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 .
Response to Amendment
3. The amendment filed on 02/12/2026 has been entered and considered by the examiner. By the amendment, claims 1, 2, 4, 9, 12, 14 and 15 are amended. In view of amendment made, the previous 101 rejection is withdrawn since the claim is directed towards producing a technical improvement in closed-loop thermal control, enabling predictive and automatic heater adjustment based on real-world system behavior. The prior art rejection is still maintained in view of the amendments made. See office action.
Applicant arguments
Applicant argues Claim 1, in contrast, requires computing the response time from measured operational and response temperatures of the heat cycle system, which reflects the actual physical transport characteristics of the thermal medium. Claim 1, however, requires alignment to synchronize the operation node and response node based on a dynamically computed physical response time, thereby preserving the causal relationship between system operation and thermal response.
Examiner response
Examiner respectfully disagrees. First, claim 1 merely recites “compute response time of the heat cycle system”. The claim does not specify how the response time is computed or require any particular technique. Under the BRI, “computing response time” requires determining or selecting a delay representing system response. Li explicitly teaches see section 3.1.3- “Meanwhile, indoor temperature response lags behind the change of system regulation variables, and the delay time is 5 min.” and further determines that see section 3.2.1- “Results indicate that 5 is the optimal value of input-layer delay coefficient, and it is in accordance with the delay time of indoor temperature against system regulation variables according to the simulation sampling data”. Thus, Li determines a system delay based on observed system behavior, which reasonably corresponds to the claimed response time.
Second, claim 1 recites “aligning the measured temperature data and a setting value of the heat cycle system to generate a training data according to the response time” but does not require any specific form of alignment such as synchronizing nodes, or preserving casual relationships as Applicant argues. Li discloses constructing training data using earlier time steps (e.g., 1st – 5th data sets) as inputs and alter time steps (e.g., 6th -200th data sets) as outputs, thereby associating input variables with delayed responses. This pairing is explicitly based on the determined delay. Under BRI, such delay-based association of input and output data constitutes “aligning …. according to the response time” because the data are organized relative to one another based on the system delay.
Finally, Applicant ‘s argument regarding difference between Wu’s district heating system and Li’s HVAC system is not persuasive. Both references address thermal systems. Li teaches a general technique for handling time delay in temperature prediction, and applying synch a known technique to Wu’s system represents a predictable use of prior art features according to their established functions.
Applicant arguments
The combination of Wu and Schmitt fails to teach or suggest "determining each of the plurality of simulation temperatures according to the temperature threshold and the temperature data to update the heating temperature" as recited in Claim 9. Schmitt discloses a binary mode- switching logic for an internal combustion engine exhaust system, which determines whether to switch between "heat-up" and "normal" modes based on a single predicted value reaching a threshold. Claim 9, in contrast, requires a multi-simulation evaluation process where each simulation temperature is assessed to update a specific heater setting. The binary logic in Schmitt does not teach or suggest this process. Moreover, the combination of Wu and Schmitt is technically inappropriate.
Examiner response
Applicant arguments is not persuasive on claim 9. Primary refences Wu teacher generating a plurality of simulation temperatures according to a temperature prediction model and secondary reference Schmitt used for determining each of the plurality of simulation temperatures according to the temperature threshold. Examiner disagrees with the fact provided by the Applicant since there are multiple simulated temperatures in Schmitt. See Schmitt para 65-Accordingly, during the simulation period a plurality of predicted temperatures are preferably calculated that are indicative of the estimated temperatures that the SCR will take in the future. The determination of whether to switch operation models is based on this predicted temperature evolution required the consideration of the simulated temperature values. (see [0016]) also see (0077])- For practical reasons, the extrema of the predicted temperature during the simulation time frame (tp) are extracted and named TSCR pred MAX and TSCR pred MIN. For the simulation step between 138 and 140 s, the TSCR pred MAX=T2 and TSCR pred MIN=T1. These extreme values are derived from the full set of simulated/predicted temperatures, and therefore necessarily reflect an evaluation of the plurality of simulation temperatures.
Furthermore, Applicant argument that Schmitt operates using “binary logic” is not persuasive, as the switching decision is based on predicted temperature behavior over time, not a single predicted value. The predicted temperature evolution comprises multiple data points, and the decision logic is based on this evolution. Binary decision still uses multi-simulation data. See [0018-0019] obtained predicted temperatures (i.e. temperatures within the predicted temperature evolution) may be compared to an upper temperature threshold. Again, this may be carried out by comparing the obtained predicted temperature to a lower temperature threshold. Importantly, Schmitt determine the trajectory reach or exceeds a threshold within the simulated horizon. See fig 5 and [0065]-However in a preferred embodiment, as will be explained below, only the extrema are memorized for decision making.
Therefore, Schmitt necessarily processes the plurality of simulated temperature to determine whether threshold conditions are met over the simulated period. The use of extrema is choice for efficient control. Schmitt further discloses that the result of this determination is used to control heating. see [0016]-The operating mode (heat-up or normal) may then be changed depending on the predicted temperature evolution. Switching between heat up and normal modes directly controls the heating applied to the exhaust system i.e., updates a heater setting of the system. See fig 5 -multiples simulation temperature over time. To determine whether the maximum predicted temperature exceeds a threshold, Schmitt must necessarily compare multiple predicted temperatures and identify the maximum. It is the Examiner position that mode switching which is equivalent to claimed update heating temperature. Wu teaches multi-region heating control, also teaches temperature constraints/thresholds and temperature-based control in a heat system. Schmitt teaches predictive simulation and threshold decision. It would have been obvious to apply Schmitt’s predictive threshold control to Wu’s heating system.
Applicant arguments
Claim 15 recites a heat cycle system with two temperature sensors disposed on an "operation node" and a "response node," where the operation node is located at the output of the heat-consuming machine and the response node at the input of the heater. Wu discloses a large- scale district heating network with general data points and temperature labels, while Li discloses sensor placement in small-room HVAC systems. Neither Wu nor Li teaches or suggests the specific sensor placement at these industrial loop coordinates.
Additionally, the processor in Claim 15 integrates real-time data from these two nodes to dynamically update the heater setting based on the physical transport lag. Neither Wu nor Li discloses such integration or dynamic control logic. Furthermore, the proposed combination lacks a proper technical motivation. Wu operates on kilometers of pipelines with significant thermal inertia, and Li observes a fixed indoor delay in a small space. A person of ordinary skill in the art would not be motivated to adopt Li's HVAC sensor logic to solve the physical delay in Wu's industrial system. The Examiner's reasoning also relies on functional similarity rather than structural correspondence, which is insufficient to establish obviousness for a system claim.
Examiner response
Applicant argues that neither Wu nor Li teaches or suggests the recited “operation node” and “response node” sensor configuration, and further contends that the combination lacks technical motivation due to differences in system scale and thermal inertia. These arguments are not persuasive. Wu discloses a district heating system (see fig 2) comprising multiple heat load areas, heat sources, and associated measurement points used for system control and optimization. As described, Wu monitors temperature conditions at various locations within the heating network and utilizes such data for coordinated operation and control of heating resources. These measurement locations correspond to nodes within the system through which thermal energy is transferred. (see fig 2) Li discloses the use of multiple temperature sensors in an HVAC system (see fig 6) including sensors positioned at different locations within a thermal control loop (e.g., upstream and downstream of heating elements), for the purpose of regulating temperature and improving control accuracy. Li further teaches using sensor feedback to dynamically adjust heating operation. It would have been obvious to a person of ordinary skill in the art to incorporate Li’s sensor placement and feedback control techniques into Wu’s heating system in order to improve temperature regulation and system responsiveness. The motivation for such combination is the well-known benefit of using multiple sensor inputs at different locations to enhance control accuracy in thermal systems. Applicant’s argument that Li is limited to small-room HVAC systems while Wu relates to large-scale district heating systems is not persuasive. The application of sensor-based feedback control is a well-understood technique that is not limited by system scale. And the claim does not require any particular scale, nor does it recite structural limitations that would preclude such adaptation. The recited “operation node” and “response node” broadly correspond to measurement points within a thermal system, which are taught by the combined references.
With respect to Applicant’s argument regarding “transport lag,” Wu explicitly considers time delay effects in district heating networks, including the delay associated with heat transfer through pipelines. Incorporating Li’s sensor placement into Wu’s system would have enabled monitoring of temperature at different points along the heat transfer path, thereby accounting for such delay in control decisions. Accordingly, the combination of Wu and Li teaches or at least suggests the claimed sensor arrangement and corresponding control functionality. The rejection of Claim 15 under 35 U.S.C. §103 is therefore maintained.
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. 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.)
wherein the temperature prediction model is used by a processor to automatically update a heater setting of the heat cycle system to maintain a temperature of a response node above a temperature threshold. (See page 123 col 1-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.)
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. also see equation 1) 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 in view of Li teaches the method for building the temperature prediction model of claim 1. 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 value 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 value 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 in view of Li teaches the method for building the temperature prediction model of claim 2. 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
Wu in view of Li teaches the method for building the temperature prediction model of claim 1. Wu 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.
wherein the processor is configured to automatically control operation of the heater by updating a temperature setting based on predicted response-node temperature. (See page 123 col 1-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.)
Wu does 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 in view of Li teaches the heat cycle system of claim 15. 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 in view of Li teaches the heat cycle system of claim 15. 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 in view of Li teaches the method for building the temperature prediction model of claim 2. 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 heater 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 alignment 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 alignment 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 alignment 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 heater 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 heater 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 heater 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
Wu in view of Li teaches the method for building the temperature prediction model of claim 1. 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 in view of Li teaches the method for building the temperature prediction model of claim 1. 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 13-14 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 et al. (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)
obtaining response temperature data of the response node (See page 123 col 1- The feedback signal (mainly includes measured average indoor temperature)
and automatically transmitting the updated heating temperature to the heater to control heating of the thermal medium. (See page 123 col 1-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.)
Wu does not teach obtaining response temperature data of the response node from a temperature sensor 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 response temperature data of the response node from a temperature sensor; (see para 61-As explained, the thermal model 10 has four inputs: the current temperature of each component TDOC feedback, TDPF feedback and TSCR feedback. These temperatures may be measured or estimated by conventional methods.)
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 0015-016- 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). According to the present invention, a predicted temperature evolution of the second exhaust aftertreatment means is regularly determined based on a thermal model. See para 65-67-Accordingly, during the simulation period a plurality of predicted temperatures are preferably calculated. 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 response temperature data of the response node from a temperature sensor 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 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 in view of Schmitt teaches the method for setting the heating temperature of claim 9. 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.
Regarding claim 14
Wu in view of Schmitt teaches the method for setting the heating temperature of claim 9. Wu teaches wherein obtaining the temperature data of the heat cycle system by the operation interface comprises: obtaining response temperature data
In the related field of invention, Li teaches obtaining response temperature data by a temperature sensor. (See para 61-As explained, the thermal model 10 has four inputs: the current temperature of each component TDOC feedback, TDPF feedback and TSCR feedback. These temperatures may be measured or estimated by conventional methods.)
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 response temperature data by a temperature sensor 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)
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 in view of Schmitt teaches the method for setting the heating temperature of claim 9. 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 in view of Schmitt and Hoyt teaches the method for setting the heating temperature of claim 10. Wu does not teach wherein determining each of the plurality of simulation temperatures 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 temperatures 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
Wu in view of Schmitt and Hoyt teaches the method for setting the heating temperature of claim 10. 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)
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 in view of Li teaches the heat cycle system of claim 15. 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.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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