Notice of Pre-AIA or AIA Status
Claims 1-20 are currently presented for Examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/03/2022, 07/01/2022 and 08/07/2025 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Form PTO-1449 is signed and attached hereto.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
The certified copy has been filed in parent Application No. TW110138945, filed on 10/20/2021.
Claim objections
Claims 1-20 are objected to because of the following informalities:
The claims have numerous issues with antecedent basis. The Examiner suggests amending the claims such that the first recitation of each distinct element uses articles such as “a”/”an”, later recitations referring back to the same distinct element uses articles such as “the”/”said”, to use disambiguating modifiers (e.g., first, second, etc.) when there are multiple distinct elements with the same base term, and that the use of modifiers for each distinct element is kept consistent. Below is a non-exhaustive list of examples of these issues: Claim 2 recites the limitation " the setting of the heat cycle system”. There is insufficient antecedent basis for this limitation in the claim. Claim 4 recites the limitation " the second data”. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
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 1-20 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.
(Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 15-20 is directed to system or machine that falls on one of statutory category.
Claims: 1-14 are directed to method or process that falls on one of statutory category.
Claim 1 recites
Step 2A, Prong 1
A method for building a temperature prediction model applicable to a heat cycle system,
wherein the method is used to measure a temperature of the heat cycle system to generate a measured temperature data, (the act of conceptually obtaining or recording that data is a mental process, and the recording can be done on paper. A person use a pen and paper to record the measured temperature data from a thermometer.)
and compute response time of the heat cycle system, (This is a calculation based on data, which can be done mentally or using mathematical formulas on paper. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas)
and the method comprises: aligning the measured temperature data and a setting value of the heat cycle system to generate a training data according to the response time; (Organizing and formatting the measured data according to the computed response time is an act of organizing information, performable mentally or on paper. It requires a mental process of comparison and synchronization by conceptually linking two disparate datasets (one measured, one a set point) based on a shared timeline and the calculated response time. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas)
and building the temperature prediction model according to a statistic model and the training data. (The development and application of a statistical model is a mathematical method and an abstract idea itself, which can be formulated and solved using mathematical principles on paper. For a simple linear model, we use a calculator and paper to calculate the values that minimize the error between the model's predictions and the actual data points. These calculations are a form of pure mental/manual computation. The idea of using measured data and response time to train a statistical model for prediction is an abstract concept of how to process information.)
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. The limitation such as “measure a temperature of the heat cycle system to generate a measured temperature data” can also be viewed as merely collecting data and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g). Claim do not recite additional elements that integrate the judicial exception. Thus, claim 1 is directed to abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the limitation such as “measure a temperature of the heat cycle system to generate a measured temperature data” can also be viewed as merely collecting data and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g) and is well-understood, routine or conventional. ((See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) Claim therefore, when taken as a whole, does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Thus, claim 1 is directed to abstract idea.
Claim 2 further recites wherein the heat cycle system comprises a heater, a heat-consuming machine, a delivery pipe, and a return pipe; the heater is configured to heat a thermal medium and transport the thermal medium with a raising temperature through the delivery pipe; the heat-consuming machine is configured to consume thermal energy of the thermal medium for processing and transport the thermal medium with a dropping temperature through the return pipe; The idea of a heat cycle system is purely a mental construct. It's an abstract design or a theoretical description of how such a system would function. The initial conception of this system in one's mind (imagining the flow of a thermal medium, the transfer of heat, etc.) is a pure mental process. A person can draw the system's components (heater, machine, pipes) and use arrows to represent the flow of the thermal medium and energy. This is a common method for engineering and design. Claim limitation “aligning the measured temperature data and the setting of the heat cycle system to generate the training data according to the response time comprises: determining a first operation node and a first response node of the heat cycle system, wherein the first operation node locates at a position where the heat-consuming machine outputs the thermal medium; obtaining an operating temperature data of the first operation node by a first temperature sensor, and obtaining a response temperature data of the first response node by a second temperature sensor, wherein the response temperature data comprises a plurality of response temperatures of the first response node at a plurality of time points; and performing following steps by a processor: obtaining a heater setting data of the heater, wherein the heater setting data comprises a plurality of heater settings of the heater at the plurality of time points; obtaining a machine setting data of the heat-consuming machine, wherein the machine setting data comprises a plurality of machine settings of the heat-consuming machine at the plurality of time points; measuring first response time between the first operation node and the first response node; and performing first data alignment according to the first response time to shift the plurality of response temperatures of the plurality of time points so as to align the plurality of response temperatures with the plurality of heater settings of the plurality of time points to generate the training data. The core steps involve gathering data points (temperatures, settings) over time and then performing a mathematical or logical operation (shifting the time stamps based on a measured response time) to align them. Calculating response time and shifting the data sets in time are fundamental mathematical and logical operations. One could write down the temperature and time data in a table, manually calculate the time difference, and then manually create a new, aligned table by shifting the time points. These can be performed mentally, with a pen and paper, or using a simple computer program. The end goal is to "generate the training data," which is an output of information organization and analysis. The process describes a sequence of data manipulation steps that could be manually planned and executed. The use of temperature sensors are merely collecting data and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g) Therefore, the described method is a series of abstract steps for data processing and analysis. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 3 recites wherein the first response time is an interval from the thermal medium performing a first heat operation at the first operation node to the thermal medium reacting to the first heat operation at first response node. The core idea is defining a specific time interval based on two events: the start of a heat operation and the observation of a reaction. This definition is a human construct for the purpose of measurement or analysis. A human can mentally conceive of this time interval and perform the calculation using a stopwatch, a calendar, or simply a mental record of time. The calculation itself is a simple subtraction of a start time from an end time (Time of Reaction - Time of Operation). This meets the criteria for a "mental process", meaning it can be practically performed in the human mind.
Claim 4 further recites wherein the heat cycle system further comprises a heat accumulator, the heater transports the thermal medium with the raising temperature to the heat accumulator through the delivery pipe, the heat accumulator provides the thermal medium to the heat-consuming machine through a supply pipe, the heat-consuming machine transports the thermal medium with the dropping temperature to the heat accumulator through the return pipe, and the method further comprises: determining a second operation node and a second response node of the heat cycle system, wherein the second operation node locates at a position where the heater outputs the thermal medium, and the second response node locates at a position where the heat accumulator receives the thermal medium; determining a third operation node and a third response node of the heat cycle system, wherein the third operation node locates at a position where the heat accumulator outputs the thermal medium, and the third response node locates at a position where the heat-consuming machine receives the thermal medium; measuring second response time between the second operation node and the second response node, wherein the second response time is an interval from the thermal medium performing a second heat operation at the second operation node to the thermal medium reacting to the second heat operation at second response node; measuring third response time between the third operation node and the third response node, wherein the third response time is an interval from the thermal medium performing a third heat operation at the third operation node to the thermal medium reacting to the third heat operation at third response node; and performing second data alignment by the processor, wherein the second data alignment shifts the plurality of machine settings of the plurality of time points to be aligned with the plurality of heating settings of the plurality of time points according to a sum of the second response time and the third response time; wherein the first data alignment further shifts the plurality of response temperatures of the plurality of time points to be aligned with the plurality of heater settings of the plurality of time points according to the sum of the second response time and the third response time, and the training data further comprises the machine setting data after being processed with the second data and the plurality of heater setting data. The "nodes," "operation times," and "response times" are abstract variables that represent a physical system but exist as concepts in the mind or as data points on paper. The "measuring" and "aligning" steps are applications of mathematical or logical rules (e.g., subtraction to find an interval, shifting data by a calculated amount). These are mental operations. These steps do not require physical machinery to execute; they are a set of instructions or a methodology for analyzing data about a physical system. The entire system relies on understanding cause and effect (e.g., heater output leading to a reaction at the accumulator) and representing these relationships as data points to be processed. This is the essence of an abstract algorithm. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 5 further recites wherein measuring the first response time between the first operation node and the first response node comprises: generating a plurality of time-delayed temperature data according to a plurality of response temperature data, wherein the plurality of time-delayed temperature data corresponds to a plurality of time-delayed length respectively; computing a plurality of correlation coefficients, wherein each of the plurality of correlation coefficients is associated with an operating temperature data and one of the plurality of time-delayed temperature data; and setting the first response time, wherein the first response time is the time-delayed length corresponding to a maximum of the plurality of correlation coefficients. The process described is a mathematical-statistical method that can be conceptualized as an abstract idea (a "mental process") but in practice requires tools (like a computer or at a minimum, pen and paper) to perform the complex, repetitive calculations for any meaningful amount of data. The operations involved (generating time-delayed data, computing correlation coefficients, finding the maximum) are mathematical and logical steps. One could, in theory, perform these steps mentally for a simple dataset. These steps are "abstract" in the sense that they describe mathematical relationships and mental/computational processes, not a physical transformation of a physical article into a different state. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 6 further recites wherein the plurality of correlation coefficients is Pearson correlation coefficient. This requires applying a specific mathematical formula (e.g., Pearson correlation) to quantify the linear relationship between the original operating temperature data and each of the time-delayed response datasets. The process described is primarily a mathematical and logical method for data analysis, which is an abstract idea that can be performed as a mental process or with pen and paper, though practically it is almost always done using a computer. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 7 and 19 further recites wherein the statistic model is linear regression model or Lasso regression model. Both linear and Lasso regression models are rooted in abstract mathematical ideas and are fundamentally a mental process of understanding relationships and making predictions. The process of building and applying these models is a mental process that can also be carried out using pen and paper with mathematical concepts. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 8 and 20 further recites wherein an estimation index of the statistic model is mean absolute error or mean absolute percentage error. Calculating Mean Absolute Error (MAE) or Mean Absolute Percentage Error (MAPE) is primarily a mental process using mathematical concepts. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Regarding claim 9
Step 2A Prong 1
A method for setting a heating temperature applicable to a heat cycle system,
wherein a temperature data of the heat cycle system is obtained by an operation interface, the heat cycle system comprises a response node, the temperature data comprises a temperature threshold corresponding to the response node, (A person mentally accesses or writes down the current system temperature data and the temperature threshold from an operation interface. This involves reading or recalling values like "current temp = 20°C" and "threshold = 22°C.")
and the method comprises: generating a plurality of simulation temperatures according to a temperature prediction model; (A person mentally conceptualizes a temperature prediction model (e.g., a simple linear equation or an algorithm) and applies it to generate a set of potential future temperatures ("simulation temperatures").
and obtaining the temperature threshold and determining each of the plurality of simulation temperatures according to the temperature threshold and the temperature data to update the heating temperature; (The person mentally recalls or writes down the temperature threshold value again. The person mentally compares the simulation temperatures to the threshold. Based on this comparison, they mentally decide on a new, updated heating temperature.)
The core logic of the method is an abstract idea that can be mentally understood and manually calculated using basic mathematical concepts. This demonstrates that the underlying process is fundamentally a mental and mathematical concept.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. The limitation such as “wherein a temperature data of the heat cycle system is obtained by an operation interface, the heat cycle system comprises a response node, the temperature data comprises a temperature threshold corresponding to the response node” can also be viewed as merely collecting data and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g). Claim do not recite additional elements that integrate the judicial exception. Thus, claim 9 is directed to abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the limitation such as “wherein a temperature data of the heat cycle system is obtained by an operation interface, the heat cycle system comprises a response node, the temperature data comprises a temperature threshold corresponding to the response node” can also be viewed as merely collecting data and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g) and is well-understood, routine or conventional. ((See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) Claim therefore, when taken as a whole, does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Thus, claim 9 is directed to abstract idea.
Claim 10 further recites wherein the temperature data further comprises a heat setting lower bound, a heat setting upper bound, and an adjustment interval, and the method further comprises performing following steps by a processor: obtaining a heater setting data and a machine setting data; and generating a plurality of simulation settings according to the heat setting lower bound and the adjustment interval, wherein each of the plurality of simulation settings is not greater than the heat setting upper bound. The individual first mentally grasps the defined parameters: a lower bound (minimum heat setting), an upper bound (maximum heat setting), and an adjustment interval (the step size). The person mentally "obtains" the current or desired heater setting data and machine setting data. The mind establishes the inclusive range of potential settings: starting at the heat setting lower bound and going up to, but not exceeding, the heat setting upper bound. The individual mentally simulates an iterative process (a loop): starting from the lower bound and repeatedly adding the adjustment interval until the next value would exceed the upper bound. The generated values (the plurality of simulation settings) are mentally noted or recorded. The entire process is an abstract idea because it describes a method for organizing and generating data based on logical rules. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 9.
Claim 11 further recites wherein generating the plurality of simulation temperatures according to the temperature prediction model comprises: inputting each of the plurality of simulation settings, the heater setting data, and the machine setting data to the temperature prediction model to generate the plurality of simulation temperatures. The act of inputting data to a temperature prediction model to generate simulation temperatures is a mental process or performed with pen and paper in principle, as it involves mathematical concepts and calculations. A human could, in theory, take the "simulation settings," "heater setting data," and "machine setting data," and use a pen and paper to manually calculate the predicted temperatures based on the underlying mathematical formulas, especially for a simple model or a limited number of simulations. This makes the underlying calculation an abstract idea. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 9.
Claim 12 further recites wherein determining each of the plurality of simulation temperature according to the temperature threshold and the temperature data to update the heating temperature comprises: determining whether each of the plurality of simulation temperatures is greater than the temperature threshold, wherein: when at least one of the plurality of simulation temperatures is not smaller than the temperature threshold, updating the heater setting data with the simulation setting corresponding to a minimum of said at least one of the plurality of simulation temperatures; and when a maximum of the plurality of simulation temperatures is smaller than the temperature threshold, updating the heater setting data with the heat setting upper bound. The core of the process involves decision-making based on a set of potential outcomes (simulation temperatures) and a predefined goal (the temperature threshold). The process is an abstract idea because it describes a method for controlling a heater based on a set of simulated outcomes and a predefined condition (a temperature threshold). It is a logical sequence of steps designed to achieve a specific result: finding the most efficient (minimum) heater setting that still meets or exceeds a target temperature threshold. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 9.
Claim 13 further recites wherein determining each of the plurality of simulation temperature according to the temperature threshold and the temperature data to update the heating temperature comprises: determining whether each of the plurality of simulation temperatures is greater than the temperature threshold, wherein: when at least one of the plurality of simulation temperatures is not smaller than the temperature threshold, updating the heater setting data with the simulation setting corresponding to a minimum of said at least one of the plurality of simulation temperatures; and when a maximum of the plurality of simulation temperatures is smaller than the temperature threshold, updating the heater setting data with the heat setting upper bound. Picturing the heat cycle—how the fluid moves, where heat is added, where it is used, and how it returns—happens in the mind. Determining the cause-and-effect relationships (e.g., the heater causes the temperature to rise, the heat-consuming machine causes the temperature to drop) is a mental exercise. Thinking about how changes in one part of the system (e.g., increasing the heater's output) would affect other parts (e.g., higher temperature in the delivery pipe) is a mental process. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 9.
Claim 14 further recites wherein obtaining the temperature data of the heat cycle system by the operation interface comprises: obtaining the response temperature data by a temperature sensor, wherein the response temperature data comprises a plurality of response temperatures of the response node at a plurality of time points. It is viewed as merely collecting data using sensor and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g) and is well-understood, routine or conventional. ((See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 9.
Regarding claim 15
Step 2A prong 1
A heat cycle system comprising: a heater heating a thermal medium; a heat-consuming machine configured to receive the thermal medium from the heater; two temperature sensors disposed on an operation node and a response node respectively, wherein the operation node locates at a position where the heat-consuming machine outputs the thermal medium, and the response node locates at a position where the heater receives the thermal medium; and a processor communicably connecting to the two temperature sensors, (The claim is directed to the abstract idea of monitoring and analyzing temperature data in a system. This is a fundamental concept or a method of organizing human activity (e.g., a basic principle of thermodynamics and process monitoring). The claim, stripped of its physical context, is merely a set of instructions to "obtain a temperature at point A, obtain a temperature at point B, and use a processor to compare or analyze this data".)
builds a temperature prediction model configured to update a temperature setting of the heater. (The development and application of a statistical model is a mathematical method and an abstract idea itself, which can be formulated and solved using mathematical principles on paper. For a simple linear model, we use a calculator and paper to calculate the values that minimize the error between the model's predictions and the actual data points. These calculations are a form of pure mental/manual computation. The idea of using measured data and response time to train a statistical model for prediction is an abstract concept of how to process information. The process involves conceptualizing a system that takes inputs (current temperature, desired temperature, outside temperature, etc.), processes them using a model (a mathematical function), and outputs an action (turn on, turn off, increase power). Thus, building a temperature prediction model to update a heater's settings can be a purely mental process using abstract mathematical concepts)
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. As discussed above with respect to the integration of the abstract idea into a practical application, the limitation such as the placement of the temperature sensors, specific "operation" and "response" nodes (e.g., input and output) while specific, describes a generic, well-understood way to measure a temperature differential relevant to the heat cycle. This activity is conventional and insignificant in the context of the overall abstract idea as discussed in MPEP 2106.05(g) The heater, heat-consuming machine, and thermal medium are the mere "field of use" or "technological environment. The processor is merely used as a generic tool to perform the data reception and analysis, which are routine computer functions. This activity is conventional and insignificant in the context of the overall abstract idea. Claim do not recite additional elements that integrate the judicial exception. Thus, claim 15 is directed to abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the limitation such as the placement of the temperature sensors, specific "operation" and "response" nodes (e.g., input and output) while specific, describes a generic, well-understood way to measure a temperature differential relevant to the heat cycle. This activity is conventional and insignificant in the context of the overall abstract idea as discussed in MPEP 2106.05(g) and is well-understood, routine or conventional. ((See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) The heater, heat-consuming machine, and thermal medium are the mere "field of use" or "technological environment. The processor is merely used as a generic tool to perform the data reception and analysis, which are routine computer functions. Claim therefore, when taken as a whole, does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Thus, claim 15 is directed to abstract idea.
Claim 16 further recites wherein the processor performs a set of instructions to build the temperature prediction model and the set of instructions comprises: obtaining a heater setting data of the heater, wherein the heater setting data comprises a plurality of heater settings of the heater at a plurality of time points; obtaining a machine setting data of the heat-consuming machine, wherein the machine setting data comprises a plurality of machine settings of the heat-consuming machine at the plurality of time points; computing a response time between the operation node and the response node; performing a data alignment to obtain a training data, wherein the data alignment shifts a plurality of response temperatures at the plurality of time points to be aligned with the plurality of heater settings at the plurality of time points at least according to the response time; and building the temperature prediction model according to a statistic model and the training data. The initial mental step involves understanding what data is needed (heater settings, machine settings, response temperatures) and how they relate in time. This requires abstract thought about inputs, outputs, and time dependencies. The human recognizes that the temperature data needs to be time-shifted to correspond correctly with the heater settings that caused those temperatures, accounting for the previously computed response time. This step involves the abstract reasoning of choosing an appropriate "statistic model" (e.g., a linear regression, a time-series model like ARIMA, or a machine learning approach). The choice depends on the perceived complexity of the relationship. Finally, the mental process involves using the aligned data to build the model, which means estimating the model's parameters (e.g., the slope and intercept of a line). This is an abstract optimization problem, aiming to minimize the difference between the model's predictions and the actual observed temperatures. Claim therefore, when taken as a whole, does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Thus, claim 16 is directed to abstract idea.
Claim 17 further recites an input interface configured to obtain a temperature threshold of the response node, a setting lower bound, a setting upper bound, and an adjustment interval of the heater; wherein the processor is communicably connected to the input interface and the set of instructions further comprises: obtaining the heater setting data of the heater and the machine setting data of the heat- consuming machine; generating a plurality of simulation settings according to the setting lower bound and the adjustment interval, wherein each of the plurality of simulation settings is not greater than the setting upper bound; inputting each of the plurality of simulation settings, the heater setting data, and the machine setting data to the temperature prediction model to generate a plurality of simulation temperatures; determining whether each of the plurality of simulation temperatures is greater than the temperature threshold, wherein when at least one of the plurality of simulation temperatures is not smaller than the temperature threshold, updating the heater setting data with the simulation setting corresponding to a minimum of said at least one of the plurality of simulation temperatures; and when a maximum of the plurality of simulation temperatures is smaller than the temperature threshold, updating the heater setting data with the setting upper bound. The process describes a control system loop designed to efficiently find the lowest possible heater setting that still satisfies a required temperature threshold, minimizing energy usage while ensuring the target temperature is met. A person can conceptualize and write down the inputs: temperature threshold, lower bound, upper bound, adjustment interval, current heater setting data, and machine setting data. A person can mentally list or write down the series of potential new settings. The individual could then, for each potential setting, apply the "temperature prediction model" (which would need to be a known formula) to determine the resulting "simulation temperature". They would then compare each simulation temperature to the threshold temperature. Based on the results of the comparisons, they would decide which rule applies (either "at least one temperature is above threshold" or "all temperatures are below threshold") and mentally or physically update the value of heater to the new chosen setting. The additional elements of “an input interface configured to obtain a temperature threshold” is viewed as merely collecting data and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g) and is well-understood, routine or conventional. ((See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014)Claim therefore, when taken as a whole, does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Thus, claim 17 is directed to abstract idea.
Claim 18 further recites a heat accumulator comprising an upper space and a lower space connected to each other, wherein the upper space receives the thermal medium heated by the heater, and the lower space receives the thermal medium passing through the heat-consuming machine. The first step is to mentally picture the system: a container (accumulator) with two distinct regions, a heater, a heat-consuming machine, and the circulating thermal medium (e.g., water, oil, air). One imagines how the system behaves over time. Hot fluid enters the top, cold fluid leaves the bottom. A thermal gradient (stratification) likely forms within the accumulator. The mental process involves predicting how factors like flow rate, heat input, and the size of the tank would affect the system's efficiency and capacity. Claim therefore, when taken as a whole, does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Thus, claim 18 is directed to abstract idea.
Claim Rejections - 35 USC § 103
7. 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.
8. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
9. Claims 1-3, 8, 15-16, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, Chenyu, et al. "Combined economic dispatch considering the time-delay of district heating network and multi-regional indoor temperature control." IEEE Transactions on Sustainable Energy 9.1 (2017): 118-127.) in view of Li, Xiuming, et al. "Modeling for indoor temperature prediction based on time-delay and Elman neural network in air conditioning system." Journal of Building Engineering 33 (2021): 101854.)
Regarding claim 1
Wu teaches a method for building a temperature prediction model applicable to a heat cycle system, (see section II and fig 1-2-The district heating system is used to distribute heat generated in a centralized location for residential and commercial heating requirements. It can be divided into three parts, heat source, district heating networks composed of insulated pipes, and heat load. See section III.A- building a temperature prediction model)
the method is used to measure a temperature of the heat cycle system to generate a measured temperature data, (see section II- The district heating network consists of a primary network (long distance heat transport network) and a secondary network (distribution after heat exchange substation). Fig. 2 shows a primary network based on two-pipe direct return system. It allows each terminal unit to be separately controlled and serviced.)
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building the temperature prediction model according to a statistic model and the training data. (see section III.A- The time delay of hot water transportation makes the heat load area division necessary. Each heat load area may receive hot water at different temperature at the same time, so it is necessary for heat users to feedback their room temperatures to dispatch center, then the center calculates new dispatch plan.
Since power systems and DHS are suffused with constraints and limits on states and inputs, a receding-horizon MPC strategy can be particularly useful within the context of online combined CHP economic dispatch. Fig. 3 shows the data interaction schema between dispatch center and each heat load area. Both the predicted indoor temperature and actual indoor temperature of all heat load areas are indispensable for dispatchers to guarantee the thermal demand of all different heat load areas. The indoor temperature is taken as the main index of heating quality. Given the time delay of hot water, dispatch center should make preparation (increase or decrease the heat output from heat source) in advance. Functions (26)–(31) can be used to build an indoor temperature prediction model.)
Wu does not teach compute response time of the heat cycle system, the method comprises: aligning the measured temperature data and a setting value of the heat cycle system to generate a training data according to the response time.
In the related field of invention, Li teaches compute response time of the heat cycle system, (see section 3.1.3-As shown in Fig. 7, indoor temperature changes along with the change of system regulation variables (supply air volume and supply air temperature). Meanwhile, indoor temperature response lags behind the change of system regulation variables, and the delay time is 5 min) and
the method comprises: aligning the measured temperature data and a setting value of the heat cycle system to generate a training data according to the response time; (see section 3.2.2 and fig 10-11- In this section, the training and validation of TDNN will be carried out, and the distribution of training and test data are shown in Fig. 10. Considering that 5 is the optimal value of input-layer delay coefficient than others, 1~5 sets of data in training and 196~200th sets of data in test are considered as the delay inputs respectively, and 6~200 th sets of data in training and 201~250 th sets of data in test are considered as training and test results to validate the network training effect. Fig. 11 shows the train and test results. MSEs of network training is 0.00152. It can be observed that network output could follow its sampling output well in the training process. In the test process, the prediction results of first 1~5 steps are better, while later predictions are not satisfactory. It can be noted that the prediction step of the 5th is just equal to the delay time of indoor temperature against system regulation variables. In practice, the 5-step prediction is able to satisfy the indoor temperature prediction control requirement, though the network generalization ability is not well and MSE is a little large after 5 steps. One possible reason is that the length of training sampling data is much more complicated and more sensitive to overfitting. Therefore, it can be crucial to implement a proper selection of the network input, which can simplify the training and lead to a better generalization ability. See fig 15)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include compute response time of the heat cycle system, the method comprises: aligning the measured temperature data and a setting value of the heat cycle system to generate a training data according to the response time as taught by Li in the system of Wu in order to build indoor temperature model in improving energy efficiency and in door thermal comfort of air conditioning system. (See abstract, Li)
Regarding claim 2
Wu further teaches the heat cycle system comprises a heater, a heat-consuming machine, a delivery pipe, and a return pipe; the heater is configured to heat a thermal medium and transport the thermal medium with a raising temperature through the delivery pipe; the heat-consuming machine is configured to consume thermal energy of the thermal medium for processing and transport the thermal medium with a dropping temperature through the return pipe; determining a first operation node and a first response node of the heat cycle system, wherein the first operation node locates at a position where the heat-consuming machine outputs the thermal medium; obtaining an operating temperature data of the first operation node, and obtaining a response temperature data of the first response node, wherein the response temperature data comprises a plurality of response temperatures of the first response node at a plurality of time points; (see fig 2)
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Examiner note: In fig 2, the "Heat source" functions as the heater, supplying a thermal medium through the upper delivery pipe (supply line) with a high temperature (TS). The "Distribution network (DN)" components act as the heat-consuming machines, where the thermal energy is consumed, and the medium returns via the lower return pipe with a lower temperature. (TR) Fig 2 explicitly labels various nodes with temperatures (T) and mass flow rates (m), consistent with the process of determining operation and response nodes and obtaining temperature data as described in the query.
and performing following steps by a processor: obtaining a heater setting data of the heater, wherein the heater setting data comprises a plurality of heater settings of the heater at the plurality of time points; obtaining a machine setting data of the heat-consuming machine, wherein the machine setting data comprises a plurality of machine settings of the heat-consuming machine at the plurality of time points; (see section III.A and fig 1-2-For instance, if the average indoor temperature of heat load area 1 drops below the lower limit according to latest predictions, the dispatch center will send control signal to heat-exchange station 1 to increase the flow rate of distribution network, or send control signals to heat sources to improve the water temperature of transmission network. see page 121-mass flow rates should within their limits to prevent pipe vibration and temperature drop of the supply/return network in the kth heat load area at period)
Examiner note: Wu describes a system where a dispatch center sends control signals to a heat-exchange station or heat sources to adjust the flow rate and water temperature. These adjustments (flow rate and water temperature) constitute the operational settings or "heater setting data" and “machine setting data” for the heating equipment.
Wu does not teach aligning the measured temperature data and the setting of the heat cycle system to generate the training data according to the response time comprises: obtaining an operating temperature data of the first operation node by a first temperature sensor, and obtaining a response temperature data of the first response node by a second temperature sensor measuring first response time between the first operation node and the first response node and performing first data alignment according to the first response time to shift the plurality of response temperatures of the plurality of time points so as to align the plurality of response temperatures with the plurality of heater settings of the plurality of time points to generate the training data;
However, Li further teaches aligning the measured temperature data and the setting of the heat cycle system to generate the training data according to the response time comprises: obtaining an operating temperature data of the first operation node by a first temperature sensor, and obtaining a response temperature data of the first response node by a second temperature sensor, (see table 1 and fig 1-supply air temperature sensor, indoor temperature sensor and air volume sensor)
measuring first response time between the first operation node and the first response node; (see section 3.1.3-As shown in Fig. 7, indoor temperature changes along with the change of system regulation variables (supply air volume and supply air temperature). Meanwhile, indoor temperature response lags behind the change of system regulation variables, and the delay time is 5 min)
and performing first data alignment according to the first response time to shift the plurality of response temperatures of the plurality of time points so as to align the plurality of response temperatures with the plurality of heater settings of the plurality of time points to generate the training data; (see section 3.2.2 and fig 10-11- In this section, the training and validation of TDNN will be carried out, and the distribution of training and test data are shown in Fig. 10. Considering that 5 is the optimal value of input-layer delay coefficient than others, 1~5 sets of data in training and 196~200th sets of data in test are considered as the delay inputs respectively, and 6~200 th sets of data in training and 201~250 th sets of data in test are considered as training and test results to validate the network training effect. Fig. 11 shows the train and test results. MSEs of network training is 0.00152. It can be observed that network output could follow its sampling output well in the training process. In the test process, the prediction results of first 1~5 steps are better, while later predictions are not satisfactory. It can be noted that the prediction step of the 5th is just equal to the delay time of indoor temperature against system regulation variables. In practice, the 5-step prediction is able to satisfy the indoor temperature prediction control requirement, though the network generalization ability is not well and MSE is a little large after 5 steps. One possible reason is that the length of training sampling data is much more complicated and more sensitive to overfitting. Therefore, it can be crucial to implement a proper selection of the network input, which can simplify the training and lead to a better generalization ability. See fig 15)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include a aligning the measured temperature data and the setting of the heat cycle system to generate the training data according to the response time comprises: obtaining an operating temperature data of the first operation node by a first temperature sensor, and obtaining a response temperature data of the first response node by a second temperature sensor measuring first response time between the first operation node and the first response node and performing first data alignment according to the first response time to shift the plurality of response temperatures of the plurality of time points so as to align the plurality of response temperatures with the plurality of heater settings of the plurality of time points to generate the training data as taught by Li in the system of Wu in order to build indoor temperature model in improving energy efficiency and in door thermal comfort of air conditioning system. (See abstract, Li)
Regarding claim 3
Wu further teaches performing a first heat operation at the first operation node to the thermal medium reacting to the first heat operation at first response node. (see fig 2 and claim 2 and see section II.C- At the heat-exchange stations the heat is transferred from transmission network to distribution network to provide adequate heat for residential and commercial heating requirements. So, the heat-exchange stations are modeled as heat load from the perspective of transmission network. So, the indoor temperature should be within a reasonable interval. See introduction- The constant flow and variables temperature (CF-VT) control strategy is adopted in this model. Its time complexity can satisfy the short-term optimal dispatch when the time interval is equal to or greater than 5min.)
Wu does not teach wherein the first response time is an interval from the thermal medium performing a first heat operation at the first operation node to the thermal medium reacting to the first heat operation at first response node.
However, Li further teaches wherein the first response time is an interval from the thermal medium performing a first heat operation at the first operation node to the thermal medium reacting to the first heat operation at first response node. (see section 3.1.3-As shown in Fig. 7, indoor temperature changes along with the change of system regulation variables (supply air volume and supply air temperature). Meanwhile, indoor temperature response lags behind the change of system regulation variables, and the delay time is 5 min. For example, in Fig. 8 (b), the supply air volume starts to change at the 9th sampling point, and the indoor temperature changes at 14th sampling point, which lags behind the change of supply air volume.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include wherein the first response time is an interval from the thermal medium performing a first heat operation at the first operation node to the thermal medium reacting to the first heat operation at first response node taught by Li in the system of Wu in order to build indoor temperature model in improving energy efficiency and in door thermal comfort of air conditioning system. (See abstract, Li)
Regarding claim 8 and 20
The combination of Wu and Kallio does not teach wherein an estimation index of the statistic model is mean absolute error or mean absolute percentage error.
In the related field of invention, Li teaches wherein an estimation index of the statistic model is mean absolute error or mean absolute percentage error. (see section 3.2.4-Fig. 14 shows the single-step and multi-step prediction (5-step) results. Unlike in TDNN, only the system input in previous one time is considered as the network input instead of 6 (5 + 1) sets of system inputs in ENN training process. The training date length is also considered as 6 (5 + 1) in a single training. It can be seen that prediction results of multi-step prediction and single-step prediction also match the sampling data well. The mean absolute errors of multi-step prediction and single-step prediction are 0.21 and 0.21 respectively, which also satisfies the indoor temperature prediction requirement.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include a wherein an estimation index of the statistic model is mean absolute error or mean absolute percentage error as taught by Li in the system of Wu in order to build indoor temperature model in improving energy efficiency and in door thermal comfort of air conditioning system. (See abstract, Li)
Regarding claim 15
Wu teaches a heat cycle system comprising: a heater heating a thermal medium; a heat-consuming machine configured to receive the thermal medium from the heater;(see fig 1-2)
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Examiner note: The "Heat source" component in the diagram represents the heater, which heats the thermal medium (indicated by the supply temperature and return temperature) The various "Distribution network" blocks (labeled DN.1 through DN.k) represent the heat-consuming parts of the system, where the thermal medium is used to provide heat to consumers. The diagram illustrates a primary network where the medium circulates in a closed loop, flowing from the heat source through supply lines (MS) to the distribution networks and returning via return lines (MR) to the heat source, forming a complete heat cycle.
wherein the processor builds a temperature prediction model configured to update a temperature setting of the heater. (See section III.A-Both the predicted indoor temperature and actual indoor temperature of all heat load areas are indispensable for dispatchers to guarantee the thermal demand of all different heat load areas. The indoor temperature is taken as the main index of heating quality. Given the time delay of hot water, dispatch center should make preparation (increase or decrease the heat output from heat source) in advance. Functions (26)–(31) can be used to build an indoor temperature prediction model. The feedback signal (mainly includes measured average indoor temperature and the mass flow rate of distribution network) will improve the real-time performance of temperature prediction.)
Wu also teaches wherein the operation node locates at a position where the heat-consuming machine outputs the thermal medium, and the response node locates at a position where the heater receives the thermal medium; (see fig 2 and page 121- Considering the heat loss and time delay, the mixed water temperature of each node can be expressed)
Examiner note: The fig 2 labeled (MR1) through (MRkend) on the return line represent the positions where the heat-consuming machines (heat-exchange stations and associated distribution networks) output the thermal medium. The point labeled (TR) at the return input of the "Heat source" represents the position where the heater receives the thermal medium.
Wu doe not teach teach two temperature sensors disposed on an operation node and a response node respectively, and a processor communicably connecting to the two temperature sensors.
In the related field of invention, Li teaches two temperature sensors disposed on an operation node and a response node respectively; and a processor communicably connecting to the two temperature sensors, (see table 1 and fig 1-supply air temperature sensor, indoor temperature sensor and air volume sensor)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include a two temperature sensors disposed on an operation node and a response node respectively, and a processor communicably connecting to the two temperature sensors as taught by Li in the system of Wu in order to build indoor temperature model in improving energy efficiency and in door thermal comfort of air conditioning system. (See abstract, Li)
Regarding claim 16
Wu teaches wherein the processor performs a set of instructions to build the temperature prediction model and the set of instructions comprises: obtaining a heater setting data of the heater, wherein the heater setting data comprises a plurality of heater settings of the heater at a plurality of time points; (see section III.A-For instance, if the average indoor temperature of heat load area 1 drops below the lower limit according to latest predictions, the dispatch center will send control signal to heat-exchange station 1 to increase the flow rate of distribution network, or send control signals to heat sources to improve the water temperature of transmission network.)
Examiner note: Wu describes a system where a dispatch center sends control signals to a heat-exchange station or heat sources to adjust the flow rate and water temperature. These adjustments (flow rate and water temperature) constitute the operational settings or "heater setting data" and “machine setting data” for the heating equipment.
obtaining a machine setting data of the heat-consuming machine, wherein the machine setting data comprises a plurality of machine settings of the heat-consuming machine at the plurality of time points; (see page 121-mass flow rates should within their limits to prevent pipe vibration and temperature drop of the supply/return network in the kth heat load area at period)
building the temperature prediction model according to a statistic model and the training data. (see section III.A- The time delay of hot water transportation makes the heat load area division necessary. Each heat load area may receive hot water at different temperature at the same time, so it is necessary for heat users to feedback their room temperatures to dispatch center, then the center calculates new dispatch plan.
Since power systems and DHS are suffused with constraints and limits on states and inputs, a receding-horizon MPC strategy can be particularly useful within the context of online combined CHP economic dispatch. Fig. 3 shows the data interaction schema between dispatch center and each heat load area. Both the predicted indoor temperature and actual indoor temperature of all heat load areas are indispensable for dispatchers to guarantee the thermal demand of all different heat load areas. The indoor temperature is taken as the main index of heating quality. Given the time delay of hot water, dispatch center should make preparation (increase or decrease the heat output from heat source) in advance. Functions (26)–(31) can be used to build an indoor temperature prediction model.)
Wu does not teach computing a response time between the operation node and the response node; performing a data alignment to obtain a training data, wherein the data alignment shifts a plurality of response temperatures at the plurality of time points to be aligned with the plurality of heater settings at the plurality of time points at least according to the response time;
In the related field of invention, Li teaches computing a response time between the operation node and the response node; (see section 3.1.3-As shown in Fig. 7, indoor temperature changes along with the change of system regulation variables (supply air volume and supply air temperature). Meanwhile, indoor temperature response lags behind the change of system regulation variables, and the delay time is 5 min)
performing a data alignment to obtain a training data,(see fig 15) wherein the data alignment shifts a plurality of response temperatures at the plurality of time points to be aligned with the plurality of heater settings at the plurality of time points at least according to the response time; (see section 3.2.2 and fig 10-11- In this section, the training and validation of TDNN will be carried out, and the distribution of training and test data are shown in Fig. 10. Considering that 5 is the optimal value of input-layer delay coefficient than others, 1~5 sets of data in training and 196~200th sets of data in test are considered as the delay inputs respectively, and 6~200 th sets of data in training and 201~250 th sets of data in test are considered as training and test results to validate the network training effect. Fig. 11 shows the train and test results. MSEs of network training is 0.00152. It can be observed that network output could follow its sampling output well in the training process. In the test process, the prediction results of first 1~5 steps are better, while later predictions are not satisfactory. It can be noted that the prediction step of the 5th is just equal to the delay time of indoor temperature against system regulation variables. In practice, the 5-step prediction is able to satisfy the indoor temperature prediction control requirement, though the network generalization ability is not well and MSE is a little large after 5 steps. One possible reason is that the length of training sampling data is much more complicated and more sensitive to overfitting. Therefore, it can be crucial to implement a proper selection of the network input, which can simplify the training and lead to a better generalization ability.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include a computing a response time between the operation node and the response node; performing a data alignment to obtain a training data, wherein the data alignment shifts a plurality of response temperatures at the plurality of time points to be aligned with the plurality of heater settings at the plurality of time points at least according to the response time as taught by Li in the system of Wu in order to build indoor temperature model in improving energy efficiency and in door thermal comfort of air conditioning system. (See abstract, Li)
Regarding claim 18
Wu further teaches a heat accumulator comprising an upper space and a lower space connected to each other, (see fig 1-heat storage) wherein the upper space receives the thermal medium heated by the heater, and the lower space receives the thermal medium passing through the heat-consuming machine. (See fig 2 and see section II. A - In order to balance heat and power demands and facilitate the wind power integration in multiple areas and time periods, the CHP units, electrical boiler, and heat storage are selected as heat sources.))
Examiner note: Fig 2 illustrates that the upper pipeline receives the thermal medium heated by the heat source, and the lower pipeline receives the thermal medium after it has passed through the heat-consuming machines (the heat-exchange stations). Fig1 shows a component labeled "Heat storage". In many thermal energy storage systems, especially those using water or other fluids as the storage medium, the heat accumulator (storage tank) naturally develops a temperature stratification, with warmer fluid accumulating in the upper space and cooler fluid in the lower space due to density differences.
10. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Wu, Chenyu, et al. "Combined economic dispatch considering the time-delay of district heating network and multi-regional indoor temperature control." IEEE Transactions on Sustainable Energy 9.1 (2017): 118-127.) in view of Li, Xiuming, et al. "Modeling for indoor temperature prediction based on time-delay and Elman neural network in air conditioning system." Journal of Building Engineering 33 (2021): 101854.) and further in view of Seshan et al. (PUB NO: US20150127284A1)
Regarding claim 4
Wu further wherein the heat cycle system further comprises a heat accumulator, the heater transports the thermal medium with the raising temperature to the heat accumulator through the delivery pipe, the heat accumulator provides the thermal medium to the heat-consuming machine through a supply pipe, the heat-consuming machine transports the thermal medium with the dropping temperature to the heat accumulator through the return pipe, (see section II. A and fig 2- In order to balance heat and power demands and facilitate the wind power integration in multiple areas and time periods, the CHP units, electrical boiler, and heat storage are selected as heat sources.)
Examiner note: The figure 2 labels the origin of the hot medium as "Heat source". Piping: The supply lines, labeled (L) (representing "delivery" or "supply" pipes), transport the thermal medium with the higher temperature away from the heat source. The return lines, labeled \(MR\) (representing "return" pipes), transport the thermal medium with the lower temperature back towards the heat source. The "Heat-exchange station" and "Distribution network" (DN.1, DN.2, etc.) act as the "heater" (heat exchanger) and "heat-consuming machine" in the system, extracting thermal energy from the medium. Fig 2 shows the medium flowing from the heat source, through the supply pipes to the heat consumers, and returning via the return pipes.
and the method further comprises: determining a second operation node and a second response node of the heat cycle system, wherein the second operation node locates at a position where the heater outputs the thermal medium, and the second response node locates at a position where the heat accumulator receives the thermal medium; determining a third operation node and a third response node of the heat cycle system, wherein the third operation node locates at a position where the heat accumulator outputs the thermal medium, and the third response node locates at a position where the heat-consuming machine receives the thermal medium; (see fig 2 and page 121- Considering the heat loss and time delay, the mixed water temperature of each node can be expressed. See introduction - The constant flow and variables temperature (CF-VT) control strategy is adopted in this model. Its time complexity can satisfy the short-term optimal dispatch when the time interval is equal to or greater than 5min.)
Examiner note: The nodes at the heat source output (supply line) and input (return line) are considered the second operation and response nodes for each time series. The nodes at the heater output and where the heat-consuming machine (Distribution Network or DN) receives the medium are considered the third operation and response nodes.
performing a second heat operation at the second operation node to the thermal medium reacting to the second heat operation at second response node; performing a third heat operation at the third operation node to the thermal medium reacting to the third heat operation at third response node; (see fig 2 and see section II.C- At the heat-exchange stations the heat is transferred from transmission network to distribution network to provide adequate heat for residential and commercial heating requirements. So, the heat-exchange stations are modeled as heat load from the perspective of transmission network. So, the indoor temperature should be within a reasonable interval.)
Wu does not teach measuring second response time between the second operation node and the second response node, wherein the second response time is an interval from the thermal medium performing a second heat operation at the second operation node to the thermal medium reacting to the second heat operation at second response node; measuring third response time between the third operation node and the third response node, wherein the third response time is an interval from the thermal medium performing a third heat operation at the third operation node to the thermal medium reacting to the third heat operation at third response node; and performing second data alignment by the processor, wherein the second data alignment shifts the plurality of machine settings of the plurality of time points to be aligned with the plurality of heating settings of the plurality of time points according to a sum of the second response time and the third response time; wherein the first data alignment further shifts the plurality of response temperatures of the plurality of time points to be aligned with the plurality of heater settings of the plurality of time points according to the sum of the second response time and the third response time, and the training data further comprises the machine setting data after being processed with the second data and the plurality of heater setting data.
However, Li teaches measuring second response time between the second operation node and the second response node, wherein the second response time is an interval from the thermal medium performing a second heat operation at the second operation node to the thermal medium reacting to the second heat operation at second response node; measuring third response time between the third operation node and the third response node, wherein the third response time is an interval from the thermal medium performing a third heat operation at the third operation node to the thermal medium reacting to the third heat operation at third response node; (see section 2.2.1-TDNN is similar to feedforward networks, except that the input weight has a delay link associated with it. This allows the network to have a finite dynamic response to time series data of inputs and obtains the delay information of network inputs. See fig 7(a)(b))
wherein the first data alignment further shifts the plurality of response temperatures of the plurality of time points to be aligned with the plurality of heater settings of the plurality of time points according to the sum of the second response time and the third response time, and the training data further comprises the machine setting data after being processed with the second data and the plurality of heater setting data. (see section 3.2.2 and fig 10-11- In this section, the training and validation of TDNN will be carried out, and the distribution of training and test data are shown in Fig. 10. Considering that 5 is the optimal value of input-layer delay coefficient than others, 1~5 sets of data in training and 196~200th sets of data in test are considered as the delay inputs respectively, and 6~200 th sets of data in training and 201~250 th sets of data in test are considered as training and test results to validate the network training effect. Fig. 11 shows the train and test results. MSEs of network training is 0.00152. It can be observed that network output could follow its sampling output well in the training process. In the test process, the prediction results of first 1~5 steps are better, while later predictions are not satisfactory. It can be noted that the prediction step of the 5th is just equal to the delay time of indoor temperature against system regulation variables. In practice, the 5-step prediction is able to satisfy the indoor temperature prediction control requirement, though the network generalization ability is not well and MSE is a little large after 5 steps. One possible reason is that the length of training sampling data is much more complicated and more sensitive to overfitting. Therefore, it can be crucial to implement a proper selection of the network input, which can simplify the training and lead to a better generalization ability. See fig 15)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include a measuring second response time between the second operation node and the second response node, wherein the second response time is an interval from the thermal medium performing a second heat operation at the second operation node to the thermal medium reacting to the second heat operation at second response node; measuring third response time between the third operation node and the third response node, wherein the third response time is an interval from the thermal medium performing a third heat operation at the third operation node to the thermal medium reacting to the third heat operation at third response node; and wherein the first data alignment further shifts the plurality of response temperatures of the plurality of time points to be aligned with the plurality of heater settings of the plurality of time points according to the sum of the second response time and the third response time, and the training data further comprises the machine setting data after being processed with the second data and the plurality of heater setting data as taught by Li in the system of Wu in order to build indoor temperature model in improving energy efficiency and in door thermal comfort of air conditioning system. (See abstract, Li)
The combination of Wu and Li does not teach performing second data alignment by the processor, wherein the second data alignment shifts the plurality of machine settings of the plurality of time points to be aligned with the plurality of heating settings of the plurality of time points according to a sum of the second response time and the third response time;
However, Seshan teaches performing second data alignment by the processor, wherein the second data alignment shifts the plurality of machine settings of the plurality of time points to be aligned with the plurality of heating settings of the plurality of time points according to a sum of the second response time and the third response time; (see para 79-Step 502 receives sensor data from the sensor system that is marked with a timestamp based on the instance of the reference time value. The timestamp, for instance, may include the reference time value, may be marked with the reference time value plus a delta value that represents an elapsed time since the reference time value, and so forth. See para 91-Step 704 applies the alignment policy to a time value of the timestamp to generate an aligned timestamp. Examples of different ways of applying an alignment policy are discussed above. An alignment policy, for instance, can be added to a time value, multiplied by the time value, utilized as a variable in a time alignment equation, and so forth. As mentioned above, an alignment policy may be based on a single parameter, multiple different parameters, an algorithm that may include a single value input and/or multiple value inputs, and so forth.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include performing second data alignment by the processor, wherein the second data alignment shifts the plurality of machine settings of the plurality of time points to be aligned with the plurality of heating settings of the plurality of time points according to a sum of the second response time and the third response time;as taught by Seshan in the system of Wu and Li in order to enable sensor data from sensor systems that operate according to different time bases to be time-aligned. This enables rich sets of time-aligned sensor data to be leveraged by various functionalities to perform different tasks. (See para 0024, Seshan)
11. Claim 5-7 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, Chenyu, et al. "Combined economic dispatch considering the time-delay of district heating network and multi-regional indoor temperature control." IEEE Transactions on Sustainable Energy 9.1 (2017): 118-127.) in view of Li, Xiuming, et al. "Modeling for indoor temperature prediction based on time-delay and Elman neural network in air conditioning system." Journal of Building Engineering 33 (2021): 101854.) and further in view of Yuan et al. (“Study on refined control and prediction model of district heating station based on support vector machine." Energy 189 (2019): 116193.)
Regarding claim 5
Li further teaches wherein measuring the first response time between the first operation node and the first response node comprises: generating a plurality of time-delayed temperature data according to a plurality of response temperature data, wherein the plurality of time-delayed temperature data corresponds to a plurality of time-delayed length respectively; (see section 3.1.3- fig 7 In this section, the simulation sampling data is used to validate the modeling method for indoor temperature prediction based on two neural networks. The simulation dataset, which includes the profiles of supply air temperature, supply air volume and indoor temperature, is shown in Fig. 8. There are 250 sets of training data, and the sampling time interval is 1 min. As shown in Fig. 7, indoor temperature changes along with the change of system regulation variables (supply air volume and supply air temperature). Meanwhile, indoor temperature response lags behind the change of system regulation variables, and the delay time is 5 min.)
The combination of Wu and Li does not teach computing a plurality of correlation coefficients, wherein each of the plurality of correlation coefficients is associated with an operating temperature data and one of the plurality of time-delayed temperature data; and setting the first response time, wherein the first response time is the time-delayed length corresponding to a maximum of the plurality of correlation coefficients.
In the related field of invention, Yuan teaches computing a plurality of correlation coefficients, wherein each of the plurality of correlation coefficients is associated with an operating temperature data and one of the plurality of time-delayed temperature data; (see section 2.3 and table 2-SPSS Statistics 17, was used to calculate the correlation coefficients between the results and input variables) and
setting the first response time, wherein the first response time is the time-delayed length corresponding to a maximum of the plurality of correlation coefficients. The heating system usually has three levels of control. (see section introduction-First-level control is centralized control of the heat source, which mainly controls supply temperature of the heat source and operation frequency of the main circulating pump. Second-level control is the control of the heat station, which mainly controls secondary temperature and operation frequency of the circulating pump, and as the secondary return temperature has a certain delay, secondary supply temperature is often to be controlled. Third-level control is the control of indoor temperature by heat users through setting the target room temperature and adjusting the control valve installed on the user side. It can be said that the regulation of the heating system is fed back to the Second-level and then to First-level through the regulation of the Third-level. See section 3.3.1-Both prediction models with and without considering BC (Eq. (19) and Eq. (20)) was established through SVR, and the correlation between predicted and actual supply temperatures for these two models is shown in Fig. 4 and Fig. 5, respectively. The predicted values corresponding to the actual values in Fig. 4 are basically distributed on the diagonal line of the graph, while predicted values in Fig. 5 are divergent. This indicates that the prediction model considering thermal inertia has a better prediction performance. In addition, in order to better characterize the prediction performance of the two models, the linear regression fitting was carried out. The fitting correlation coefficient (R2) for Figs. 4 and 5 is 0.968 and 0.882 respectively. It further demonstrated that the secondary pipeline supply temperature could be well predicted by considering the building thermal inertia.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include computing a plurality of correlation coefficients, wherein each of the plurality of correlation coefficients is associated with an operating temperature data and one of the plurality of time-delayed temperature data; and setting the first response time, wherein the first response time is the time-delayed length corresponding to a maximum of the plurality of correlation coefficients as taught by Yuan in the system of Wu and Li in order to provide refined management in the heating station, thus improve the energy efficiency, reduce the heating consumption, and alleviate air pollution. (See abstract, Yuan)
Regarding claim 6
The combination of Wu and Li does not teach wherein the plurality of correlation coefficients is Pearson correlation coefficient.
In the related field of invention, Yuan further teaches wherein the plurality of correlation coefficients is Pearson correlation coefficient. (see equation 6 and table 2)
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Regarding claim 7 and 19
Wu does not teach wherein the statistic model is linear regression model or Lasso regression model.
However, Yuan further teaches wherein the statistic model is linear regression model or Lasso regression model. (See section 2.2 -SVM is a supervised machine learning method, which belongs to generalized linear regression. see section 3.3.1- This indicates that the prediction model considering thermal inertia has a better prediction performance. In addition, in order to better characterize the prediction performance of the two models, the linear regression fitting was carried out.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include wherein the statistic model is linear regression model or Lasso regression model as taught by Yuan in the system of Wu and Li in order to provide refined management in the heating station, thus improve the energy efficiency, reduce the heating consumption, and alleviate air pollution. (See abstract, Yuan)
11. Claims 9 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, Chenyu, et al. "Combined economic dispatch considering the time-delay of district heating network and multi-regional indoor temperature control." IEEE Transactions on Sustainable Energy 9.1 (2017): 118-127.) in view of Schmitt etl. (PUB No: US20130289857A1).
Regarding claim 9
Wu teaches a method for setting a heating temperature applicable to a heat cycle system, (see section III.A- The indoor temperature is taken as the main index of heating quality. Given the time delay of hot water, dispatch center should make preparation (increase or decrease the heat output from heat source) in advance. For instance, if the average indoor temperature of heat load area 1 drops below the lower limit according to latest predictions, the dispatch center will send control signal to heat-exchange station 1 to increase the flow rate of distribution network, or send control signals to heat sources to improve the water temperature of transmission network. See section II.A- In order to balance heat and power demands and facilitate the wind power integration in multiple areas and time periods, the CHP units, electrical boiler, and heat storage are selected as heat sources. Both electrical boiler and heat storage can improve the flexibility of CHP units)
wherein a temperature data of the heat cycle system is obtained by an operation interface, the heat cycle system comprises a response node,
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and the method comprises: generating a plurality of simulation temperatures according to a temperature prediction model; (see section III.A-Both the predicted indoor temperature and actual indoor temperature of all heat load areas are indispensable for dispatchers to guarantee the thermal demand of all different heat load areas. The indoor temperature is taken as the main index of heating quality. Given the time delay of hot water, dispatch center should make preparation (increase or decrease the heat output from heat source) in advance. Functions (26)–(31) can be used to build an indoor temperature prediction model. The feedback signal (mainly includes measured average indoor temperature and the mass flow rate of distribution network) will improve the real-time performance of temperature prediction.)
and the temperature data comprises a temperature threshold corresponding to the response node, (See page 122-The return temperature of heat exchanger is required to exceed a threshold to ensure the load-serving quality and lower than an upper limit to prevent steam forming)
Wu does not teach obtaining the temperature threshold and determining each of the plurality of simulation temperatures according to the temperature threshold and the temperature data to update the heating temperature.
However, Schmitt teaches obtaining the temperature threshold and determining each of the plurality of simulation temperatures according to the temperature threshold and the temperature data to update the heating temperature. (see para 0015- The aim of the heat-up mode is to increase the temperature in the exhaust system and hence of the exhaust aftertreatment means, i.e. to perform the “thermal management” of the exhaust line. When these engine combustion modes (normal/heat-up) are controlled in a coordinate manner with respect to the second exhaust aftertreatment means, they may be said to be associated therewith. In heat-up mode, any appropriate measures may be taken that result in an increase in the temperature of the exhaust gases arriving at the second exhaust aftertreatment means (as compared to normal mode), namely by acting on engine settings/control parameters to heat-up the temperature of engine-out gases or by injecting fuel in the exhaust line that is burned in the DOC. The thermal control of the SCR-catalyst may then be performed in the engine ECU by comparing the predicted temperature values to a threshold to decide whether or not to switch from one operating mode to the other (from heat-up to normal or vice-versa).)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include obtaining the temperature threshold and determining each of the plurality of simulation temperatures according to the temperature threshold and the temperature data to update the heating temperature as taught by Schmitt in the system of Wu in order to control of an internal combustion engine equipped with an oxidation catalyst and a NOx treatment device such as a SCR catalyst, thus reduce the concentration of combustion byproducts and/or products of incomplete combustion. (See para 001-002, Schmitt)
Regarding claim 13
Wu further teaches wherein the heat cycle system comprises a heater, a heat-consuming machine, a delivery pipe, and a return pipe, the heater heats a thermal medium and transports the thermal medium with a raising temperature through the delivery pipe, and the heat-consuming machine consumes thermal energy of the thermal medium for processing and transport the thermal medium with a dropping temperature through the return pipe. (see fig 2)
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Examiner note: A Heat source (heater) that warms a thermal medium. A supply node and a series of pipes, which function as the delivery pipes for the hot medium. Multiple heat-consuming machines, labeled as Heat-exchange station and DN (Distribution network), which are connected to the main supply line. These stations consume the thermal energy. A response node and a series of pipes which function as the return pipes for the cooled medium.
12. Claims 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, Chenyu, et al. "Combined economic dispatch considering the time-delay of district heating network and multi-regional indoor temperature control." IEEE Transactions on Sustainable Energy 9.1 (2017): 118-127.) in view of *Schmitt etl. (PUB No: US20130289857A1) and still further in view of Hoyt et al. (Hoyt, Tyler, Edward Arens, and Hui Zhang. "Extending air temperature setpoints: Simulated energy savings and design considerations for new and retrofit buildings." Building and Environment 88 (2015): 89-96.).
Regarding claim 10
Wu further teaches and the method further comprises performing following steps by a processor: obtaining a heater setting data and a machine setting data; (see section III.A-For instance, if the average indoor temperature of heat load area 1 drops below the lower limit according to latest predictions, the dispatch center will send control signal to heat-exchange station 1 to increase the flow rate of distribution network, or send control signals to heat sources to improve the water temperature of transmission network. see page 121-mass flow rates should within their limits to prevent pipe vibration and temperature drop of the supply/return network in the kth heat load area at period)
The combination of Wu and Schmitt does not teach wherein the temperature data further comprises a heat setting lower bound, a heat setting upper bound, and an adjustment interval, and generating a plurality of simulation settings according to the heat setting lower bound and the adjustment interval, wherein each of the plurality of simulation settings is not greater than the heat setting upper bound.
In the related of invention, Hoyt teaches wherein the temperature data further comprises a heat setting lower bound, a heat setting upper bound, (see page 91 col 1 -while the heating setpoint is varied in the range of 17.8–21.1 °C (64–70 °F).)
and an adjustment interval, (See page 91-To carry out the parametric simulations, the software JEPlus was used. This software allows the user to parameterize fields in an EnergyPlus model and specify a discrete set of values for these fields. Upon execution, the set of values will supply the parameterized fields in the model, and the simulations are automated. In our case, the heating and cooling setpoints during occupied hours are parameterized in the reference models for each climate. Summary results were collected and hourly results stored for detailed zone temperature analysis. A total of 1638 simulations were carried out comprising 7 climates, 6 model types, and 39 distinct setpoint combinations (including 29 cooling setpoints, 11 heating setpoints, and 1 baseline combination).
Examiner note: Each discrete values is effectively adjustment interval.
and generating a plurality of simulation settings according to the heat setting lower bound and the adjustment interval, wherein each of the plurality of simulation settings is not greater than the heat setting upper bound. (See page 91 -A smaller set of simulations were carried out to examine whether independent heating and cooling savings calculated in the large parametric are additive. 7 distinct temperature setpoint ranges were considered in this analysis: 20.6–23.3 °C (69–74 °F), 20.0–24.4 °C (68–76 °F), 19.4–25.6 °C (67–78 °F), 18.9–26.7 °C (66–80 °F), 18.3–27.8 °C (65–82 °F), 17.8–28.9 °C (64–84 °F), and 17.2–30.0 °C (63–86 °F). As in the main analysis, these simulations are carried out for 7 climates and 6 model types, totaling 294 simulations.)
Examiner note: It performs 294 parametric simulations across setpoints and other parameters; each simulation uses a setpoint within the defined bounds.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include wherein the temperature data further comprises a heat setting lower bound, a heat setting upper bound, and an adjustment interval, and the method further comprises performing following steps by a processor: obtaining a heater setting data and a machine setting data; and generating a plurality of simulation settings according to the heat setting lower bound and the adjustment interval, wherein each of the plurality of simulation settings is not greater than the heat setting upper bound as taught by Hoyt in the system of Wu and Schmitt in order to fully realize energy savings from widening thermostat temperature setpoints. (See abstract, Hoyt)
Regarding claim 12
Wu does not teach wherein determining each of the plurality of simulation temperature according to the temperature threshold and the temperature data to update the heating temperature comprises: determining whether each of the plurality of simulation temperatures is greater than the temperature threshold, wherein: when at least one of the plurality of simulation temperatures is not smaller than the temperature threshold, updating the heater setting data with the simulation setting corresponding to a minimum of said at least one of the plurality of simulation temperatures; and when a maximum of the plurality of simulation temperatures is smaller than the temperature threshold, updating the heater setting data with the heat setting upper bound.
However, Schmitt further teaches wherein determining each of the plurality of simulation temperature according to the temperature threshold and the temperature data to update the heating temperature comprises: determining whether each of the plurality of simulation temperatures is greater than the temperature threshold, wherein: when at least one of the plurality of simulation temperatures is not smaller than the temperature threshold, updating the heater setting data with the simulation setting corresponding to a minimum of said at least one of the plurality of simulation temperatures; (See para 015-018-The normal mode may for example correspond to engine settings designed to optimize emissions and fuel consumption. In doing so, the obtained predicted temperatures (i.e. temperatures within the predicted temperature evolution) may be compared to an upper temperature threshold and the heat-up mode may be stopped when it is determined that the predicted temperature has reached or exceeds such upper temperature threshold. See also para 67-70-The thermal control of the SCR-catalyst may then be performed in the engine ECU by comparing the predicted temperature values to a threshold to decide whether or not to switch from one operating mode to the other (from heat-up to normal or vice-versa). Still in FIG. 3, the dashed line indicates the so-called “maximum” decision threshold (TTH — MAX) for deciding whether or not the heat-up mode may be ended, while ensuring that the SCR will subsequently operate in a desired temperature range. At t=now, the temperatures correspond to the current, real temperatures of each component (DOC, DPF and SCR). On the right of the t=now line, all temperatures are simulated, supposing that the engine operating mode is switched from “heat up” to “normal” and assuming that the SCR will reach a steady state temperature TSCR future 1 (the long term temperature associated with the normal mode. As can be seen, the simulation reveals that TSCR will reach TTH — MAX. Therefore, the engine operating mode can be switched to normal from that moment on)
and
when a maximum of the plurality of simulation temperatures is smaller than the temperature threshold, updating the heater setting data with the heat setting upper bound. (see para 15-The aim of the heat-up mode is to increase the temperature in the exhaust system and hence of the exhaust aftertreatment means, i.e. to perform the “thermal management” of the exhaust line. When these engine combustion modes (normal/heat-up) are controlled in a coordinate manner with respect to the second exhaust aftertreatment means, they may be said to be associated therewith. In heat-up mode, any appropriate measures may be taken that result in an increase in the temperature of the exhaust gases arriving at the second exhaust aftertreatment means (as compared to normal mode), namely by acting on engine settings/control parameters to heat-up the temperature of engine-out gases or by injecting fuel in the exhaust line that is burned in the DOC. see para 71-The inverse situation will now be explained with respect to FIG. 4. At t=0 (now) the engine is currently operated in the normal mode and the current temperatures are those indicated by the points on the vertical line. On the right of the vertical line t=now, the simulated temperatures are represented for the future, simulated time period, with the hypothesis of a switch to the “heat up” mode and a long term temperature TSCR future 2. As can be seen, if the heat up mode was entered at t=now, the SCR temperature would continue dropping down to a minimum and level off towards a steady state temperature. Detecting the minimum of the predicted temperatures allows deciding when to switch from normal to heat up. A proper selection of the minimum threshold value will avoid a sensible temperature drop of the SCR catalyst. In FIG. 4, the simulated SCR temperature drops down to the minimum threshold level indicated TTH — MIN, so that the mode is switched to “heat up” again.)
Examiner note: “When” limitation is conditional. Thus, a reference does not need to perform both branches of when condition.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include wherein determining each of the plurality of simulation temperature according to the temperature threshold and the temperature data to update the heating temperature comprises: determining whether each of the plurality of simulation temperatures is greater than the temperature threshold, wherein: when at least one of the plurality of simulation temperatures is not smaller than the temperature threshold, updating the heater setting data with the simulation setting corresponding to a minimum of said at least one of the plurality of simulation temperatures; and when a maximum of the plurality of simulation temperatures is smaller than the temperature threshold, updating the heater setting data with the heat setting upper bound.as taught by Schmitt in the system of Wu and Hoyt in order to control of an internal combustion engine equipped with an oxidation catalyst and a NOx treatment device such as a SCR catalyst, thus reduce the concentration of combustion byproducts and/or products of incomplete combustion. (See para 001-002, Schmitt)
13. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Wu, Chenyu, et al. "Combined economic dispatch considering the time-delay of district heating network and multi-regional indoor temperature control." IEEE Transactions on Sustainable Energy 9.1 (2017): 118-127.) in view of *Schmitt etl. (PUB No: US20130289857A1) and still further in view of Hoyt et al. (Hoyt, Tyler, Edward Arens, and Hui Zhang. "Extending air temperature setpoints: Simulated energy savings and design considerations for new and retrofit buildings." Building and Environment 88 (2015): 89-96.) and still further in view of Li, Xiuming, et al. "Modeling for indoor temperature prediction based on time-delay and Elman neural network in air conditioning system." Journal of Building Engineering 33 (2021): 101854.)
Regarding claim 11
The combination of Wu, Schmitt and Hoyt does not teach generating the plurality of simulation temperatures according to the temperature prediction model comprises: inputting each of the plurality of simulation settings, the heater setting data, and the machine setting data to the temperature prediction model to generate the plurality of simulation temperatures.
In the related of invention, Li teaches wherein generating the plurality of simulation temperatures according to the temperature prediction model comprises: inputting each of the plurality of simulation settings, the heater setting data, and the machine setting data to the temperature prediction model to generate the plurality of simulation temperatures. (See fig 3 and section 2.2.1 and 3.4.1- According to indoor temperature regulation principle of air conditioning systems, the schematic and architecture of TDNN for indoor temperature prediction is illustrated in Fig. 3. Where, SAT represents supply air temperature; SAQ represents supply air volume; T represents indoor temperature. Supply air fan control loop adopts conventional constant static pressure method, which regulates fan speed to remain supply air pressure within the range of its setting value. See section 3.1.3- In this section, the simulation sampling data is used to validate the modeling method for indoor temperature prediction based on two neural networks. The simulation dataset, which includes the profiles of supply air temperature, supply air volume and indoor temperature, is shown in Fig. 8. There are 250 sets of training data, and the sampling time interval is 1 min.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include a generating the plurality of simulation temperatures according to the temperature prediction model comprises: inputting each of the plurality of simulation settings, the heater setting data, and the machine setting data to the temperature prediction model to generate the plurality of simulation temperatures as taught by Li in the system of Wu, Schmitt and Hoyt in order to build indoor temperature model in improving energy efficiency and in door thermal comfort of air conditioning system. (See abstract, Li)
14. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Wu, Chenyu, et al. "Combined economic dispatch considering the time-delay of district heating network and multi-regional indoor temperature control." IEEE Transactions on Sustainable Energy 9.1 (2017): 118-127.) in view of *Schmitt etl. (PUB No: US20130289857A1) and and still further in view of Li, Xiuming, et al. ("Modeling for indoor temperature prediction based on time-delay and Elman neural network in air conditioning system." Journal of Building Engineering 33 (2021): 101854.)
Regarding claim 14
Wu teaches wherein obtaining the temperature data of the heat cycle system by the operation interface comprises: obtaining the response temperature data
In the related field of invention, Li teaches obtaining the response temperature data by a temperature sensor. (see table 1 and fig 1-supply air temperature sensor, indoor temperature sensor and air volume sensor)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include a two temperature sensors disposed on an operation node and a response node respectively, and a processor communicably connecting to the two temperature sensors as taught by Li in the system of Wu and Schmitt in order to build indoor temperature model in improving energy efficiency and in door thermal comfort of air conditioning system. (See abstract, Li)
15. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Wu, Chenyu, et al. "Combined economic dispatch considering the time-delay of district heating network and multi-regional indoor temperature control." IEEE Transactions on Sustainable Energy 9.1 (2017): 118-127.) in view of Li, Xiuming, et al. ("Modeling for indoor temperature prediction based on time-delay and Elman neural network in air conditioning system." Journal of Building Engineering 33 (2021): 101854.)in view of *Schmitt etl. (PUB No: US20130289857A1) and still further in view of Hoyt et al. (Hoyt, Tyler, Edward Arens, and Hui Zhang. "Extending air temperature setpoints: Simulated energy savings and design considerations for new and retrofit buildings." Building and Environment 88 (2015): 89-96.) and still further Schmitt etl. (PUB No: US20130289857A1))
Regarding claim 17
Wu further teaches an input interface configured to obtain a temperature threshold of the response node, (See page 122-The return temperature of heat exchanger is required to exceed a threshold to ensure the load-serving quality and lower than an upper limit to prevent steam forming)
wherein the processor is communicably connected to the input interface and the set of instructions further comprises: obtaining a heater setting data and a machine setting data; (see section III.A-For instance, if the average indoor temperature of heat load area 1 drops below the lower limit according to latest predictions, the dispatch center will send control signal to heat-exchange station 1 to increase the flow rate of distribution network, or send control signals to heat sources to improve the water temperature of transmission network. see page 121-mass flow rates should within their limits to prevent pipe vibration and temperature drop of the supply/return network in the kth heat load area at period)
Li further teaches inputting each of the plurality of simulation settings, the heater setting data, and the machine setting data to the temperature prediction model to generate a plurality of simulation temperatures; (See fig 3 and section 2.2.1 and 3.4.1- According to indoor temperature regulation principle of air conditioning systems, the schematic and architecture of TDNN for indoor temperature prediction is illustrated in Fig. 3. Where, SAT represents supply air temperature; SAQ represents supply air volume; T represents indoor temperature. Supply air fan control loop adopts conventional constant static pressure method, which regulates fan speed to remain supply air pressure within the range of its setting value. See section 3.1.3- In this section, the simulation sampling data is used to validate the modeling method for indoor temperature prediction based on two neural networks. The simulation dataset, which includes the profiles of supply air temperature, supply air volume and indoor temperature, is shown in Fig. 8. There are 250 sets of training data, and the sampling time interval is 1 min.)
The combination of Wu and Li does not teach wherein the temperature data further comprises a heat setting lower bound, a heat setting upper bound, and an adjustment interval, and generating a plurality of simulation settings according to the heat setting lower bound and the adjustment interval, wherein each of the plurality of simulation settings is not greater than the heat setting upper bound, generating a plurality of simulation settings according to the setting lower bound and the adjustment interval, wherein each of the plurality of simulation settings is not greater than the setting upper bound; determining whether each of the plurality of simulation temperatures is greater than the temperature threshold, wherein when at least one of the plurality of simulation temperatures is not smaller than the temperature threshold, updating the heater setting data with the simulation setting corresponding to a minimum of said at least one of the plurality of simulation temperatures; and when a maximum of the plurality of simulation temperatures is smaller than the temperature threshold, updating the heater setting data with the setting upper bound.
However, Hoyt teaches wherein the temperature data further comprises a heat setting lower bound, a heat setting upper bound, (see page 91 col 1 -while the heating setpoint is varied in the range of 17.8–21.1 °C (64–70 °F).)
and an adjustment interval, (See page 91-To carry out the parametric simulations, the software JEPlus was used. This software allows the user to parameterize fields in an EnergyPlus model and specify a discrete set of values for these fields. Upon execution, the set of values will supply the parameterized fields in the model, and the simulations are automated. In our case, the heating and cooling setpoints during occupied hours are parameterized in the reference models for each climate. Summary results were collected and hourly results stored for detailed zone temperature analysis. A total of 1638 simulations were carried out comprising 7 climates, 6 model types, and 39 distinct setpoint combinations (including 29 cooling setpoints, 11 heating setpoints, and 1 baseline combination).
Examiner note: Each discrete values is effectively adjustment interval.
and generating a plurality of simulation settings according to the heat setting lower bound and the adjustment interval, wherein each of the plurality of simulation settings is not greater than the heat setting upper bound. (See page 91 -A smaller set of simulations were carried out to examine whether independent heating and cooling savings calculated in the large parametric are additive. 7 distinct temperature setpoint ranges were considered in this analysis: 20.6–23.3 °C (69–74 °F), 20.0–24.4 °C (68–76 °F), 19.4–25.6 °C (67–78 °F), 18.9–26.7 °C (66–80 °F), 18.3–27.8 °C (65–82 °F), 17.8–28.9 °C (64–84 °F), and 17.2–30.0 °C (63–86 °F). As in the main analysis, these simulations are carried out for 7 climates and 6 model types, totaling 294 simulations.)
Examiner note: It performs 294 parametric simulations across setpoints and other parameters; each simulation uses a setpoint within the defined bounds.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include obtaining the temperature threshold and determining each of the plurality of simulation temperatures according to the temperature threshold and the temperature data to update the heating temperature as taught by Hoyt in the system of Wu and Li in order to fully realize energy savings from widening thermostat temperature setpoints. (See abstract, Hoyt)
The combination of Wu, Li and Hoyt does not teach wherein determining each of the plurality of simulation temperature according to the temperature threshold and the temperature data to update the heating temperature comprises: determining whether each of the plurality of simulation temperatures is greater than the temperature threshold, wherein: when at least one of the plurality of simulation temperatures is not smaller than the temperature threshold, updating the heater setting data with the simulation setting corresponding to a minimum of said at least one of the plurality of simulation temperatures when a maximum of the plurality of simulation temperatures is smaller than the temperature threshold, updating the heater setting data with the heat setting upper bound.
However, Schmitt further teaches wherein determining each of the plurality of simulation temperature according to the temperature threshold and the temperature data to update the heating temperature comprises: determining whether each of the plurality of simulation temperatures is greater than the temperature threshold, wherein: when at least one of the plurality of simulation temperatures is not smaller than the temperature threshold, updating the heater setting data with the simulation setting corresponding to a minimum of said at least one of the plurality of simulation temperatures; (See para 015-018-The normal mode may for example correspond to engine settings designed to optimize emissions and fuel consumption. In doing so, the obtained predicted temperatures (i.e. temperatures within the predicted temperature evolution) may be compared to an upper temperature threshold and the heat-up mode may be stopped when it is determined that the predicted temperature has reached or exceeds such upper temperature threshold. See also para 67-70-The thermal control of the SCR-catalyst may then be performed in the engine ECU by comparing the predicted temperature values to a threshold to decide whether or not to switch from one operating mode to the other (from heat-up to normal or vice-versa). Still in FIG. 3, the dashed line indicates the so-called “maximum” decision threshold (TTH — MAX) for deciding whether or not the heat-up mode may be ended, while ensuring that the SCR will subsequently operate in a desired temperature range. At t=now, the temperatures correspond to the current, real temperatures of each component (DOC, DPF and SCR). On the right of the t=now line, all temperatures are simulated, supposing that the engine operating mode is switched from “heat up” to “normal” and assuming that the SCR will reach a steady state temperature TSCR future 1 (the long term temperature associated with the normal mode. As can be seen, the simulation reveals that TSCR will reach TTH — MAX. Therefore, the engine operating mode can be switched to normal from that moment on)
and
when a maximum of the plurality of simulation temperatures is smaller than the temperature threshold, updating the heater setting data with the heat setting upper bound. (see para 15-The aim of the heat-up mode is to increase the temperature in the exhaust system and hence of the exhaust aftertreatment means, i.e. to perform the “thermal management” of the exhaust line. When these engine combustion modes (normal/heat-up) are controlled in a coordinate manner with respect to the second exhaust aftertreatment means, they may be said to be associated therewith. In heat-up mode, any appropriate measures may be taken that result in an increase in the temperature of the exhaust gases arriving at the second exhaust aftertreatment means (as compared to normal mode), namely by acting on engine settings/control parameters to heat-up the temperature of engine-out gases or by injecting fuel in the exhaust line that is burned in the DOC. see para 71-The inverse situation will now be explained with respect to FIG. 4. At t=0 (now) the engine is currently operated in the normal mode and the current temperatures are those indicated by the points on the vertical line. On the right of the vertical line t=now, the simulated temperatures are represented for the future, simulated time period, with the hypothesis of a switch to the “heat up” mode and a long term temperature TSCR future 2. As can be seen, if the heat up mode was entered at t=now, the SCR temperature would continue dropping down to a minimum and level off towards a steady state temperature. Detecting the minimum of the predicted temperatures allows deciding when to switch from normal to heat up. A proper selection of the minimum threshold value will avoid a sensible temperature drop of the SCR catalyst. In FIG. 4, the simulated SCR temperature drops down to the minimum threshold level indicated TTH — MIN, so that the mode is switched to “heat up” again.)
Examiner note: Although the claim 17 uses conditional language (e.g., “when at least one…”, “when a maximum…”), the claim is drawn to a system comprising a processor executing instructions. Conditional steps are not optional in an apparatus or system claim because the claimed system must be configured with the capability to perform all recited functions, irrespective of whether the triggering conditions actually occur during operation. Examiner rejection reflects the prior art on both conditional branches of the logic in claim 17, because the system must possess the ability to perform all claimed operations.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of time delay in the DHN as disclosed by Wu to include wherein determining each of the plurality of simulation temperature according to the temperature threshold and the temperature data to update the heating temperature comprises: determining whether each of the plurality of simulation temperatures is greater than the temperature threshold, wherein: when at least one of the plurality of simulation temperatures is not smaller than the temperature threshold, updating the heater setting data with the simulation setting corresponding to a minimum of said at least one of the plurality of simulation temperatures; and when a maximum of the plurality of simulation temperatures is smaller than the temperature threshold, updating the heater setting data with the heat setting upper bound.as taught by Schmitt in the system of Wu, Li and Hoyt in order to control of an internal combustion engine equipped with an oxidation catalyst and a NOx treatment device such as a SCR catalyst, thus reduce the concentration of combustion byproducts and/or products of incomplete combustion. (See para 001-002, Schmitt)
Conclusion
16. All claims 1-20 are rejected.
17. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PURSOTTAM GIRI whose telephone number is (469)295-9101. The examiner can normally be reached 7:30-5:30 PM, Monday to Friday.
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/PURSOTTAM GIRI/
Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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