, the workflow information can transferred via the manufacturing network environment and can be embodied as a set of data comprising a series of tasks which are comprising modification steps to be applied to the work piece. The current task of such a series of tasks can in particular explicitly or implicitly comprise a spatial dependency or spatial relation in-between the work piece and at least one external tool, component or probe provided by one of the multiple mobile vehicles and/or by the manufacturing environment. The spatial dependency can in particular be extracted in form of some relative spatial information that is comprised in the current task as a requirement].
Regarding claim 4, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above.
Further, Rana teaches the forming-machine equipment testing system further comprises a location host computer [0008-0011 — By employing the present invention already to complete simulation and virtual testing scenarios throughout the whole product lifecycle… fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network… At least one mobile vehicle is configured as a mobile measurement vehicle comprising at least one spatial measurement sensor unit; 0060, Fig. 6 — the invention also relates to a method for manufacturing a work piece within a manufacturing environment by a system comprising multiple autonomously moving mobile vehicles; 0080, Fig. 1 — a manufacturing environment 1; 0111, Fig. 2 — a static part scenario, in which larger work pieces 2 are manufactured, e.g. airplane cabins or wings], wherein the forming-machine equipment testing system comprises a scheduling host computer [0067 — A device or system according to the present invention comprises computation units, microcontrollers, microcomputers, DSPs, TPUs or programmable or hardwired digital logics, wherefore the present invention can involve a computer program product with program code being stored on a machine readable medium; 0083-0085, Fig. 5 — other equipment in the manufacturing environment 1 can comprise such an edge computation unit 33, like the shown entities 3,3t,9,5a,5m,5a,6,13,35,10, etc… a cloud computing system 8… an edge computation system can also comprise intermediary computer systems 7, e.g. at the edge of a station of a production line, of a production line or of a factory, which processes data in close proximity to the corresponding data source; 0139, Fig. 5 — At the shown manufacturing environment 1, cars are manufactured as work pieces which is done according to the invention by orchestrating a plurality of mobile vehicles 5, which are cooperating to establish the required tasks to manufacture the car 2, and which mobile vehicles 5 are equipped with the here symbolized edge-client computation units 33, which are configured to run edge computation agents], and determining, by the scheduling host computer, the equipment transport task for the equipment comprises:
receiving, by the scheduling host computer, a specified location input externally and determining the equipment transport task for the equipment based on the specified location [0010 — The mobile vehicle therein at least comprises a spatial localization system for deriving a location of the mobile vehicle in the manufacturing environment... The mobile vehicle also comprises a communication interface providing a data link to at least one other mobile vehicle and/or to a fog- and/or cloud-computation and storage system (input externally), in particular configured for cloud analytics which can comprise an analysis by big data processing. The fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network. The data link can therein e.g. be established directly in-between the mobile vehicles for direct interaction of the vehicles and/or swarm intelligence, indirectly via a common gateway, hub or control system, or exclusively or optionally by central fog- and/or cloud computation unit… The spatial dependency can in particular be extracted in form of some relative spatial information that is comprised in the current task as a requirement (determining the equipment transport task for the equipment based on the specified location); 0149-0154, Fig. 6 —Box 42 symbolizes an automatic deployment of a subset of mobile vehicles at the manufacturing environment to the work piece, which is done automatically according to a capability information provided by a local computation system at the mobile vehicle. Therein, the subset comprises at least one mobile measurement vehicle having a spatial measurement sensor unit.]; the method further comprising:
after the intelligent transport device transports the equipment to the specified location, receiving, by the location host computer, a target test task input externally and delivering the target test task to the equipment [0010 — The mobile vehicle therein at least comprises a spatial localization system for deriving a location of the mobile vehicle in the manufacturing environment... The mobile vehicle also comprises a communication interface providing a data link to at least one other mobile vehicle and/or to a fog- and/or cloud-computation and storage system (input externally), in particular configured for cloud analytics which can comprise an analysis by big data processing. The fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network. The data link can therein e.g. be established directly in-between the mobile vehicles for direct interaction of the vehicles and/or swarm intelligence, indirectly via a common gateway, hub or control system, or exclusively or optionally by central fog- and/or cloud computation unit… An edge computation unit comprised at the mobile vehicle is configured for a local data analysis at the mobile vehicle by intelligent, deployable edge analytics software agents. The edge computation unit at the mobile vehicle can comprises a central processing unit, a memory and program code configured for a local data analysis at the mobile vehicle, in particular of data derived by the mobile vehicle and of data derived from the task information and from the manufacturing environment…This local data analysis can preferably be established by intelligent, deployable edge analytics software agents, comprising at least one program-code-module configured to be transferred by the manufacturing environment network and to be executes by the edge client, preferably in real time by a stream analytics agent at the edge computation unit which is configured to compute real time analysis on an online stream of data at the manufacturing environment network. Such edge analytics software agents can e.g. also be deployable via the communication interface from the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system. The edge computation unit can therein at least partially operate in real time and/or by a stream analytics agent, and can be configured to interact with the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system via the communication interface. The invention can comprise an automatic deployment (delivery) of a workflow information for the processing of the work-piece for the at least one mobile vehicle, which workflow comprises at least one current task (target test task input). In particular, the workflow information can transferred via the manufacturing network environment and can be embodied as a set of data comprising a series of tasks which are comprising modification steps to be applied to the work piece. The current task of such a series of tasks can in particular explicitly or implicitly comprise a spatial dependency or spatial relation in-between the work piece and at least one external tool, component or probe provided by one of the multiple mobile vehicles and/or by the manufacturing environment. The spatial dependency can in particular be extracted in form of some relative spatial information that is comprised in the current task as a requirement].
Regarding claim 7, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above.
Further, Rana teaches after the target test task is executed in the target location, obtaining, by the scheduling host computer, equipment data corresponding to the target test task executed by the equipment; determining, by the scheduling host computer, whether the equipment data meets an equipment backhauling task; and in response to the equipment data meeting the equipment backhauling task, instructing, by the scheduling host computer, the intelligent transport device to transport the equipment back to an original location of the equipment [0148 — A software agent at the edge-computation system 33 of the machine 3 detects such and automatically dispatches an according information about this anomaly to a higher-level factory control system… The UAV 5a (unmanned aerial vehicles/intelligent transportation device) autonomously navigates to the production machine and takes pictures. Those pictures which are returned from the inspection UAV are then processed by an image recognition and classification system… a decision tree of a machine fault analysis algorithm then automatically determines that any additionally consumed energy has to be manifested somewhere, and that heat is a common manifestation. Therefore, the system automatically releases the visual inspection UAV of its duty (which then autonomously returns to its charging base) and negotiates with one of the mobile vehicles 5 in the manufacturing environment that is equipped with a thermal imager. As this thermal imager vehicle 5 is presently occupied and the state of the production machine is not classified to be critical but could get critical in near future if unattended, the vehicle is scheduled to autonomously navigate to the production machine at its next availability timeslot and to report back thermal images from multiple views of the production machine].
Regarding claim 8, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above.
Further, Rana teaches computer-readable storage medium, storing a computer program, wherein when the computer program is executed by a processor, the processor implements the method according to claim 1 [0067 — the present invention can involve a computer program product with program code being stored on a machine readable medium; 0060, Fig. 6 — the invention also relates to a method for manufacturing a work piece within a manufacturing environment by a system comprising multiple autonomously moving mobile vehicles].
Regarding claim 9, Rana teaches a system for executing a forming-machine equipment task [0008-0011 — By employing the present invention already to complete simulation and virtual testing scenarios throughout the whole product lifecycle… fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network… At least one mobile vehicle is configured as a mobile measurement vehicle comprising at least one spatial measurement sensor unit; 0060, Fig. 6 — the invention also relates to a method for manufacturing a work piece within a manufacturing environment by a system comprising multiple autonomously moving mobile vehicles; 0080, Fig. 1 — a manufacturing environment 1; 0111, Fig. 2 — a static part scenario, in which larger work pieces 2 are manufactured, e.g. airplane cabins or wings], comprising:
a scheduling host computer [0067 — A device or system according to the present invention comprises computation units, microcontrollers, microcomputers, DSPs, TPUs or programmable or hardwired digital logics, wherefore the present invention can involve a computer program product with program code being stored on a machine readable medium; 0083-0085, Fig. 5 — other equipment in the manufacturing environment 1 can comprise such an edge computation unit 33, like the shown entities 3,3t,9,5a,5m,5a,6,13,35,10, etc… a cloud computing system 8… an edge computation system can also comprise intermediary computer systems 7, e.g. at the edge of a station of a production line, of a production line or of a factory, which processes data in close proximity to the corresponding data source; 0139, Fig. 5 — At the shown manufacturing environment 1, cars are manufactured as work pieces which is done according to the invention by orchestrating a plurality of mobile vehicles 5, which are cooperating to establish the required tasks to manufacture the car 2, and which mobile vehicles 5 are equipped with the here symbolized edge-client computation units 33, which are configured to run edge computation agents], wherein the scheduling host computer stores a task start time of equipment [0010 — fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network; 0148 — the vehicle is scheduled to autonomously navigate to the production machine at its next availability timeslot] and types of equipment [0044, 0086-0087, Figs. 1-2 and 5 — Mobile vehicles according to the invention which are equipped with sensor units 15, like e.g. laser scanner units 15s or laser tracker units 15t… the mobile vehicles can be equipped with one or more measurement tools 15, actuator tools 16 and/or machining tools 17, either fixedly or in an exchangeable manner. The lower level task can then comprise the detailed information on how to execute the upper level task at a specific work piece for a specific step in the manufacturing and measuring chain, e.g. where, in which order and how], and the scheduling host computer is configured to:
determine an equipment transport task for the equipment after a task start time of the equipment is reached [0148 — the vehicle is scheduled to autonomously navigate to the production machine at its next availability timeslot];
determine whether the equipment meets the equipment transport task [0149-0154, Fig. 6 —Box 42 symbolizes an automatic deployment of a subset of mobile vehicles at the manufacturing environment to the work piece, which is done automatically according to a capability information provided by a local computation system at the mobile vehicle. Therein, the subset comprises at least one mobile measurement vehicle having a spatial measurement sensor unit.]; and
in response to the equipment meeting the equipment transport task, instruct an intelligent transport device to transport the equipment to a corresponding target location, so as to execute a test task for the equipment [0149-0154, Fig. 6 —Box 42 symbolizes an automatic deployment of a subset of mobile vehicles at the manufacturing environment to the work piece, which is done automatically according to a capability information provided by a local computation system at the mobile vehicle. Therein, the subset comprises at least one mobile measurement vehicle having a spatial measurement sensor unit.; 0086, Figs. 1-2 and 5 — Mobile vehicles according to the invention which are equipped with sensor units 15, like e.g. laser scanner units 15s or laser tracker units 15t for accurate spatial measurement].
But Rana fails to clearly specify a task start time corresponding to each type of equipment.
However, Xu that a computer stores a task start time corresponding to each type of equipment [0052-0055 — AGV (automated guided vehicles) system 22 includes an adapter 221, a scheduling manager 222, and a plurality of AGV controllers. The adapter 221 receives a transport order from the order generator 22, and converts the transport order into a plurality of AGV transport tasks and sends the AGV transport tasks to the scheduling manager 222 according to types of materials transported by different AGVs and maximum carrying capacities — scheduling/start time is based on different AGV carrying capacities (types) — Quantities and types of… the virtual AGVs in the emulator 21 may vary according to different factory environments.].
Rana and Xu are analogous art. They relate to manufacturing systems, particularly involving automated vehicles.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by Rana, by incorporating the above limitations, as taught by Xu.
One of ordinary skill in the art would have been motivated to do this modification so that equipment is appropriately scheduled based on the individual capacity/capability of the equipment, as suggested by Xu [0052].
Regarding claim 10, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 2.
Regarding claim 12, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 4.
Regarding claim 15, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above.
Further, Rana teaches the scheduling host computer is further configured to: after the target test task is executed in the target location, obtain equipment data corresponding to the target test task executed by the equipment; determine whether the equipment data meets an equipment backhauling task; and in response to the equipment data meeting the equipment backhauling task, instruct the intelligent transport device to transport the equipment back to an original location of the equipment [0148 — A software agent at the edge-computation system 33 of the machine 3 detects such and automatically dispatches an according information about this anomaly to a higher-level factory control system… The UAV 5a (unmanned aerial vehicles/intelligent transportation device) autonomously navigates to the production machine and takes pictures. Those pictures which are returned from the inspection UAV are then processed by an image recognition and classification system… a decision tree of a machine fault analysis algorithm then automatically determines that any additionally consumed energy has to be manifested somewhere, and that heat is a common manifestation. Therefore, the system automatically releases the visual inspection UAV of its duty (which then autonomously returns to its charging base) and negotiates with one of the mobile vehicles 5 in the manufacturing environment that is equipped with a thermal imager. As this thermal imager vehicle 5 is presently occupied and the state of the production machine is not classified to be critical but could get critical in near future if unattended, the vehicle is scheduled to autonomously navigate to the production machine at its next availability timeslot and to report back thermal images from multiple views of the production machine].
Claim(s) 3, 11 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Rana and Xu in view of Pedersen U.S. Patent Publication No. 20160085584 (hereinafter Pedersen).
Regarding claim 3, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above
Further, Rana teaches the location host computer [0008-0011 — By employing the present invention already to complete simulation and virtual testing scenarios throughout the whole product lifecycle… fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network… At least one mobile vehicle is configured as a mobile measurement vehicle comprising at least one spatial measurement sensor unit; 0060, Fig. 6 — the invention also relates to a method for manufacturing a work piece within a manufacturing environment by a system comprising multiple autonomously moving mobile vehicles; 0080, Fig. 1 — a manufacturing environment 1; 0111, Fig. 2 — a static part scenario, in which larger work pieces 2 are manufactured, e.g. airplane cabins or wings], wherein the forming-machine equipment testing system comprises a scheduling host computer [0067 — A device or system according to the present invention comprises computation units, microcontrollers, microcomputers, DSPs, TPUs or programmable or hardwired digital logics, wherefore the present invention can involve a computer program product with program code being stored on a machine readable medium; 0083-0085, Fig. 5 — other equipment in the manufacturing environment 1 can comprise such an edge computation unit 33, like the shown entities 3,3t,9,5a,5m,5a,6,13,35,10, etc… a cloud computing system 8… an edge computation system can also comprise intermediary computer systems 7, e.g. at the edge of a station of a production line, of a production line or of a factory, which processes data in close proximity to the corresponding data source; 0139, Fig. 5 — At the shown manufacturing environment 1, cars are manufactured as work pieces which is done according to the invention by orchestrating a plurality of mobile vehicles 5, which are cooperating to establish the required tasks to manufacture the car 2, and which mobile vehicles 5 are equipped with the here symbolized edge-client computation units 33, which are configured to run edge computation agents] and determining, by the location host computer, the corresponding target test task based on the equipment type [0010 — The mobile vehicle therein at least comprises a spatial localization system for deriving a location of the mobile vehicle in the manufacturing environment... The mobile vehicle also comprises a communication interface providing a data link to at least one other mobile vehicle and/or to a fog- and/or cloud-computation and storage system (input externally), in particular configured for cloud analytics which can comprise an analysis by big data processing. The fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network. The data link can therein e.g. be established directly in-between the mobile vehicles for direct interaction of the vehicles and/or swarm intelligence, indirectly via a common gateway, hub or control system, or exclusively or optionally by central fog- and/or cloud computation unit… An edge computation unit comprised at the mobile vehicle is configured for a local data analysis at the mobile vehicle by intelligent, deployable edge analytics software agents. The edge computation unit at the mobile vehicle can comprises a central processing unit, a memory and program code configured for a local data analysis at the mobile vehicle, in particular of data derived by the mobile vehicle and of data derived from the task information and from the manufacturing environment…This local data analysis can preferably be established by intelligent, deployable edge analytics software agents, comprising at least one program-code-module configured to be transferred by the manufacturing environment network and to be executes by the edge client, preferably in real time by a stream analytics agent at the edge computation unit which is configured to compute real time analysis on an online stream of data at the manufacturing environment network. Such edge analytics software agents can e.g. also be deployable via the communication interface from the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system. The edge computation unit can therein at least partially operate in real time and/or by a stream analytics agent, and can be configured to interact with the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system via the communication interface. The invention can comprise an automatic deployment (delivery) of a workflow information for the processing of the work-piece for the at least one mobile vehicle, which workflow comprises at least one current task (target test task input). In particular, the workflow information can transferred via the manufacturing network environment and can be embodied as a set of data comprising a series of tasks which are comprising modification steps to be applied to the work piece. The current task of such a series of tasks can in particular explicitly or implicitly comprise a spatial dependency or spatial relation in-between the work piece and at least one external tool, component or probe provided by one of the multiple mobile vehicles and/or by the manufacturing environment. The spatial dependency can in particular be extracted in form of some relative spatial information that is comprised in the current task as a requirement].
But the combination of Rana and Xu fails to clearly specify obtaining a graphic code of the equipment, and determining an equipment type of the equipment based on the graphic code; and determining the corresponding target task based on the equipment type.
However, Pedersen teaches obtaining a graphic code of the equipment, and determining an equipment type of the equipment based on the graphic code; and determining the corresponding target task based on the equipment type [0053 — The individual tasks depicted in FIG. 2 require particular equipment and/or personnel for their execution as indicated in FIG. 1. That equipment and/or personnel are referred to herein as task execution agents. Example task execution agents (301) are further listed in FIG. 3 and may include, for example, computers, storage units, transportation equipment, manufacturing equipment, factories, personnel and/or communications equipment. The particular equipment required for an individual task will, of course, depend on the nature of the specified task; 0076-0078 — Bar coding, RFID tagging and toll tag technology provide heretofore unavailable location data for virtually any item. Connection of barcode reader (503) to the task execution controller (500) is illustrated in FIG. 5. The barcode reader (503) may be used to identify particular items or materials required or produced by the various task execution agents (401) as identified in FIGS. 1-4. For example, standard items of commerce including, for example, manufactured goods and products may be identified by barcoding. Barcodes may also be used to identify various task execution agents including computers, storage units, manufacturing equipment, communications equipment and other equipment used as task execution agents].
Rana, Xu and Pedersen are analogous art. They relate to manufacturing systems and Rana and Xu relate to manufacturing systems, particularly involving automated vehicles.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Rana and Xu by incorporating the above limitations, as taught by Pedersen.
One of ordinary skill in the art would have been motivated to do this modification in order to more easily identify a task associated with equipment using a well-known method for identifying and tracking items, as suggested by Pedersen [0053, 0076].
Regarding claim 11, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 3.
Regarding claim 14, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above
Further, Rana teaches the scheduling host computer [0008-0011 — By employing the present invention already to complete simulation and virtual testing scenarios throughout the whole product lifecycle… fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network… At least one mobile vehicle is configured as a mobile measurement vehicle comprising at least one spatial measurement sensor unit; 0060, Fig. 6 — the invention also relates to a method for manufacturing a work piece within a manufacturing environment by a system comprising multiple autonomously moving mobile vehicles; 0080, Fig. 1 — a manufacturing environment 1; 0111, Fig. 2 — a static part scenario, in which larger work pieces 2 are manufactured, e.g. airplane cabins or wings], wherein the forming-machine equipment testing system comprises a scheduling host computer [0067 — A device or system according to the present invention comprises computation units, microcontrollers, microcomputers, DSPs, TPUs or programmable or hardwired digital logics, wherefore the present invention can involve a computer program product with program code being stored on a machine readable medium; 0083-0085, Fig. 5 — other equipment in the manufacturing environment 1 can comprise such an edge computation unit 33, like the shown entities 3,3t,9,5a,5m,5a,6,13,35,10, etc… a cloud computing system 8… an edge computation system can also comprise intermediary computer systems 7, e.g. at the edge of a station of a production line, of a production line or of a factory, which processes data in close proximity to the corresponding data source; 0139, Fig. 5 — At the shown manufacturing environment 1, cars are manufactured as work pieces which is done according to the invention by orchestrating a plurality of mobile vehicles 5, which are cooperating to establish the required tasks to manufacture the car 2, and which mobile vehicles 5 are equipped with the here symbolized edge-client computation units 33, which are configured to run edge computation agents] and determining, by the location host computer, the corresponding target test task based on the equipment type [0010 — The mobile vehicle therein at least comprises a spatial localization system for deriving a location of the mobile vehicle in the manufacturing environment... The mobile vehicle also comprises a communication interface providing a data link to at least one other mobile vehicle and/or to a fog- and/or cloud-computation and storage system (input externally), in particular configured for cloud analytics which can comprise an analysis by big data processing. The fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network. The data link can therein e.g. be established directly in-between the mobile vehicles for direct interaction of the vehicles and/or swarm intelligence, indirectly via a common gateway, hub or control system, or exclusively or optionally by central fog- and/or cloud computation unit… An edge computation unit comprised at the mobile vehicle is configured for a local data analysis at the mobile vehicle by intelligent, deployable edge analytics software agents. The edge computation unit at the mobile vehicle can comprises a central processing unit, a memory and program code configured for a local data analysis at the mobile vehicle, in particular of data derived by the mobile vehicle and of data derived from the task information and from the manufacturing environment…This local data analysis can preferably be established by intelligent, deployable edge analytics software agents, comprising at least one program-code-module configured to be transferred by the manufacturing environment network and to be executes by the edge client, preferably in real time by a stream analytics agent at the edge computation unit which is configured to compute real time analysis on an online stream of data at the manufacturing environment network. Such edge analytics software agents can e.g. also be deployable via the communication interface from the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system. The edge computation unit can therein at least partially operate in real time and/or by a stream analytics agent, and can be configured to interact with the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system via the communication interface. The invention can comprise an automatic deployment (delivery) of a workflow information for the processing of the work-piece for the at least one mobile vehicle, which workflow comprises at least one current task (target test task input). In particular, the workflow information can transferred via the manufacturing network environment and can be embodied as a set of data comprising a series of tasks which are comprising modification steps to be applied to the work piece. The current task of such a series of tasks can in particular explicitly or implicitly comprise a spatial dependency or spatial relation in-between the work piece and at least one external tool, component or probe provided by one of the multiple mobile vehicles and/or by the manufacturing environment. The spatial dependency can in particular be extracted in form of some relative spatial information that is comprised in the current task as a requirement] and determine whether the equipment type of the equipment is an equipment type corresponds to the current test task [0010 — The mobile vehicle therein at least comprises a spatial localization system for deriving a location of the mobile vehicle in the manufacturing environment... The mobile vehicle also comprises a communication interface providing a data link to at least one other mobile vehicle and/or to a fog- and/or cloud-computation and storage system (input externally), in particular configured for cloud analytics which can comprise an analysis by big data processing. The fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network. The data link can therein e.g. be established directly in-between the mobile vehicles for direct interaction of the vehicles and/or swarm intelligence, indirectly via a common gateway, hub or control system, or exclusively or optionally by central fog- and/or cloud computation unit… An edge computation unit comprised at the mobile vehicle is configured for a local data analysis at the mobile vehicle by intelligent, deployable edge analytics software agents. The edge computation unit at the mobile vehicle can comprises a central processing unit, a memory and program code configured for a local data analysis at the mobile vehicle, in particular of data derived by the mobile vehicle and of data derived from the task information and from the manufacturing environment…This local data analysis can preferably be established by intelligent, deployable edge analytics software agents, comprising at least one program-code-module configured to be transferred by the manufacturing environment network and to be executes by the edge client, preferably in real time by a stream analytics agent at the edge computation unit which is configured to compute real time analysis on an online stream of data at the manufacturing environment network. Such edge analytics software agents can e.g. also be deployable via the communication interface from the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system. The edge computation unit can therein at least partially operate in real time and/or by a stream analytics agent, and can be configured to interact with the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system via the communication interface. The invention can comprise an automatic deployment (delivery) of a workflow information for the processing of the work-piece for the at least one mobile vehicle, which workflow comprises at least one current task (target test task input). In particular, the workflow information can transferred via the manufacturing network environment and can be embodied as a set of data comprising a series of tasks which are comprising modification steps to be applied to the work piece. The current task of such a series of tasks can in particular explicitly or implicitly comprise a spatial dependency or spatial relation in-between the work piece and at least one external tool, component or probe provided by one of the multiple mobile vehicles and/or by the manufacturing environment. The spatial dependency can in particular be extracted in form of some relative spatial information that is comprised in the current task as a requirement].
But the combination of Rana and Xu fails to clearly specify obtain a graphic code of the equipment and determine the equipment type of the equipment based on the graphic code.
However, Pedersen teaches obtain a graphic code of the equipment and determine the equipment type of the equipment based on the graphic code [0053 — The individual tasks depicted in FIG. 2 require particular equipment and/or personnel for their execution as indicated in FIG. 1. That equipment and/or personnel are referred to herein as task execution agents. Example task execution agents (301) are further listed in FIG. 3 and may include, for example, computers, storage units, transportation equipment, manufacturing equipment, factories, personnel and/or communications equipment. The particular equipment required for an individual task will, of course, depend on the nature of the specified task; 0076-0078 — Bar coding, RFID tagging and toll tag technology provide heretofore unavailable location data for virtually any item. Connection of barcode reader (503) to the task execution controller (500) is illustrated in FIG. 5. The barcode reader (503) may be used to identify particular items or materials required or produced by the various task execution agents (401) as identified in FIGS. 1-4. For example, standard items of commerce including, for example, manufactured goods and products may be identified by barcoding. Barcodes may also be used to identify various task execution agents including computers, storage units, manufacturing equipment, communications equipment and other equipment used as task execution agents].
Rana, Xu and Pedersen are analogous art. They relate to manufacturing systems and Rana and Xu relate to manufacturing systems, particularly involving automated vehicles.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Rana and Xu by incorporating the above limitations, as taught by Pedersen.
One of ordinary skill in the art would have been motivated to do this modification in order to more easily identify a task associated with equipment using a well-known method for identifying and tracking items, as suggested by Pedersen [0053, 0076].
Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Rana and Xu in view of Bosworth et al. U.S. Patent Publication No. 20210258719 (hereinafter Bosworth).
Regarding claim 5, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above
Further, Rana teaches determining, by the scheduling host computer, whether an equipment type of the equipment is an equipment type corresponding to a current test task; and in response to the equipment type of the equipment being the equipment type corresponding to the current test task, determining that the equipment meets the equipment transport task [0149-0154, Fig. 6 —Box 42 symbolizes an automatic deployment of a subset of mobile vehicles at the manufacturing environment to the work piece, which is done automatically according to a capability information provided by a local computation system at the mobile vehicle. Therein, the subset comprises at least one mobile measurement vehicle having a spatial measurement sensor unit.; 0086, Figs. 1-2 and 5 — Mobile vehicles according to the invention which are equipped with sensor units 15, like e.g. laser scanner units 15s or laser tracker units 15t for accurate spatial measurement; 0044, 0086-0087, Figs. 1-2 and 5 — Mobile vehicles according to the invention which are equipped with sensor units 15, like e.g. laser scanner units 15s or laser tracker units 15t… the mobile vehicles can be equipped with one or more measurement tools 15, actuator tools 16 and/or machining tools 17, either fixedly or in an exchangeable manner. The lower level task can then comprise the detailed information on how to execute the upper level task at a specific work piece for a specific step in the manufacturing and measuring chain, e.g. where, in which order and how (types of equipment)].
But the combination of Rana and Xu fails to clearly specify determining, by the computer, whether the target location corresponding to the equipment meets an admission condition; in response to the target location meeting the admission condition, performing an action.
However, Bosworth teaches determining, by the computer, whether the target location corresponding to the equipment meets an admission condition; in response to the target location meeting the admission condition, performing an action [0046-0049, Fig. 5 — controller 202 determines 404 if the identified vehicle 102 is authorized to enter the restricted access area 106. For example, the controller 202 may retrieve a list or access a database stored in the memory 208 that lists the vehicles 102 or known features/thresholds of vehicles that are authorized to enter the restricted access area 106, and determines if the identified vehicle 102 in the list or database of authorized vehicle 102. Alternatively, the list or database accessed to identify the vehicle 102 that is approaching the restricted access area 106 may also indicate if the vehicle 102 is authorized to enter the restricted access area or may indicate which (if there are more than one) restricted access areas the AV is authorized to enter… , if no other object is detected approaching the restricted access area, the authorized vehicle 102 is permitted at 410 to enter the restricted access area 106. Permitting the authorized vehicle 102 to enter the restricted access area may be an active permission, such as by opening a door, gate, or other barrier in the entrance 108; 0030, Fig. 3 — processor 206 may include, but is not limited to, a general purpose central processing unit (CPU), a microcontroller, a reduced instruction set computer (RISC) processor, an application specific integrated circuit (ASIC), a programmable logic circuit (PLC), and/or any other circuit or processor].
Rana, Xu and Bosworth are analogous art. They relate to manufacturing systems, particularly involving automated vehicles.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Rana and Xu by incorporating the above limitations, as taught by Bosworth.
One of ordinary skill in the art would have been motivated to do this modification to prevent dispatching a vehicle/transport device to a location that the vehicle cannot enter.
Regarding claim 13, the combination of Rana and Xu teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 5.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Rana, Xu and Bosworth in view of Pedersen.
Regarding claim 6, the combination of Rana, Xu and Bosworth teaches all the limitations of the base claims as outlined above
Further, Rana teaches the scheduling host computer [0008-0011 — By employing the present invention already to complete simulation and virtual testing scenarios throughout the whole product lifecycle… fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network… At least one mobile vehicle is configured as a mobile measurement vehicle comprising at least one spatial measurement sensor unit; 0060, Fig. 6 — the invention also relates to a method for manufacturing a work piece within a manufacturing environment by a system comprising multiple autonomously moving mobile vehicles; 0080, Fig. 1 — a manufacturing environment 1; 0111, Fig. 2 — a static part scenario, in which larger work pieces 2 are manufactured, e.g. airplane cabins or wings], wherein the forming-machine equipment testing system comprises a scheduling host computer [0067 — A device or system according to the present invention comprises computation units, microcontrollers, microcomputers, DSPs, TPUs or programmable or hardwired digital logics, wherefore the present invention can involve a computer program product with program code being stored on a machine readable medium; 0083-0085, Fig. 5 — other equipment in the manufacturing environment 1 can comprise such an edge computation unit 33, like the shown entities 3,3t,9,5a,5m,5a,6,13,35,10, etc… a cloud computing system 8… an edge computation system can also comprise intermediary computer systems 7, e.g. at the edge of a station of a production line, of a production line or of a factory, which processes data in close proximity to the corresponding data source; 0139, Fig. 5 — At the shown manufacturing environment 1, cars are manufactured as work pieces which is done according to the invention by orchestrating a plurality of mobile vehicles 5, which are cooperating to establish the required tasks to manufacture the car 2, and which mobile vehicles 5 are equipped with the here symbolized edge-client computation units 33, which are configured to run edge computation agents] and determining, by the location host computer, the corresponding target test task based on the equipment type [0010 — The mobile vehicle therein at least comprises a spatial localization system for deriving a location of the mobile vehicle in the manufacturing environment... The mobile vehicle also comprises a communication interface providing a data link to at least one other mobile vehicle and/or to a fog- and/or cloud-computation and storage system (input externally), in particular configured for cloud analytics which can comprise an analysis by big data processing. The fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network. The data link can therein e.g. be established directly in-between the mobile vehicles for direct interaction of the vehicles and/or swarm intelligence, indirectly via a common gateway, hub or control system, or exclusively or optionally by central fog- and/or cloud computation unit… An edge computation unit comprised at the mobile vehicle is configured for a local data analysis at the mobile vehicle by intelligent, deployable edge analytics software agents. The edge computation unit at the mobile vehicle can comprises a central processing unit, a memory and program code configured for a local data analysis at the mobile vehicle, in particular of data derived by the mobile vehicle and of data derived from the task information and from the manufacturing environment…This local data analysis can preferably be established by intelligent, deployable edge analytics software agents, comprising at least one program-code-module configured to be transferred by the manufacturing environment network and to be executes by the edge client, preferably in real time by a stream analytics agent at the edge computation unit which is configured to compute real time analysis on an online stream of data at the manufacturing environment network. Such edge analytics software agents can e.g. also be deployable via the communication interface from the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system. The edge computation unit can therein at least partially operate in real time and/or by a stream analytics agent, and can be configured to interact with the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system via the communication interface. The invention can comprise an automatic deployment (delivery) of a workflow information for the processing of the work-piece for the at least one mobile vehicle, which workflow comprises at least one current task (target test task input). In particular, the workflow information can transferred via the manufacturing network environment and can be embodied as a set of data comprising a series of tasks which are comprising modification steps to be applied to the work piece. The current task of such a series of tasks can in particular explicitly or implicitly comprise a spatial dependency or spatial relation in-between the work piece and at least one external tool, component or probe provided by one of the multiple mobile vehicles and/or by the manufacturing environment. The spatial dependency can in particular be extracted in form of some relative spatial information that is comprised in the current task as a requirement] and determine whether the equipment type of the equipment is an equipment type corresponds to the current test task [0010 — The mobile vehicle therein at least comprises a spatial localization system for deriving a location of the mobile vehicle in the manufacturing environment... The mobile vehicle also comprises a communication interface providing a data link to at least one other mobile vehicle and/or to a fog- and/or cloud-computation and storage system (input externally), in particular configured for cloud analytics which can comprise an analysis by big data processing. The fog- and/or cloud-computation and storage system can be embodied as stationary computation and storage system external from the mobile vehicle, for example located on-site or off-site of the manufacturing environment, and can comprise at least a computation and data storage unit and a data link to a manufacturing environment network. The data link can therein e.g. be established directly in-between the mobile vehicles for direct interaction of the vehicles and/or swarm intelligence, indirectly via a common gateway, hub or control system, or exclusively or optionally by central fog- and/or cloud computation unit… An edge computation unit comprised at the mobile vehicle is configured for a local data analysis at the mobile vehicle by intelligent, deployable edge analytics software agents. The edge computation unit at the mobile vehicle can comprises a central processing unit, a memory and program code configured for a local data analysis at the mobile vehicle, in particular of data derived by the mobile vehicle and of data derived from the task information and from the manufacturing environment…This local data analysis can preferably be established by intelligent, deployable edge analytics software agents, comprising at least one program-code-module configured to be transferred by the manufacturing environment network and to be executes by the edge client, preferably in real time by a stream analytics agent at the edge computation unit which is configured to compute real time analysis on an online stream of data at the manufacturing environment network. Such edge analytics software agents can e.g. also be deployable via the communication interface from the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system. The edge computation unit can therein at least partially operate in real time and/or by a stream analytics agent, and can be configured to interact with the at least one other mobile vehicle and/or the fog- and/or cloud-computation and storage system via the communication interface. The invention can comprise an automatic deployment (delivery) of a workflow information for the processing of the work-piece for the at least one mobile vehicle, which workflow comprises at least one current task (target test task input). In particular, the workflow information can transferred via the manufacturing network environment and can be embodied as a set of data comprising a series of tasks which are comprising modification steps to be applied to the work piece. The current task of such a series of tasks can in particular explicitly or implicitly comprise a spatial dependency or spatial relation in-between the work piece and at least one external tool, component or probe provided by one of the multiple mobile vehicles and/or by the manufacturing environment. The spatial dependency can in particular be extracted in form of some relative spatial information that is comprised in the current task as a requirement].
But the combination of Rana and Xu fails to clearly specify obtaining a graphic code of the equipment and determining the equipment type of the equipment based on the graphic code.
However, Pedersen teaches obtaining a graphic code of the equipment and determining the equipment type of the equipment based on the graphic code [0053 — The individual tasks depicted in FIG. 2 require particular equipment and/or personnel for their execution as indicated in FIG. 1. That equipment and/or personnel are referred to herein as task execution agents. Example task execution agents (301) are further listed in FIG. 3 and may include, for example, computers, storage units, transportation equipment, manufacturing equipment, factories, personnel and/or communications equipment. The particular equipment required for an individual task will, of course, depend on the nature of the specified task; 0076-0078 — Bar coding, RFID tagging and toll tag technology provide heretofore unavailable location data for virtually any item. Connection of barcode reader (503) to the task execution controller (500) is illustrated in FIG. 5. The barcode reader (503) may be used to identify particular items or materials required or produced by the various task execution agents (401) as identified in FIGS. 1-4. For example, standard items of commerce including, for example, manufactured goods and products may be identified by barcoding. Barcodes may also be used to identify various task execution agents including computers, storage units, manufacturing equipment, communications equipment and other equipment used as task execution agents].
Rana, Xu, Bosworth and Pedersen are analogous art. They relate to manufacturing systems and Rana, Xu and Bosworth relate to manufacturing systems, particularly involving automated vehicles.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Rana, Xu and Bosworth by incorporating the above limitations, as taught by Pedersen.
One of ordinary skill in the art would have been motivated to do this modification in order to more easily identify a task associated with equipment using a well-known method for identifying and tracking items, as suggested by Pedersen [0053, 0076].
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kobari U.S. Patent Publication No. 20210362617 discloses a system and method for charging batteries using a charging vehicle.
Note that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERNARD G. LINDSAY whose telephone number is (571)270-0665. The examiner can normally be reached Monday through Friday from 8:30 AM to 5:30 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on (571)272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119