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 .
This action is in response to the applicant’s communication filed on 11/18/2024
Claims 1-11 are pending
Claim Objections
Claim 6 objected to because of the following informalities: “a water temperature, a damper angle” should be amended to “a water temperature, and a damper angle” for proper grammatical form. Appropriate correction is required.
Claim 7 objected to because of the following informalities: “installed IN the field” should be amended to “installed in the field”. Appropriate correction is required.
Claim 8 objected to because of the following informalities: “a part … another part … a still another part” is grammatically awkward and does not clearly identify the respective portions of the collected operation data. It is suggested that the claim be amended to recite “a first part”, “a second part”, and “a third part” respectively. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “communication unit” in claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 2, 7, and 8 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 2 recites “equally controlling boilers that are designated as operating boilers”. It is unclear what aspect of the boilers is required to be controlled equally, for example, whether the boilers are required to have equal loads, equal outputs, equal fuel inputs, equal control commands, or some other equal operating parameter. The specification states that the boilers are “equally controlled” but does not further define or provide objective boundaries for the term.
Claim 7 recites the limitation "the field" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites that “a still another part is reprocessed with a last value before collection”. It is unclear what is meant by “before collection” because claim 7 recites that the operation data is reprocessed after being collected, and it is unclear whether “a last value before collection” refers to a last value preceding a current data sample, a unit-time collection interval, a missing data point, or some other collection event. Although the specification describes an ffill operation in which “the collected data is reprocessed with a last value”, the specification does not clarify the meaning of “before collection”.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2 and 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Batey et al. US 4,864,972 A (hereinafter Batey) in view of Manoharan et al. USPGPUB 2021/0303998 A1 (hereinafter Manoharan).
Regarding claim 1, Batey teaches a boiler control method comprising (Col. 3, lines 4-6, “subject invention provides a method and apparatus for determining optimum boiler combination for operation in a multi-boiler heating plant”; Col. 4, lines 47-51, “the computer 100 selects the boiler or boiler combination for operation at the current temperature and signals the boiler controller 110 through computer interface 109 for automatic operation of the boilers selected):
collecting operation data of boilers (Col. 4, lines 58-64, “the fuel consumption for each boiler per degree day is continuously monitored as is the current outdoor air temperature. Thus, fuel flow meters 122, 124, 126, 128 attached to the flow inlet of each boiler is inputted into the computer 100 which processes the data to adjust the fuel consumption profile of the boilers in operation”);
deriving operating boiler combinations (Col. 4, lines 31-36, “computer aided analysis is employed to determine the boiler selection based on current outdoor air temperature and fuel consumption per degree day for each possible boiler combination.”; Col. 4, lines 46-48, “Based on the fuel consumption profile 116, the computer 100 selects the boiler or boiler combination for operation at the current temperature.” – Batey’s computer analyzes the possible boiler combinations and selects the boiler or boiler combination for operation, which corresponds to deriving operating boiler combinations because the operating boiler combination is determined from the possible boiler combinations based on the operating data.); and
controlling operation of the boilers according to the derived operating boiler combinations (Col. 4, lines 47-51, “the computer 100 selects the boiler or boiler combination for operation at the current temperature and signals the boiler controller 110 through computer interface 109 for automatic operation of the boilers selected”).
Batey does not explicitly teach inputting the collected operation data to an AI model that is trained to receive operation data and to derive operating boiler combinations.
However, Manoharan teaches inputting the collected operation data to an AI model that is trained to receive operation data (Par. [0004], “obtaining, via one or more hardware processors, an input data comprising time series data pertaining (i) design parameters of a first set of chillers deployed in a building, (ii) a leaving chilled water temperature (LCWT) of each of the first set of chillers, and (iii) required cooling load provided by each of the first set of chillers collected for a pre-defined time-interval; training, via the one or more hardware processors, a pre-trained transfer learning (TL) model based on the input data to obtain a re-trained TL model … reading, by the deep RL agent executed by the one or more hardware processors and deployed in the first set of target chillers, (i) a leaving chilled water temperature (LCWT), (ii) a returned chilled water temperature (RCWT) of each of the first set of chillers, and (iii) ambient temperature associated with the building”; Par. [0056], “The learned RL agent ( e.g., trained RL agent) can suggest the chiller ON/OFF sequence as well a LCWT setpoint from the given chiller state and cooling load requirement” – the time series data and the LCWT, RCWT, ambient temperature, chiller state, and cooling load requirement correspond to the operation data received by the trained AI model, and the chiller ON/OFF sequence corresponds to the operating boiler combinations in the modified Batey system.).
Batey and Manoharan are analogous art because they contain functional similarities. They both relate to determining which units of a multiple-unit thermal system should operate based on system operating conditions and load requirements to reduce energy consumption.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above multiple-boiler control method, as taught by Batey, and incorporate the trained reinforcement-learning based ON/OFF combination selection technique, as taught by Manoharan.
One of ordinary skill in the art would have been motivated to improve the boiler combination selection by considering future operating states and operational constraints, as suggested by Manoharan (Par. [0076]).
Regarding claim 2, the combination of Batey and Manoharan teaches all the limitations of the base claims as outlined above.
Batey further teaches wherein controlling comprises equally controlling boilers that are designated as operating boilers in the operating boiler combinations (Col. 6, lines 61-68, “when more than one boiler is operated simultaneously, the effect of various loading levels on the multiple boilers (50-50, 40-60, 60-40, 30-70, 70-30, for example) is monitored at each degree day interval to identify the optimal fuel valve position for the optimum boilers at each degree day (outdoor air temperature) interval. Automatic variation of the fuel valve positions is made by the computer” – the 50-50 loading level corresponds to equally controlling the boilers because, when two boilers are operated simultaneously at a 50-50 loading level, each operating boiler is controlled to provide an equal portion of the load.), and
wherein collecting comprises collecting the operation data from any one of the operating boilers (Col. 4, lines 58-64, “the fuel consumption for each boiler per degree day is continuously monitored as is the current outdoor air temperature. Thus, fuel flow meters 122, 124, 126, 128 attached to the flow inlet of each boiler is inputted into the computer 100 which processes the data to adjust the fuel consumption profile of the boilers in operation”).
Regarding claim 10, Batey teaches a boiler control system comprising (Col. 3, lines 4-6, “subject invention provides a method and apparatus for determining optimum boiler combination for operation in a multi-boiler heating plant”; Col. 4, lines 47-51, “the computer 100 selects the boiler or boiler combination for operation at the current temperature and signals the boiler controller 110 through computer interface 109 for automatic operation of the boilers selected”):
a control system configured to collect operation data of boilers (Col. 4, lines 58-64, “the fuel consumption for each boiler per degree day is continuously monitored as is the current outdoor air temperature. Thus, fuel flow meters 122, 124, 126, 128 attached to the flow inlet of each boiler is inputted into the computer 100 which processes the data to adjust the fuel consumption profile of the boilers in operation”), and to derive operating boiler combinations (Col. 4, lines 31-36, “computer aided analysis is employed to determine the boiler selection based on current outdoor air temperature and fuel consumption per degree day for each possible boiler combination.”; Col. 4, lines 46-48, “Based on the fuel consumption profile 116, the computer 100 selects the boiler or boiler combination for operation at the current temperature.” - Batey’s computer analyzes the possible boiler combinations and selects the boiler or boiler combination for operation, which corresponds to deriving operating boiler combinations because the operating boiler combination is determined from the possible boiler combinations based on the operating data.); and
a controller configured to control operations of the boilers according to the operating boiler combinations derived by the control system (Col. 4, lines 47-51, “the computer 100 selects the boiler or boiler combination for operation at the current temperature and signals the boiler controller 110 through computer interface 109 for automatic operation of the boilers selected” – computer 100 corresponds to the control system because computer 100 selects the boiler or boiler combination for operation, and boiler controller 110 corresponds to the controller because computer 100 signals boiler controller 110 for automatic operation of the selected boilers. Thus, boiler controller 110 controls operation of the boilers according to the operating boiler combination derived by computer 100.).
Batey does not explicitly teach inputting the collected operation data to an AI model that is trained to receive operation data and to derive operating boiler combinations.
However, Manoharan teaches inputting the collected operation data to an AI model that is trained to receive operation data (Par. [0004], “obtaining, via one or more hardware processors, an input data comprising time series data pertaining (i) design parameters of a first set of chillers deployed in a building, (ii) a leaving chilled water temperature (LCWT) of each of the first set of chillers, and (iii) required cooling load provided by each of the first set of chillers collected for a pre-defined time-interval; training, via the one or more hardware processors, a pre-trained transfer learning (TL) model based on the input data to obtain a re-trained TL model … reading, by the deep RL agent executed by the one or more hardware processors and deployed in the first set of target chillers, (i) a leaving chilled water temperature (LCWT), (ii) a returned chilled water temperature (RCWT) of each of the first set of chillers, and (iii) ambient temperature associated with the building”; Par. [0056], “The learned RL agent ( e.g., trained RL agent) can suggest the chiller ON/OFF sequence as well a LCWT setpoint from the given chiller state and cooling load requirement” – the time series data and the LCWT, RCWT, ambient temperature, chiller state, and cooling load requirement correspond to the operation data received by the trained AI model, and the chiller ON/OFF sequence corresponds to the operating boiler combinations in the modified Batey system).
Batey and Manoharan are analogous art because they contain functional similarities. They both relate to determining which units of a multiple-unit thermal system should operate based on system operating conditions and load requirements to reduce energy consumption.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above multiple-boiler control system, as taught by Batey, and incorporate the trained reinforcement-learning based ON/OFF combination selection technique, as taught by Manoharan.
One of ordinary skill in the art would have been motivated to improve the boiler combination selection by considering future operating states and operational constraints, as suggested by Manoharan (Par. [0076]).
Regarding claim 11, Batey teaches an optimal boiler operation number control system comprising (Col. 3, lines 4-6, “subject invention provides a method and apparatus for determining optimum boiler combination for operation in a multi-boiler heating plant”; Col. 4, lines 47-51, “the computer 100 selects the boiler or boiler combination for operation at the current temperature and signals the boiler controller 110 through computer interface 109 for automatic operation of the boilers selected):
a communication unit configured to collect operation data of boilers (Col. 4, lines 41-44, “The current air temperature is continuously monitored by conventional temperature sensor 114 inputted into the computer.”; Col. 4, lines 61-64, “fuel flow meters 122, 124, 126, 128 attached to the flow inlet of each boiler is inputted into the computer 100 which processes the data to adjust the fuel consumption profile of the boilers in operation”; Col. 9, lines 2-5, “fuel flow meters on the fuel inlet of each boiler in communication with said computer to provide said computer with the current fuel consumption of the boilers in operation” – the sensor and meter communication inputs to computer 100 correspond to the communication unit configured to collect boiler operation data.);
a processor configured to derive operating boiler combinations and to derive operating boiler combinations (Col. 4, lines 31-36, “computer aided analysis is employed to determine the boiler selection based on current outdoor air temperature and fuel consumption per degree day for each possible boiler combination.”; Col. 4, lines 46-48, “Based on the fuel consumption profile 116, the computer 100 selects the boiler or boiler combination for operation at the current temperature.” – computer 100 analyzes the possible boiler combinations and selects the boiler or boiler combination for operation, which corresponds to a processor configured to derive operating boiler combinations because computer 100 determines the operating boiler combination from the possible boiler combinations based on the operating data.).
Batey does not explicitly teach inputting the collected operation data to an AI model that is trained to receive operation data.
However, Manoharan teaches inputting the collected operation data to an AI model that is trained to receive operation data (Par. [0004], “obtaining, via one or more hardware processors, an input data comprising time series data pertaining (i) design parameters of a first set of chillers deployed in a building, (ii) a leaving chilled water temperature (LCWT) of each of the first set of chillers, and (iii) required cooling load provided by each of the first set of chillers collected for a pre-defined time-interval; training, via the one or more hardware processors, a pre-trained transfer learning (TL) model based on the input data to obtain a re-trained TL model … reading, by the deep RL agent executed by the one or more hardware processors and deployed in the first set of target chillers, (i) a leaving chilled water temperature (LCWT), (ii) a returned chilled water temperature (RCWT) of each of the first set of chillers, and (iii) ambient temperature associated with the building”; Par. [0056], “The learned RL agent ( e.g., trained RL agent) can suggest the chiller ON/OFF sequence as well a LCWT setpoint from the given chiller state and cooling load requirement” – the time series data and the LCWT, RCWT, ambient temperature, chiller state, and cooling load requirement correspond to the operation data received by the trained AI model, and the chiller ON/OFF sequence corresponds to the operating boiler combinations in the modified Batey system).
Batey and Manoharan are analogous art because they contain functional similarities. They both relate to determining which units of a multiple-unit thermal system should operate based on system operating conditions and load requirements to reduce energy consumption.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above multiple-boiler control system, as taught by Batey, and incorporate the trained reinforcement-learning based ON/OFF combination selection technique, as taught by Manoharan.
One of ordinary skill in the art would have been motivated to improve the boiler combination selection by considering future operating states and operational constraints, as suggested by Manoharan (Par. [0076]).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Batey et al. US 4,864,972 A (hereinafter Batey) in view of Manoharan et al. USPGPUB 2021/0303998 A1 (hereinafter Manoharan), and further in view of Park et al. USPGPUB 2020/0166206 A1 (hereinafter Park).
Regarding claim 3, the combination of Batey and Manoharan teaches all the limitations of the base claims as outlined above.
Batey and Manoharan do not explicitly teach wherein the collected operation data is operation data which has a correlation with boiler efficiency greater than or equal to a reference value.
However, Park teaches wherein the collected operation data is operation data which has a correlation with boiler efficiency greater than or equal to a reference value (Par. [0038], “the real-time data includes operation data and a state binary value of the boiler. The operation data includes a value measured through a plurality of sensors with respect to the boiler, and a control value for controlling the boiler.”; Par. [0041], “The data analyzer 220 analyzes a correlation between the data based on a tag of the data, clusters the data, and selects input data whose correlation degree is a predetermined value or more for model output data through the correlation analysis for the design of the combustion model.” – In the combined system, Park’s correlation analysis is applied to Batey’s boiler efficiency, such that the selected operation data has a correlation with boiler efficiency greater than or equal to the reference value.).
Batey, Manoharan, and Park are analogous art because they contain functional similarities. They all relate to controlling and optimizing thermal systems based on collected operating data.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above AI-based multiple boiler control method, as taught by Batey and Manoharan, and incorporate selecting operation data having a correlation with boiler efficiency greater than or equal to a predetermined value, as taught by Park.
One of ordinary skill in the art would have been motivated to improve the selection of operation data used for boiler optimization, as suggested by Park (Par. [0041]).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Batey et al. US 4,864,972 A (hereinafter Batey) in view of Manoharan et al. USPGPUB 2021/0303998 A1 (hereinafter Manoharan) and Park et al. USPGPUB 2020/0166206 A1 (hereinafter Park), and further in view of Kobayashi JPH 0420701 A (hereinafter Kobayashi).
Regarding claim 4, the combination of Batey, Manoharan, and Park teaches all the limitations of the base claims as outlined above.
Batey, Manoharan, and Park do not explicitly teach wherein the boiler efficiency is calculated by the following equation:
Boiler Efficiency=(Water Supply for Unit Time)/(Amount of Fuel for Unit Time).
However, Kobayashi teaches wherein the boiler efficiency is calculated by the following equation:
Boiler Efficiency=(Water Supply for Unit Time)/(Amount of Fuel for Unit Time) (Page 4, “the boiler efficiency η is the evaporation multiple E. , / B, that is, by measuring El and B, the boiler efficiency can be calculated”; Page 4, “B = the amount of fuel consumed by boiler I”; Page 5, “The load may be measured as the water supply amount W instead of the evaporation amount E”; Page 5, “8.8 ', 8 "is a fuel flow detecting device, 9.9', 9" is a steam flow detecting device, 10.10 ', 10 "is a feed water flow detecting device” – Kobayashi teaches calculating boiler efficiency from the ratio E/B, and teaches using water supply amount W instead of evaporation amount E, and measures both water supply and fuel using respective flow-rate detectors, thereby providing water supply and fuel amounts per unit time.).
Batey, Manoharan, Park, and Kobayashi are analogous art because they contain functional similarities. They all relate to controlling and optimizing boiler or thermal system operation based on operating data and efficiency.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above AI-based multiple boiler control method, as taught by Batey, Manoharan, and Park, and incorporate calculating boiler efficiency based on water supply amount per unit time and fuel amount per unit time, as taught by Kobayashi.
One of ordinary skill in the art would have been motivated to improve the determination and control of boiler efficiency during actual operation, as suggested by Kobayashi (Page 4).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Batey et al. US 4,864,972 A (hereinafter Batey) in view of Manoharan et al. USPGPUB 2021/0303998 A1 (hereinafter Manoharan) and Park et al. USPGPUB 2020/0166206 A1 (hereinafter Park), and further in view of Zhang et al. CN 111006240 A (hereinafter Zhang).
Regarding claim 5, the combination of Batey, Manoharan, and Park teaches all the limitations of the base claims as outlined above.
Batey, Manoharan, and Park do not explicitly teach wherein the correlation between the operation data and the boiler efficiency is analyzed through a PCC method.
However, Zhang teaches wherein the correlation between the operation data and the boiler efficiency is analyzed through a PCC method (Page 5, “Perform a pairwise correlation calculation on the independent variable X and the furnace temperature Y to obtain the Pearson correlation coefficient”; Page 3, “The Pearson correlation calculation formula is used to calculate the correlation between the independent variable and the furnace temperature, and the correlation coefficient is obtained to determine the influence of the selected variable operating parameters on the furnace temperature” – in the combined system, Zhang’s Pearson correlation coefficient method is applied to Park’s correlation analysis, wherein the correlated output corresponds to Batey’s boiler efficiency, such that the correlation between the operation data and boiler efficiency is analyzed through a PCC method.).
Batey, Manoharan, Park, and Zhang are analogous art because they contain functional similarities. They all relate to analyzing boiler or thermal-system operating data for optimizing operation.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above AI-based multiple boiler control method, as taught by Batey, Manoharan, and Park, and incorporate using a Pearson correlation coefficient method to perform correlation analysis, as taught by Zhang.
One of ordinary skill in the art would have been motivated to reduce the amount of input data and calculation required for boiler-data analysis, as suggested by Zhang (Page 3).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Batey et al. US 4,864,972 A (hereinafter Batey) in view of Manoharan et al. USPGPUB 2021/0303998 A1 (hereinafter Manoharan) and Park et al. USPGPUB 2020/0166206 A1 (hereinafter Park), and further in view of Chen et al. USPGPUB 2022/0187065 A1 (hereinafter Chen) and Maeng et al. USPGPUB 2020/0173650 A1 (hereinafter Maeng).
Regarding claim 6, the combination of Batey, Manoharan, and Park teaches all the limitations of the base claims as outlined above.
Batey further teaches wherein collecting comprises collecting a boiler pressure, an exhaust gas temperature, a boiler body temperature (Col. 5, line 65 – Col. 6, line 2, “other boiler efficiency parameters can be measured and monitored by sensors 150 and fed to computer 100. Thus, flue gas temperature, flue gas oxygen flue gas CO, opacity, boiler temperature and/or (02) pressure, and other boiler efficiency parameters can be monitored” – Batey collects fuel-consumption data through the respective fuel-flow meter of each boiler, which corresponds to collecting the operation data from any one of the operating boilers. Flue gas temperature corresponds to the exhaust gas temperature, boiler temperature corresponds to the boiler body temperature, and pressure corresponds to the boiler pressure.), and an external air temperature (Col. 5, lines 32-34, “a computer system continuously receives fuel use and outdoor air temperature information”).
Batey, Manoharan, and Park do not explicitly teach collecting a scale temperature, a water temperature, and a damper angle.
However, Chen teaches collecting a scale temperature (Par. [0075], “determining that a scale generation amount in the water heating device reaches a preset threshold when at least one of the following judgment conditions is met: the first temperature is not less than a preset temperature threshold;”; Par. [0080], “the first temperature of the heat exchange zone may be acquired using the first temperature detector 1, and the first temperature is different before and after scaling in the heat exchange zone. The first temperature acquired after scaling is higher than that before scaling.” – the first temperature corresponds to the scale temperature because measured temperature is used to determine the scale generation amount.) and a water temperature (Par. [0081], “the second temperature indicating the water temperature in the water heating device may be acquired using the second temperature detector 2”).
Batey, Manoharan, Park, and Chen are analogous art because they contain functional similarities. They all relate to collecting and analyzing operating data of boilers or other thermal systems for monitoring, optimization, or control.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above AI-based boiler control method, as taught by Batey, Manoharan, and Park, and incorporate collecting scale-related temperature and water temperature, as taught by Chen.
One of ordinary skill in the art would have been motivated to improve monitoring operating conditions affecting heat-transfer efficiency and facilitate timely detection and removal of scale, as suggested by Chen (Par. [0003]; Par. [0038]).
Batey, Manoharan, Park, and Chen do not explicitly teach collecting a damper angle.
However, Maeng teaches collecting a damper angle (Par. [0063], “Examples of the input data may include damper angles of a primary air and a secondary air, a damper angle of a combustion air nozzle (OFA)”; Par. [0049], “The operation data of the boiler includes measurements received from various sensors installed in the currently operating boiler, or control values that can be monitored by the boiler control system”; Par. [0055], “the pre-processor 20 serves to collect data associated with the operation of the boiler and process the data into a form suitable for future modeling” – the damper angles are input data included in the boiler operation data collected by the pre-processor, which corresponds to collecting a damper angle.).
Batey, Manoharan, Park, Chen, and Maeng are analogous art because they contain functional similarities. They all relate to collecting and analyzing operating data of boilers or other thermal systems for monitoring, optimization, or control.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above AI-based boiler control method, as taught by Batey, Manoharan, Park, and Chen, and incorporate collecting damper angle data, as taught by Maeng.
One of ordinary skill in the art would have been motivated to improve combustion efficiency by collecting additional boiler operating data for use in boiler modeling and optimization, as suggested by Maeng (Par. [0047]).
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Batey et al. US 4,864,972 A (hereinafter Batey) in view of Manoharan et al. USPGPUB 2021/0303998 A1 (hereinafter Manoharan), and further in view of Herlocker et al. USPGPUB 2022/0261393 A1 (hereinafter Herlocker).
Regarding claim 7, the combination of Batey and Manoharan teaches all the limitations of the base claims as outlined above.
Batey further teaches collecting operation data from boilers installed IN the field (Col. 4, lines 58-64, “the fuel consumption for each boiler per degree day is continuously monitored as is the current outdoor air temperature. Thus, fuel flow meters 122, 124, 126, 128 attached to the flow inlet of each boiler is inputted into the computer 100 which processes the data to adjust the fuel consumption profile of the boilers in operation” – the fuel-consumption and outdoor air temperature data correspond to operation data collected from boilers installed and operating in the boiler heating plant.)
Batey and Manoharan do not explicitly teach wherein the AI model is trained with operation data which is reprocessed after being collected.
However, Herlocker teaches wherein the AI model is trained with operation data which is reprocessed after being collected (Par. [0023], “the sensor data 206 originate at the physical system 200 which includes assets 202 and sensors 204. The sensor measurement data 206 is then transmitted to sensor data storage 208 (e.g., cloud storage). In response to queries, portions of sensor data are extracted from the storage and transmitted as sensor data streams 220 to a processor 216 that performs analysis and monitoring computations.”; Par. [0025], “the sensor data streams 106 that were retrieved from the data store are automatically synchronized and upsampled, if needed, to ensure that they have the same period and starting times. The resulting synchronized and resampled sensor data streams 112 are then used” – Herlocker collects and stores sensor operation data and subsequently reprocesses the collected data by synchronizing and resampling the data; Par. [0072], “A row stream can be constructed by specifying multiple metric streams as an input … Each input metric stream is automatically synchronized as needed”; Par. [0078], “Row streams may be used to build machine learning models over data derived from multiple entities” – the synchronized metric streams containing the sensor data are used to construct a row stream, and the resulting row stream is used to build the machine learning model.).
Batey, Manoharan, and Herlocker are analogous art because they contain functional similarities. They all relate to collecting operating or sensor data from physical systems and processing the collected data for computerized analysis.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above AI-based boiler control method, as taught by Batey and Manoharan, and incorporate reprocessing the collected operation data before using the data to train the AI model, as taught by Herlocker.
One of ordinary skill in the art would have been motivated to improve the ability to reliably process operation data obtained from multiple sensors by synchronizing and resampling the collected data to have the same period and starting times, as suggested by Herlocker (Par. [0025]).
Regarding claim 8, the combination of Batey, Manoharan, and Herlocker teaches all the limitations of the base claims as outlined above.
Herlocker further teaches wherein a part of the collected operation data is reprocessed by summing for a unit time (Par. [0046], “The duration of the window of the resulting stream can also be specified”; Par. [0047], “mean(FLOW) over 1 h”; Par. [0048], “Window operations produce a single value from all the values of one or more streams within a specified window. Examples of window operations are as follows:”; Par. [0051], “sum: The sum of the non-missing values” - Herlocker teaches a specified one-hour unit-time window and teaches summing the values within a specified window.), and another part is reprocessed into a median value within the unit time (Par. [0048] – [0057], “Window operations produce a single value from all the values of one or more streams within a specified window. Examples of window operations are as follows: … median: Median of the non-missing values.”), and a still another part is reprocessed with a last value before collection (Par. [0035], “time series fill may be used to interpolate between pre-existing values of the stream in one of two methods. Filled in points may either use linear interpolation or a fill-forward approach where the last known value is repeated. The default is fill-forward” – “a last value before collection” is interpreted to encompass a previously known value preceding the value being supplied during reprocessing. The fill-forward operation reprocesses the time-series data using the last previously known value.).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Batey et al. US 4,864,972 A (hereinafter Batey) in view of Manoharan et al. USPGPUB 2021/0303998 A1 (hereinafter Manoharan), and further in view of Hyodo USPGPUB 2016/0201895 A1 (hereinafter Hyodo).
Regarding claim 9, the combination of Batey and Manoharan teaches all the limitations of the base claims as outlined above.
Batey and Manoharan do not explicitly teach wherein the boilers have a common steam header, and boilers are additionally installed.
However, Hyodo teaches wherein the boilers have a common steam header, and boilers are additionally installed (Par. [0026], “The boiler system 1 includes a boiler group 2 mixedly provided with step value control boilers 20A and proportional control boilers 20B, a steam header 6 configured to collect steam generated by the plurality of boilers 20A and 20B”; Par. [0030], “The steam header 6 is connected, through a steam pipe 11, to each of the boilers 20A and 20B included in the boiler group 2.”; Par. [0038], “The boiler group 2 according to the present embodiment includes three step value control boilers 20A and two proportional control boilers 20B” – Hyodo provides a plurality of boilers together in the boiler group and connects each of the boilers to the common steam header, which corresponds to boilers being additionally installed in the common-header boiler system.).
Batey, Manoharan, and Hyodo are analogous art because they contain functional similarities. They all relate to selecting and controlling combinations of multiple heating or cooling units based on system operating conditions and load requirements.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above AI-based boiler control method, as taught by Batey and Manoharan, and incorporate installing a plurality of boilers connected to a common steam header, as taught by Hyodo.
One of ordinary skill in the art would have been motivated to improve the supply of steam from the plurality of boilers by regulating pressure differences and pressure variations among the boilers, as suggested by Hyodo (Par. [0031]).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Petrus et al. [USPGPUB 2021/0149350 A1] teaches a machine-learning based heating-plant control system that uses boiler operating/performance data to predict boiler energy consumption and the quantity of boilers required to operate to satisfy a building load.
Pouchak et al. [USPGPUB 2005/0230490 A1] teaches controlling a multi-boiler system by determining the number of boilers to operate based on heating load and operating the boilers according to an efficiency-optimized staging scheme.
Conclusion
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/PETER XU/ Examiner, Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119
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