Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
The following claims have been rejected or allowed for the following reasons:
Claim(s) 1-20 is rejected under 35 USC § 103.
Information Disclosure Statement
The information disclosure statement/statements (IDS) were filed on 5/21/24, 10/18/25 and 7/28/26. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Darolfi (US 20210114408 A1), in further view of Attar (US 20240017408 A1), in further view of Czinger (US 20180339456 A1).
Darolfi/Attar/Czinger
Regarding claim 1 Darolfi teaches A system of one or more robots that implement an autonomous technician, the system comprising: (Darolfi abstract reads “Systems, methods and apparatus for automated vehicle wheel removal and replacement are provided. One system includes a computer system with applications for scheduling the replacement of tires for the vehicle. An electronically controlled lift device and robotic apparatus is configured for interaction with the computer system.”);
and installing the component in the apparatus. (Darolfi [0041] reads “Among other features, this specification describes a system to automatically remove and dismount a wheel from a vehicle for tire replacement, and then replace the wheel once a tire has been mounted.”);
Darolfi does not teach the autonomous technician configured to perform operations comprising: verifying a component at a first location for an apparatus that is located at a second location using one or more sensors of the one or more robots that implement the autonomous technician, including verifying compatibility of the component with the apparatus and verifying a condition of the component; transporting the component to the second location;
Attar in analogous art, teaches the autonomous technician configured to perform operations comprising: verifying a component at a first location for an apparatus that is located at a second location using one or more sensors of the one or more robots that implement the autonomous technician, (Attar [0428 - 0429] reads “Referring back to FIG. 15 , one or more imaging sensors 1504 a-1504 d may also be installed around staging areas 1310. These imaging sensors can be used to identify, detect and verify which building parts are located at which staging areas. This can be used by the perception software system to guide the assembly robot to picking-up the correct building part from the correct staging area 1310. Imaging sensors 1506 a-1506 b may also be mounted to, or around, the assembly building platform 210. Images captured by these sensors can provide a view of both the assembly on the building platform 210, as well as a view of the assembly robot 204 positioned over the building platform 210.”);
and verifying a condition of the component; (Attar [0434] reads “At 1902 a, an image of a target building part is captured. The target building part may be, for instance, a stud which requires quality inspection. The images can be captured using image sensors in the perception system. In one example, the images are captured using one or more imaging sensors 1502 on the assembly robot 204 (FIG. 16 ). The images can be captured from various angles and perspectives.” And [0435] reads “At 1904 a, the captured images are analyzed to determine one or more properties of the corresponding building part. For example, this can involve determining whether the building part includes any cracks (e.g., images 1702-1706 in FIG. 17 ), or edge imperfections (1706-1708 in FIG. 17 ). In the case of studs, this can also involve determining whether the stud has a stud crown, etc.“ and [0438] reads “At 1906 a, a determination is made as to whether the building part meets a pre-determined minimum quality threshold. This determination is made based on the one or more properties determined at act 1904 a. For example, the minimum quality threshold may involve determining that a building part does not include any imperfections (e.g., cracks or edge imperfections). Therefore, if any imperfections are detected at act 1904 a, then the minimum quality threshold is not met.”);
transporting the component to the second location; (Attar [0176] reads “In at least one embodiment, cell “factories” may include transportation, or conveyance systems for transporting parts and components between cells. For example, automated guided vehicles (AGVs) can transport raw material, pre-cut material as well as partially or fully assembled building structures between robotic cells. In these cases, the cloud software platform 106 may also manage and coordinate an inter-cell transport or conveyance system.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Darolfi with that of Attar to include a sensor system that could identify parts. This would allow the system to make a more accurate assessment of the work that it is conducting and reduce potential assembly errors before they arise. (Attar [0001] reads “The present disclosure generally relates to assembly and manufacturing of building structures, including building structures used in the assembly of housing units as well as other infrastructure, and in particular, to methods, systems and devices for automated assembly of building structures.”);
Darolfi/Attar does not teach including verifying compatibility of the component with the apparatus
Czinger in analogous art, teaches including verifying compatibility of the component with the apparatus (Czinger [0053] reads “In some exemplary embodiments, each station includes an area designated for the dedicated performance of one of the tasks above, with automated constructors moving to the station or otherwise residing at the station as required. For example, an automated constructor tasked with inspecting a part can move to a station to perform the inspection. The automated constructor can inspect tolerances of a part being assembled on the fly to ensure that the part is meeting one or more specifications. If a part is not within the specification, the automated constructor can communicate this information to a central controller or another automated constructor, and the part can either be remedied to fall within specifications, or it can be removed from active assembly in the event the problem cannot be fixed via available remedial measures or if the problem is serious in nature.” It would be appreciated by one with ordinary skill in the art that in a manufacturing or assembly situation specifications and tolerances are used to ensure that part mate in the manner intended.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have combined the teachings of Darolfi/Attar with that of Czinger to include a machine learning process for the instillation of components. This would allow the system to easily adapt to new parts, models, or components. (Czinger [0002] reads “Traditional manufacturing facilities can involve significant inflexible factory infrastructure to produce products at volume. For example, a factory may use fixed robotic assembly systems operating on assembly lines to achieve efficient production at volume. The inflexible factory infrastructure may often limit the manufacturing systems to manufacture only a handful of models of transport structures such as vehicles, motorcycles, boats, aircraft and the like, and even then, each model of the transport structure may be expensive to tool. In the event that a factory is configured, permanently or in the long-term, to produce one or more underperforming models, the factory may face financial trouble because of the factory and tooling amortization costs and operate at a loss. More specifically, the factory in these instances may be underutilized because of factory inflexibility, the need for tooling amortization prior to changing factory resources from producing the underperforming products to producing new products or more commercially successful existing products, and other constraints.”);
Regarding claim 2 Darolfi/Attar/Czinger teaches The system as described in claim 1, wherein the verifying compatibility of the component with the apparatus includes: assessing the component at the first location using the one or more sensors; determining an identifier of the component based on an output of the one or more sensors during the assessing; (Attar [0025] reads “In another broad aspect, there is provided a robotic assembly cell for assembling building structures comprising: a perception sensor system; one or more assembly robots; and at least one processor operable to: receive instructions corresponding to an assembly sequence for assembling a building structure; determine a plurality of building parts required for assembling the building structure based on the assembly sequence; identify each building part within a facility, based on sensor data from the perception sensor system;”);
and matching the identifier to the apparatus. (Darolfi [0092] reads “When the ordered tires are physically received, the tires are either scanned if the tire has a bar code, and the system 100 updates the tire inventory with an updated quantity for that tire type, and/or the system 100 receives a manual input that updates the quantity for the respective tire type. Each of the type of tires may have an associated stock keeping unit (SKU) for inventory management purposes.”);
Regarding claim 3 Darolfi/Attar/Czinger teaches The system as described in claim 1, wherein the autonomous technician includes a dynamic toolhead, (Darolfi [0130] reads “The robotic apparatus 150 may use or be fitted with one or more sockets for the removal of lug nuts. The sockets may be detachably affixed to a torque wrench end of the robotic apparatus 150.”);
and installing the component in the apparatus with the autonomous technician includes: detecting a shape of a fastener to be rotated during the installing; (Darolfi [0121] reads “The system 100 may detect if the lug nuts have locks. The system 100 may detect a pattern on the surface of a lug nut by analyzing an image and determining that the lug nut may have a lock. The particular lug nut lock pattern may be associated in the general vehicle database with a required robotic tool attachment.”);
and adjusting the dynamic toolhead based on the detected shape of the fastener. (Darolfi [0133] reads “The robotic apparatus 150 may select or be fitted with different socket sizes. In one example, the robotic apparatus chooses from 6 different socket sizes, 3 metric sizes (17 mm, 19 mm, 21 mm) and 3 standard size (¾″, 13/16″, ⅞″). The robotic apparatus 150 may have a tray or compartment where the group of sockets may be stored, and the selected socket may be attached to the torque wrench of the tooling end of the robotic apparatus 150.”);
Regarding claim 4 Darolfi/Attar/Czinger teaches The system as described in claim 1, wherein the autonomous technician includes: a locomotion framework; (Darolfi [0107] reads “ Additionally, the robotic apparatus 150 may move into a location suitable for the lug nut and wheel removal operation. For example, if two robotic apparatus 150 are used on one side of a vehicle, the system may move the robotic apparatus to positions based on the axle distance obtained from the general vehicle database for the particular vehicle.”);
and a reasoning module configured to receive feedback from the one or more sensors Darolfi [0048] reads “Referring to FIG. 1, an exemplary system 100 for the automated removal and replacement of a wheel and tire is disclosed. The system 100 can be a system of one or more computers 102, 104, 106, 108, 110 (generally referred to as 102) including software executing a method system on one or more computers 102, which is in communication with, or maintains one or more databases 112 of information.”);
and issue commands to the locomotion framework based on the received feedback. (Darolfi [0107] reads ”Also, in response to the check-in event, the robotic apparatus 150 may activate sensors, and/or select an appropriate socket based on the size of lug nut for the particular vehicle as determined by the system 100 from a general vehicle database, or as determined from an input from a user interface used to check-in the vehicle. Additionally, the robotic apparatus 150 may move into a location suitable for the lug nut and wheel removal operation.”);
Regarding claim 5 Darolfi/Attar/Czinger teaches The system as described in claim 2, wherein the autonomous technician includes a component recognition machine learning model stored in memory, the component recognition machine learning model trained on data including a plurality of component identifiers, and the determining of the identifier of the component is performed using the component recognition machine learning model. (Darolfi [0293] reads “The system 100 may train a machine learning model to identify, classify and/or infer from a digital image of a vehicle wheel the type of lug nut pattern, the type of lug nut (i.e., generally referred to as a wheel fastener) and/or the type of lug nut lock. The system 100 may use any suitable machine learning training technique, including, but are not limited to a neural net based algorithm, such as Artificial Neural Network, Deep Learning;“);
Regarding claim 6 Darolfi/Attar/Czinger teaches The system as described in claim 1 wherein the autonomous technician includes an installation procedure machine learning model stored in memory, the installation procedure machine learning model trained on data including a plurality of installation instructions, and installing the component in the apparatus is performed using the installation procedure machine learning model. (Czinger [0061] reads “For example, an automated constructor may be configured to receive instructions on performing a set of one or more vehicle manufacturing processes, and further configured to subsequently carry out the received instructions. An automated constructor may have pre-programmed instructions to perform one or more vehicle manufacturing processes. Alternatively or in addition, the automated constructor may receive real-time instructions to perform one or more vehicle manufacturing processes”. And [0222] reads “Here, automated constructors 2202, 2204 have equipped themselves with effectors for manipulating the COTS carbon fiber panel 2206 to assemble and/or install the panel having a node or extrusion. In this illustration, an end of the panel 2206 includes extrusion 2208. The automated constructors 2202, 2204 cooperate, using machine-learning, autonomous programs or direction from a control station, to assemble panel 2206 and insert extrusion 2208 in the appropriate location.” It would have been obvious to one with ordinary skill in the art, that the combination of machine learning in the installation procedure with the given instructions of how the component is to be installed would equate to the machine learning model being trained on the proper method of installation of a component.);
Regarding claim 7 Darolfi/Attar/Czinger teaches The system as described in claim 1, wherein the autonomous technician includes a component recognition machine learning model (Darolfi [0293] reads “The system 100 may train a machine learning model to identify, classify and/or infer from a digital image of a vehicle wheel the type of lug nut pattern, the type of lug nut (i.e., generally referred to as a wheel fastener) and/or the type of lug nut lock. The system 100 may use any suitable machine learning training technique, including, but are not limited to a neural net based algorithm, such as Artificial Neural Network, Deep Learning;“);
the component recognition machine learning model trained on data including a plurality of component identifiers (Darolfi [0293] reads “The system 100 may train a machine learning model to identify, classify and/or infer from a digital image of a vehicle wheel the type of lug nut pattern, the type of lug nut (i.e., generally referred to as a wheel fastener) and/or the type of lug nut lock. The system 100 may use any suitable machine learning training technique, including, but are not limited to a neural net based algorithm, such as Artificial Neural Network, Deep Learning;“);
and an installation procedure machine learning model, (Czinger [0222] reads “Here, automated constructors 2202, 2204 have equipped themselves with effectors for manipulating the COTS carbon fiber panel 2206 to assemble and/or install the panel having a node or extrusion. In this illustration, an end of the panel 2206 includes extrusion 2208. The automated constructors 2202, 2204 cooperate, using machine-learning, autonomous programs or direction from a control station, to assemble panel 2206 and insert extrusion 2208 in the appropriate location.”);
and the installation procedure machine learning model trained on data including a plurality of installation instructions, and an output of the component recognition machine learning model is an input of the installation procedure machine learning model. (Czinger [0061] reads “For example, an automated constructor may be configured to receive instructions on performing a set of one or more vehicle manufacturing processes, and further configured to subsequently carry out the received instructions. An automated constructor may have pre-programmed instructions to perform one or more vehicle manufacturing processes. Alternatively or in addition, the automated constructor may receive real-time instructions to perform one or more vehicle manufacturing processes”. And [0222] reads “Here, automated constructors 2202, 2204 have equipped themselves with effectors for manipulating the COTS carbon fiber panel 2206 to assemble and/or install the panel having a node or extrusion. In this illustration, an end of the panel 2206 includes extrusion 2208. The automated constructors 2202, 2204 cooperate, using machine-learning, autonomous programs or direction from a control station, to assemble panel 2206 and insert extrusion 2208 in the appropriate location.” It would have been obvious to one with ordinary skill in the art, that the combination of machine learning in the installation procedure with the given instructions of how the component is to be installed would equate to the machine learning model being trained on the proper method of installation of a component.);
Regarding claim 8 Darolfi/Attar/Czinger teaches The system as described in claim 1, wherein the autonomous technician includes a location and routing module, and transporting the component to the second location includes: receiving transaction data associated with the component, the transaction data including location data describing the second location; (Guy [0037] reads “The searching engine also can perform a search based on the location of the user. A user can access the searching engine via a mobile device and generate a search query. Using the search query and the user's location, the searching engine can return relevant search results for products, services, offers, auctions, and so forth to the user.”);
generating a route to the second location via the location and routing module based on the location data; (Czinger [0100] reads “For instance, the control system may provide a specific travel path for an automated constructor. In another instance, the control system may provide a target destination, and the automated constructor may be programmed to reach the target destination by following any path. The automated constructor may follow one or more parameters in determining a path or may freely determine the path on the fly. In another instance, the control system may provide a target destination and parameters, such as allowed paths and disallowed paths, allowed areas and disallowed areas in the facility, preferred paths, preferred areas, and/or time constraints. The automated constructor may, within the provided parameters, reach the target destination.”);
and transporting the component along the route to the second location. (Czinger [0112] reads “Thereupon, in step 2020, the component may be transferred in an automated fashion from the first automated constructor having the 3-D printer to a second automated constructor.”);
Regarding claim 9 Darolfi/Attar/Czinger teaches The system as described in claim 1, wherein the autonomous technician includes a first robot configured to perform the verifying of the component at the first location for the apparatus that is located at the second location, (Darolfi [0092] reads “When the ordered tires are physically received, the tires are either scanned if the tire has a bar code, and the system 100 updates the tire inventory with an updated quantity for that tire type, and/or the system 100 receives a manual input that updates the quantity for the respective tire type. Each of the type of tires may have an associated stock keeping unit (SKU) for inventory management purposes.”);
and a second robot configured to perform the installing of the component in the apparatus. (Darolfi [0041] reads “Among other features, this specification describes a system to automatically remove and dismount a wheel from a vehicle for tire replacement, and then replace the wheel once a tire has been mounted.”);
Regarding claim 10 Darolfi/Attar/Czinger teaches The system as described in claim 1, wherein the autonomous technician includes a network communication module configured to retrieve component installation data via wired or wireless communications responsive to determining an identifier of the component using the one or more sensors. (Darolfi [0057] reads “The system 100 may use a computer network 126 for communication to one or more computers 102 of the system 100. As described herein, the computer network 120, may include, for example, a local area network (LAN), a virtual LAN (VLAN), a wireless local area network (WLAN), a virtual private network (VPN), cellular network, wireless network, the Internet, or the like, or a combination thereof. Communication among devices of may be performed using any suitable communications protocol such as TCP/IP or EtherNET IP.”);
Regarding claim 11 Darolfi/Attar/Czinger teaches The system as described in claim 10, wherein the network communication module retrieves the component installation data from a service provider system including a listing of the component. (Czinger [0061] reads “For example, an automated constructor may be configured to receive instructions on performing a set of one or more vehicle manufacturing processes, and further configured to subsequently carry out the received instructions. An automated constructor may have pre-programmed instructions to perform one or more vehicle manufacturing processes. Alternatively or in addition, the automated constructor may receive real-time instructions to perform one or more vehicle manufacturing processes.”);
Regarding claim 12 Darolfi/Attar/Czinger teaches The system as described in claim 10, wherein the first location is associated with a seller of the component and the second location is associated with a buyer of the component. (Darolfi [0089] reads “For example, some of the information received via the user interface by the system 100 includes one or more of the following customer information and vehicle information. The customer information may include customer number, address, billing information.” And [0097] reads “A vehicle arrives at a physical location for a scheduled tire change job. The physical location ideally is a structure with one or more bays where a vehicle may enter the bay for replacement of the tires.” And [0093] reads “Ideally, the tires are stored in a physical location where a robotic tire retrieval apparatus may obtain them. The system 100 may store information about a physical location of the tires in inventory. For example, the system 100 may store bin data with row and column positions of where a tire is located.”);
Regarding claim 13 Darolfi teaches A method implemented by one or more robots of an autonomous technician, (Darolfi abstract reads “Systems, methods and apparatus for automated vehicle wheel removal and replacement are provided. One system includes a computer system with applications for scheduling the replacement of tires for the vehicle. An electronically controlled lift device and robotic apparatus is configured for interaction with the computer system.”);
and installing, by the autonomous technician, the component in the apparatus. (Darolfi [0041] reads “Among other features, this specification describes a system to automatically remove and dismount a wheel from a vehicle for tire replacement, and then replace the wheel once a tire has been mounted.”);
Darolfi does not teach the method comprising: verifying, by the autonomous technician, a component at a first location for an apparatus that is located at a second location using one or more sensors of the one or more robots that implement the autonomous technician, and verifying a condition of the component; transporting, by the autonomous technician, the component to the second location;
Attar in analogous art, teaches the method comprising: verifying, by the autonomous technician, a component at a first location for an apparatus that is located at a second location using one or more sensors of the one or more robots that implement the autonomous technician, (Attar [0428 - 0429] reads “Referring back to FIG. 15 , one or more imaging sensors 1504 a-1504 d may also be installed around staging areas 1310. These imaging sensors can be used to identify, detect and verify which building parts are located at which staging areas. This can be used by the perception software system to guide the assembly robot to picking-up the correct building part from the correct staging area 1310. Imaging sensors 1506 a-1506 b may also be mounted to, or around, the assembly building platform 210. Images captured by these sensors can provide a view of both the assembly on the building platform 210, as well as a view of the assembly robot 204 positioned over the building platform 210.”);
and verifying a condition of the component; (Attar [0434] reads “At 1902 a, an image of a target building part is captured. The target building part may be, for instance, a stud which requires quality inspection. The images can be captured using image sensors in the perception system. In one example, the images are captured using one or more imaging sensors 1502 on the assembly robot 204 (FIG. 16 ). The images can be captured from various angles and perspectives.” And [0435] reads “At 1904 a, the captured images are analyzed to determine one or more properties of the corresponding building part. For example, this can involve determining whether the building part includes any cracks (e.g., images 1702-1706 in FIG. 17 ), or edge imperfections (1706-1708 in FIG. 17 ). In the case of studs, this can also involve determining whether the stud has a stud crown, etc.“ and [0438] reads “At 1906 a, a determination is made as to whether the building part meets a pre-determined minimum quality threshold. This determination is made based on the one or more properties determined at act 1904 a. For example, the minimum quality threshold may involve determining that a building part does not include any imperfections (e.g., cracks or edge imperfections). Therefore, if any imperfections are detected at act 1904 a, then the minimum quality threshold is not met.”);
transporting, by the autonomous technician, the component to the second location; (Attar [0176] reads “In at least one embodiment, cell “factories” may include transportation, or conveyance systems for transporting parts and components between cells. For example, automated guided vehicles (AGVs) can transport raw material, pre-cut material as well as partially or fully assembled building structures between robotic cells. In these cases, the cloud software platform 106 may also manage and coordinate an inter-cell transport or conveyance system.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Darolfi with that of Attar to include a sensor system that could identify parts. This would allow the system to make a more accurate assessment of the work that it is conducting and reduce potential assembly errors before they arise. (Attar [0001] reads “The present disclosure generally relates to assembly and manufacturing of building structures, including building structures used in the assembly of housing units as well as other infrastructure, and in particular, to methods, systems and devices for automated assembly of building structures.”);
Darolfi/Attar does not teach wherein the verifying includes verifying compatibility of the component with the apparatus.
Czinger in analogous art, teaches wherein the verifying includes verifying compatibility of the component with the apparatus (Czinger [0053] reads “In some exemplary embodiments, each station includes an area designated for the dedicated performance of one of the tasks above, with automated constructors moving to the station or otherwise residing at the station as required. For example, an automated constructor tasked with inspecting a part can move to a station to perform the inspection. The automated constructor can inspect tolerances of a part being assembled on the fly to ensure that the part is meeting one or more specifications. If a part is not within the specification, the automated constructor can communicate this information to a central controller or another automated constructor, and the part can either be remedied to fall within specifications, or it can be removed from active assembly in the event the problem cannot be fixed via available remedial measures or if the problem is serious in nature.” It would be appreciated by one with ordinary skill in the art that in a manufacturing or assembly situation specifications and tolerances are used to ensure that part mate in the manner intended.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have combined the teachings of Darolfi/Attar with that of Czinger to include a machine learning process for the instillation of components. This would allow the system to easily adapt to new parts, models, or components. (Czinger [0002] reads “Traditional manufacturing facilities can involve significant inflexible factory infrastructure to produce products at volume. For example, a factory may use fixed robotic assembly systems operating on assembly lines to achieve efficient production at volume. The inflexible factory infrastructure may often limit the manufacturing systems to manufacture only a handful of models of transport structures such as vehicles, motorcycles, boats, aircraft and the like, and even then, each model of the transport structure may be expensive to tool. In the event that a factory is configured, permanently or in the long-term, to produce one or more underperforming models, the factory may face financial trouble because of the factory and tooling amortization costs and operate at a loss. More specifically, the factory in these instances may be underutilized because of factory inflexibility, the need for tooling amortization prior to changing factory resources from producing the underperforming products to producing new products or more commercially successful existing products, and other constraints.”);
Regarding claim 14 Darolfi/Attar/Czinger teaches The method as described in claim 13, wherein verifying the component at the first location for the apparatus that is located at the second location includes: assessing the component, by the autonomous technician, using the one or more sensors of the autonomous technician; determining, by the autonomous technician, an identifier of the component based on an output of the one or more sensors during the assessing; (Darolfi [0092] reads “When the ordered tires are physically received, the tires are either scanned if the tire has a bar code, and the system 100 updates the tire inventory with an updated quantity for that tire type, and/or the system 100 receives a manual input that updates the quantity for the respective tire type. Each of the type of tires may have an associated stock keeping unit (SKU) for inventory management purposes.”);
and matching, by the autonomous technician, the identifier to the apparatus. (Darolfi [0090] reads “The system 100 determines a suggested tire type based on the provided vehicle information. For example, the system 100 may include an inventory determination process that interacts with a database 112 having stored information describing different tires that may fit a vehicle's wheel, and retrievable from the database 112 based on the vehicle year, make and model.”);
Regarding claim 15 Darolfi/Attar/Czinger teaches The method as described in claim 13, wherein transporting the component to the second location includes: receiving, by the autonomous technician, location data describing the second location; (Attar [0196] reads “More particularly, robotic cell capability data may refer to the aggregate, or any portion, of the hardware and/or software capabilities of the robotic cell 104. By way of non-limiting examples, this data can include: (i) the number of assembly robots in the robotic cell, (ii) the positional location of each assembly robots within the cell (e.g., left or right of the building platform 210 … (v) assembly robot hardware limitations, and/or (vi) information about other devices or systems located within the robotic cell (e.g., a pre-cut station).” And [0260] reads “At 302 c, server 106 can identify the facility capability by accessing facility capability data. Facility capability data can include, for example, the number of robotic assembly cells 104 in the facility, as well robot cell capability data corresponding to one or more of the robotic cells 104.”);
generating, by the autonomous technician, a route to the second location based on the location data; (Czinger [0100] reads “For instance, the control system may provide a specific travel path for an automated constructor. In another instance, the control system may provide a target destination, and the automated constructor may be programmed to reach the target destination by following any path. The automated constructor may follow one or more parameters in determining a path or may freely determine the path on the fly. In another instance, the control system may provide a target destination and parameters, such as allowed paths and disallowed paths, allowed areas and disallowed areas in the facility, preferred paths, preferred areas, and/or time constraints. The automated constructor may, within the provided parameters, reach the target destination.”);
and transporting, by the autonomous technician, the component along the route to the second location. (Czinger [0112] reads “Thereupon, in step 2020, the component may be transferred in an automated fashion from the first automated constructor having the 3-D printer to a second automated constructor.”);
Regarding claim 16 Darolfi/Attar/Czinger teaches The method as described in claim 13, wherein verifying the component at the first location for the apparatus that is located at the second location is performed by the autonomous technician using a first machine learning model of the autonomous technician, (Darolfi [0293] reads “The system 100 may train a machine learning model to identify, classify and/or infer from a digital image of a vehicle wheel the type of lug nut pattern, the type of lug nut (i.e., generally referred to as a wheel fastener) and/or the type of lug nut lock. The system 100 may use any suitable machine learning training technique, including, but are not limited to a neural net based algorithm, such as Artificial Neural Network, Deep Learning;“);
and installing the component in the apparatus is performed by the autonomous technician using a second machine learning model of the autonomous technician. (Czinger [0061] reads “For example, an automated constructor may be configured to receive instructions on performing a set of one or more vehicle manufacturing processes, and further configured to subsequently carry out the received instructions. An automated constructor may have pre-programmed instructions to perform one or more vehicle manufacturing processes. Alternatively or in addition, the automated constructor may receive real-time instructions to perform one or more vehicle manufacturing processes”. And [0222] reads “Here, automated constructors 2202, 2204 have equipped themselves with effectors for manipulating the COTS carbon fiber panel 2206 to assemble and/or install the panel having a node or extrusion. In this illustration, an end of the panel 2206 includes extrusion 2208. The automated constructors 2202, 2204 cooperate, using machine-learning, autonomous programs or direction from a control station, to assemble panel 2206 and insert extrusion 2208 in the appropriate location.” It would have been obvious to one with ordinary skill in the art, that the combination of machine learning in the installation procedure with the given instructions of how the component is to be installed would equate to the machine learning model being trained on the proper method of installation of a component.);
Regarding claim 17 Darolfi/Attar/Czinger teaches The method as described in claim 16, wherein the first machine learning model is trained on data describing a plurality of known component identifiers, (Darolfi [0293] reads “The system 100 may train a machine learning model to identify, classify and/or infer from a digital image of a vehicle wheel the type of lug nut pattern, the type of lug nut (i.e., generally referred to as a wheel fastener) and/or the type of lug nut lock. The system 100 may use any suitable machine learning training technique, including, but are not limited to a neural net based algorithm, such as Artificial Neural Network, Deep Learning;“);
and the second machine learning model is trained on data describing a plurality of installation protocols. (Czinger [0061] reads “For example, an automated constructor may be configured to receive instructions on performing a set of one or more vehicle manufacturing processes, and further configured to subsequently carry out the received instructions. An automated constructor may have pre-programmed instructions to perform one or more vehicle manufacturing processes. Alternatively or in addition, the automated constructor may receive real-time instructions to perform one or more vehicle manufacturing processes”. And [0222] reads “Here, automated constructors 2202, 2204 have equipped themselves with effectors for manipulating the COTS carbon fiber panel 2206 to assemble and/or install the panel having a node or extrusion. In this illustration, an end of the panel 2206 includes extrusion 2208. The automated constructors 2202, 2204 cooperate, using machine-learning, autonomous programs or direction from a control station, to assemble panel 2206 and insert extrusion 2208 in the appropriate location.” It would have been obvious to one with ordinary skill in the art, that the combination of machine learning in the installation procedure with the given instructions of how the component is to be installed would equate to the machine learning model being trained on the proper method of installation of a component.);
Regarding claim 18 Darolfi/Attar/Czinger teaches The method as described in claim 13, further comprising: verifying, by the autonomous technician, a condition of the apparatus that is located at the second location by: acquiring, by the autonomous technician, image data describing the apparatus using the one or more sensors of the autonomous technician; (Darolfi [0293] reads “The system 100 may train a machine learning model to identify, classify and/or infer from a digital image of a vehicle wheel the type of lug nut pattern, the type of lug nut (i.e., generally referred to as a wheel fastener) and/or the type of lug nut lock. The system 100 may use any suitable machine learning training technique, including, but are not limited to a neural net based algorithm, such as Artificial Neural Network, Deep Learning;“);
detecting, by the autonomous technician, the condition of the apparatus based on the image data; (Darolfi [0336] reads “The system 100 may be configured to evaluate the conditions of threads of a wheel stud and take an action in response to determining the condition of the threads. In one embodiment, the system 100 uses computer vision system to obtain digital imagery or 3-dimensional data of the threads of the wheel studs. The system 100 may obtain a 360-degree imagery or other sensor data for each of the wheel studs. Based on the obtained digital imagery or 3-dimensional sensor data of the threads, the system 100 may determine whether the threads are clean and/or clear of burns or dents.”);
and determining, by the autonomous technician, whether the detected condition of the apparatus matches a described condition of the apparatus using a machine learning model. (Czinger [0171] reads “he disassembled components may be recycled, refurbished, or discarded based on their respective condition. The condition of a disassembled component can be inspected, for example by a sensor, such as the sensor 7600 in FIG. 7. Alternatively, one or more sensors may be located on a robot, such as the disassembling automated constructor, to inspect the disassembled components. The condition of the disassembled parts may be embedded on the disassembled parts in some embodiments, such as via an identification matrix (such as the matrices 8110 a, 8110 b, 8310 b in FIG. 8) identifying the phase of the life cycle of the disassembled parts. The control system may give instructions, or one or more robots may be pre-programmed, to determine whether a part is to be recycled, refurbished, or discarded”. And [0053] reads “For example, an automated constructor tasked with inspecting a part can move to a station to perform the inspection. The automated constructor can inspect tolerances of a part being assembled on the fly to ensure that the part is meeting one or more specifications. If a part is not within the specification, the automated constructor can communicate this information to a central controller or another automated constructor, and the part can either be remedied to fall within specifications, or it can be removed from active assembly in the event the problem cannot be fixed via available remedial measures or if the problem is serious in nature.”);
Regarding claim 19 Darolfi teaches One or more computer-readable storage media storing executable instructions which, responsive to execution by an autonomous technician implemented by one or more robots, (Darolfi abstract reads “Systems, methods and apparatus for automated vehicle wheel removal and replacement are provided. One system includes a computer system with applications for scheduling the replacement of tires for the vehicle. An electronically controlled lift device and robotic apparatus is configured for interaction with the computer system.”);
and installing the component in the apparatus. (Darolfi [0041] reads “Among other features, this specification describes a system to automatically remove and dismount a wheel from a vehicle for tire replacement, and then replace the wheel once a tire has been mounted.”);
Darolfi does not teach cause the autonomous technician to perform operations comprising: verifying a component at a first location for an apparatus that is located at a second location using one or more sensors of the one or more robots that implement the autonomous technician, including verifying compatibility of the component with the apparatus and verifying a condition of the component; transporting the component to the second location;
Attar in analogous art, teaches cause the autonomous technician to perform operations comprising: verifying a component at a first location for an apparatus that is located at a second location using one or more sensors of the one or more robots that implement the autonomous technician, (Attar [0428 - 0429] reads “Referring back to FIG. 15 , one or more imaging sensors 1504 a-1504 d may also be installed around staging areas 1310. These imaging sensors can be used to identify, detect and verify which building parts are located at which staging areas. This can be used by the perception software system to guide the assembly robot to picking-up the correct building part from the correct staging area 1310. Imaging sensors 1506 a-1506 b may also be mounted to, or around, the assembly building platform 210. Images captured by these sensors can provide a view of both the assembly on the building platform 210, as well as a view of the assembly robot 204 positioned over the building platform 210.”);
and verifying a condition of the component; (Attar [0434] reads “At 1902 a, an image of a target building part is captured. The target building part may be, for instance, a stud which requires quality inspection. The images can be captured using image sensors in the perception system. In one example, the images are captured using one or more imaging sensors 1502 on the assembly robot 204 (FIG. 16 ). The images can be captured from various angles and perspectives.” And [0435] reads “At 1904 a, the captured images are analyzed to determine one or more properties of the corresponding building part. For example, this can involve determining whether the building part includes any cracks (e.g., images 1702-1706 in FIG. 17 ), or edge imperfections (1706-1708 in FIG. 17 ). In the case of studs, this can also involve determining whether the stud has a stud crown, etc.“ and [0438] reads “At 1906 a, a determination is made as to whether the building part meets a pre-determined minimum quality threshold. This determination is made based on the one or more properties determined at act 1904 a. For example, the minimum quality threshold may involve determining that a building part does not include any imperfections (e.g., cracks or edge imperfections). Therefore, if any imperfections are detected at act 1904 a, then the minimum quality threshold is not met.”);
transporting the component to the second location; (Attar [0176] reads “In at least one embodiment, cell “factories” may include transportation, or conveyance systems for transporting parts and components between cells. For example, automated guided vehicles (AGVs) can transport raw material, pre-cut material as well as partially or fully assembled building structures between robotic cells. In these cases, the cloud software platform 106 may also manage and coordinate an inter-cell transport or conveyance system.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Darolfi with that of Attar to include a sensor system that could identify parts. This would allow the system to make a more accurate assessment of the work that it is conducting and reduce potential assembly errors before they arise. (Attar [0001] reads “The present disclosure generally relates to assembly and manufacturing of building structures, including building structures used in the assembly of housing units as well as other infrastructure, and in particular, to methods, systems and devices for automated assembly of building structures.”);
Darolfi/Attar does not teach including verifying compatibility of the component with the apparatus.
Czinger in analogous art, teaches including verifying compatibility of the component with the apparatus (Czinger [0053] reads “In some exemplary embodiments, each station includes an area designated for the dedicated performance of one of the tasks above, with automated constructors moving to the station or otherwise residing at the station as required. For example, an automated constructor tasked with inspecting a part can move to a station to perform the inspection. The automated constructor can inspect tolerances of a part being assembled on the fly to ensure that the part is meeting one or more specifications. If a part is not within the specification, the automated constructor can communicate this information to a central controller or another automated constructor, and the part can either be remedied to fall within specifications, or it can be removed from active assembly in the event the problem cannot be fixed via available remedial measures or if the problem is serious in nature.” It would be appreciated by one with ordinary skill in the art that in a manufacturing or assembly situation specifications and tolerances are used to ensure that part mate in the manner intended.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have combined the teachings of Darolfi/Attar with that of Czinger to include a machine learning process for the instillation of components. This would allow the system to easily adapt to new parts, models, or components. (Czinger [0002] reads “Traditional manufacturing facilities can involve significant inflexible factory infrastructure to produce products at volume. For example, a factory may use fixed robotic assembly systems operating on assembly lines to achieve efficient production at volume. The inflexible factory infrastructure may often limit the manufacturing systems to manufacture only a handful of models of transport structures such as vehicles, motorcycles, boats, aircraft and the like, and even then, each model of the transport structure may be expensive to tool. In the event that a factory is configured, permanently or in the long-term, to produce one or more underperforming models, the factory may face financial trouble because of the factory and tooling amortization costs and operate at a loss. More specifically, the factory in these instances may be underutilized because of factory inflexibility, the need for tooling amortization prior to changing factory resources from producing the underperforming products to producing new products or more commercially successful existing products, and other constraints.”);
Regarding claim 20 Darolfi/Attar/Czinger teaches The one or more computer-readable storage media as described in claim 19, the operations further comprising: inputting data describing the component to a first machine learning model for the verifying of the component; (Czinger [0188] reads “The system may further collect test results and inspection measurements at each critical step of the manufacturing and assembly process. A full database and history of a manufacturing and assembly process for a specific vehicle could be generated for all vehicles manufactured by the facility. The database may be provided to the customer. Alternatively or in addition, the information in the database may be analyzed, such as for research and development.” And [0177] reads “In other exemplary embodiments, one or more robots, such as automated constructors, may include a machine-based learning algorithm (or suite of algorithms) for dynamically learning tasks on the fly. In these embodiments, such robots may learn tasks, or details about tasks, such as spot-welding based on observation via the robots' sensors and/or direct experience. For instance, if an error occurs during the course of a particular task being executed by the automated constructor, the automated constructor's machine-based learning capabilities may enable it to identify the cause of the error as well as possible or likely resolutions. In another exemplary embodiment, the machine-based learning algorithms are embedded within the robots themselves and coordinated in real-time (or near real-time), periodically, or otherwise by instructions from the control system that may govern machine learning algorithms such as settings, activation, etc.”);
generating a route from the first location to the second location to be travelled by the autonomous technician for the transporting of the component to the second location; (Czinger [0100] reads “For instance, the control system may provide a specific travel path for an automated constructor. In another instance, the control system may provide a target destination, and the automated constructor may be programmed to reach the target destination by following any path. The automated constructor may follow one or more parameters in determining a path or may freely determine the path on the fly. In another instance, the control system may provide a target destination and parameters, such as allowed paths and disallowed paths, allowed areas and disallowed areas in the facility, preferred paths, preferred areas, and/or time constraints. The automated constructor may, within the provided parameters, reach the target destination.”);
inputting data describing one or more installation protocols to a second machine learning model for the installing of the component in the apparatus; (Czinger [0061] reads “For example, an automated constructor may be configured to receive instructions on performing a set of one or more vehicle manufacturing processes, and further configured to subsequently carry out the received instructions. An automated constructor may have pre-programmed instructions to perform one or more vehicle manufacturing processes. Alternatively or in addition, the automated constructor may receive real-time instructions to perform one or more vehicle manufacturing processes”. And [0222] reads “Here, automated constructors 2202, 2204 have equipped themselves with effectors for manipulating the COTS carbon fiber panel 2206 to assemble and/or install the panel having a node or extrusion. In this illustration, an end of the panel 2206 includes extrusion 2208. The automated constructors 2202, 2204 cooperate, using machine-learning, autonomous programs or direction from a control station, to assemble panel 2206 and insert extrusion 2208 in the appropriate location.” It would have been obvious to one with ordinary skill in the art, that the combination of machine learning in the installation procedure with the given instructions of how the component is to be installed would equate to the machine learning model being trained on the proper method of installation of a component.);
and verifying a condition of the apparatus that is located at the second location by inputting data describing the apparatus to a third machine learning model. (Czinger [0171] reads “he disassembled components may be recycled, refurbished, or discarded based on their respective condition. The condition of a disassembled component can be inspected, for example by a sensor, such as the sensor 7600 in FIG. 7. Alternatively, one or more sensors may be located on a robot, such as the disassembling automated constructor, to inspect the disassembled components. The condition of the disassembled parts may be embedded on the disassembled parts in some embodiments, such as via an identification matrix (such as the matrices 8110 a, 8110 b, 8310 b in FIG. 8) identifying the phase of the life cycle of the disassembled parts. The control system may give instructions, or one or more robots may be pre-programmed, to determine whether a part is to be recycled, refurbished, or discarded”. And [0053] reads “For example, an automated constructor tasked with inspecting a part can move to a station to perform the inspection. The automated constructor can inspect tolerances of a part being assembled on the fly to ensure that the part is meeting one or more specifications. If a part is not within the specification, the automated constructor can communicate this information to a central controller or another automated constructor, and the part can either be remedied to fall within specifications, or it can be removed from active assembly in the event the problem cannot be fixed via available remedial measures or if the problem is serious in nature.”);
Response to arguments
Applicant argues < Critically, Darolfi does not disclose verifying a condition of a component and verifying compatibility of a component at one location for an apparatus located at a different, remote location, as claimed.> [Remarks Page 9 Second paragraph]. The examiner respectfully disagrees. The current rejection of record relies upon the teachings of Czinger to cover the limitations regarding the compatibility between components that are to be assembled together. Czinger does this by describing a specific robot that preforms inspection of previously machined and manufactured parts. This inspection includes determination that that the parts fall within desired specifications and tolerances. One with ordinary skill in the art in the field of manufacturing would know that tolerances would be used to ensure that manufactured parts would fit together in the method intended by the engineer. This would be known as Geometric Dimensioning and Tolerancing to one with ordinary skill in the art. Therefore, the combination teaches the claimed invention.
Applicant argues < However, Attar does not describe the ability to determine a condition and compatibility of the building parts with the building structure. Attar simply identifies that the building parts are at the right location.> [Page 10 Spanning Paragraph]. The examiner respectfully disagrees. Attar in analogous art, is relied upon to teach that there is a minimum quality of building part that could be used in a given assembly process. (Attar [0434] reads “At 1902 a, an image of a target building part is captured. The target building part may be, for instance, a stud which requires quality inspection. The images can be captured using image sensors in the perception system. In one example, the images are captured using one or more imaging sensors 1502 on the assembly robot 204 (FIG. 16 ). The images can be captured from various angles and perspectives.” And [0435] reads “At 1904 a, the captured images are analyzed to determine one or more properties of the corresponding building part. For example, this can involve determining whether the building part includes any cracks (e.g., images 1702-1706 in FIG. 17 ), or edge imperfections (1706-1708 in FIG. 17 ). In the case of studs, this can also involve determining whether the stud has a stud crown, etc.“ and [0438] reads “At 1906 a, a determination is made as to whether the building part meets a pre-determined minimum quality threshold. This determination is made based on the one or more properties determined at act 1904 a. For example, the minimum quality threshold may involve determining that a building part does not include any imperfections (e.g., cracks or edge imperfections). Therefore, if any imperfections are detected at act 1904 a, then the minimum quality threshold is not met.”); Further Czinger is relied upon to teach limitations about the fitment and the tolerances associated with manufactured parts. Therefore, the combination teaches the claimed invention.
Other references not Cited
Throughout examination other references were found that could read onto the prior art. Though these references were not used in this examination they could be used in future examination and could read on the contents of the current disclosure. These references are, Ghanem (US 20220016762 A1). Ghanem in analogous art, could be used to teach limitations regarding autonomous robotic technicians that operate in a facility. This include some teachings about the identification of material and specifications of that material that is going to be used. However, Ghanem was not selected for this rejection because other pieces of prior art were found to more broadly cover the current limitations.
Conclusion
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/JOHN MARTIN O'MALLEY/Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658
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