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. Claim Rejections - 35 USC § 112 The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites the following: 1. A method of automatic decision-making for re-feeding, comprising: obtaining basic source data of re-feeding nodes for respective furnaces of respective series of a plurality of types in a re-feeding 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 re-feeding 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 a critical feeding quality, a critical crystal position, and a critical sensor weight in the re-feeding 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 feeding quality, a crystal position, and a sensor weight of a re-feeding 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 feeding quality, the crystal position, and the sensor weight; comparing the process parameters of the feeding quality, the crystal position, and the sensor weight respectively with the critical feeding quality, the critical crystal position, and the critical sensor weight to obtain a comparison result, and determining, based on the comparison result, whether respective values of the process parameters of the re-feeding node where the 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 an abnormality occurs in a current re-feeding process to obtain a second determination result, and make a decision based on the second determination result (emphasis added). MPEP 2163.03 states the following: While there is a presumption that an adequate written description of the claimed invention is present in the specification as filed. In re Wertheim, 541 F.2d 257, 262, 191 USPQ 90, 96 (CCPA 1976), a question as to whether a specification provides an adequate written description may arise in the context of an original claim . An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc). The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. "Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement. "Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002) (emphasis added). Here, Examiner finds claim 1 lacks written description support because at least the bolded elements in the claim recite functional elements and a desired result and the specification fails to s ufficiently identify how t hose function al elements are performed and/ or how the desired result s are achieved . For example, with respect to “establishing respective models for the plurality of the parameters by deep learning based on the data set ,” the specification is silent on how “deep learning” establishes the respective mode ls . The specification merely repeats the claim language verbatim and states : The deep learning is based on a conventional deep learning model in the art of machine learning. For example, the deep learning may be based on at least one of a convolution neural network, a recurrent neural network, a generative adversarial network, or deep reinforcement learning, which are well known in the art. See p ara graph 27 of specification. Examiner finds th is passage wholly inadequate with respect to the written description requirement. It fails to indicate how deep learning achieves the result of “e stablishing respective models for the plurality of the parameters . . . on the data set .” Stated another way, the specification fails to provide any guidance as to what “based on conventional deep learning ” entails other than to merely state that deep learning is “based on a conventional deep learning model.” See id. Examiner finds this is a fatal flaw and the specification is wholly inadequate with respect to the bolded elements above and 35 USC 112(a). Th e above analysis applies to the at least the remaining elements below: performing analysis, calculation, fitting and optimization on each of the models by the deep learning to obtain a critical feeding quality, a critical crystal position, and a critical sensor weight in the re-feeding 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 feeding quality, a crystal position, and a sensor weight of a re-feeding node for current furnace of current series of current type; performing data analysis on the first determination result by the deep learning to determine whether an abnormality occurs in a current re-feeding process to obtain a second determination result, and make a decision based on the second determination result (emphasis added). That is, there is no disclosure as to how deep learning performs the recited results (i.e. how deep learning performs the analysis, calculation, fitting, and/or optimization recited in the claim). Examiner finds merely repeating the claim language in the specification is insufficient. See MPEP 2163.03 . For the reasons above, claim 1 is rejected under 35 USC 112(a), written description. Claims 9 and 17 are rejected for the reasons given above for claim 1. The dependent claims are rejected because the inherit the deficiencies of their respective parent claims. Claim Rejections - 35 USC § 112 The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. MPEP 2164.01(a) states the following: In order to determine compliance with the enablement requirement of 35 U.S.C. 112(a), the Federal Circuit developed a framework of factors in In re Wands, 858 F.2d 731, 737, 8 USPQ2d 1400, 1404 (Fed. Cir. 1988), referred to as the Wands factors to assess whether any necessary experimentation required by the specification is "reasonable" or is "undue." Consistent with Amgen Inc. et al. v. Sanofi et al., 598 U.S. 594, 2023 USPQ2d 602 (2023), the Wands factors continue to provide a framework for assessing enablement in a utility application or patent, regardless of technology area. See Guidelines for Assessing Enablement in Utility Applications and Patents in View of the Supreme Court Decision in Amgen Inc. et al. v. Sanofi et al., 89 FR 1563 (January 10, 2024). These factors include, but are not limited to: (A) The breadth of the claims; (B) The nature of the invention; (C) The state of the prior art; (D) The level of one of ordinary skill; (E) The level of predictability in the art; (F) The amount of direction provided by the inventor; (G) The existence of working examples; and (H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure Claim 1 recites the following: 1. A method of automatic decision-making for re-feeding, comprising: obtaining basic source data of re-feeding nodes for respective furnaces of respective series of a plurality of types in a re-feeding 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 re-feeding 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 a critical feeding quality, a critical crystal position, and a critical sensor weight in the re-feeding 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 feeding quality, a crystal position, and a sensor weight of a re-feeding 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 feeding quality, the crystal position, and the sensor weight; comparing the process parameters of the feeding quality, the crystal position, and the sensor weight respectively with the critical feeding quality, the critical crystal position, and the critical sensor weight to obtain a comparison result, and determining, based on the comparison result, whether respective values of the process parameters of the re-feeding node where the 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 an abnormality occurs in a current re-feeding process to obtain a second determination result, and make a decision based on the second determination result (emphasis added). With respect to (A), Examiner finds the scope of enablement provided to one skilled in the art by the disclosure is not commensurate with the scope of protection sought by the claims. Examiner finds claim 1, particularly the bolded elements in claim 1 are very broad. Th ese bolded elements cover any and all deep learning models . They cover any and all ways to train , tune, retrain , configure, use, and/or implement deep learning models . The disclosure only mentions conventional deep learning models and that the deep learning is “based on a conventional model.” Paragraph 27 of specification (Spec) . Examiner finds this is inadequate. The factor does not favor enablement. With respect to (B) and (C), the nature of the invention is monocrystal production manufacturing and machine learning/ deep learning. Examiner finds one skilled in the art of machine learning and deep learning at the time of filing would have likely known how to train and implement conventional deep learning models . See Spec at paragraph 27 . However, Examiner finds the state of the prior art is such that one skilled are would not have known how to train an d/or implement the specific deep leaning models recited in the claims, i.e. at least the following: 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 a critical feeding quality, a critical crystal position, and a critical sensor weight in the re-feeding 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 feeding quality, a crystal position, and a sensor weight of a re-feeding node for current furnace of current series of current type ; performing data analysis on the first determination result by the deep learning to determine whether an abnormality occurs in a current re-feeding process to obtain a second determination result, and make a decision based on the second determination result (emphasis added). The disclosure only mentions conventional models and that the claimed deep learning is “based on a conventional deep learning model.” Paragraph 27 of specification . Examiner finds it is not enough to merely mention these models without explaining how they are trained/used/configured/implemented to achieve the claimed invention. These factors do not favor enablement. With respect to (D), “[w]here different arts are involved in the invention, the specification is enabling if it enables persons skilled in each art to carry out the aspect of the invention applicable to their specialty.” MPEP 2164.05(b). Here, Examiner finds the disclosure requires ordinary skill in the machine learning/ deep learning arts and ordinary skill in monocrystal production manufacturing. Examiner finds the specification does not enable one skilled in the art of deep learning to enable the full scope of the bolded elements above. For example, there is no disclosure as to how to train /use/configure/implement a deep learning model to perform the tasks recited in the claims . In fact, the specification is virtually silent on deep learning other than acknowledging that conventional deep learning models were known in the art. See Spec para. 27 . This factor does not favor enablement. With respect to (E), Examiner finds the level of predictability in the deep learning arts is relatively high. This factor favors e nablement. With respect to (F), Examiner finds there is no direction provided by the invent or as to how to “establish. . respective models for the plurality of the parameters by deep learning based on the data set; perform. . . analysis, calculation, fitting and optimization on each of the models by the deep learning to obtain a critical feeding quality, a critical crystal position, and a critical sensor weight in the re-feeding process for monocrystal pulling-up;. . . perform. . . analysis and calculation on each of the models by the deep learning to obtain first basic source data of a feeding quality, a crystal position, and a sensor weight of a re-feeding node for current furnace of current series of current type; and /or perform. . . data analysis on the first determination result by the deep learning to determine whether an abnormality occurs in a current re-feeding process to obtain a second determination result, and make a decision based on the second determination result . . .” (emphasis added). Again, Examiner finds merely mentioning conventional deep learning models (see Spec para. 27) does not rise to the level “guidance” as to how to implement/train/ use/configure conventional deep learning models to achieve the claimed invention . This factor does not favor enablement. With respect to (G), the disclosure provides no working examples. This factor does not favor enablement. With respect to (H), training and using a deep learning model (and other machine learning models) necessarily requires experimentation such as retraining the data based on new incoming data, selecting parameters to train the model on , and tuning hyperparameters of the model. However, Examiner finds that because the specification is virtually silent on how to train/use/configure/implement a deep learning model as it relate s to the claimed invention (i.e. as it relate s to monocrystal production manufacturing), the resulting experimentation required by one skilled in the art of machine learning would be undue. This factor does not favor enablement. For the reasons above, claim 1 is rejected under 35 USC 112(a) , enablement. Claims 9 and 17 are rejected for the reasons given above for claim 1. The dependent claims are rejected because the inherit the deficiencies of their respective parent claims. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claim s 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 recites “ comparing the process parameters of the feeding quality, the crystal position, and the sensor weight respectively with the critical feeding quality, the critical crystal position, and the critical sensor weight to obtain a comparison result, and determining, based on the comparison result, whether respective values of the process parameters of the re-feeding node where the monocrystal is located are reasonable to obtain a first determination result ” (emphasis added). One skilled in the art could not determine the scope of claim 1 because there is no objective standard or example in the specification or the prior art as how to ascertain the degree of “reasonable ness” required for the claim. See MPEP 2173.05(b) (“Thus, when a term of degree is used in the claim, the examiner should determine whether the specification provides some standard for measuring that degree. . . If the specification does not provide some standard for measuring that degree, a determination must be made as to whether one of ordinary skill in the art could nevertheless ascertain the scope of the claim . . . When a subjective term is used in the claim, the examiner should determine whether the specification supplies some objective standard for measuring the scope of the term. . . ). This renders the claim vague and indefinite. Claims 9 and 17 are rejected for the reasons given above for claim 1. The dependent claims are rejected because the inherit the deficiencies of their respective parent claims. Conclusion The following prior art is relevant to Applicant’s specification: Yousif, Artificial Neural Network Modelling and Experimental Evaluation of Dust and Thermal Energy Impact on Monocrystalline and Polycrystalline Photovoltaic Modules, 4 June 2022. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT ALBERT M PHILLIPS, III whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3256 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 10a-6:30pm EST M-F . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Ann J Lo can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-9767 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ALBERT M PHILLIPS, III/ Primary Examiner, Art Unit 2159