DETAILED ACTION
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 1-29 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 written description 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.
The claims recite “customized machine learning algorithm”, but the original disclosure does not disclose any details in regard to customized machine learning algorithm (see Ariad, 598 F.3d at 1349-50, 94 USPQ2d (2010) at 1171 (2010), “an adequate written description of a claimed genus requires more than a generic statement of an invention’s boundaries.”). As an example, beginning on page 12 of the Original Disclosure states “The system further comprises a machine learning-based mathematical model for providing a range of temperatures of the external surface of the vessel over such region of interest correlated to a level of risk of operation of the vessel. This model is developed and trained using a dataset comprising data that include refractory material size….and molten materials, according to machine learning techniques,” but the Original Disclosure does not further discloses any required details on machine learning-based mathematical model itself or how the model uses the data to train, etc and use it to determine the risk as claimed. As such, due to lack of details in regard to machine learning-based mathematical model and training, the original disclosure amounts to nothing more than a “wish” or “plan” of generically using the customized machine learning algorithm (see Ariad, Id., at 1179, “Rather, we held that original claim language to “a DNA coding for interferon activity” failed to provide an adequate written description as it amounted to no more than a “wish” or “plan” for obtaining the claimed DNA rather than a description of the DNA itself.” and Fiers vs Revel, 984 F2d 1164, 25 USPQ2d 1601, Id., at 1605 "As we stated in Amgen and reaffirmed above, such a disclosure just represents a wish or arguably a plan, for obtaining the DNA") and outlining of the goals (see In re Wilder, 736 F2d 1516, 222 USPQ 369, Id., at 372-373 “In our view the board correctly read the Objects of the Invention as doing little more than outlining goals appellants hope the claimed invention achieves and the problems the invention will hopefully ameliorate. But the invention that achieves these general objectives must still be described.)
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 1-29 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 is an apparatus claim, but the limitation “a customized machine learning algorithm,” is not an apparatus; rather, it is an algorithm. As such, claim 1 is mixing an apparatus with an algorithm, which is indefinite. Similarly, claim 22 is indefinite, as it is a method claims, but is mixing the customized machine learning algorithm, as well as mixing with the apparatus limitation, “a data processing subsystem comprising…”
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.
6. Claims 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without being integrated into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Utilizing the two step process adopted by the Supreme Court (Alice Corp vs CLS Bank Int'l, US Supreme Court, 110 USPQ2d 1976 (2014) and the recent 101 guideline, Federal Register Vol. 84, No., Jan 2019)), determination of the subject matter eligibility under the 35 USC 101 is as follows: Specifically, the Step 1 requires claim belongs to one of the four statutory categories (process, machine, manufacture, or composition of matter). If Step 1 is satisfied, then in the first part of Step 2A (Prong one), identification of any judicial recognized exceptions in the claim is made. If any limitation in the claim is identified as judicial recognized exception, then proceeding to the second part of Step 2A (Prong two), determination is made whether the identified judicial exception is being integrated into practical application. If the identified judicial exception is not integrated into a practical application, then in Step 2B, the claim is further evaluated to see if the additional elements, individually and in combination, provide “inventive concept” that would amount to significantly more than the judicial exception. If the element and combination of elements do not amount to significantly more than the judicial recognized exception itself, then the claim is ineligible under the 35 USC 101.
Looking at the claims, the claims satisfy the first part of the test 1A, namely the claims are directed to two of the four statutory class, apparatus and method. In Step 2A Prong one, we next identify any judicial exceptions in the claims. In Claim 1 (as a representative example), we recognize that the limitations “a customized machine learning-based algorithm, process a first set of data comprising a first of said at least two groups of temperatures measured over said region or interest of said external surface of said vessel, second set of data comprising at least one operational parameter related to a processing of said one or more types of molten material, and a third set of data comprising said measured set of temperatures of said region of interest of said external surface of said vessel, and to operate said customized machine learning-based algorithm, wherein said risk of operation of said vessel is calculated, in real time while said vessel is in operation processing said one or more types of molten material, based on a correlation of said second of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel and a range of variations from said first of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel with a level of an element selected from a group consisting of said risk of operation of said vessel and a penetration of said one or more types of molten material within said refractory material of said vessel, according to at least one output of said customized machine learning-based algorithm, and wherein said first set of data, said second set of data, and said third set of data for at least one of a plurality of vessels, including said vessel, are processed using said customized machine learning-based algorithm to create a customized machine learning-based mathematical model,” are abstract ideas, as they involve a combination of mental process and usage of mathematical concept. Furthermore, the lack of detail on the customized machine-learning-based algorithm adds to the abstractness of the algorithm. Similar rejections are made for other independent and dependent claims. With the identification of abstract ideas, we proceed to Step 2A, Prong two, where with additional elements and taken as a whole, we evaluate whether the identified abstract idea is being integrated into a practical application.
In Step 2A, Prong two, the claims additionally recite “manufacturing vessel comprises a refractory material having at least one internal wall and at least
one external wall opposite said at least one internal wall, wherein said at least one internal wall of said refractory material of said vessel is exposed to one or more types of molten material different from said refractory material, said system comprising: a. a thermal scanning subsystem comprising at least one first sensor to collect data for measuring at least two groups of temperatures over a region of interest of an external surface of said vessel,” “at least one first sensor comprises an element selected from a group of an infrared camera, a thermal scanner, a thermal imaging camera, and a mesh formed by one or more sections of optical fiber laid out in proximity to said external surface of said vessel,” “at least one second sensor to collect information related to an element selected from a group of said first set of data and said second set of data, at least one second sensor comprises an element selected from a group of an ultrasound unit, a laser scanner, a LIDAR device, a radar, and a stereovision camera,” “at least one second sensor is disposed in a location selected from a group consisting of being in physical contact with said refractory material, being at least partly embedded in said refractory material, and being at least partly embedded in at least one casted portion of said refractory material,” “one laser scanner configured to perform one or more laser scans of a predefined area of said at least one internal wall of said refractory material while said vessel is empty,” “first of said plurality of laser scans and performing a second of said plurality of laser scans,” but said limitations are merely directed to insignificant data collection activity involving manufacturing vessel. The claims additionally recite “a data processing system comprising a computer-based processor and data storage,” but said limitations are merely general-purpose computer for implementing the abstract idea. Furthermore, the claims additionally recite “enhancing a maintenance plan of said vessel, after calculating said risk of operation of said vessel,” but said limitation is an insignificant post-solution activity, recited at high level of generality. The claims do not improve the functioning of any sensors or machines. The claims also do not improve other technology, as the claimed invention simply uses measured sensor data in generic machine-learning (despite the fact that it labels it as “customized machine learning”. The claims also do not improve the machine learning itself. At most, the claim is an improvement in the abstract idea of determining the risk of operation of manufacturing vessel. However, new or improved abstract idea is still abstract idea, and not eligible under the 101. In short, the claims do not provide sufficient evidence to show that they are more than a drafting effort to monopolize the abstract idea. As such, the abstract idea is not integrated into a practical application. Consequently, with the identified abstract idea not being integrated into a practical application, we proceed to Step 2B and evaluate whether the additional elements provide “inventive concept” that would amount to significantly more than the abstract idea.
In Step 2B, the claims additionally recite ““manufacturing vessel comprises a refractory material having at least one internal wall and at least one external wall opposite said at least one internal wall, wherein said at least one internal wall of said refractory material of said vessel is exposed to one or more types of molten material different from said refractory material, said system comprising: a. a thermal scanning subsystem comprising at least one first sensor to collect data for measuring at least two groups of temperatures over a region of interest of an external surface of said vessel,” “at least one first sensor comprises an element selected from a group of an infrared camera, a thermal scanner, a thermal imaging camera, and a mesh formed by one or more sections of optical fiber laid out in proximity to said external surface of said vessel,” “at least one second sensor to collect information related to an element selected from a group of said first set of data and said second set of data, at least one second sensor comprises an element selected from a group of an ultrasound unit, a laser scanner, a LIDAR device, a radar, and a stereovision camera,” “at least one second sensor is disposed in a location selected from a group consisting of being in physical contact with said refractory material, being at least partly embedded in said refractory material, and being at least partly embedded in at least one casted portion of said refractory material,” “one laser scanner configured to perform one or more laser scans of a predefined area of said at least one internal wall of said refractory material while said vessel is empty,” “first of said plurality of laser scans and performing a second of said plurality of laser scans,” but said limitations are merely directed to data collection activity involving manufacturing vessel that are well-understood, routine and conventional. The additionally recite “a data processing system comprising a processor and data storage,” but said limitations are merely general-purpose computer that is also well-understood, routine and conventional. Furthermore, the claims additionally recite “enhancing a maintenance plan of said vessel, after calculating said risk of operation of said vessel,” but said limitation is an insignificant post-solution activity, recited at high level of generality, without a particular end use. As such, the claims do not provide additional elements that would amount to significantly more than the abstract idea.
In Summary, the claims recite abstract idea without being integrated into a practical application, and do not provide additional elements that would amount to significantly more than the abstract idea. As such, taken as a whole, the claims are ineligible under the 35 USC 101.
Claim Rejections - 35 USC § 103
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.
Claims 1-12, 15, 22-23 and 25-29 are rejected under 35 U.S.C. 103 as being unpatentable over Lammer et al., US-PGPUG 2016/0282049 (hereinafter Lammer) (cited by the Applicant) in views of Bonin et al., US-PGPUB 2013/0120738 (hereinafter Bonin) and Gerritsen et al., US Pat No. 7,976,770 (hereinafter Gerritsen) (cited by the Applicant)
Regarding Claims 1 and 22. Lammer discloses calculating a risk of operation of a manufacturing vessel (Abstract; Paragraph [0002]), wherein said manufacturing vessel comprises a refractory material having at least one internal wall and at least one external wall opposite said at least one internal wall, wherein said at least one internal wall of said refractory material of said vessel is exposed to one or more types of molten material different from said refractory material (Paragraph [0002]; Fig. 1), comprising
b. a customized machine learning-based algorithm (Claim 9, machine learning-based algorithm in the form of neural network, in actual operation); and c. a data processing subsystem comprising a computer-based processor further comprising a data storage device and an executable computer code configured to process a first set of data, comprising a first temperatures measured over said region of interest of said external surface of said vessel corresponding to at least one prior heat of said vessel, a second set of data comprising at least one operational parameter related to a processing of said one or more types of molten material (Paragraph [0017]; Paragraph [0018] and Claim 1, production data during use, including amount of molten, temperature and temperature profiles, composition of molten, treatment time, tapping times, etc., Paragraph [0019],wall thickness of the lining at the point of greatest wear, etc; Paragraph [0020]), and to operate said customized machine learning-based algorithm (Claim 9, neural network), according to at least one output of said customized machine learning-based algorithm, and wherein said first set of data, said second set of data, and said third set of data are processed using said customized machine learning-based algorithm to create a customized machine learning-based mathematical model (Paragraphs [0021]-[0022]; Claims 1, 8 and 9, calculation model generated from the measured data with usage of neural network)
Lammer does not disclose a thermal scanning subsystem comprising at least one first sensor to collect data for measuring at least two groups of temperatures of a region of interest of an external surface of said vessel, and a third set of data comprising a second of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel corresponding to a current heat of an ongoing campaign of said vessel, and wherein said risk of operation of said vessel is calculated in real time while said vessel is in operation processing said one or more types of molten material, based on a correlation of said second of said at least two groups of temperatures measured of said region of interest of said external surface of said vessel and a range of variations from said first of said at least two groups of temperatures measured over a region of interest of said external surface of said vessel with a level of element selected from a group consisting of said risk of operating one or more vessels, and a penetration of said one or more types of molten material within said refractory material of said vessel (claim 18: d. determining a distribution of ranges of said first of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel associated to said level of said risk of operation of said vessel, according to said machine learning-based mathematical model, wherein said distribution of ranges of said first of said at least two groups of
temperatures measured over said region of interest of said external surface of said vessel provides an expected safe range of said second of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel while said vessel is in operation processing said one or more types of molten material and said level of said risk of operating said vessel.)
Bonin discloses monitoring the integrity of the manufacturing vessel (Paragraphs [0011]-[0013]) comprising a refractory material having at least one internal wall and at least one external wall opposite said at least one internal wall, wherein said at least one internal wall of said refractory material of said vessel is exposed to a molten material different from said refractory material (Paragraphs [0004]; Fig. 1), said system comprising: a. a thermal scanning subsystem comprising at least one first sensor to collect data for measuring at least two groups of temperatures over a region of interest of an external vessel (Fig. 2, thermographic system 21, and laser system 25; Paragraph [0016]; [0027], [0030], laser scanner 25; Paragraph [0031], combination of thermographic system 21 and laser scanner 25; Fig. 4, different temperatures ranges on the y-axis, or groups of temperature ranges, such as 1700-1500, 1500-1300, etc.), and a third set of data comprising a second of said at last two groups of temperature measured over said region of interest of said external surface of said vessel (Fig. 4, different temperature ranges on the y-axis, or groups of temperature ranges), and wherein said risk of operation of said vessel is calculated, in real time while said vessel is in operation processing said one or more types of molten material, based on a correlation of said measured set of temperatures of said region of interest of said external surface of said vessel and a range of variations of said measured set of temperatures with a level of risk of operating one or more vessels, including said vessel, (Claim 18: determining a distribution of ranges of said set of temperatures corresponding to said region of interest of said external surface of said vessel associated to a level of said risk of operation of said vessel, according to said machine learning-based mathematical model, wherein said distribution of ranges of said first of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel provides an expected safe range of said second of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel while said vessel is in operation processing said one or more types of molten material and said level of said risk of operating said vessel (Paragraph [0031], correlating internal refractory thickness with the external temperature verifies the internal thickness measurements; Paragraph [0032], correlation between hot spots and local reduction in thickness; Paragraph [0036]-[0037], lining profile with temperature profile as shown in Figs 4 and 5; Fig. 6-7),
Gerritsen disclose diagnostic system for determining the operating condition of a metallurgical vessel in real time (Abstract; Fig. 1; Col. 4, lines 22-27; Col. 3, lines 12-27; Background section)
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Bonin and Gerritsen in Lammer and have a thermal scanning subsystem comprising at least one first sensor to collect data for measuring at least two groups of temperatures of a region of interest of an external surface of said vessel, and a third set of data comprising a second of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel corresponding to a current heat of an ongoing campaign of said vessel, and wherein said risk of operation of said vessel is calculated in real time while said vessel is in operation processing said one or more types of molten material, based on a correlation of said second of said at least two groups of temperatures measured of said region of interest of said external surface of said vessel and a range of variations from said first of said at least two groups of temperatures measured over a region of interest of said external surface of said vessel with a level of element selected from a group consisting of said risk of operating one or more vessels, and a penetration of said one or more types of molten material within said refractory material of said vessel (claim 22: d. determining a distribution of ranges of said first of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel associated to said level of said risk of operation of said vessel, according to said machine learning-based mathematical model, wherein said distribution of ranges of said first of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel provides an expected safe range of said second of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel while said vessel is in operation processing said one or more types of molten material and said level of said risk of operating said vessel, so as to accurately monitoring the integrity of the vessel in real time.
Regarding Claim 2. Bonn discloses said first set of data further comprises at least one element selected from a group consisting of a number of heats undergone by said vessel, a contact time of said one or more types of molten material with said refractory material of said vessel, and a thickness of said refractory material of said vessel, corresponding to said at least one prior heat of said vessel, wherein said at least one prior heat of said vessel is immediately preceding said current heat of said ongoing campaign (Paragraphs [0009]; [0011])
Regarding Claim 3. Lammer discloses said executable computer code operates said customized machine learning-based algorithm by providing one or more inputs to be used by said customized machine learning-based algorithm to create said machine learning-based mathematical model and by processing one or more outputs of said customized machine learning-based mathematical model (Claim 9, neural network)
Regarding Claim 4. Lammer discloses said first set of data comprises at least one element selected from a group consisting of a remaining thickness, a rate of degradation, an erosion profile of said at least one internal wall, a type, a quality, an original and an actual chemical composition, an operational age, and a number of heats of, a presence of one or more cracks in, and a level or rate of penetration of said one or more types of molten material into said refractory material before operating said vessel, a historical information related to a maintenance of an outer shell material of said vessel, including its audit reports, age, design and observed geometrical variations, ahistorical information related to a maintenance of said refractory material including a type, an amount, and a location of one or more additives and one or more replaced parts applied to said refractory material, a physical design of said refractory material, said at least one operational parameter, and at least one operational parameter in addition to said at least one operational parameter, corresponding to a prior operation of said at least one of said plurality of vessels, including said vessel (Paragraph [0019],wall thickness of the lining at the point of greatest wear)
Regarding Claim 5. Lammer discloses said physical design of said refractory material comprises one or more elements selected from a group consisting of said type, a shape, a dimension, a number of layers, and a layout of a physical disposition of said refractory material of said at least one of said plurality of vessels, including said vessel Fig. 1), and Bonin likewise discloses in Paragraph [0036], number of layers, etc)
Regarding Claim 6. Lammer discloses said second set of data comprises at least one element selected from a group consisting of a remaining thickness of said refractory material prior to operating said vessel; an amount, an average and a peak processing temperatures; a heating and a cooling temperature profiles; a set of treatment times for said one or more types of molten material being or to be processed using said vessel; a type and a chemical composition of said one or more types of molten material being or to be processed using said vessel; a thickness and a composition of a slag buildup in said at least one internal wall of said refractory material of said vessel; an ambient temperature surrounding said vessel; a number of tapping times using said vessel; a pouring and a tapping method for said one or more types of molten material to be poured and tapped into and out of said vessel; a preheating temperature profile while said vessel is empty; a time during which said one or more types of molten material is in contact with said refractory material; a stirring time; intensity of stirring; a flow rate of inert gas applied to said vessel during stirring; an electric power applied; duration of electric power applied; duration of time between two tappings; a physical and a chemical set of attributes and amounts of one or more additives used or to be used in processing said one or more types of molten material to process a desired grade of said one or more types of molten material; said at least one operational parameter; and at least one operational parameter in addition to said at least one operational parameter, for processing said one or more types of molten material using said at least one of said plurality of vessels, including said vessel (Paragraph [0018], amount of molten, temperature, composition of molten, treatment time, tapping times, etc.; Paragraph [0019],wall thickness of the lining at the point of greatest wear
Regarding Claim 7. Lammer discloses customized machine learning-based model is created by determining a correlation of said first set of data and said second set of data (Paragraphs [0021]-[0022]; Claim 9, generating neural network model)
Lammer does not disclose said third set of data for at least one element selected from a group of one or more types of said refractory material.
Bonin discloses one or more types of said refractory material (Paragraph [0036], refractory brick; Paragraph [0004], refractory material in brick form or cast in monolithic blocks)
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Bonin in Lammer and have the machine learning-based model created by determining a correlation of said first set of data and said second set of data and third set of data for at least one element selected from a group of one or more types of said refractory material, so as to accurately monitor the integrity of the vessel.
Regarding Claim 8. Bonin discloses said data processing subsystem is configured to process said at least two groups of temperatures over said region of interest of said external surface of said vessel, corresponding to a plurality of residual thicknesses of said region of interest of said external surface of said vessel for said at least one prior heat and said current heat of said vessel, to calculate said risk of operation of said vessel based on a variability of said at least two groups of temperatures over said region of interest of said external surface of said vessel for said at least one prior heat and said current heat of said vessel (Paragraph [0031], correlating internal refractory thickness with the external temperature verifies the internal thickness measurements; Paragraph [0032], correlation between hot spots and local reduction in thickness; Paragraph [0036]-[0037], lining profile with temperature profile as shown in Figs 4 and 5, including previous melting cycle; Fig. 6-7)
Regarding Claim 9. Bonin discloses said at least one first sensor comprises an element selected from a group consisting of an infrared camera, a thermal scanner, a thermal imaging camera, and a mesh formed by one or more sections of optical fiber laid out in proximity to said external surface of said vessel (Paragraph [0027], Fig. 2, infrared camera as radiation detector 22)
Regarding Claim 10. Bonin discloses at least one second sensor to collect information related to an element selected from a group consisting of said first set of data and said second set of data Paragraph [0027], refractory thickness measuring system 25, or laser as described in Paragraph [0030])
Regarding Claim 11. Bonin discloses said at least one second sensor comprises an element selected from a group consisting of an ultrasound unit, a laser scanner, a LIDAR device, a radar, and a stereovision camera (Paragraph [0027], refractory thickness measuring system 25, or laser as described in Paragraph [0030])
Regarding Claim 12. Bonin discloses said second sensor comprises at least one laser scanner configured to perform a plurality of laser scans of a predefined area of said at least one internal wall of said refractory material while said vessel is empty (Fig. 2).
Regarding Claim 15. Bonin discloses correlation between hot spots and local reduction in lining thickness wherein said data processing subsystem further comprises a second-level algorithm for identifying a potential development of a hotspot in a specific locality of said region of interest of said external surface of said vessel and said data processing subsystem is further configured to perform an action selected from a group consisting of estimating a remaining operational life of said vessel and enhancing a maintenance plan of said vessel, after calculating said risk of operation of said vessel (Paragraph [0032], correlating between hot spots and local reduction in lining thickness)
Regarding Claim 23. Bonin discloses e. measuring said second of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel while said vessel is in operation processing said one or more types of molten material, f. comparing said second of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel while said vessel is in operation processing said one or more types of molten material with said distribution of ranges of said first of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel, g. calculating said level of said risk of operation of said vessel, according to said comparison of said second of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel while said vessel is in operation processing said one or more types of molten material with said distribution of ranges of said first of said at least two groups of temperatures measured over said region of interest of said external surface of said vessel (Fig. 4-5, different groups of temperature ranges, and their comparisons; Paragraph [0031], correlating internal refractory thickness with the external temperature verifies the internal thickness measurements; Paragraph [0032], correlation between hot spots and local reduction in thickness; Paragraph [0036]-[0037], lining profile with temperature profile as shown in Figs 4 and 5; Fig. 6-7),
Regarding Claim 25. Bonn discloses said at least one first sensor comprises an element selected from a group consisting of an infrared camera, a thermal scanner, a thermal imaging camera, and a mesh formed by one or more sections of optical fiber laid out in proximity to said external surface of said vessel (Paragraph [0027], Fig. 2, infrared camera as radiation detector 22)
Regarding Claim 26. Bonn discloses said executable computer code is further configured to operate at least one signal processing method selected to process data according to a characteristic of said refractory material of said vessel (Paragraph [0036]-[0037], type of refractory material, namely refractory brick. Although Bonin does not disclose selecting a method as claimed, with different type of refractory material, such as cast in monolithic block, it would have been obvious to use different processing method taking into consideration that different types of refractory material respond differently to operation temperature cycle, resulting in different thickness and temperature profiles)
Regarding Claim 27. Bonin discloses a second-level algorithm for identifying a potential development of a hotspot in a specific locality of said region of interest of said external surface of said vessel (Paragraph [0032], correlating between hot spots and local reduction in lining thickness)
Regarding Claim 28. Bonn discloses a second sensor is used to collect at least a portion of an element selected from a group consisting of said first set of data and said second set of data, and wherein said at least one second sensor comprises an element selected from a group consisting of an ultrasound unit, a laser scanner, a LIDAR device, a radar, and a stereovision camera (Paragraph [0027], refractory thickness measuring system 25, or laser as described in Paragraph [0030])
Regarding Claim 29. Bonn discloses said first set of data comprises at least one element selected from a group consisting of a remaining thickness, a rate of degradation, an erosion profile of said at least one internal wall, a type, a quality, an original and an actual chemical composition, an operational age, and a number of heats of, a presence of one or more cracks in, and a level or rate of penetration of said one or more types of molten material into said refractory material before operating said vessel, a historical information related to a maintenance of an outer shell material of said vessel, including its audit reports, age, design and observed geometrical variations, a historical information related to a maintenance of said refractory material including a type, an amount, and a location of one or more additives and one or more replaced parts applied to said refractory material, a physical design of said refractory material, said at least one operational parameter, and at least one operational parameter in addition to said at least one operational
parameter, corresponding to a prior operation of said at least one of said plurality of vessels, including said vessel (Paragraph [0019],wall thickness of the lining at the point of greatest wear); wherein said second set of data comprises at least one element selected from a group consisting of a remaining thickness of said refractory material prior to operating said vessel; an amount, an average and a peak processing temperatures; a heating and a cooling temperature profiles; a set of treatment times for said one or more types of molten material being or to be processed using said vessel; a type and a chemical composition of said one or more types of molten material being or to be processed using said vessel; a thickness and a composition of a slag buildup in said at least one internal wall of said refractory material of said vessel; an ambient temperature surrounding said vessel; a number of tapping times using said vessel; a pouring and a tapping method for said one or more types of molten material to be poured and tapped into and out of said vessel; a preheating temperature profile while said vessel is empty; a time during which said one or more types of molten material is in contact with said refractory material; a stirring time; intensity of stirring; a flow rate of inert gas applied to said vessel during stirring; an electric power applied; duration of electric power applied; duration of time between two tappings; a physical and
a chemical set of attributes and amounts of one or more additives used or to be used in processing said one or more types of molten material to process a desired grade of said one or more types of molten material; said at least one operational parameter; and at least one operational parameter in addition to said at least one operational parameter, for processing said one or more types of molten material using said at least one of said plurality of vessels, including said vessel; and wherein said third set of data includes said measured set of temperatures of said region of interest of said external surface of said refractory material (Paragraph [0018], amount of molten, temperature, composition of molten, treatment time, tapping times, etc.)
9. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Lammer et al., US-PGPUG 2016/0282049 in views of Bonin, US-PGPUB 2013/0120738 and Gerritsen, US Pat No. 7,976,770 as applied to Claim 12, and further in view of Makupa et al., “A non-destructive technique for health assessment of fire-damaged concrete elements using terrestrial laser scanning,” J. Civil Struct Health Monit (2016) (hereinafter Makupa)
Regarding Claim 13. Bonin discloses laser scanning a vessel has undergone a plurality of heats in between performing a first of said plurality of laser scans to calculate the remaining thickness (Paragraph [0031]; Figs. 4-5). Note: Bonin discusses the affect of heating on the refractory materials, showing the visual contrast between heating effect on different parts of the refractory materials. As such, one of ordinary skill in the art would recognize that there would be structural contrast likewise between the pre-heating and after-heating in the refractory materials even without doing multiple laser scanning.
The modified Lammer does not disclose performing a second of said plurality of laser scans
Makupa discloses the thermal effect on concrete structures by laser scanning prior to heat and after the heat (Page 668, Section 2.4, Heating exposure technique, Pages 671-672, Section 6.1, Results section)
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Makupa in the modified Lammer and wherein said vessel has undergone a plurality of heats in between performing a first of said plurality of laser scans and performing a second of said plurality of laser scans to calculate the remaining thickness of said refractory material, so as to easily visualize the heating effect on the thickness.
10. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Lammer et al., US-PGPUG 2016/0282049 in views of Bonin, US-PGPUB 2013/0120738 and Gerritsen, US Pat No. 7,976,770 as applied to Claim 11, and further in view of Chandra et al., US-PGPUB 2021/0018469 (hereinafter Chandra)
Regarding Claim 14. The modified Lammer does not disclose said at least one second sensor is disposed in a location selected from a group of being in physical contact with said refractory material, being at least partly embedded in said refractory material, and being at least partly embedded in at least one casted portion of said refractory material.
Chndra discloses acousto-ultrasonic technique to measure thickness of the manufacturing vessel (Paragraph [0005]), where the ultrasonic sensors would at least be physically contact with the refractor material to measure vibration.
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Chandra in modified Lammer and have at least one second sensor disposed in a location selected from a group of being in physical contact with said refractory material, being at least partly embedded in said refractory material, and being at least partly embedded in at least one casted portion of said refractory material, so as to accurately determine the integrity of the vessel.
11. Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Lammer et al., US-PGPUG 2016/0282049 in views of Bonin, US-PGPUB 2013/0120738 and Gerritsen, US Pat No. 7,976,770 as applied to Claim 22 above, and further in view of Richter, US-PGPUB 2021/0096093 (cited by the Applicant).
Regarding Claim 24. Bonin discloses processing at least one element selected from a group consisting of said first set of data, said second set of data, said third set of data, a range of normal temperatures over said region of interest of said external surface of said vessel corresponding to said current heat of said ongoing campaign, and said level of said risk of operating said vessel to analyze, and provide information to perform an action selected from a group consisting of estimating a remaining operational life of said vessel and improving a maintenance plan of said vessel (Figs. 4-6; Paragraph [0032])
The modified Lammer does not disclose forecasting.
Richer discloses predicting the future status of a refractory lining (Abstract; Fig. 3, 6; Paragraphs [0085]-[0092]; [0011])
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Richter in the modified Lammer and perform processing at least one element selected from a group consisting of said first set of data, said second set of data, said third set of data, a range of normal temperatures over said region of interest of said external surface of said vessel corresponding to said current heat of said ongoing campaign, and said level of said risk of operating said vessel to analyze, and provide information to perform an action selected from a group consisting of estimating a remaining operational life of said vessel and improving a maintenance plan of said vessel, with efficiency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Trojer et al., US-PGPUB 2021/0098143
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/HYUN D PARK/Primary Examiner, Art Unit 2857
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