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 § 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 – 16, 18 and 19 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 monitoring and/or controlling 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, 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), the method at least partially being performed via a computing unit (controller 112; paragraph 28; figure 1A), wherein the method comprising:
receiving, via an input interface, real-time process data from the equipment (at 202; paragraphs 29 and 51; figure 2);
determining, via the computing unit, 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
providing as output data, via an output interface, the subset of the real-time process data (at 302 and display panel at 308 and 312; paragraph 63; figure 3).
Regarding claim 2, Baseman teaches the method of claim 1, wherein the method further comprises: computing, via the computing unit, at least one performance parameter of the chemical product related to the input material, the computing being performed based on the subset of the real-time process data and historical process data (at 304; paragraph 63; figure 3).
Regarding claim 3, Baseman teaches the method of claim 2, wherein the output data include the at least one performance parameter (at 304; paragraph 63; figure 3).
Regarding claim 4, Baseman teaches the method of claim 1, wherein the method further comprises: providing, via an interface an object identifier comprising input material data; wherein the input material data is indicative of one or more properties of the input material (e.g., at 204, a product identifier and quality measure of the product identified by the product identifier; paragraph 53).
Regarding claim 5, Baseman teaches the method of claim 4, wherein the method further comprises: appending, to the object identifier, the subset of the real-time process data (e.g., at 204, a product identifier and quality measure of the product identified by the product identifier; paragraph 53).
Regarding claim 6, Baseman teaches the method of claim 4, wherein the method further comprises: appending, to the object identifier, the at least one performance parameter (e.g., at 204, a product identifier and quality measure of the product identified by the product identifier; paragraph 53).
Regarding claim 7, 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 8, Baseman teaches the method of claim 1, wherein the processing of the at least two packages (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) is managed by means of corresponding data objects, 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 9, Baseman teaches the method of claim 1, wherein a data object is generated in response to a trigger signal being provided via the equipment (e.g., the IoT device connectivity message platform is configured to communicate with sensors at all stages to receive real-time operating condition data of a stage when a product is being processing; paragraph 52; e.g., at 204, a product identifier and quality measure of the product identified by the product identifier; paragraph 53).
Regarding claim 10, Baseman teaches the method of claim 9, wherein the trigger signal is provided in response to the output of a corresponding sensor being arranged at each of an equipment unit of the equipment (e.g., the IoT device connectivity message platform is configured to communicate with sensors at all stages to receive real-time operating condition data of a stage when a product is being processing; paragraph 52; 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 1, wherein the equipment comprises a plurality of physically separated equipment zones, such that the output data comprise sub-sets of the real-time process data from each of the equipment zones and/or at least one performance parameter computed at each of the equipment zones (e.g., the IoT device connectivity message platform is configured to communicate with sensors at all stages to receive real-time operating condition data of a stage when a product is being processing; paragraph 52; e.g., at 204, a product identifier and quality measure of the product identified by the product identifier; paragraph 53).
Regarding claim 12, Baseman teaches the method of claim 1, wherein the output data form a time-dependent data stream (e.g., the IoT connectivity messaging platform is configured to control and receive real time sensor data from the sensors at every specified interval of time, for example, continuously; paragraph 52).
Regarding claim 13, Baseman teaches the method of claim 1, wherein the output data and/or the time-dependent data stream (e.g., the IoT connectivity messaging platform is configured to control and receive real time sensor data from the sensors at every specified interval of time, for example, continuously; paragraph 52) are/is provided to a human machine interface ("HMI") system (e.g., display 28; paragraph 73).
Regarding claim 14, Baseman teaches the method of claim 13, wherein the HMI system is at least partially a display device (e.g., display 28; paragraph 73).
Regarding claim 15, Baseman teaches the method of claim 13, wherein the HMI system is at least partially an augmented reality ("AR") and/or virtual reality ("VR") device (e.g., the computer system can also communicate with external devices 26 such as a display 28 or other known external device that enable the computer system to communicate with a one or more other computing devices or users, which would implicitly include an AR or VR device; paragraphs 67 and 73).
Regarding claim 16, Baseman teaches the method of claim 13, wherein the HMI system is at least partially an audio device (the computer system can include other peripheral devices such as personal computer systems which include an audio device comprising a speaker; paragraphs 67 and 73).
Regarding claim 18, Baseman teaches a system for monitoring and/or controlling a production process, the system being configured to perform the method of claim 1 (figure 5; paragraphs 67 – 71).
Regarding claim 19, Baseman teaches a computer program, or a non-transitory computer readable medium storing the program, comprising instructions which, when the program is executed by a suitable computing, cause the computing unit to carry out the method of claim 1 (paragraphs 71 and 76 – 80).
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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BRIAN J. SINES
Primary Patent Examiner
Art Unit 1796
/BRIAN J. SINES/Primary Examiner, Art Unit 1796