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 .
This communication is responsive to application filed on 08/27/2023.
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/20/2023 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 § 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.
The term “reasonable” in claim 1 (line 22) is a relative term which renders the claim indefinite. The term “reasonable” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Does this claim fall within at least one statutory category?):
Claims 1-8 are directed to a method.
Claims 9-16 are directed to a system.
Claims 17-20 are directed to a product.
Therefore, claims 1-20 fall into at least one of the four statutory categories.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
A method of automatic decision-making for welding, comprising:
obtaining basic source data of welding nodes for respective furnaces of respective series of a plurality of types in a welding process for monocrystal pulling-up [insignificant extra solution, e.g. mere data-gathering];
processing the obtained basic source data to filter and convert the basic source data into a plurality of parameters easily identified and marked in the welding nodes, and obtaining a data set of respective values of the plurality of parameters [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)];
establishing respective models for the plurality of the parameters by deep learning based on the data set [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)] and/or [mathematical concepts];
performing analysis, calculation, fitting and optimization on each of the models by the deep learning to obtain an optimal welding time model, an optimal welding power model, and an optimal welding temperature model in the welding process for monocrystal pulling-up [mathematical concepts];
performing analysis and calculation on each of the models by the deep learning to obtain first basic source data of a welding time, a welding power, and a welding temperature of a welding node for current furnace of current series of current type [mathematical concepts];
processing the obtained first basic source data to filter and convert the first basic source data into process parameters, easily identified and marked, of the welding time, the welding power, and the welding temperature [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)];
comparing the process parameters of the welding time, the welding power, and the welding temperature respectively with the optimal welding time model, the optimal welding power model, and the optimal welding temperature model to obtain a comparison result, and determining, based on the comparison result, whether respective values of the process parameters of the welding node where a monocrystal is located are reasonable to obtain a first determination result [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)]; and
performing data analysis on the first determination result by the deep learning to determine whether a current welding process is within an optimal range to obtain a second determination result, and make a decision based on the second determination result [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): The claim is directed to the judicial exception.
Claim 1 recites additional element of “obtaining”. The additional element of “receiving” is insignificant pre-solution (i.e. data gathering). The method recites using “deep learning”. When determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. See MPEP 2106.05(f). Here, there are no details about a particular DNN or how the DNN operates to derive the models other than that it is being used to perform analysis and calculation. The DNN is used to generally apply the abstract idea (i.e., perform the mathematical calculation using the recited mathematical concepts) without placing any limitation on how the DNN operates to derive the models as a function of the input signal. In addition, the limitation recites only the idea of performing analysis, calculation, fitting and optimizing of the models using a DNN without details on how this is accomplished. The claim omits any details as to how the DNN solves a technical problem, and instead recites only the idea of a solution or outcome. Also, the claim invokes a generic DNN merely as a tool for making the recited mathematical calculation rather than purporting to improve the technology or a computer. See MPEP 2106.05(f). Therefore, the limitation represents no more than mere instructions to apply the judicial exception on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers. Accordingly, the additional element(s) of each of this claim does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): At Step 2A, Prong Two, the second additional element in steps “using a deep learning,” was found to represent no more than mere instructions to apply the judicial exception on a computer using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. Further, the first additional element in step (a) was found to be insignificant extra-solution activity. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the re-evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). Here, the step of obtaining basic source data is mere data gathering that is recited at a high level of generality, See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iv) - Obtaining information about transactions. Therefore, this limitation remains insignificant extra solution activity even upon reconsideration and does not amount to significantly more.
As per claim 2, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per claim 3, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per claim 4, the claim falls into [insignificant post solution, data output].
As per claim 5, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per claim 6, the claim falls into [insignificant extra solution, e.g. mere data-gathering.
As per claim 7, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per claim 8, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per claim 9, independent claim 9 recites limitations analogous in scope to those of independent claim 1, and as such are similar rejected. Further, claim 9 recites additional elements of “a processor” and “a memory”. The components recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). Accordingly, the additional element(s) of each of these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as discussed above with respect to the integration of the abstract into a practical application, the additional elements of “processors” and “memory” amount to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As per Claims 10-20, claims 10-20 recite limitations analogous in scope to those of claims 2-8, and as such are similar rejected.
Allowable Subject Matter
Claims 1-20 are allowable over prior arts.
The following is a statement of reasons for the indication of allowable subject matter:
Zhang et al (Y. M. Zhang, Q. Y. Wang, Y. K. Liu, “Adaptive Intelligent Welding Manufacturing” pgs. 63-83, 2021) discloses Optimal design of the welding procedure gives the desired welding results under nominal welding conditions, applying the designed procedure will produce welding results that are different from the desired ones, make corrections and adjust some of the welding parameters from those specified in the design, make corrections and adjustments, their performance is limited by their physical constraints and skill level, extracting the needed information from signals from the sensors, predicting the responses of the welding process to the adjustments on welding parameters, and optimizing the adjustments, modeling of process dynamics, and control system design, establish the scientific foundation to correlate them to the weld quality or process variables of our concern, automate the learning such that the needed intelligence for robotic welding adaptation can be directly and automatically learned from experimental data after the physical phenomena being represented by the experimental data has been appropriately selected to make sure they are fundamentally correlated to that with which we are concerned. Some adaptation abilities may also be learned from skilled human welders. In addition, human-robot collaborative welding may incorporate adaptations from humans with the welding robots, analyzes and identifies the challenges in adaptive robotic welding, reviews efforts devoted to solve these challenges, analyzes the principles and nature of the methods behind these efforts, and introduces modern approaches, including machine learning/deep learning, learning from humans, and human-robot collaboration, to solve these challenges (Abstract).
Fujii et al (US Publication No. 2002/0008086 A1) discloses 8) calculating the temperature distribution of a base-metal, from which a nugget diameter is estimated, correcting the temperature distribution according to the amount of movement of electrode tip observed during welding [0018]; Performing the comparison between the two values over and over again until the predetermined ending time allows the predicted value to reach closer to the target value on the completion of welding. If the difference between the two values persists, the welding time should be adjusted, setting it slightly longer or shorter [0122].
Ivkovich et al (US Patent No. 12, 246, 399 B2) discloses tracking using machine learning techniques are described, analyzes feature characteristics related to one or more welds to identify, determine characteristics of, and/or label one or more parts repeatedly assembled by the welds, identifying parts assembled from the welds may make it possible to do part based analytics (e.g., related to part quality, cost, production efficiency, etc.), as opposed to just weld based analytics, on past welding data, identifying a part assembled from several welds results in an ordering of those several welds used to create the part, which can make it easier to compare/contrast similar welds across parts, determining the characteristics of the parts can assist in configuring certain part tracking systems, thereby reducing the expertise, time, and personnel required (Abstract).
However, none of the cited prior art references of record fully anticipate or render obvious the independent claims in particular the limitation of: “obtaining basic source data of welding nodes for respective furnaces of respective series of a plurality of types in a welding process for monocrystal pulling-up; processing the obtained basic source data to filter and convert the basic source data into a plurality of parameters easily identified and marked in the welding nodes, and obtaining a data set of respective values of the plurality of parameters; establishing respective models for the plurality of the parameters by deep learning based on the data set; performing analysis, calculation, fitting and optimization on each of the models by the deep learning to obtain an optimal welding time model, an optimal welding power model, and an optimal welding temperature model in the welding process for monocrystal pulling-up; performing analysis and calculation on each of the models by the deep learning to obtain first basic source data of a welding time, a welding power, and a welding temperature of a welding node for current furnace of current series of current type; processing the obtained first basic source data to filter and convert the first basic source data into process parameters, easily identified and marked, of the welding time, the welding power, and the welding temperature; comparing the process parameters of the welding time, the welding power, and the welding temperature respectively with the optimal welding time model, the optimal welding power model, and the optimal welding temperature model to obtain a comparison result, and determining, based on the comparison result, whether respective values of the process parameters of the welding node where a monocrystal is located are reasonable to obtain a first determination result; and performing data analysis on the first determination result by the deep learning to determine whether a current welding process is within an optimal range to obtain a second determination result, and make a decision based on the second determination result” in combination with the remaining steps recited in claims 1, 9, 17.
Conclusion
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/ Primary Examiner, Art Unit 2189 10/13/2026
