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 .
Specification
The lengthy specification has not been checked to the extent necessary to
determine the presence of all possible minor errors. Applicant's cooperation is
requested in correcting any errors of which applicant may become aware in the
specification.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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 3 and 5-7 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 claims 3, 5 and 6, the phrase "or alike" renders the claim(s) indefinite because the claim(s) include(s) elements not actually disclosed (those encompassed by "or alike"), thereby rendering the scope of the claim(s) unascertainable. See MPEP § 2173.05(d).
Regarding claim 7, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
As to claim 1,
Step 1: Claim 1 is directed to a system. Therefore, the claim is eligible under Step 1 for being directed to machine.
Step 2A Prong One
Claim 1 recites
a) a front-end interface to receive a plurality of optimization constraints at the input form in text boxes of the visual display device; (input data)
b) a server database to store the data corresponding to each optimizing constraint; (store data, generic computer function)
c) a back-end interface that executes on the stored data to provide various recommendations using an optimizing model; (mental process and generic computer function)
and d) a communication network that transmits optimized recommendations to the visual display device, (output data)
wherein, the optimization system is characterized to solve multivariate third-degree polynomial equations using machine learning. (mental process)
The claimed concept is a method of providing recommendation by evaluating data based on mathematic relationship directed to “Mental Process” and/or “Mathematical Concepts” grouping. These limitations can be performed in a human mind or using pen and paper.
Therefore, claim 1 is an abstract idea.
Step 2A Prong Two
The receiving data step is recited at a high level of generality (i.e., as a general means of receiving input for use in the evaluation step) and amounts to mere data collecting, which is a form of insignificant extra-solution activity.
The displaying step of a model is recited at a high level of generality (i.e. as a general means of outputting data) and amounts to mere data outputting, which is a form of insignificant extra-solution activity.
The claim recites additional elements such as “a front-end interface, server database, back-end interface, communication network and visual display device”. Each of the additional limitations is no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. See applicant’s specification [0036] for generic computer description.
The judicial exception is not integrated into a practical application.
Step 2B:
The same analysis of Step 2A Prong Two applies here in 2B. The present claim does not recite any limitation that would integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(d).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, claim 1 is not patent eligible. Same conclusion for dependent claims of claim 1. See below.
2. The system as claimed in claim 1, wherein the optimization query is selected from a group of optimization constraints that are required to minimize the electricity consumption in caustic soda production. (mental process)
3. The system as claimed in claim 1, wherein the user can enter various optimizing constraints at the text box of the front-end interface including but not limited to minimum current density, maximum current density, membrane efficiency, K efficiency, Rectifier efficiency, power source, elements or alike for a plurality of electrolytes. (input data, data description)
4. The system as claimed in claim 1, wherein the recommendation optimized by the back-end interface includes aggregate required data of coal mix, power costs, power load, chlorine evacuation, and production cost for different caustic soda plants. (data description)
5. The system as claimed in claim 1, wherein the optimization system coding languages can be selected from Python, HTML, CSS, Javascript, JQuery, React, Angular JS, or alike. (mental process)
6. The system as claimed in claim 1, wherein the front-end and back-end framework can be selected from Django, ReactJS, Express, Rails, Laravel, Spring, Angular, HTML, Vue, Ember, Backbone, or alike. (mental process)
7. The system as claimed in claim 1, wherein the optimization system or at least a portion thereof can be implemented using one or more computing device or system that includes one or more server, such as network server or cloud servers including Google Cloud performance (GCP), Azure, and AWS. (mental process)
In particular, the claim limitations do not recite a combination of additional elements that tie or “integrate the invention into a practical application”.
Thus, claims 1-7 are not patent eligible.
Claim Rejections - 35 USC § 103
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 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, 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.
Claim(s) 1-3 and 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (US 20190041842 A1), hereinafter Cella, in view of Seibert et al (NPL: Optimal Feature Selection Based on Chemical Engineer Concepts and Proposal Soft Sensor to Predict f-CaO in Clinker Using Industrial Data, 2017), hereinafter Seibert.
Claim 1. Cella discloses An optimization system for optimizing a query configured by a client/user hosted at an input form, the input form is implementable on a visual display system and the visual display system is coupled to the optimization system, the system comprising:
Cella: [0178] [0182] Fig. 1-6 discloses optimization system comprises a front-end interface, server database, back-end interface, communication network and visual display device.
Cella discloses a) a front-end interface to receive a plurality of optimization constraints at the input form in text boxes of the visual display device;
Cella: [0220] “Embodiments of the methods and systems disclosed herein may include expert system GUIs. In embodiments, the system undertakes a graphical approach to defining smart bands and diagnoses for the expert system. The entry of symptoms, rules, or more generally smart bands for creating a particular machine diagnosis, may be tedious and time consuming. One means of making the process more expedient and efficient is to provide a graphical means by use of wiring. The proposed graphical interface consists of four major components: a symptom parts bin, diagnoses bin, tools bin, and graphical wiring area (“GWA”). … Each part may be assigned additional properties. For example, a spectral peak part may be assigned a frequency or order (multiple) of running speed. Some parts may be pre-defined or user defined such as a 1×, 2×, 3× running speed, 1×, 2×, 3× gear mesh, 1×, 2×, 3× blade pass, number of motor rotor bars x running speed, and so on.”
See [0189] for additional detail for GUI.
See [0184-0185] for additional detail for optimization.
Cella discloses b) a server database to store the data corresponding to each optimizing constraint;
Cella: [0384] “… In embodiments, data may then be fed into a raw data server 5058 which may store the stream data 5050 in a stream data repository 5060. In embodiments, this data storage area is typically meant for storage until the data is copied off of the DAQ instrument 5002 and verified. The DAQ API 5052 may also direct the local data control application 5062 to extract and process the recently obtained stream data 5050 and convert it to the same or lower sampling rates of sufficient length to effect one or more desired resolutions. …”
Cella discloses c) a back-end interface that executes on the stored data to provide various recommendations using an optimizing model; and
Cella: [0728] “The iteration of the expert system may result in any number of downstream actions based on analysis of data from the smart band. In an embodiment, the expert system may determine that the system should either keep or modify operational parameters, equipment or a weighting of a neural net model given a desired goal, such as a specified/threshold output production rate, specified/threshold generation rate, an operational efficiency/failure rate, a financial efficiency/profit goal, a power efficiency/resource utilization, an avoidance of a fault condition, an avoidance of a dangerous condition or catastrophic failure, and the like. In embodiments, the adjustments may be based on determining context of an industrial system, such as understanding a type of equipment, its purpose, its typical operating modes, the functional specifications for the equipment, the relationship of the equipment to other features of the environment (including any other systems that provide input to or take input from the equipment), the presence and role of operators (including humans and automated control systems), and ambient or environmental conditions. … The expert system may be seeded with a model for operation of the pipeline in a manner that results in a specified profit goal, such as indicating a given flow rate of material through the pipeline based on the current market sale price for the material and the cost of getting the material into the pipeline. As it acquires data and iterates, the model will predict whether the profit goal will be achieved given the current data. Based on the results of the iteration of the expert system, a recommendation may be made (or a control instruction may be automatically provided) to operate the pipeline at a higher flow rate, to keep it operational for longer or the like. …”
See [0310] for optimization model.
Cella discloses d) a communication network that transmits optimized recommendations to the visual display device,
Cella: [0395] “FIG. 25 depicts a display 5200 whose viewable content 5202 may be accessed locally or remotely, wholly or partially. In many embodiments, the display 5200 may be part of the DAQ instrument 5002, may be part of the PC or connected device 5038 that may be part of the DAQ instrument 5002, or its viewable content 5202 may be viewable from associated network connected displays. In further examples, the viewable content 5202 of the display 5200 or portions thereof may be ported to one or more relevant network addresses. …”
See [00849-0851] for additional detail for optimization recommendation.
Cella does not appear to explicitly disclose wherein, the optimization system is characterized to solve multivariate third-degree polynomial equations using machine learning.
However, Seibert disclose on (page 4)
PNG
media_image1.png
118
896
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Greyscale
See “Multiple Linear Regression (MLR) and Polynomial Regression (PolR)”
(page 11) and Table 1
PNG
media_image2.png
206
900
media_image2.png
Greyscale
Cella and Seibert are analogous art because they are from the “same field of endeavor” industrial data analysis.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Cella and Seibert before him or her, to modify the method of Cella to include the Multiple Linear Regression feature of Seibert because this combination improves the performance of optimization model.
The suggestion/motivation for doing so would have been Seibert (page 4) Linear Eqs are easy to solve and have low computational costs, so, with some simple mathematical manipulations, no-linear effects can be analyzed in a faster and more effective way when MLR is applied in combination with the linearization process [4] [10].
Therefore, it would have been obvious to combine Seibert to obtain the invention as specified in the instant claim(s).
Claim 2. The system as claimed in claim 1, Cella discloses wherein the optimization query is selected from a group of optimization constraints that are required to minimize the electricity consumption in caustic soda production.
Cella [1184-1185] “A hydrogen production and use system 1000 as disclosed herein may comprise one or more of the following elements as depicted in FIGS. 179 and 180. An electrolytic cell 1101 is detailed in FIG. 180, which shows an exploded view of the cell consisting of steel electrodes separated by nylon membranes inside polyvinyl chloride (“PVC”) gaskets sandwiched by acrylic sheets. The cell may comprise an alkaline electrolytic cell that separates water into its constituent components of hydrogen and oxygen. A mixture tank, such as a concentrated alkaline mixture tank may serve as the electrolyte source for the electrolytic cell. The alkali mixture may be prepared by mixing a base like potassium hydroxide (“KOH”) or sodium hydroxide (“NaOH”) with water. In case of KOH, in embodiments the concentration may be around 20%. The membrane for separation of gases within the cell may be made from a variety of materials. One such material is a nylon sheet with catalyst coating that has enough thread count to allow ion transfer and minimal gas transfer. … In this methodology, any additional electrolyte that flows out with the gas gets re-circulated into the alkaline mixture tank. The two bubbling tanks may be connected together, such as at the bottom, to ensure pressure maintenance across them. Dehumidifiers may also be included. The gas passed through the bubblers may have excess moisture content that reduces the combustion efficiency. … The power supply may supply a desired voltage that may be optimized according to the conditions of the system, such as the water temperature, pressure, etc. The voltage per cell may vary, such as from 1.4 v to 2.3 v, and the current density may be as low as 44 mA/cm.sup.2 for maximum efficiency. As the current density is low, the efficiency tends to be high.”
Claim 3. The system as claimed in claim 1, Cella discloses wherein the user can enter various optimizing constraints at the text box of the front-end interface including but not limited to minimum current density, maximum current density, membrane efficiency, K efficiency, Rectifier efficiency, power source, elements or alike for a plurality of electrolytes.
Cella: [1185] “The power supply may supply a desired voltage that may be optimized according to the conditions of the system, such as the water temperature, pressure, etc. The voltage per cell may vary, such as from 1.4 v to 2.3 v, and the current density may be as low as 44 mA/cm.sup.2 for maximum efficiency. As the current density is low, the efficiency tends to be high.”
Claim 5. The system as claimed in claim 1, Cella discloses wherein the optimization system coding languages can be selected from Python, HTML, CSS, Javascript, JQuery, React, Angular JS, or alike.
Cella [0411] “In embodiments, the LabVIEW™ tools may generate JSCRIPT™ code and JAVA™ code that may be edited postcompilation. The NXG™ tools may generate Web VI's that may not require any specialized driver and only some RESTful™ services which may be readily installed from any browser. It will be appreciated in light of the disclosure that because various applications may be run inside a browser, the applications may be run on any operating system, such as Windows™, Linux™, and Android™ operating systems especially for personal devices, mobile devices, portable connected devices, and the like.”
Claim 6. The system as claimed in claim 1, Cella discloses wherein the front-end and back-end framework can be selected from Django, ReactJS, Express, Rails, Laravel, Spring, Angular, HTML, Vue, Ember, Backbone, or alike.
Cella [0411] “In embodiments, portable connected devices 5850 such as a tablet 5852 and a smart phone 5854 may connect the CDMS 5832 using web APIs 5860 and 5862, respectively, as depicted in FIG. 35. The APIs 5860, 5862 may be configured to execute in a browser and may permit access via a cloud network facility 5870 of all (or some of) the functions previously discussed as accessible through the PARA Server 5800. In embodiments, computing devices of a user 5880 such as computing devices 5882, 5884, 5888 may also access the cloud network facility 5870 via a browser or other connection in order to receive the same functionality. In embodiments, thin-client apps which do not require any other device drivers and may be facilitated by web services supported by cloud services 5890 and cloud data 5892. In many examples, the thin-client apps may be developed and reconfigured using, for example, the visual high-level LabVIEW™ programming language with NXG™ Web-based virtual interface subroutines. In embodiments, thin client apps may provide high-level graphing functions such as those supported by LabVIEW™ tools. …”
Claim 7. The system as claimed in claim 1, Cella discloses wherein the optimization system or at least a portion thereof can be implemented using one or more computing device or system that includes one or more server, such as network server or cloud servers including Google Cloud performance (GCP), Azure, and AWS.
Cella [0411] “In embodiments, portable connected devices 5850 such as a tablet 5852 and a smart phone 5854 may connect the CDMS 5832 using web APIs 5860 and 5862, respectively, as depicted in FIG. 35. The APIs 5860, 5862 may be configured to execute in a browser and may permit access via a cloud network facility 5870 of all (or some of) the functions previously discussed as accessible through the PARA Server 5800. In embodiments, computing devices of a user 5880 such as computing devices 5882, 5884, 5888 may also access the cloud network facility 5870 via a browser or other connection in order to receive the same functionality. In embodiments, thin-client apps which do not require any other device drivers and may be facilitated by web services supported by cloud services 5890 and cloud data 5892. In many examples, the thin-client apps may be developed and reconfigured using, for example, the visual high-level LabVIEW™ programming language with NXG™ Web-based virtual interface subroutines. In embodiments, thin client apps may provide high-level graphing functions such as those supported by LabVIEW™ tools. …”
Allowable Subject Matter
Claim 4 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and overcome 101 rejection.
The following is a statement of reasons for the indication of allowable subject matter:
Cella et al (US 20190041842 A1) teaches an intelligent system which include machine learning systems, such as for learning on one or more data sets. The one or more data sets may include information collected using local data collection systems or other information from input sources, such as to recognize states, objects, events, patterns, conditions, or the like that may, in turn, be used for processing by the host system as inputs to components of the platform and portions of the industrial IoT data collection, monitoring and control system, or the like. Learning may be human-supervised or fully-automated, such as using one or more input sources to provide a data set, along with information about the item to be learned. Machine learning may use one or more models, rules, semantic understandings, workflows, or other structured or semi-structured understanding of the world, such as for automated optimization of control of a system or process based on feedback or feed forward to an operating model for the system or process.
Seibert et al (NPL: Optimal Feature Selection Based on Chemical Engineer Concepts and Proposal Soft Sensor to Predict f-CaO in Clinker Using Industrial Data, 2017) teaches a method for investigating six months of industrial data with a combination of deep knowledge of the system and statical tools to determine the optimal feature set between operational and quality parameters that impact the f-CaO at clinker; with the features sub-set defined, the second step was applying the machine learning techniques in the data to develop a soft sensor to predict f-CaO. The results obtained in the Multiple Linear Regression model demonstrated the importance of the feature selection step.
Constantz et al (US 2011/0277474 A1) teaches a method for determining energy consumption of separating carbon dioxide from the multi-component gaseous stream and assessing the determined energy consumption of separating carbon dioxide from the multi-component gaseous stream to identify any desired adjustments to the energy consumption.
These references taken either alone or in combination with the prior art of record fail to disclose limitations, including:
Claim 4. “wherein the recommendation optimized by the back-end interface includes aggregate required data of coal mix, power costs, power load, chlorine evacuation, and production cost for different caustic soda plants.”
in combination with the remaining elements and features of the claimed invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHUEN-MEEI GAN whose telephone number is (469)295-9127. The examiner can normally be reached Monday-Friday 9:00 am to 4:00 pm EST.
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/CHUEN-MEEI GAN/Primary Examiner, Art Unit 2189