DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-18 were previously pending and subject to a final office action mailed 05/12/2025. Claims 1, 7 and 13 were amended; claims 4, 6, 10, 12, 16 and 18 were cancelled, and no claim was added in a reply filed 09/04/2026. Therefore claims 1-3, 5, 7-9, 11, 13-15 and 17 are currently pending and subject to the nonfinal office action below.
Response to Arguments
Applicant’s arguments with respect to 103 rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments, see remarks p. 12-14, filed 09/04/2026, with respect to 101 rejection have been fully considered and are persuasive. The 101 rejection of claims 1-18 has been withdrawn.
Applicant’s arguments regarding interpretation under 35 USC 112(f) have been considered and are persuasive in view of the amendments. Independent claims 1, 7 and 13 now expressly recite that the factory energy management system (FEMS) comprises one or more processors and one or more control devices, identify the monitoring and analysis functions performed by the processors, and require control of energy consumption through the recited control devices. Accordingly, when the limitation is considered as a whole, it recites sufficient structure for performing the claimed functions such that prong C of the three prong analysis of MPEP 2181 is not satisfied. The FEMS limitation will therefore not be interpreted under 35 USC 112(f).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3, 5, 7-9, 11, 13-15 and 17 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1/7/13 recite “transmitting the generated control settings to the loT-based ventilation system installed in a prescribed space of the factories, the industrial facilities, and the plants, controlling the loT-based ventilation system according to the generated control settings to adjust operation of the loT-based ventilation system in the prescribed space, collecting indoor air information from at least one loT-based sensor installed in the prescribed space, and adjusting the generated control settings based on the collected indoor air information” The limitation is new matter because Examiner is unable to find support for it in the specification. The specification describes a test bed implementation in which facility materials and construction methods are applied to a test bed, a sensor installed in the test bed is monitored, and a ventilation device “installed in the test bed” is controlled while indoor air information is collected and analyzed (paragraph 55-60).
The disclosure then separately explains that, based on the results of the test bed simulation, the system “may provide settings” for an IoT based ventilation system intended to optimize indoor air in a prescribed space (paragraph 62-64) the specification further states that device type, number, installation location, and operating/stopping conditions can be derived, and that a consultant may customize the derived results through a user interface.
The passage above disclose a test bed sensing/control, simulation, derivation/provision of settings for a prescribed space ventilation system. They do not describe the claimed sequence of: generated settings, transmission to the ventilation system installed in the prescribed operational space, operation of that installed system according to the transmitted settings, collection of resulting indoor-air information from a sensor int hat prescribed space and then adjustment of the previously generated settings based upon that resulting feedback.
In particular, paragraph 62 and 77, relied upon by Applicant for the transmitting limitation, disclose providing settings based on simulation results, but do not disclose transmitting those settings to an installed prescribed space ventilation system for execution.
Paragraph 31, 56 and 64 disclose energy management control generally, control of a ventilation device in the test bed, and derivation of operating/stopping conditions. They do not disclose controlling the prescribed space ventilation system according to the generated settings.
Paragraph 48 and 60 disclose sensors and sensor data collection, including test bed sensors, but do not disclose collecting post control indoor air feedback from a sensor installed in the claimed prescribed operational space as part of the claimed sequence.
Paragraph 63-64 disclose optimized settings and user customization of derived results. They do not disclose adjusting the generated control settings based on subsequently collected indoor air information from the controlled prescribed space system.
The fact that the specification separately disclose sensors, ventilation controls, generated settings, and a prescribes space does not itself provide written description support for the newly claimed ordered closed loop relationship among those elements.
Accordingly, the limitation is new matter.
Claims 1/7/13 recite “calculating a carbon reduction amount by applying a carbon emission factor based on integrated operation data collected from both the factory energy management system (FEMS) and the loT-based ventilation system,_the integrated operation data including the collected indoor air information from the adjusted loT-based ventilation system,” the limitation is new matter because Examiner is unable to find support for it in the specification.
The specification does not disclose that the big data server may collect data from the FEMS, an IoT based ventilation system, and a test bed. It also discloses using energy use data from the FEMS and ventilation system to train or apply energy optimization models. (paragraph 33 and 42).
However, the disclosure of collecting data from both sources for storage, analysis, or machine learning purposes does not disclose that the subsequently claimed carbon reduction calculation itself is performed based on an integrated dataset composed of operation data from both systems.
To the contrary, the originally filed carbon reduction disclosure states that the carbon reduction amount may be calculated based on analyzed energy performance and operation data provided by the FEMS (paragraph 66). The ESG disclosure separately for analysis of data from the FEMS or the ventilation system and not expressly requiring both (paragraph 68-69).
The distinction is also reflecte din the claims as originally published. Original claims 6, 12, and 18 required calculating the carbon reduction amount based on data collected from a FEMS or a ventilation system rather than both systems.
Thus, the original disclosure provides three different concepts:
The server can receive information from both systems;
The information from both systems can be used for certain energy modeling/optimization process; and
ESG/carbon calculations ca use FEMS or ventilation system information.
However, the specification does not reasonably convey possession of the newly claimed fourth concept of using both the FEMS and the IoT ventilation system data as the basis for the carbon reduction calculation.
The additional requirement that the integrated operation data include “the collected indoor air information from the adjusted IoT based ventilation system” is even further removed from the originally filed specification. As explained above, the specification does not describe the claimed prescribed space feedback loop producing an adjusted ventilation system whose resulting indoor air information is then incorporated into the integrated dataset used for the carbon reduction calculation.
Accordingly, the limitation is new matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
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.
Claim(s) 1-3, 5, 7-9, 11, 13-15 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ko (KR 102565319) in view of Choi (WO 2022182092) and Risbeck (US 2023/0250988).
As per claim 1/13, Ko discloses a method for achieving carbon neutrality, comprising:
acquiring, via an energy information acquisition device including at least one sensor and a communication interface, energy information comprising at least one of power, flow rate, gas, temperature, humidity, pressure, or weather data from factories, industrial facilities, and plants ([0052] [For example, measuring devices such as watt-hour meters, flow meters, temperature/humidity, and illuminance sensors required according to the shape, space size, and type of facilities of the proposed building; And control devices such as lighting, boilers, air conditioners, heaters, gas, etc., and automatically set and provide facility operation information settings, monitoring plans, IoT-based data collection plans, etc. for optimal energy management.]”, [0023] [The online network referred to in the present invention may be a core network integrated with a wired public network, a wireless mobile communication network, or a portable Internet, etc. Several services exist, namely Hyper Text Transfer Protocol (HTTP), Hyper Text Transfer Protocol Secure (HTTPS), Telnet, File Transfer Protocol (FTP), Domain Name System (DNS), Simple Mail Transfer Protocol (SMTP), MQTT Protocol ( Message Queueing Telemetry Transport), IPFS (Inter Planetary File System), etc., and may refer to a worldwide open computer network structure, which is not limited to these examples and comprehensively refers to a data communication network capable of transmitting and receiving data in various forms. will do.] [0024] [The terminal in the present invention should be interpreted as including various communication means such as desktop, tablet, netbook, PDA (personal digital assistant), PMP (portable multimedia player), smart phone, wearable smart device, etc., web-based or separate Various functions provided by the server can be executed through software/applications of the server.]”, “0022] [ESG-based smart green construction carbon neutral platform (10, hereinafter carbon neutral platform) according to an embodiment of the present invention includes a consultant terminal 100, a smart green construction carbon neutral system (200, hereinafter carbon neutral system), an architect terminal ( 300), BEMS (Building Energy Management System, 400) and big data server 500, and each device can communicate with each other by being connected to a network.]”);
analyzing, by at least one processor, energy performance of the buildings based on the acquired energy information, and deriving energy saving factors and renewable energy production factors ( paragraph 26, “analyzes the energy performance of the building, derives energy saving factors and renewable energy production factors, evaluates harmful substances in indoor building materials, verifies the performance of eco-friendly indoor building materials/construction methods, and It performs control, monitoring and optimization analysis on energy consumption facilities inside and outside the building, and quantitatively measures/evaluates the ESG performance of the building and provides it.]”);
verifying, by the at least one processor, performance of facility materials and facility construction methods using simulation results generated from the acquired energy information, and optimizing internal air by generating control settings for an loT-based ventilation system and collecting sensor information and controlling an IoT ventilation apparatus (paragraph 59, “Air quality optimization unit 230 evaluates harmful substances in indoor building materials, verifies the performance of eco-friendly indoor building materials or eco-friendly construction methods, and proposes optimization of indoor air quality.”, “[0062] [The IoT simulation unit 232 builds a test bed to which eco-friendly materials and eco-friendly construction methods are applied, and analyzes and simulates indoor air quality by applying IoT-based monitoring/control technology.]”, paragraph 63, 69-71, [0063] [IoT simulation unit 232 collects sensor information installed in the IoT-based test bed, IoT-based smart installed in the test bed, B [attributes={}; value=[]], collects and analyzes indoor air quality data for a specific environment by controlling the air conditioning ventilator.] [0067] [The big data interlocking unit 233 collects data in real time from various sensors and control devices installed in the test bed, stores/manages it in the big data server 600, and instructs data purification/analysis and visualization.] [0068] [Data collected from the test bed may include types of eco-friendly indoor building materials, eco-friendly construction methods, amount of harmful substances, building structure information, indoor temperature/humidity, air flow, inflow air volume, fine dust concentration, etc.] [0069] [The air quality improvement unit 234 reflects the indoor air quality analysis results collected from the simulation performed on the test bed to configure an IoT-based smart air conditioning ventilation system that optimizes indoor air when a specific eco-friendly indoor building material and eco-friendly construction method are applied. suggest.]); and
quantitatively measuring and evaluating, by the at least one processor, environmental, social, and governance (ESG) achievement rates of the [0075] [The carbon reduction calculation unit 241 calculates the carbon emission factor in the content analyzed/suggested by the zero energy optimization unit 220 or various operational data such as the process, transportation, environment, and waste of the building or business site provided by the BEMS 400. Calculate the amount of carbon reduction by applying .]).
configuring and providing a building energy management system (FEMS) by reflecting results of analyzing the energy performance ([0050] [The energy optimization unit 223 configures and proposes a building energy management system (BEMS) for energy optimization by reflecting the results of energy performance analysis of the building.]… [0051] [Reflect the energy performance analysis results analyzed/simulated by the energy analysis unit 221 and the energy saving unit 222 to modify all related design/construction information so that it can be applied to design and construction, and maintain a pleasant indoor environment of the building And for efficient energy management, an IoT-based BEMS (400) is configured and proposed. ])
the BEMS comprising one or more control devices ([0052] [For example, measuring devices such as watt-hour meters, flow meters, temperature/humidity, and illuminance sensors required according to the shape, space size, and type of facilities of the proposed building; And control devices such as lighting, boilers, air conditioners, heaters, gas, etc., and automatically set and provide facility operation information settings, monitoring plans, IoT-based data collection plans, etc. for optimal energy management.])
monitor, analyze and control energy consumption of the building based on the reflected results (paragraph 28, 50-53, [0028] [BEMS (400) in the present invention monitors energy use details to maintain a pleasant indoor environment and efficiently manage energy in a building, and provides an optimized building energy management plan based on IoT integrated with measurement, control, management, and operation. It is an energy optimization system. ] [0050] [The energy optimization unit 223 configures and proposes a building energy management system (BEMS) for energy optimization by reflecting the results of energy performance analysis of the building.] [0051] [Reflect the energy performance analysis results analyzed/simulated by the energy analysis unit 221 and the energy saving unit 222 to modify all related design/construction information so that it can be applied to design and construction, and maintain a pleasant indoor environment of the building And for efficient energy management, an IoT-based BEMS (400) is configured and proposed. ] [0052] [For example, measuring devices such as watt-hour meters, flow meters, temperature/humidity, and illuminance sensors required according to the shape, space size, and type of facilities of the proposed building; And control devices such as lighting, boilers, air conditioners, heaters, gas, etc., and automatically set and provide facility operation information settings, monitoring plans, IoT-based data collection plans, etc. for optimal energy management.] [0053] [In addition, the energy optimization unit 223 can derive and apply the configuration and setting information of the BEMS 400 through an energy optimization algorithm learned from the operation data of the BEMS 400 managed by the big data server. ])
reflecting the energy performance analysis results in the energy management system configuration and setting (“according to the reflected results”) (paragraph 50-53, [0050] [The energy optimization unit 223 configures and proposes a building energy management system (BEMS) for energy optimization by reflecting the results of energy performance analysis of the building.] [0051] [Reflect the energy performance analysis results analyzed/simulated by the energy analysis unit 221 and the energy saving unit 222 to modify all related design/construction information so that it can be applied to design and construction, and maintain a pleasant indoor environment of the building And for efficient energy management, an IoT-based BEMS (400) is configured and proposed. ] [0052] [For example, measuring devices such as watt-hour meters, flow meters, temperature/humidity, and illuminance sensors required according to the shape, space size, and type of facilities of the proposed building; And control devices such as lighting, boilers, air conditioners, heaters, gas, etc., and automatically set and provide facility operation information settings, monitoring plans, IoT-based data collection plans, etc. for optimal energy management.] [0053] [In addition, the energy optimization unit 223 can derive and apply the configuration and setting information of the BEMS 400 through an energy optimization algorithm learned from the operation data of the BEMS 400 managed by the big data server. ])
evaluating ESG evaluation indicators for greenhouse gas emissions, energy consumption, a renewable energy usage ratio, waste emissions, water consumption, air pollutant emissions, indoor fine dust concentration, and indoor air pollutant generation ([0078] [ESG performance indicator calculation unit 242 manages ESG evaluation indicators, analyzes IoT data collected from the BEMS 400 or air conditioning ventilation system installed in the building, and analyzes greenhouse gas emissions, energy consumption, renewable energy use rate, waste Evaluate at least one ESG evaluation index among emissions, water consumption, air pollutant emissions, use of eco-friendly certified products, indoor fine dust concentration, and indoor air pollutant generation amount.]).
However, Ko does not explicitly disclose but Choi discloses a communication interface (lines 192-194, The power measurement unit 240 may be provided on a power line that delivers power to each factory or 192 industrial facility entered in the industrial complex, and checks the power supplied to the energy use place 10 in 193 real time, and the data wirelessly It can be transmitted to the integrated control unit 300”) and that the buildings are factories, industrial facilities, and plants (lines 12-15, 52-54, Choi discloses “an industrial complex energy management system configured to efficiently manage the energy required by various factories or industrial facilities lined up in the industrial complex, and in detail, each plant or It relates to an energy management system configured to cover the energy required by industrial facilities… it includes a power amount measuring unit for measuring the power consumption of the energy use, the integrated control unit, based on the data measured by the power amount measuring unit can control the compressed air supply unit and the power generation unit.”)
A factory energy management system (FEMS) (lines 189-190, “The industrial complex energy management system 100 according to an embodiment of the present invention 189 may include a power amount measuring unit 240 for measuring the power usage of the energy use place.”)
The FEMS comprising one or more control devices (lines 219-220, “The integrated control unit 300 may control the compressed air supply unit 210 and the power generation unit 219 220 based on the data measured by the power measurement unit 240 .”)
Controlling, by the FEMS, energy consumption of the factories, the industrial facilities, and the plants via the one or more control devices (lines 202-219, 283-299, 319-324).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Choi in the teaching of Ko, in order to provide a configuration that can supply various types of energy required by the energy users of the industrial complex (please see Choi lines 91-92). Furthermore, the combination does not merely substitute the acronym “FEMS” and “BEMS”. Ko teaches configuring the energy management architecture based upon energy performance analysis results, while Choi teaches factor/industrial energy management architecture that receives measured industrial energy information, analyzes energy consumption, and controls physical industrial equipment responsive to that information.
However, Ko in view of Choi does not disclose but Risbeck discloses transmitting the generated control settings to the loT-based ventilation system installed in a prescribed space of the factories, the industrial facilities, and the plants, controlling the loT-based ventilation system according to the generated control settings to adjust operation of the loT-based ventilation system in the prescribed space, collecting indoor air information from at least one loT-based sensor installed in the prescribed space, and adjusting the generated control settings based on the collected indoor air information ([0005] Some embodiments relate to a building management system (BMS) for executing an indoor air quality (IAQ) analysis of a building, the BMS including a controller including memory and one or more processors configured to obtain IAQ data from one or more sensors within the building, wherein the IAQ data is associated with at least one of a plurality of environment species, obtain building automation system (BAS) data, identify one or more unknown parameters from the IAQ data and BAS data of two or more of the plurality of environment species, estimate the one or more unknown parameters based on inputting the IAQ data and the BAS data into an optimization model, and wherein the optimization model analyzes predicted concentrations of the plurality of environment species subject to the two or more of the plurality of environment species evolving according to a single-species concentration model, and provide the estimated one or more unknown parameters to one or more predictive models configured to predict values of a control objective for one or more building zones as a function of control decision variables for HVAC equipment. [0212]… For example, controller 310 can use the recommended equipment configurations to automatically enable, disable, or alter the operation of HVAC equipment in accordance with the recommended equipment configurations (e.g., enabling the set of HVAC equipment associated with the lowest cost equipment configuration identified by the simulations/optimizations). Similarly, controller 310 can use the recommended operating parameters to generate and provide control signals to the HVAC equipment (e.g., operating the HVAC equipment in accordance with the recommended operating parameters)…. [0214] Controller 310 can perform or initiate one or more automated actions using the design data and/or the operational data. In some embodiments, the automated actions include automated control actions such as generating and providing control signals to UV lights 306, AHU 304, one or more VAV units, or other types of airside HVAC equipment that operate to provide airflow to one or more building zones… [0434] At block 4170, controller 310 can modify a control strategy for the one or more building zones based on satisfying a ratio between the estimated occupancy and the estimated ventilation rate (e.g., improving a value of the predicted values). For example, after determining an occupancy time series and a ventilation time series the controller 310 may determine a ratio of occupants to ventilation/airflow rate is incorrect. For example, the higher the number of occupants the greater than airflow rate, but if controller determines the estimated occupancy is high (e.g., close to a building or room capacity) but the ventilation rate is low (e.g., off or on standby with minimal air circulation) the ratio may be considered incorrect and a control strategy for the building may be modified. In some embodiments, one or more instructions generated by the processing circuits are used to implement a control strategy that adjusts at least one control of the HVAC equipment. The control strategy is based on the time series outdoor airflow rate, which can be maintained during the ventilation schedule. The BMS uses the time series outdoor airflow rate to adjust the HVAC equipment, for example, by adjusting the airflow rate, temperature, and humidity, to ensure that the building's IAQ meets the desired standards. This includes maintaining the desired outdoor airflow rate, which is calculated using the IAQ data and occupancy estimates. The BMS continuously monitors the IAQ data, and adjusts the HVAC equipment in real-time to ensure that the ventilation schedule is followed, and the building's IAQ is maintained. [0438] At blocks 4210 and 4220, controller 310 can obtain IAQ data from one or more sensors within the building, wherein the IAQ data is associated with at least one of a plurality of environment species, and obtain building automation system (BAS) data. Blocks 4210 and 4220 include similar features and functionality as described in detail with reference to blocks 4110 and 4120 of FIG. 41. [0440] In some embodiments, the estimated at least one airflow parameter is an estimated ventilation rate. In particular, controller 310 can compare an expected ventilation rate of the HVAC system with the estimated ventilation rate and in response to the expected ventilation rate and the estimated ventilation rate diverging from each other, modify a control strategy for the one or more building zones based on the estimated ventilation rate and a ventilation schedule for the one or more building zones.).
The BEMS comprising one or more processors, the one or more processors being configured to operate HVAC equipment to affect an environmental condition of the building in accordance with a selected set of optimization results (paragraph 13);
Calculating based on integrated operation data collected from both (paragraph 430 teaches composite dataset and states that “the composite dataset can be used in performing infection metric analysis, Pareto optimization, sustainability metric analysis, energy cost analysis” and that “using the composite dataset can improve accuracy of simulations or predictions of models and analysis. [0431] Referring again to FIGS. 11-19, the controller 1110 (or 310) can be configured to perform any of the Pareto optimization techniques described herein to perform a historical analysis for the building 10 that the HVAC system 300 serves. For example, the controller 1110 can use the composite dataset including IAQ measurements, BAS data, and space characteristics to supplement or substitute the modeling data 1218 and/or a data model 1202 that is based on historical data of the building 10, weather conditions, occupancy data, etc. In some embodiments, the controller 1110 is configured to perform the simulation and Pareto optimization techniques to determine different sets of values for hospitalization risk (e.g., subset of infection risk), the energy cost (described above), and infection risk (described above), carbon reduction, etc., and determine which of these sets are feasible, infeasible, Pareto optimal, etc., and compare the different Pareto optimal solutions to estimated actual energy consumption (e.g., as read on a meter or other energy consumption sensor), and estimated hospitalization risks that are determined based on the composite dataset, and/or historical data of the building 10 or the HVAC system 300.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations above as taught by Risbeck in the teaching of Ko in view of Choi, in order to ensure that the ventilation schedule is followed, and the building's IAQ is maintained. (please see Risbeck paragraph 434).
As per claim 7, Ko discloses an apparatus for achieving carbon neutrality, comprising: at least one processor; a memory storing instructions which, when executed by the at least one processor, cause the apparatus to: (paragraph 22-25)
receive energy information from an energy information acquisition device comprising at least one sensor and a communication interface ([0052] [For example, measuring devices such as watt-hour meters, flow meters, temperature/humidity, and illuminance sensors required according to the shape, space size, and type of facilities of the proposed building; And control devices such as lighting, boilers, air conditioners, heaters, gas, etc., and automatically set and provide facility operation information settings, monitoring plans, IoT-based data collection plans, etc. for optimal energy management.]”, [0023] [The online network referred to in the present invention may be a core network integrated with a wired public network, a wireless mobile communication network, or a portable Internet, etc. Several services exist, namely Hyper Text Transfer Protocol (HTTP), Hyper Text Transfer Protocol Secure (HTTPS), Telnet, File Transfer Protocol (FTP), Domain Name System (DNS), Simple Mail Transfer Protocol (SMTP), MQTT Protocol ( Message Queueing Telemetry Transport), IPFS (Inter Planetary File System), etc., and may refer to a worldwide open computer network structure, which is not limited to these examples and comprehensively refers to a data communication network capable of transmitting and receiving data in various forms. will do.] [0024] [The terminal in the present invention should be interpreted as including various communication means such as desktop, tablet, netbook, PDA (personal digital assistant), PMP (portable multimedia player), smart phone, wearable smart device, etc., web-based or separate Various functions provided by the server can be executed through software/applications of the server.]”, “0022] [ESG-based smart green construction carbon neutral platform (10, hereinafter carbon neutral platform) according to an embodiment of the present invention includes a consultant terminal 100, a smart green construction carbon neutral system (200, hereinafter carbon neutral system), an architect terminal ( 300), BEMS (Building Energy Management System, 400) and big data server 500, and each device can communicate with each other by being connected to a network.]”) ;
please see claim 1 rejection analysis for the rest of claim 7 rejection.
As per claim 2/8/14, Ko discloses wherein deriving the energy saving factors and the renewable energy production factors includes performing energy modeling by reflecting information including at least one or a combination of shape information, weather information, insulation and heat generation information, facility information, facility efficiency information, and facility operation information of buildings (paragraphs 40-41, “[The energy analysis unit 221 performs energy modeling by reflecting building shape information, air conditioning facility information, meteorological data, insulation/heat information, equipment efficiency information, and air conditioning device operation schedules, and performs simulations on the cooling and heating load of the building. , analyze energy consumption, carbon emissions and thermal comfort”), and analyzing at least one or a combination of energy consumption, carbon emissions, and heat circulation of the factories, the industrial facilities, and the plants through energy simulation (paragraph 78, “analyzes greenhouse gas emissions, energy consumption, renewable energy use rate, waste Evaluate at least one ESG evaluation index among emissions, water consumption, air pollutant emissions, use of eco-friendly certified products, indoor fine dust concentration, and indoor air pollutant generation amount.]”).
However, Ko does not disclose but Choi discloses that the buildings are the factories, the industrial facilities, and the plants (lines 12-15, 52-54)(please see claim 1 rejection for combination rationale).
As per claim 3/9/15, Ko discloses wherein deriving the energy saving factors and the renewable energy production factors includes deriving passive technology elements or active technology elements which are energy-saving elements, through machine learning-based energy optimization models and data mining (paragraph 45, “through the machine learning-based energy optimization model learned using the energy consumption data collected from the BEMS (400) and IoT-based smart air conditioning ventilation system managed by the big data server (500), the passive and active technology elements of the building can be predicted.].”)
As per claim 5/11/17, Ko discloses wherein the generating of the control settings for the loT-based ventilation system comprises: controlling theloT-based ventilation device in a test bed to which the facility materials and the facility construction methods are applied, collecting indoor air information from at least one loT-based sensor installed in the test bed, performing a simulation for indoor air based on the collected indoor air information, and deriving the control settings for the loT-based ventilation system based on results of the simulation (paragraph 62-66, “[The IoT simulation unit 232 builds a test bed to which eco-friendly materials and eco-friendly construction methods are applied, and analyzes and simulates indoor air quality by applying IoT-based monitoring/control technology.]… [IoT simulation unit 232 collects sensor information installed in the IoT-based test bed, IoT-based smart installed in the test bed, B [attributes={}; value=[]], collects and analyzes indoor air quality data for a specific environment by controlling the air conditioning ventilator.]… [At this time, in the test bed, a mock-up of a specific space to which eco-friendly indoor building materials and eco-friendly construction methods are applied, IoT-based sensors for measuring the concentration of indoor harmful substances such as formaldehyde and volatile organic compounds and fine dust, and IoT-based smart , B[attributes={}; value=[]], an air-conditioning ventilation device is installed, which is to measure the effect of indoor building materials, construction methods, and air-conditioning ventilation systems that affect indoor air quality.]”).
Conclusion
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3629
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