DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Examiner’s Note
This Office Action is in response to application filed on 4/4/2024, where claims 1-20 are currently pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7, 9, and 17 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.
Claims 7 and 17 are rejected to because of the following: each of claims 7 and 17 recites “displacement of the printing head in a direction perpendicular to the motion”. The element “motion” is recited with the article “the”, which indicates it is referring to a prior element of the same name. However, there is no such prior element recited in either the instant claim nor in the claim it depends from. Therefore, it is unclear to one of ordinary skill in the art, which element it is referring to As such, renders the claim indefinite.
Claim 9 is rejected to because of the following: claim 9 recites “the relationship between the process parameters and result”. The element “process parameters” is recited with the article “the”, which indicates it is referring to a prior element of the same name. However, there is no such prior element recited in either the instant claim nor in the claim it depends from. Therefore, it is unclear to one of ordinary skill in the art, which element it is referring to As such, renders the claim indefinite.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 4, 10-12, 14, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cella et al., (US 20190146477 A1) (hereinafter Cella).
Referring to claim 1, Cella teaches a manufacturing system comprising:
a tool configured to interact with or produce a product (¶ [1069], “a system for data collection in an industrial environment may be deployed for automotive production line robotic assembly systems…major components of a production line robotic assembly system including motors, linkages, tool handlers, positioning systems and the like”);
at least one sensor that provides sensor information on the quality of the operation of the tool relative to the product (¶ [0420], “An example system for data collection in an industrial environment includes a data collection system that monitors at least one signal for a set of collection band parameters and, upon detection of a parameter from the set of collection band parameters, configures portions of the system and performs collection of data from a set of sensors based on the detected parameter.” ¶ [1069], “major components of a production line robotic assembly system including motors, linkages, tool handlers, positioning systems and the like along with corresponding sensors for the particular installation of the production line robotic assembly system”. ¶ [1266], “data collection systems and methods may facilitate intelligent, situational, context-aware collection, summarization, storage, processing, transmitting, and/or organization of data, such as by one or more data collectors (such as any of the wide range of data collector embodiments described throughout this disclosure)…The described self-organization functionality of data collection in an industrial environment may improve various parameters of such data collection, as well as parameters of the processes, applications, and products that depend on data collection, such as data quality parameters”. ¶ [1414], “system includes an automated robotic handling system, including a number of components such as actuators, gear boxes, and/or rail guides. The system includes a number of sensors that determine various parameters related to the components”. ¶ [1506], “manufacturing body and frame components of trucks and cars, certain detailed data regarding paint color, surface curvature, and other quality control measures may be captured and stored at high resolution”.); and
a controller configured to control operation of the tool based on a predetermined manufacturing process (¶ [0200], “The host processing system 112, referred to for convenience in some cases as the host system 112, may include various systems, components, methods, processes, facilities, and the like for enabling automated, or automation-assisted processing of the data, such as for monitoring one or more environments 104 or networks 110 or for remotely controlling one or more elements in a local environment 104 or in a network 110.” ¶ [1069], “a system for data collection in an industrial environment may be deployed for automotive production line robotic assembly systems.” Examiner recognizes the predetermined manufacturing process as the assembly of automotive production.) and further configured to dynamically adjust at least one parameter of the predetermined manufacturing process to thereby dynamically adjust operation of the tool based on qualitative performance information derived from the sensor information (¶ [1266], “data collection in an industrial environment featuring self-organization functionality. Such data collection systems and methods may facilitate intelligent, situational, context-aware collection, summarization, storage, processing, transmitting, and/or organization of data, such as by one or more data collectors (such as any of the wide range of data collector embodiments described throughout this disclosure), a central headquarters or computing system, and the like. The described self-organization functionality of data collection in an industrial environment may improve various parameters of such data collection, as well as parameters of the processes, applications, and products that depend on data collection, such as data quality parameters”. ¶ [1414], “An example system includes an automated robotic handling system…The system includes a number of sensors that determine various parameters related to the components…The sensor information is conveyed to a target storage system, including at least partially through a network communicatively coupled to the automated robotic handling system…The network management circuit, a related expert system, and/or a related machine learning algorithm, updates the sensor data transmission protocol to ensure efficient network utilization, sufficient delivery of data to support system control, diagnostics, improvement and/or efficiency updates to handling efficiency, and/or other determinations planned for the data outside of the system, to reduce resource utilization of data transmission, and/or to respond to system noise factors, variability, and/or changes in the system or related aspects such as cost or environment parameters.” ¶ [1506], “manufacturing body and frame components of trucks and cars, certain detailed data regarding paint color, surface curvature, and other quality control measures may be captured and stored at high resolution, but for ongoing operational purposes”.) applied as feedback to a closed-loop control policy learned through machine reinforcement learning (¶ [0309], “the platform 100 may include the local data collection system 102 deployed in the environment 104 using machine learning to enable derivation-based learning outcomes from computers without the need to program them. The platform 100 may, therefore, learn from and make decisions on a set of data, by making data-driven predictions and adapting according to the set of data. In embodiments, machine learning may involve performing a plurality of machine learning tasks by machine learning systems, such as supervised learning, unsupervised learning, and reinforcement learning.” ¶ [0899], “In embodiments, a monitoring system for data collection in an industrial environment, comprising: a plurality of input sensors communicatively coupled to a data collector having a controller; a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn received output data patterns indicative of a state, wherein the data collection band circuit alters the at least one collection parameter for the at least one of the plurality of sensors based on one or more of the learned received output data patterns and the state…17. The system of clause 1, wherein the monitoring system keeps or modifies operational parameters of an item of equipment in the environment based on the determined state.”)
Referring to claim 2, Cella further teaches a system according to claim 1, wherein the tool performs an additive manufacturing process (¶ [1338], “The plurality of sensor inputs can be configured to sense at least one of an operational mode, a fault mode, and a health status of at least one target system. Examples of such target systems include…a printing system”).
Referring to claim 4, Cella further teaches a system according to claim 1, wherein the at least one sensor comprises:
at least one camera;
a 3D laser scanner; and/or
a coordinate measuring machine (¶ [0934], camera).
Referring to claim 10, Cella further teaches a system according to claim 1, wherein the controller is manufacturing process agnostic such that the controller can be used on different types of manufacturing processes (¶ [1145], car, truck, or industrial vehicle).
Claim Rejections - 35 USC § 103
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.
Claims 3, 8, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Cella as applied to claims 1 and 11 above, and in view of Zhao et al., (WO 2023012964 A1) (hereinafter Zhao).
Referring to claim 3, Cella teaches a system according to claim 1. However, Cella does not explicitly teach the tool performs a subtractive manufacturing process.
Zhao teaches the tool performs a subtractive manufacturing process (Pg. 2 lines 42-47, “Provided is a machine learning device that generates a learning model for, when the machined surface quality of an unknown machined surface is to be determined, correctly evaluating the machined surface quality on the basis of an evaluation value that serves as a criterion for determining the appearance of the surface.” Pg. 4 lines 31-43, CNC)
Cella and Zhao are analogous art to the claimed invention because they are concerning with using machine learning in manufacturing system (i.e., same field of endeavor).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Cella and Zhao before them to substitute the CNC as taught by Zhao for the generic tool of Cella. Because both Cella and Zhao teach methods of using machine learning in manufacturing system, it would have been obvious to one skilled in the art to substitute one known method for the other to achieve the predictable result of utilizing machine learning. The motivation would have been to increase usability of the system by allowing to be used on various machines.
Referring to claim 8, Cella teaches a system according to claim 1. However, Cella does not explicitly teach the tool comprises a CNC machine.
Zhao teaches the tool comprises a CNC machine (Pg. 4 lines 31-43, CNC).
Cella and Zhao are analogous art to the claimed invention because they are concerning with using machine learning in manufacturing system (i.e., same field of endeavor).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Cella and Zhao before them to substitute the CNC as taught by Zhao for the generic tool of Cella. Because both Cella and Zhao teach methods of using machine learning in manufacturing system, it would have been obvious to one skilled in the art to substitute one known method for the other to achieve the predictable result of utilizing machine learning. The motivation would have been to increase usability of the system by allowing to be used on various machines.
Regarding claims 13 and 18, these claims recite the method performed by the manufacturing system of claims 3 and 8 respectively; therefore, the same rationale of rejection is applicable.
Claims 5-7, 9, 15-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Cella as applied to claims 1 and 11 above, and in view of Ren et al., (WO 2019112655 A1) (hereinafter Ren).
Referring to claim 5, Cella further teaches a system according to claim 1, wherein:
the tool comprises a 3D printer having a material dispenser (¶ [1127], 3D printer);
…the closed-loop control policy uses qualitative performance information (¶ [1266], “data collection in an industrial environment featuring self-organization functionality. Such data collection systems and methods may facilitate intelligent, situational, context-aware collection, summarization, storage, processing, transmitting, and/or organization of data, such as by one or more data collectors (such as any of the wide range of data collector embodiments described throughout this disclosure), a central headquarters or computing system, and the like. The described self-organization functionality of data collection in an industrial environment may improve various parameters of such data collection, as well as parameters of the processes, applications, and products that depend on data collection, such as data quality parameters”. ¶ [1414], “An example system includes an automated robotic handling system…The system includes a number of sensors that determine various parameters related to the components…The sensor information is conveyed to a target storage system, including at least partially through a network communicatively coupled to the automated robotic handling system…The network management circuit, a related expert system, and/or a related machine learning algorithm, updates the sensor data transmission protocol to ensure efficient network utilization, sufficient delivery of data to support system control, diagnostics, improvement and/or efficiency updates to handling efficiency, and/or other determinations planned for the data outside of the system, to reduce resource utilization of data transmission, and/or to respond to system noise factors, variability, and/or changes in the system or related aspects such as cost or environment parameters.” ¶ [1506], “manufacturing body and frame components of trucks and cars, certain detailed data regarding paint color, surface curvature, and other quality control measures may be captured and stored at high resolution, but for ongoing operational purposes”.) derived from the images (¶ [0308], “the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor multiple signals that may be carried by a plurality of physical, electronic, and symbolic formats or signals. The platform 100 may employ signal processing including a plurality of mathematical, statistical, computational, heuristic, and linguistic representations and processing of signals and a plurality of operations needed for extraction of useful information from signal processing operations such as techniques for representation, modeling, analysis, synthesis, sensing, acquisition, and extraction of information from signals…signal processing applies to almost all disciplines and applications in the industrial environment such as audio and video processing, image processing, wireless communications, process control, industrial automation, financial systems, feature extraction, quality improvements such as noise reduction, image enhancement, and the like. Signal processing for images may include pattern recognition for manufacturing inspections, quality inspection, and automated operational inspection and maintenance.”)
Cella teaches the limitations above. However, Cella does not explicitly teach the at least one sensor comprises at least one camera configured to provide images of a location around the deposition.
Ren teaches the at least one sensor comprises at least one camera configured to provide images of a location around the deposition (¶ [0026], “During the printing, position and speed sensors 224 detect the position and speed of the tooltip 218 (or the actuators controlling it) and pass position and speed data to feature detection 226. Additional sensors 222 detect additional sensor data regarding tooptip 218 and/or the printed part 220 being manufactured. Additional sensors 220 can include, for example, infrared (IR) sensors, IR cameras, visible light or other cameras”. ¶ [0029], “The’feature detection’ block 226 can identify printing problems, including faults in the part that has been or is being printed. It can do so, for example, based on computer vision and sensor fusion algorithms such as deep learning or artificial neural networks. These issues can be determined from the additional sensor data (of additional sensors 222) and/or the position and speed data (of position and speed sensors 224), such as from the camera or the IR camera”.)
Cella and Ren are analogous art to the claimed invention because they are concerning with using sensor in manufacturing system (i.e., same field of endeavor).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Cella and Ren before them to substitute the image sensor as taught by Ren for the generic sensor of Cella. Because both Cella and Ren teach methods of using sensor in manufacturing system, it would have been obvious to one skilled in the art to substitute one known method for the other to achieve the predictable result of utilizing sensor in manufacturing system. The motivation would have been to feature detection to identify manufacturing problems as suggested by Ren (¶ [0004]-[0005]).
Referring to claim 6, Cella teaches a system according to claim 5. However, Cella does not explicitly teach the qualitative performance information comprises both deposition and variance of deposition.
Ren further teaches the qualitative performance information comprises both deposition and variance of deposition (¶ [0035], “S is the set of states, including the ID of different defects/problems and their severity levels. s.sup.k is a specific state at the time instance k, where s.sup.k 6 S.” ¶ [0037], “r is the current reward. If the current part has no defects, the r is the largest, such as 1. If there are many defects, r is reduced to a small number, such as 0.”)
Referring to claim 7, Cella teaches a system according to claim 5. However, Cella does not explicitly teach the at least one parameter comprises (1) the velocity at which the printing head is moving and/or (2) displacement of the printing head in a direction perpendicular to the motion
Ren further teaches the at least one parameter comprises (1) the velocity at which the printing head is moving and/or (2) displacement of the printing head in a direction perpendicular to the motion (¶ [0026], “During the printing, position and speed sensors 224 detect the position and speed of the tooltip 218 (or the actuators controlling it) and pass position and speed data to feature detection 226.” ¶ [0029], “The’feature detection’ block 226 can identify printing problems, including faults in the part that has been or is being printed. It can do so, for example, based on computer vision and sensor fusion algorithms such as deep learning or artificial neural networks. These issues can be determined from the additional sensor data (of additional sensors 222) and/or the position and speed data (of position and speed sensors 224)”.)
Referring to claim 9, Cella further teaches a system according to claim 1, wherein the controller utilizes a policy network for controlling the manufacturing process (¶ [0350], “methods and systems are disclosed herein for a cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices, including a cloud-based policy automation engine for IoT, enabling creation, deployment and management of policies that apply to IoT devices. Policies can relate to data usage to an on-device storage system that stores fused data from multiple industrial sensors, or what data can be provided to whom in a self-organizing marketplace for IoT sensor data…Embodiments include training a model to determine what policies should be deployed in an industrial data collection system.” ¶ [0828], “an expert system for processing a plurality of inputs collected from sensors in an industrial environment, comprising: A modular neural network, where the expert system uses one type of neural network for recognizing a pattern and a different neural network for self-organizing an activity in the industrial environment.”), the policy network trained using a learning environment that models the relationship between the process parameters (¶ [0350], “Embodiments include training a model to determine what policies should be deployed in an industrial data collection system.” ¶ [0352], “As noted above, methods and systems are disclosed herein for a self-organizing data marketplace for industrial IoT data, where available data elements are organized in the marketplace for consumption by consumers based on training a self-organizing facility with a training set and feedback from measures of marketplace success. Embodiments include organizing a set of data pools in a self-organizing data marketplace based on utilization metrics for the data pools. Embodiments include training a model to determine pricing for data in a data marketplace. The data marketplace is fed with data streams from a self-organizing swarm of industrial data collectors”.)
Cella teaches the limitations above. However, Cella does not explicitly teach a reward function that penalizes or rewards the policy depending on how well the policy performed.
Ren teaches a reward function that penalizes or rewards the policy depending on how well the policy performed (¶ [0036], “Q(s, a) is a reward function, which maps the current state (e.g., detected problems or printing parameters) to a product quality metric as a reward value (e.g., adjusted toolpath or settings). In other words, Q: S → Pr(d = a|S). Q(s, a) can be implemented, for example, as a reward table with every possible s in a row (alternately, column) and every possible a a column (alternately, row). The reward table can be stored in a storage or memory of the local printing device 216, the AM control system 250, or the cloud data repository 228. In this example, reward table 232 is stored on the AM control system 250. Q(s, a) or the reward table 232 can be stored in a Deep Q-learning Network (DQN), which is a type of neural network.”)
Cella and Ren are analogous art to the claimed invention because they are concerning with manufacturing system with policy network (i.e., same field of endeavor).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Cella and Ren before them to substitute the policy network with reward function as taught by Ren for the generic policy network of Cella. Because both Cella and Ren teach methods of utilizing policy network in manufacturing system, it would have been obvious to one skilled in the art to substitute one known method for the other to achieve the predictable result of utilizing policy network in manufacturing system. The motivation would have been to using reinforcement learning to address manufacturing issue and optimizing production as suggested by Ren (¶ [0004]-[0005]).
Regarding claims 15-17 and 19, these claims recite the method performed by the manufacturing system of claims 5-7 and 9 respectively; therefore, the same rationale of rejection is applicable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US 9,855,698 (Perez) – discloses automatic process control of additive manufacturing.
US 20240278506 (Toncelli) – discloses method for additive manufacturing where qualitative characteristics are determined using camera.
US 20240142941 (Mehr) – discloses system for robotic additive manufacturing comprising sensors that generate sensor data.
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/MONG-SHUNE CHUNG/
Primary Examiner, Art Unit 2118
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