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 .
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 18 – 21 and 23 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.
Regarding claim 18, the phrase “preferably” which is considered equivalent to the phrases: “such as,” “for example,” “in particular,” “specifically,” and “or the like,” is considered indefinite. The metes and bounds of the claim scope are not clear. It is not clear if the claim limitation is optional or not. Furthermore, without knowing this information, one of ordinary skill in the art would not know whether they are infringing on the claims or not.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 – 4, 7 – 14, 17 – 21, 23, 25 and 26 is/are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Baseman et al. (US 2018/0292812 A1; hereinafter “Baseman”).
Regarding claim 1, Baseman teaches throughout the publication a method for improving a production process for manufacturing a chemical product at an industrial plant (paragraphs 3, 4, 18, 24, 25, 26 and 81), the industrial plant comprising at least one equipment (e.g., processing units; paragraph 51) and one or more computing units (controller 112; paragraph 28; figure 1A), and the product being manufactured by processing, via the equipment (paragraph 28), at least one input material using the production process (e.g., at 210, the selected process variables and the genealogy data are input to a machine learning algorithm such as a decision tree algorithm; paragraphs 56 – 58), wherein the method comprises:
receiving, via an input interface, real-time process data from the equipment (at 202; paragraphs 29 and 51; figure 2);
determining, via any of the computing units, a subset of the real-time process data (at 208; paragraph 55; figure 2); the subset of the real-time process data being indicative of the process parameters and/or equipment operating conditions that the input material is processed under; and
computing, using at least a part of the subset of the real-time process data, at least one state related to the input material and/or the equipment (e.g., paragraphs 56 and 57).
Regarding claim 2, Baseman teaches the method of claim 1, wherein the computation of the at least one of the states is performed also using input material data, the input material data being indicative of one or more properties of the input material (e.g., paragraphs 55 and 56).
Regarding claim 3, Baseman teaches the method of claim 1, wherein at least one of the computed states is a state of a chemical reaction that the input material undergoes for transforming to the chemical product (e.g., paragraphs 25, 55 and 56).
Regarding claim 4, Baseman teaches the method of claim 1, wherein at least one of the computed states is a value wherein, the value is an energy value indicative of the energy used for the production of the chemical product (e.g., operating conditions such as temperature and material composition of the product; paragraphs 53 – 58) or the value is a regulatory value to be compiled by the industrial plant and/or the chemical product.
Regarding claim 7, Baseman teaches the method of claim 4, wherein the method further comprises: adjusting, via the equipment, the production process; wherein the adjusting of the production process is performed in response to at least one of the computed states (e.g., paragraphs 59 – 61).
Regarding claim 8, Baseman teaches the method of claim 7, wherein the adjusting of the production process is performed such that at least one of the states approaches or reaches their corresponding desired or expected state (e.g., paragraphs 59 – 61).
Regarding claim 9, Baseman teaches the method of claim 1, wherein the state is computed via a model (e.g., a machine learning model or decision tree model; paragraphs 32, 33, 49, 56).
Regarding claim 10, Baseman teaches the method of claim 2, wherein the method further comprises: providing, via an interface, an object identifier; the object identifier being appended with the input material data (e.g., at 204, a product identifier and quality measure of the product identified by the product identifier; paragraph 53).
Regarding claim 11, Baseman teaches the method of claim 10, wherein the method also comprises: appending, to the object identifier (e.g., at 204, a product identifier and quality measure of the product identified by the product identifier; paragraph 53), the subset of real-time process data (e.g., paragraphs 19, 52, 59 and 61).
Regarding claim 12, Baseman teaches the method of claim 10, wherein the method further comprises: appending, to the object identifier, the at least one of the states (e.g., at 204, a product identifier and quality measure of the product identified by the product identifier; paragraph 53).
Regarding claim 13, Baseman teaches the method of claim 1, wherein the input material for the processing via the equipment is divided into at least two packages wherein the size of a package is fixed or is determined based on an input material weight or amount, for which considerably constant process parameters or equipment operation parameters can be provided by the equipment (e.g., at 202, a manufacturing facility may have processing units that process material or intermediary product to produce a final or end product that implicitly include packaging of the product; paragraph 51; figure 2).
Regarding claim 14, Baseman teaches the method of claim 1, wherein the processing of the at least two packages is managed by means of corresponding data objects (e.g., at 202, a manufacturing facility can have processing units that process material or intermediary product to produce a final or end product that implicitly include packaging of the product; paragraph 51; figure 2), each of which at least including an object identifier (e.g., at 204, a product identifier and quality measure of the product identified by the product identifier; paragraph 53).
Regarding claim 17, Baseman teaches the method of claim 1, wherein a respective production process is monitored and/or controlled via an individual machine learning (ML) model (e.g., a machine learning model or decision tree model; paragraphs 32, 33, 49, 56), the individual ML model being trained using historical process data or historical data (e.g., training data or historian data; paragraph 56) in order to reflect reaction kinetics or physio-chemical processes related to the respective production process (paragraphs 53, 54 and 63).
Regarding claim 18, Baseman teaches the method of claim 17, wherein the individual ML model is trained using training data (e.g., training data or historian data; paragraph 56) that include data from an appended object identifier (e.g., at 204, a product identifier and quality measure of the product identified by the product identifier; paragraph 53), the training being done via the computing unit (controller 112; paragraph 28; figure 1A).
Regarding claim 19, Baseman teaches the method of claim 18, wherein the industrial plant comprises an Internet-of-Things (IoT) Edge device or component (paragraphs 18, 27, 52) and wherein the underlying ML system (e.g., a machine learning model or decision tree model; paragraphs 32, 33, 49, 56) is implemented to find or create an algorithm, which is deployed to the IoT Edge device or component, in order to use the accordingly created or found algorithm for controlling the IoT Edge device.
Regarding claim 20, Baseman teaches the method of claim 18, wherein providing an abstraction layer which includes an object database (paragraphs 54) and which serves as an abstraction layer for the production equipment, for the corresponding input materials and for package-related data.
Regarding claim 21, Baseman teaches the method of claim 20, wherein the abstraction layer connects to certain processing and/or ML components within a Cloud computing platform (paragraph 66), wherein for this connection, a data streaming protocol is used, and wherein streamed and received product data is used by the ML system to find or create algorithms for getting additional data related to an underlying chemical product, wherein the additional data concern predictable product quality control (QC) data of the underlying chemical product (e.g., at 204, a product identifier and quality measure of the product identified by the product identifier; paragraph 53).
Regarding claim 23, Baseman teaches the method of claim 18, wherein the training data for training the ML model also comprise historical and/or current laboratory test data, or data from the past and/or recent samples (paragraph 56),
Regarding claim 25, Baseman teaches throughout the publication a system for improving a production process for manufacturing a chemical product at an industrial plant (paragraphs 3, 4, 18, 24, 25, 26 and 81) by processing at least one input material via at least one equipment (e.g., processing units; paragraph 51), the system comprising one or more computing units (controller 112; paragraph 28; figure 1A), wherein the system is configured to:
receive, via an input interface, real-time process data from the equipment (at 202; paragraphs 29 and 51; figure 2);
determine, via any of the computing units, a subset of the real-time process data (at 208; paragraph 55; figure 2); the subset of the real-time process data being indicative of the process parameters and/or equipment operating conditions that the input material is processed under; and
compute, using at least a part of the subset of the real-time process data, at least one state related to the input material and/or the equipment (e.g., paragraphs 56 and 57).
Regarding claim 26, Baseman teaches a computer program, or a non-transitory computer readable medium storing the program (paragraphs 75 – 81), comprising instructions which, when executed by any one or more suitable computing units (controller 112; paragraph 28; figure 1A), operatively coupled to at least one equipment (e.g., processing units; paragraph 51) for manufacturing a chemical product at an industrial plant by processing least one input material using a production process, causes any of the computing units to:
receive, via an input interface, real-time process data from the equipment (at 202; paragraphs 29 and 51; figure 2);
determine a subset of the real-time process data (at 208; paragraph 55; figure 2); the subset of the real-time process data being indicative of the process parameters and/or equipment operating conditions that the input material is processed under; and
compute, using at least a part of the subset of the real-time process data, at least one state related to the input material and/or the equipment (e.g., paragraphs 56 and 57).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN J. SINES whose telephone number is (571)272-1263. The examiner can normally be reached 9 AM-5 PM EST M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Elizabeth A Robinson can be reached at (571) 272-7129. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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BRIAN J. SINES
Primary Patent Examiner
Art Unit 1796
/BRIAN J. SINES/Primary Examiner, Art Unit 1796