system

Abstract

We provide the system. [Solution] A means of collecting building information that meets specific conditions using a terminal, A means for analyzing the energy-saving effect of converting to light-emitting diodes based on the building information using the aforementioned information processing device, A means for creating the aforementioned proposal for light-emitting diodes for the constituent elements, Means for notifying the operator of the above proposal, A means of monitoring the property's condition in real time and providing information, A means of showing the economic effects and subsidy information related to the aforementioned proposal, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In aging buildings, the increase in power consumption due to inefficient lighting equipment and the accompanying maintenance cost burden are problems. Also, in situations where the occupancy rate is decreasing, means for reducing maintenance costs and improving the attractiveness of the building are required, but currently, no method for efficiently achieving this has been established. Furthermore, there is a lack of information on administrative support and related services associated with the conversion to light-emitting diodes, and there are insufficient appropriate proposals for improvement.

Means for Solving the Problems

[0005] This system uses an information processing device to collect building data that meets specific conditions, such as the age of the building, and includes a means to analyze the power reduction effect of converting to LED lighting based on that data. Next, based on the analysis results, it creates a proposal for effective LED lighting conversion and notifies the user. Furthermore, by including the results of subsidy information related to LED lighting conversion in the proposal, it maximizes the economic benefits. In addition, by incorporating options such as smart office conversion and communication network improvement into the proposal, it supports the overall improvement of the building's value.

[0006] An "information processing device" is a computer system capable of collecting, analyzing, and processing data.

[0007] "Building data" refers to data that includes information such as the building's address, year of construction, occupancy rate, current lighting equipment, and electricity consumption.

[0008] "Light-emitting diode (LED) conversion" refers to replacing existing lighting equipment within a building with lighting equipment that uses light-emitting diodes.

[0009] "Means for analyzing the effect" refers to a method or system for calculating and evaluating the power reduction effect obtained by converting to light-emitting diodes, based on the collected data.

[0010] "Means for creating proposals" refers to a method or system for documenting the optimal light-emitting diode (LED) conversion strategy based on the analysis results.

[0011] "Means of notifying users" refers to communication methods for providing proposed information to the building owner or manager.

[0012] "Subsidy information" refers to information on financial support from the government that can be used when converting to LEDs.

[0013] "Smart office transformation" refers to improving office efficiency and convenience by utilizing IoT technology and the latest IT solutions.

[0014] "Communication network improvement options" refer to additional technical options that can be used to improve the performance of communication equipment in a building. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention aims to improve the efficiency of managing aging buildings and enhance their value by using an information processing system. Servers, terminals, and users each play a crucial role in the operation of this system. The system is constructed using an information processing device and functions as follows:

[0037] First, the server collects data on buildings across Japan that are 40 years old or older. This data includes information such as location, building size, current lighting equipment type, power consumption, and occupancy rate. Based on the collected data, the server selects buildings that meet specific criteria.

[0038] Next, based on the data collected by the server, a generative AI model is used to analyze the power reduction effect of converting to LEDs. This analysis calculates the expected reduction in electricity costs for each building and the payback period for the initial costs of converting to LEDs.

[0039] Next, based on the analysis results, the server creates a proposal for building owners to implement LED lighting. This proposal includes details on the economic benefits of implementation and relevant subsidy programs. Furthermore, options for smart office solutions and improvements to the communication network are also considered and presented in the proposal.

[0040] Once the proposal is complete, the server notifies the user (building owner). The user can access the proposal using a terminal and review its contents. If the user has detailed questions about the proposal, they can inquire with the server via a dedicated terminal and receive relevant information immediately.

[0041] As a concrete example, let's say the user is the owner of a 50-year-old building. The server analyzes the building's data and presents a proposal showing that converting to LED lighting is expected to result in a 30% annual power saving. Furthermore, it might include information about the possibility of utilizing subsidies from the national and local governments to cover 50% of the initial investment.

[0042] In this way, the invention functions as a system that improves the efficient management and operation of aging buildings, thereby bringing economic benefits to building owners.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server connects to a database and collects data on buildings across Japan that are 40 years old or older. This data includes information such as location, age of construction, building size, current lighting equipment, power consumption, and occupancy rate. The server organizes this data and stores it in the database.

[0046] Step 2:

[0047] The system uses building data stored on the server to activate a generating AI model. The AI ​​model performs analysis to predict the power reduction effect of converting each building to LED lighting. Specifically, it compares current power consumption with predicted consumption after LED installation to calculate the potential cost savings.

[0048] Step 3:

[0049] Based on the analysis results, the server generates a proposal for LED conversion. This proposal includes estimated power cost savings, payback period for initial investment, information on applicable subsidies, and also presents options for smart office implementation and network improvements.

[0050] Step 4:

[0051] The server notifies the building owner (user) of the generated proposal. The notification is made through a portal exclusively for building owners, and the user can access the proposal.

[0052] Step 5:

[0053] Users review the proposal using a dedicated terminal. If users wish to obtain more detailed information based on the proposal, they can query the server via the terminal to receive additional information.

[0054] Step 6:

[0055] After the server accepts user inquiries and approvals, it begins the process of arranging LED conversion work with partner construction companies. Once the construction schedule is finalized, the user will be notified, and construction will begin. As part of after-sales service, support will also be provided if any proposed smart office modifications or network improvements are necessary.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In the case of aging structures, there is a need to improve efficient management and operation, and to provide economic benefits to the structure owners. To achieve this, effective data analysis and proposal development methods are necessary.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for collecting structural information that meets certain conditions, means for analyzing the effects of converting structures into light-emitting devices using a generated AI model, and means for creating proposals for converting structures into light-emitting devices for the structure owners. This enables effective management and operational improvement of structures.

[0061] An "information processing system" is a collection of computers used for collecting, analyzing, and processing data.

[0062] "Structural information" refers to detailed data about buildings, such as their location, size, energy consumption, and occupancy rate.

[0063] A "generative AI model" is an artificial intelligence model that learns from data and uses that data to make predictions and perform analyses.

[0064] "Light-emitting device conversion" refers to replacing conventional lighting fixtures with light-emitting diodes or similar energy-efficient lighting devices.

[0065] A "proposal" is a document created based on the analysis results, outlining improvement suggestions and economic benefits for the structure owner.

[0066] "Structure owner" refers to an individual or organization that owns a particular building or structure.

[0067] "Grant information" refers to information regarding financial support or subsidies from the government or related organizations.

[0068] "Office automation" refers to the use of technological means to automate tasks and management processes with the aim of improving efficiency.

[0069] "Communication network improvement options" refers to various technologies and methods for improving the efficiency or expanding communication infrastructure.

[0070] This invention functions as an information processing system that provides economic benefits to structure owners by streamlining the management and operation of aging structures and promoting their integration with light-emitting devices. This system is implemented as follows.

[0071] First, the server uses an information processing system to collect information on structures across Japan that are 40 years old or older. The hardware used includes high-performance computer servers for data collection and processing. This includes an API interface that allows access to public databases and datasets provided by local governments via network connectivity.

[0072] Next, the server utilizes a generative AI model to analyze the collected data. This AI model learns from past data and predicts the improvement in energy efficiency through the use of light-emitting devices. Specifically, it analyzes the power consumption patterns of each structure and simulates the reduction effect of using light-emitting diodes. This simulation calculates the payback period for the initial costs of using light-emitting devices and the expected cost reduction effect.

[0073] Based on the analysis results, the server creates a proposal for the structure owner to implement light-emitting devices. This proposal, based on the AI ​​model's analysis results, includes information on the economic benefits of implementation and available subsidies. A text generation program is used to create the proposal. Furthermore, to clearly show the structure owner what improvements they can actually implement, options such as office automation and communication network improvements are also included.

[0074] After the proposal is created, the server notifies the building owner, allowing the user to access the proposal using a dedicated terminal and review the details. If the user has questions about the proposal, they can inquire with the server from the dedicated terminal, and the server will provide an immediate answer. For example, if the user is the owner of a 50-year-old building, the proposal might show a 30% reduction in power consumption through LED lighting and indicate that 50% of the initial investment will be covered by a subsidy program.

[0075] Example of a prompt:

[0076] "Based on electricity consumption data for a 50-year-old building, please generate a proposal for efficiency improvements through the use of LEDs."

[0077] Thus, this invention utilizes data analysis and AI technology to improve the efficiency and cost-effectiveness of structural management.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server collects structural information. As input, it queries a database of buildings across Japan that are 40 years old or older, obtaining information such as location, size, current power consumption, and occupancy rate. This information is typically obtained from real estate databases and public datasets via APIs. As output, it organizes this information by structure and generates a dataset to be passed on to the next step.

[0081] Step 2:

[0082] The server analyzes the collected structural information. The dataset generated in step 1 is used as input. The generated AI model uses this dataset to analyze the power consumption patterns of each structure and simulate the effects of incorporating light-emitting devices. Specifically, the AI ​​model uses a machine learning algorithm to learn patterns from past data and predicts the amount of electricity cost reduction and the payback period for initial costs. The output is an analysis result that includes the estimated effect of incorporating light-emitting devices.

[0083] Step 3:

[0084] The server generates a proposal based on the analysis results. The analysis results obtained in Step 2 are used as input. The proposal includes the economic benefits of using light-emitting devices, details of relevant grant programs, and options for office automation and communication network improvements. Specifically, text generation software combines these elements and documents them in a format easily understandable to the building owner. The completed proposal is generated as output.

[0085] Step 4:

[0086] The server notifies the user (structure owner) of the proposal. The proposal created in step 3 is used as input. A notification is sent to the user's device via email or a dedicated app, and an access link is provided. Specifically, the notification system sends the proposal information to the user, making it easy for the user to access the proposal. The output is the notification received by the user and the access link to the proposal.

[0087] Step 5:

[0088] Users can request more detailed information about the proposal. As input, the user's inquiry request is sent to the server via a dedicated terminal. The server searches its database and immediately provides the user with relevant information. Specifically, the server's internal data processing system analyzes the user's request, gathers the necessary information, and responds to the user. The output provides specific information in response to the user's request.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] Managing aging buildings can lead to problems such as increased energy consumption and rising operating costs due to a lack of proper maintenance and operation. Furthermore, building owners often lack effective management strategies and renovation proposals, making it difficult to maximize asset value. Therefore, there is a need for efficient means to improve building value and promote energy conservation.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means for collecting building information that meets specific conditions using a terminal, means for analyzing the energy-saving effect of converting to light-emitting diodes based on the building information using an information processing device, and means for creating proposals for converting to light-emitting diodes for the constituent elements. This makes it possible to monitor the status of the property in real time, provide effective energy-saving proposals, and further maximize the value of the building and operational efficiency by utilizing subsidy information.

[0094] A "terminal" is a device used for inputting or outputting information, and has the function of connecting to a computer network to exchange data.

[0095] "Building information" refers to data about a building, including details such as its location, size, facilities, and energy consumption.

[0096] An "information processing device" is a computer system used to collect, process, and analyze data.

[0097] "Light-emitting diode conversion" refers to replacing conventional lighting equipment with light-emitting diodes (LEDs) to improve power efficiency and reduce energy consumption.

[0098] "Energy-saving effect" refers to the improvement in efficiency and cost reduction achieved by reducing energy consumption.

[0099] "Components" are the individual parts or components necessary to form the whole.

[0100] A "proposal" refers to a document or notice that outlines methods or plans for achieving a specific objective.

[0101] An "operator" is a person whose role is to operate or manage a system or piece of equipment.

[0102] "Funding information" refers to data about programs and schemes that provide financial support for specific activities or improvements.

[0103] This invention is a system that uses an information processing system to streamline the management of aging buildings and improve their value. The system functions as follows, with the server, terminal, and user elements working together.

[0104] The server's role is to collect information on buildings across Japan that are above a certain age. This building information includes location, building size, current equipment status, and energy consumption. Based on the collected information, the server selects buildings that meet specific criteria and uses an information processing device to analyze the energy-saving effects of converting to LEDs. A generated AI model is used in this analysis to calculate the power reduction effect and the payback period for initial costs.

[0105] Next, the server generates a proposal for LED conversion for each component based on the analysis results. This proposal includes information on energy-saving effects and subsidies, clearly outlining the economic benefits. Options for smart office implementation and communication network improvements are also presented.

[0106] The user receives and reviews this proposal using their device. Specifically, the proposal presents a plan to reduce energy consumption by 30% annually by installing LED lighting in the user's 50-year-old building. The proposal also includes the possibility of utilizing a subsidy program to cover 50% of the initial investment.

[0107] An example of a prompt message would be: "We are considering introducing a new LED lighting system. It is expected to reduce electricity consumption by 30% annually in our 50-year-old building, and we are also eligible for subsidies from the local government. Please tell us about the specific process and the expected economic effects."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server collects information on buildings across Japan that are above a certain age. This process collects data such as location, size, equipment status, and energy consumption. It retrieves relevant information through databases and the internet and stores the collected data in the system.

[0111] Step 2:

[0112] The server selects buildings that meet specific criteria based on the collected building information. It uses a complete list of building information as input and filters it to extract information that matches the criteria. The criteria are set based on factors such as the building's age and energy consumption.

[0113] Step 3:

[0114] The server uses an information processing device to analyze the energy-saving effects of converting selected buildings to LED lighting. It uses information about the selected buildings as input and utilizes a generated AI model to evaluate the energy-saving effects. The output includes analysis results such as power reduction effects and initial cost payback periods for each building.

[0115] Step 4:

[0116] Based on the analysis results, the server will create a proposal for LED conversion. This will include economic benefits and information on subsidies. The server will format the necessary data for the proposal and present it in a visually easy-to-understand format using tables and graphs.

[0117] Step 5:

[0118] The server notifies the user of the created proposal. The user accesses the proposal using their terminal and reviews its contents. The notification system sends an alert to the user, guiding them to log in to view the details.

[0119] Step 6:

[0120] The user reviews the presented proposal and, if they have any questions or further requests, contacts the server via their device. The inquiry involves entering prompts generated using a generative AI model, and the server immediately provides additional information.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention is a system that combines an information processing device and an emotion engine to support the conversion of aging buildings to LEDs, and further provides suggestions that take into account the emotions of users. The embodiments of this system are described in detail below.

[0123] The system consists of a server, terminals, and users. First, the server collects data on buildings across Japan that are 40 years old or older, organizing information such as location, age, occupancy rate, type of lighting equipment, and power consumption, and storing it in a database. Based on this, the server uses a generative AI model to analyze the power reduction effect of converting to LED lighting in each building. Based on the results of this analysis, it creates a proposal document detailing the potential for power reduction and the possibility of utilizing subsidies.

[0124] Next, when the server provides this proposal to a user (building owner) connected to the system, it uses an emotion engine to analyze the user's emotions in real time. This emotion engine captures the user's reaction using voice tone and facial expression analysis, and can adjust the proposal content based on the obtained emotion data. For example, if the server detects that the user is feeling uneasy about the proposal, it will provide additional, easy-to-understand support information.

[0125] After the proposal is presented, the user reviews it. The user can access the proposal using a dedicated terminal and examine its contents in detail. If further information is needed regarding the proposal, the user can query the server via the terminal and receive supplementary information immediately.

[0126] For example, if a user is the owner of their own building and expresses doubt during the explanation of the proposal, the server will provide actual implementation case videos and details of success stories to address their concerns. In this way, the system aims to dynamically optimize the proposal content using an emotion engine to enhance user understanding and acceptance.

[0127] Ultimately, after the server receives approval from the user for the proposal, it arranges for the LED conversion work with a partner construction company. Furthermore, depending on the user's requests, it offers options for smart office implementation and communication network improvements, aiming to enhance the overall value of the building. Through this process, the invention achieves building management with greater economic and functional value.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server collects data on buildings over 40 years old from real estate databases and public sources. This data includes address, year of construction, occupancy rate, type of lighting fixtures, and current electricity consumption. The server filters the collected information to identify target buildings.

[0131] Step 2:

[0132] The server processes data on identified buildings and uses a generated AI model to analyze the effects of converting to LEDs. Specifically, it compares current power consumption with predicted power consumption after LED conversion to calculate the amount of electricity cost reduction. It also investigates the possibility of subsidy eligibility.

[0133] Step 3:

[0134] Based on the analysis results, the server creates a proposal for each building owner to implement LED lighting. The proposal includes details on potential electricity cost savings, subsidy utilization, implementation costs, and projected cash flow after implementation.

[0135] Step 4:

[0136] The server activates an emotion engine when presenting a proposal to the user. It monitors the user's voice, facial expressions, and reaction speed, and analyzes their emotions in real time. This allows the server to evaluate how the user feels about the proposal.

[0137] Step 5:

[0138] The user receives the proposal via their device and reviews its contents. If the user expresses concerns or doubts, the server adjusts the proposal or provides supplementary materials based on the results of the emotion engine. For example, it might add specific implementation examples or FAQs.

[0139] Step 6:

[0140] If the user approves the proposal or has further questions, they will notify the server using their device. The server will then coordinate the construction schedule with the contractor and provide any necessary additional information, based on the user's instructions. It will also provide detailed information about smart office and network improvement options.

[0141] This system allows users to receive efficient and emotionally sensitive suggestions, enabling a smooth LED conversion process.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] In modern society, there are many older buildings, and there is a need for efficient management and optimization of energy consumption. Furthermore, proposals that take into account not only energy efficiency but also the needs and feelings of users are required. However, the current challenge is that there are not enough systems available that meet these requirements.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for collecting information about buildings that meet specific conditions using an information processing device, means for using an artificial intelligence model to analyze the effects of converting buildings to light-emitting diodes based on the information about the buildings, and means for adjusting suggestions using an emotion recognition system and notifying the user. This makes it possible to provide optimal suggestions that take into account both improved energy efficiency and the user's emotions.

[0147] An "information processing device" refers to a computer system for collecting, organizing, and storing data, and is designed to efficiently process data related to buildings.

[0148] "Information about buildings" refers to data about the characteristics of a building, including its location, age, occupancy rate, type of lighting equipment, and electricity consumption.

[0149] "Light-emitting diode (LED) conversion" refers to replacing conventional lighting equipment with energy-efficient light-emitting diode (LED) lighting, a technology aimed at reducing power consumption.

[0150] An "artificial intelligence model" is a program that uses machine learning algorithms to analyze data and evaluate the effectiveness of improving the energy efficiency of buildings.

[0151] An "emotion recognition system" is a technology that analyzes a user's emotions in real time through methods such as speech recognition and facial expression analysis, making it possible to adjust the suggested content according to the user's emotions.

[0152] "Adjusting a proposal" is the process of modifying or strengthening the content of a proposal based on the user's feelings to provide the user with the most relevant information.

[0153] This invention provides a system that offers efficient suggestions for building management by combining an information processing device and emotion recognition technology.

[0154] The server functions as an information processing device, collecting information on buildings across Japan that are 40 years old or older. This information includes the building's location, age, occupancy rate, type of lighting equipment, and power consumption. The server can use Amazon Web Services (AWS®) or Google Cloud Platform (GCP) to organize this data and store it in a database.

[0155] Based on the collected information, the server uses a generative AI model to analyze the power reduction effect of switching to LEDs. Specifically, it uses machine learning algorithms to evaluate the potential for energy efficiency improvement for each building. An example of a prompt message in this case would be, "Calculate the power reduction effect of introducing LEDs in buildings that are 40 years old or older."

[0156] Based on the analysis results, the server will create a proposal that includes power reduction effects and the possibility of utilizing subsidies. The proposal will be documented using Microsoft Office or Adobe Acrobat and generated in PDF or Word format.

[0157] Once the proposal is complete, the server uses an emotion recognition system to adjust the proposal based on the user's emotions. Using voice recognition software and facial recognition cameras, it analyzes the user's reactions in real time and provides easy-to-understand information and additional support as needed.

[0158] Ultimately, users can view the proposal via a dedicated terminal. The proposal includes actual implementation examples and success stories as concrete examples. If a user requests additional information, they can query the server through the terminal and receive the necessary supplementary information immediately. Through this process, the invention promotes improvements in building management and enhances user satisfaction.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The server begins collecting information. It accesses real estate databases and official statistical databases to gather information on buildings 40 years old or older across the country. The input includes data on the building's location, age, occupancy rate, type of lighting equipment, and power consumption. This information is converted into a digital format and stored in the database. The output is building data stored in an organized format.

[0162] Step 2:

[0163] The analysis is performed based on building information collected by the server. A generative AI model is used, with the prompt "Calculate the power reduction effect of introducing LEDs in buildings over 40 years old" as input. For data processing, a machine learning algorithm is used to analyze the energy consumption data of each building and evaluate the power reduction effect of switching to LEDs. The output is the predicted energy reduction rate and the possible cost reduction for each building.

[0164] Step 3:

[0165] The server generates a proposal based on the analysis results. The analysis results are incorporated into a template, and a PDF or Word document is generated using Microsoft Office or Adobe Acrobat. This proposal includes power reduction potential, available subsidy information, and estimated implementation costs. The input is the analysis results and relevant subsidy information, and the output is the completed proposal.

[0166] Step 4:

[0167] The server activates an emotion recognition system to provide the proposal to the user. Using speech recognition software and a camera, it monitors the user's facial expressions and voice tone in real time. The input is user reaction data to the proposal. Based on this, the system determines the user's emotions and, if necessary, adds additional information or clearer explanations to the proposal. The output is a proposal optimized for the user.

[0168] Step 5:

[0169] Users access and review proposals via a dedicated terminal. Input consists of the proposal itself and any supplementary information related to it. If further details are needed during the review process, users can send a request for supplementary information to the server via the terminal. The output consists of additional information obtained by the user, supporting decision-making based on the proposal's content.

[0170] (Application Example 2)

[0171] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0172] There is a need for effective proposals to reduce power consumption in aging buildings and promote the adoption of LED lighting. Furthermore, these proposals must be presented to building managers and owners in a way that is easily understandable and convincing. In particular, a key challenge is providing a system that can detect and appropriately address any concerns or questions that recipients may have in real time.

[0173] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0174] In this invention, the server includes means for collecting building information that meets specific conditions using an information processing device, means for analyzing the effects of LED conversion based on the building information using the information processing device, means for creating a proposal for LED conversion for the building, emotion analysis means for analyzing user emotion data, and means for adjusting the proposal content based on the emotion data. This reduces the psychological burden on the recipient of the proposal and enables smoother decision-making regarding LED conversion.

[0175] An "information processing device" is a device used to collect, analyze, and process data, and is used to handle information about buildings.

[0176] "Building information" refers to data about a specific building, such as its location, age, occupancy rate, type of lighting equipment, and electricity consumption.

[0177] "Light-emitting diode conversion" is a technical method that reduces power consumption by replacing existing lighting equipment with light-emitting diode (LED) lighting.

[0178] "Analytical methods" refer to processes and methods for evaluating specific effects or results based on collected data.

[0179] A "proposal" refers to a report that includes improvement measures and plans for building owners and managers, based on the analyzed data.

[0180] "Emotional analysis methods" are technologies that analyze emotions from a user's voice and facial expressions to evaluate their psychological state.

[0181] "Emotional data" refers to numerical data and information that represents the user's emotions, and is used to adjust the content of the suggestions.

[0182] "Adjustment methods" refer to techniques for adapting or modifying proposals based on analyzed sentiment data.

[0183] This invention is a system that combines an information processing device and emotion analysis technology, and is implemented to promote the conversion of aging buildings to light-emitting diodes (LEDs). The server collects information on older buildings nationwide and forms a building information database. The collected information includes location, age of the building, occupancy rate, type of existing lighting equipment, and power consumption. The server uses a generative AI model built with Python and TENSORFLOW® to analyze this data and evaluate the power reduction effect of converting to LEDs. Based on this evaluation, it creates a proposal that includes details on the potential for power reduction, the availability of subsidies, and the necessary capital investment.

[0184] Users (building owners and managers) access this proposal via smartphones or dedicated terminals. To make the proposal easier to understand, the server analyzes the user's emotional data in real time using sentiment analysis tools. Specifically, it uses the smartphone's camera and microphone to collect the user's facial expressions and voice tone. This makes it possible to detect any anxieties or questions the user may have about the proposal. Based on the emotional data, information is provided in a format that is easy for the user to understand. For example, if anxiety is detected, the server may display videos of past success stories or provide graphs and charts to make the proposal easier to understand.

[0185] As a concrete example of implementation, consider a scenario where a user receives a proposal to convert their company building to LED lighting. In this case, if the user has questions, additional information to address those questions is immediately provided, deepening their understanding of the proposal.

[0186] By utilizing generative AI models, suggestions can be dynamically optimized, facilitating user understanding. An example of a prompt might be: "Please enter information about the building. Would you like to know about the power savings from LED lighting? Also, please optimize the suggestions while analyzing my emotions in real time." In this way, better building management and increased economic value become possible.

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The server collects information on older buildings across Japan. It takes location, age, occupancy rate, lighting equipment type, and power consumption from a building database as input, and outputs a structured building information database. This process systematically organizes the information necessary for subsequent analysis based on the collected data.

[0190] Step 2:

[0191] The server analyzes building information using a generative AI model. Using the building information acquired in Step 1 as input, it evaluates the power reduction effects of LED conversion and the feasibility of utilizing subsidies. The evaluation results are included in the proposal as output. Specifically, it performs data pattern matching and simulations to find the most efficient method of LED conversion.

[0192] Step 3:

[0193] The server generates a proposal based on the analysis results. Using the evaluation results from Step 2 as input, it outputs a comprehensive explanation of the potential for power reduction, details of necessary capital investments, and subsidy information. Specifically, it organizes the proposal content in a user-friendly format and includes charts, graphs, and attached documents to aid visual understanding.

[0194] Step 4:

[0195] The user retrieves the proposal via their device and reviews its contents. The input involves providing authentication information to access the proposal data on the device, and the output is the detailed proposal content. Specifically, the device downloads the proposal sent from the server and displays it to the user.

[0196] Step 5:

[0197] The server acquires real-time emotional data from users using emotion analysis tools. It uses the user's facial expressions and voice tone, collected through the device's camera and microphone, as input, and outputs the analysis results. Specifically, it deploys machine learning algorithms to identify the user's psychological state.

[0198] Step 6:

[0199] The server adjusts the suggestions based on emotional data. It uses the emotional analysis results obtained in step 5 as input and outputs optimized suggestions after adjustment. Specifically, it enhances the suggestions by adding success stories and graphs for users who have anxieties or questions.

[0200] Step 7:

[0201] The user gains a deeper understanding of the optimized proposal and makes a final decision. Inputs include receiving the adjusted proposal, and outputs include approving the LED conversion for the building or requesting further information. Specific actions include communication for feedback on the proposal and question-and-answer sessions.

[0202] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0209] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0213] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0218] This invention aims to improve the efficiency of managing aging buildings and enhance their value by using an information processing system. Servers, terminals, and users each play a crucial role in the operation of this system. The system is constructed using an information processing device and functions as follows:

[0219] First, the server collects data on buildings across Japan that are 40 years old or older. This data includes information such as location, building size, current lighting equipment type, power consumption, and occupancy rate. Based on the collected data, the server selects buildings that meet specific criteria.

[0220] Next, based on the data collected by the server, a generative AI model is used to analyze the power reduction effect of converting to LEDs. This analysis calculates the expected reduction in electricity costs for each building and the payback period for the initial costs of converting to LEDs.

[0221] Next, based on the analysis results, the server creates a proposal for building owners to implement LED lighting. This proposal includes details on the economic benefits of implementation and relevant subsidy programs. Furthermore, options for smart office solutions and improvements to the communication network are also considered and presented in the proposal.

[0222] Once the proposal is complete, the server notifies the user (building owner). The user can access the proposal using a terminal and review its contents. If the user has detailed questions about the proposal, they can inquire with the server via a dedicated terminal and receive relevant information immediately.

[0223] As a concrete example, let's say the user is the owner of a 50-year-old building. The server analyzes the building's data and presents a proposal showing that converting to LED lighting is expected to result in a 30% annual power saving. Furthermore, it might include information about the possibility of utilizing subsidies from the national and local governments to cover 50% of the initial investment.

[0224] In this way, the invention functions as a system that enables efficient management and improved operation of aging buildings, thereby bringing economic benefits to building owners.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server connects to a database and collects data on buildings across Japan that are 40 years old or older. This data includes information such as location, age of construction, building size, current lighting equipment, power consumption, and occupancy rate. The server organizes this data and stores it in the database.

[0228] Step 2:

[0229] The system uses building data stored on the server to activate a generating AI model. The AI ​​model performs analysis to predict the power reduction effect of converting each building to LED lighting. Specifically, it compares current power consumption with predicted consumption after LED installation to calculate the potential cost savings.

[0230] Step 3:

[0231] Based on the analysis results, the server generates a proposal for LED conversion. This proposal includes estimated power cost savings, payback period for initial investment, information on applicable subsidies, and also presents options for smart office implementation and network improvements.

[0232] Step 4:

[0233] The server notifies the building owner (user) of the generated proposal. The notification is made through a portal exclusively for building owners, and the user can access the proposal.

[0234] Step 5:

[0235] Users review the proposal using a dedicated terminal. If users wish to obtain more detailed information based on the proposal, they can query the server via the terminal to receive additional information.

[0236] Step 6:

[0237] After the server accepts user inquiries and approvals, it begins the process of arranging LED conversion work with partner construction companies. Once the construction schedule is finalized, the user will be notified, and construction will begin. As part of after-sales service, support will also be provided if any proposed smart office modifications or network improvements are necessary.

[0238] (Example 1)

[0239] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0240] In aging structures, there is a need to improve efficient management and operation, and to provide economic benefits to the structure owners. To achieve this, effective data analysis and proposal development methods are necessary.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes means for collecting structural information that meets certain conditions, means for analyzing the effects of converting structures into light-emitting devices using a generated AI model, and means for creating proposals for converting structures into light-emitting devices for the structure owners. This enables effective management and operational improvement of structures.

[0243] An "information processing system" is a collection of computers used for collecting, analyzing, and processing data.

[0244] "Structural information" refers to detailed data about buildings, such as their location, size, energy consumption, and occupancy rate.

[0245] A "generative AI model" is an artificial intelligence model that learns from data and uses that data to make predictions and perform analyses.

[0246] "Light-emitting device conversion" refers to replacing conventional lighting fixtures with light-emitting diodes or similar energy-efficient lighting devices.

[0247] A "proposal" is a document created based on the analysis results, outlining improvement suggestions and economic benefits for the structure owner.

[0248] "Structure owner" refers to an individual or organization that owns a particular building or structure.

[0249] "Grant information" refers to information regarding financial support or subsidies from the government or related organizations.

[0250] "Office automation" refers to the use of technological means to automate tasks and management processes with the aim of improving efficiency.

[0251] "Communication network improvement options" refers to various technologies and methods for improving the efficiency or expanding communication infrastructure.

[0252] This invention functions as an information processing system that provides economic benefits to structure owners by streamlining the management and operation of aging structures and promoting their integration with light-emitting devices. This system is implemented as follows.

[0253] First, the server uses an information processing system to collect information on structures across Japan that are 40 years old or older. The hardware used includes high-performance computer servers for data collection and processing. This includes an API interface that allows access to public databases and datasets provided by local governments via network connectivity.

[0254] Next, the server utilizes a generative AI model to analyze the collected data. This AI model learns from past data and predicts the improvement in energy efficiency through the use of light-emitting devices. Specifically, it analyzes the power consumption patterns of each structure and simulates the reduction effect of using light-emitting diodes. This simulation calculates the payback period for the initial costs of using light-emitting devices and the expected cost reduction effect.

[0255] Based on the analysis results, the server creates a proposal for the structure owner to implement light-emitting devices. This proposal, based on the AI ​​model's analysis results, includes information on the economic benefits of implementation and available subsidies. A text generation program is used to create the proposal. Furthermore, to clearly show the structure owner what improvements they can actually implement, options such as office automation and communication network improvements are also included.

[0256] After the proposal is created, the server notifies the building owner, allowing the user to access the proposal using a dedicated terminal and review the details. If the user has questions about the proposal, they can inquire with the server from the dedicated terminal, and the server will provide an immediate answer. For example, if the user is the owner of a 50-year-old building, the proposal might show a 30% reduction in power consumption through LED lighting and indicate that 50% of the initial investment will be covered by a subsidy program.

[0257] Example of a prompt:

[0258] "Please generate a proposal for efficiency improvements using LEDs, based on electricity consumption data from a 50-year-old building."

[0259] Thus, this invention utilizes data analysis and AI technology to improve the efficiency and cost-effectiveness of structural management.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] The server collects structural information. As input, it queries a database of buildings across Japan that are 40 years old or older, obtaining information such as location, size, current power consumption, and occupancy rate. This information is typically obtained from real estate databases and public datasets via APIs. As output, it organizes this information by structure and generates a dataset to be passed on to the next step.

[0263] Step 2:

[0264] The server analyzes the collected structural information. The dataset generated in step 1 is used as input. The generated AI model uses this dataset to analyze the power consumption patterns of each structure and simulate the effects of incorporating light-emitting devices. Specifically, the AI ​​model uses a machine learning algorithm to learn patterns from past data and predicts the amount of electricity cost reduction and the payback period for initial costs. The output is an analysis result that includes the estimated effect of incorporating light-emitting devices.

[0265] Step 3:

[0266] The server generates a proposal based on the analysis results. The analysis results obtained in Step 2 are used as input. The proposal includes the economic benefits of using light-emitting devices, details of relevant grant programs, and options for office automation and communication network improvements. Specifically, text generation software combines these elements and documents them in a format easily understandable to the building owner. The completed proposal is generated as output.

[0267] Step 4:

[0268] The server notifies the user (structure owner) of the proposal. The proposal created in step 3 is used as input. A notification is sent to the user's device via email or a dedicated app, and an access link is provided. Specifically, the notification system sends the proposal information to the user, making it easy for the user to access the proposal. The output is the notification received by the user and the access link to the proposal.

[0269] Step 5:

[0270] Users can request more detailed information about the proposal. As input, the user's inquiry request is sent to the server via a dedicated terminal. The server searches its database and immediately provides the user with relevant information. Specifically, the server's internal data processing system analyzes the user's request, gathers the necessary information, and responds to the user. The output provides specific information in response to the user's request.

[0271] (Application Example 1)

[0272] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] Managing aging buildings can lead to problems such as increased energy consumption and rising operating costs due to a lack of proper maintenance and operation. Furthermore, building owners often lack effective management strategies and renovation proposals, making it difficult to maximize asset value. Therefore, there is a need for efficient means to improve building value and promote energy conservation.

[0274] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0275] In this invention, the server includes means for collecting building information that meets specific conditions using a terminal, means for analyzing the energy-saving effect of converting to light-emitting diodes based on the building information using an information processing device, and means for creating proposals for converting to light-emitting diodes for the constituent elements. This makes it possible to monitor the status of the property in real time, provide effective energy-saving proposals, and further maximize the value of the building and operational efficiency by utilizing subsidy information.

[0276] A "terminal" is a device used for inputting or outputting information, and has the function of connecting to a computer network to exchange data.

[0277] "Building information" refers to data about a building, including details such as its location, size, facilities, and energy consumption.

[0278] An "information processing device" is a computer system used to collect, process, and analyze data.

[0279] "Light-emitting diode conversion" refers to replacing conventional lighting equipment with light-emitting diodes (LEDs) to improve power efficiency and reduce energy consumption.

[0280] "Energy-saving effect" refers to the improvement in efficiency and cost reduction achieved by reducing energy consumption.

[0281] "Components" are the individual parts or components necessary to form the whole.

[0282] A "proposal" refers to a document or notice that outlines methods or plans for achieving a specific objective.

[0283] An "operator" is a person whose role is to operate or manage a system or piece of equipment.

[0284] "Subsidy Information" refers to data related to programs or systems that provide economic support for specific activities or improvements.

[0285] This invention is a system that uses an information processing system to streamline the management of aging buildings and enhance their value. The system functions as follows with the cooperation of servers, terminals, and users.

[0286] The server has the role of collecting building information of buildings with a certain age or more across the country. The building information includes location, building scale, current equipment status, energy consumption, etc. Based on the collected information, the server selects buildings that meet specific conditions and analyzes the energy-saving effect by converting to light-emitting diodes using an information processing device. A generated AI model is utilized in this analysis to calculate the power reduction effect and the payback period of the initial cost.

[0287] Next, based on the analysis results, the server creates a proposal for converting to light-emitting diodes for each component. This proposal document includes the energy-saving effect and subsidy information, and clearly shows the economic benefits. It also introduces options for smart office conversion and communication network improvement.

[0288] The user receives this proposal document using a terminal and checks the content. Specifically, a proposal is shown that for a building owned by the user with a history of 50 years, an annual energy reduction of 30% is expected by introducing LED lighting. At this time, the proposal also includes the possibility that 50% of the initial investment may be compensated by utilizing the subsidy system.

[0289] An example of the prompt text is as follows: "We are considering introducing a new LED lighting system. For a building with a history of 50 years, an annual power reduction effect of 30% is expected, and the subsidy system from the local government can also be utilized. Please tell us about the specific process and expected economic effects."

[0290] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0291] Step 1:

[0292] The server collects information on buildings across Japan that are above a certain age. This process collects data such as location, size, equipment status, and energy consumption. It retrieves relevant information through databases and the internet and stores the collected data in the system.

[0293] Step 2:

[0294] The server selects buildings that meet specific criteria based on the collected building information. It uses a complete list of building information as input and filters it to extract information that matches the criteria. The criteria are set based on factors such as the building's age and energy consumption.

[0295] Step 3:

[0296] The server uses an information processing device to analyze the energy-saving effects of converting selected buildings to LED lighting. It uses information about the selected buildings as input and utilizes a generated AI model to evaluate the energy-saving effects. The output includes analysis results such as power reduction effects and initial cost payback periods for each building.

[0297] Step 4:

[0298] Based on the analysis results, the server will create a proposal for LED conversion. This will include economic benefits and information on subsidies. The server will format the necessary data for the proposal and present it in a visually easy-to-understand format using tables and graphs.

[0299] Step 5:

[0300] The server notifies the user of the created proposal. The user accesses the proposal using their terminal and reviews its contents. The notification system sends an alert to the user, guiding them to log in to view the details.

[0301] Step 6:

[0302] The user reviews the presented proposal and, if they have any questions or further requests, contacts the server via their device. The inquiry involves entering prompts generated using a generative AI model, and the server immediately provides additional information.

[0303] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0304] This invention is a system that combines an information processing device and an emotion engine to support the conversion of aging buildings to LEDs, and further provides suggestions that take into account the emotions of users. The embodiments of this system are described in detail below.

[0305] The system consists of a server, terminals, and users. First, the server collects data on buildings across Japan that are 40 years old or older, organizing information such as location, age, occupancy rate, type of lighting equipment, and power consumption, and storing it in a database. Based on this, the server uses a generative AI model to analyze the power reduction effect of converting to LED lighting in each building. Based on the results of this analysis, it creates a proposal document detailing the potential for power reduction and the possibility of utilizing subsidies.

[0306] Next, when the server provides this proposal to a user (building owner) connected to the system, it uses an emotion engine to analyze the user's emotions in real time. This emotion engine captures the user's reaction using voice tone and facial expression analysis, and can adjust the proposal content based on the obtained emotion data. For example, if the server detects that the user is feeling uneasy about the proposal, it will provide additional, easy-to-understand support information.

[0307] After presenting the proposal content, the user checks the proposal. The user can access the proposal document using a dedicated terminal and examine the content in detail. If further information regarding the proposal is needed, the user can inquire the server via the terminal and receive supplementary information immediately.

[0308] As a specific example, if the user is the owner of a company building and shows suspicion during the explanation of the proposal document, the server provides actual introduction case videos and details of successful cases to resolve the doubts. In this way, the system aims to dynamically optimize the proposal content by the emotion engine and enhance the user's understanding and acceptance.

[0309] Finally, after the server receives approval of the proposal from the user, it arranges for the light-emitting diode construction work with the partner construction company. Also, in response to the user's requests, it provides options for smart office conversion and improvement of the communication network to enhance the value of the entire building. Through such a process, the invention realizes building management with higher economic and functional value.

[0310] The following explains the processing flow.

[0311] Step 1:

[0312] The server collects building data of buildings over 40 years old from the real estate database and public information sources. This data includes the address, construction year, occupancy rate, types of lighting equipment, and current power consumption. The server filters the collected information to identify the target buildings.

[0313] Step 2:

[0314] The server processes the data of the identified buildings and analyzes the effect of converting to light-emitting diodes using the generated AI model. Specifically, it compares the current power consumption with the predicted power consumption after converting to light-emitting diodes and calculates the reduction amount of the power cost. It also investigates the applicability of subsidies.

[0315] Step 3:

[0316] Based on the analysis results, the server creates a proposal for each building owner to implement LED lighting. The proposal includes details on potential electricity cost savings, subsidy utilization, implementation costs, and projected cash flow after implementation.

[0317] Step 4:

[0318] The server activates an emotion engine when presenting a proposal to the user. It monitors the user's voice, facial expressions, and reaction speed, and analyzes their emotions in real time. This allows the server to evaluate how the user feels about the proposal.

[0319] Step 5:

[0320] The user receives the proposal via their device and reviews its contents. If the user expresses concerns or doubts, the server adjusts the proposal or provides supplementary materials based on the results of the emotion engine. For example, it might add specific implementation examples or FAQs.

[0321] Step 6:

[0322] If the user approves the proposal or has further questions, they will notify the server using their device. The server will then coordinate the construction schedule with the contractor and provide any necessary additional information, based on the user's instructions. It will also provide detailed information about smart office and network improvement options.

[0323] This system allows users to receive efficient and emotionally sensitive suggestions, enabling a smooth LED conversion process.

[0324] (Example 2)

[0325] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0326] In modern society, there are many older buildings, and there is a need for efficient management and optimization of energy consumption. Furthermore, proposals that take into account not only energy efficiency but also the needs and feelings of users are required. However, the current challenge is that there are not enough systems available that meet these requirements.

[0327] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0328] In this invention, the server includes means for collecting information about buildings that meet specific conditions using an information processing device, means for using an artificial intelligence model to analyze the effects of converting buildings to light-emitting diodes based on the information about the buildings, and means for adjusting suggestions using an emotion recognition system and notifying the user. This makes it possible to provide optimal suggestions that take into account both improved energy efficiency and the user's emotions.

[0329] An "information processing device" refers to a computer system for collecting, organizing, and storing data, and is designed to efficiently process data related to buildings.

[0330] "Information about buildings" refers to data about the characteristics of a building, including its location, age, occupancy rate, type of lighting equipment, and electricity consumption.

[0331] "Light-emitting diode (LED) conversion" refers to replacing conventional lighting equipment with energy-efficient light-emitting diode (LED) lighting, a technology aimed at reducing power consumption.

[0332] An "artificial intelligence model" is a program that uses machine learning algorithms to analyze data and evaluate the effectiveness of improving the energy efficiency of buildings.

[0333] An "emotion recognition system" is a technology that analyzes a user's emotions in real time through methods such as speech recognition and facial expression analysis, making it possible to adjust the suggested content according to the user's emotions.

[0334] "Adjusting a proposal" is the process of modifying or strengthening the content of a proposal based on the user's feelings to provide the user with the most relevant information.

[0335] This invention provides a system that offers efficient suggestions for building management by combining an information processing device and emotion recognition technology.

[0336] The server functions as an information processing device, collecting information on buildings across Japan that are 40 years old or older. This information includes the building's location, age, occupancy rate, type of lighting equipment, and power consumption. The server can use Amazon Web Services (AWS) or Google Cloud Platform (GCP) to organize this data and store it in a database.

[0337] Based on the collected information, the server uses a generative AI model to analyze the power reduction effect of switching to LEDs. Specifically, it uses machine learning algorithms to evaluate the potential for energy efficiency improvement for each building. An example of a prompt message in this case would be, "Calculate the power reduction effect of introducing LEDs in buildings that are 40 years old or older."

[0338] Based on the analysis results, the server will create a proposal that includes power reduction effects and the possibility of subsidy utilization. The proposal will be documented using Microsoft Office or Adobe Acrobat and generated in PDF or Word format.

[0339] Once the proposal is complete, the server uses an emotion recognition system to adjust the proposal based on the user's emotions. Using voice recognition software and facial recognition cameras, it analyzes the user's reactions in real time and provides easy-to-understand information and additional support as needed.

[0340] Ultimately, users can view the proposal via a dedicated terminal. The proposal includes actual implementation examples and success stories as concrete examples. If a user requests additional information, they can query the server through the terminal and receive the necessary supplementary information immediately. Through this process, the invention promotes improvements in building management and enhances user satisfaction.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The server begins collecting information. It accesses real estate databases and official statistical databases to gather information on buildings 40 years old or older across the country. The input includes data on the building's location, age, occupancy rate, type of lighting equipment, and power consumption. This information is converted into a digital format and stored in the database. The output is building data stored in an organized format.

[0344] Step 2:

[0345] The analysis is performed based on building information collected by the server. A generative AI model is used, with the prompt "Calculate the power reduction effect of introducing LEDs in buildings over 40 years old" as input. For data processing, a machine learning algorithm is used to analyze the energy consumption data of each building and evaluate the power reduction effect of switching to LEDs. The output is the predicted energy reduction rate and the possible cost reduction for each building.

[0346] Step 3:

[0347] The server generates a proposal based on the analysis results. The analysis results are incorporated into a template, and a PDF or Word document is generated using Microsoft Office or Adobe Acrobat. This proposal includes power reduction potential, available subsidy information, and estimated implementation costs. The input is the analysis results and relevant subsidy information, and the output is the completed proposal.

[0348] Step 4:

[0349] The server activates an emotion recognition system to provide the proposal to the user. Using speech recognition software and a camera, it monitors the user's facial expressions and voice tone in real time. The input is user reaction data to the proposal. Based on this, the system determines the user's emotions and, if necessary, adds additional information or clearer explanations to the proposal. The output is a proposal optimized for the user.

[0350] Step 5:

[0351] Users access and review proposals via a dedicated terminal. Input consists of the proposal itself and any supplementary information related to it. If further details are needed during the review process, users can send a request for supplementary information to the server via the terminal. The output consists of additional information obtained by the user, supporting decision-making based on the proposal's content.

[0352] (Application Example 2)

[0353] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0354] There is a need for effective proposals to reduce power consumption in aging buildings and promote the adoption of LED lighting. Furthermore, these proposals must be presented to building managers and owners in a way that is easily understandable and convincing. In particular, a key challenge is providing a system that can detect and appropriately address any concerns or questions that recipients may have in real time.

[0355] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0356] In this invention, the server includes means for collecting building information that meets specific conditions using an information processing device, means for analyzing the effects of LED conversion based on the building information using the information processing device, means for creating a proposal for LED conversion for the building, emotion analysis means for analyzing user emotion data, and means for adjusting the proposal content based on the emotion data. This reduces the psychological burden on the recipient of the proposal and enables smoother decision-making regarding LED conversion.

[0357] An "information processing device" is a device used to collect, analyze, and process data, and is used to handle information about buildings.

[0358] "Building information" refers to data about a specific building, such as its location, age, occupancy rate, type of lighting equipment, and electricity consumption.

[0359] "Light-emitting diode conversion" is a technical method that reduces power consumption by replacing existing lighting equipment with light-emitting diode (LED) lighting.

[0360] "Analytical methods" refer to processes and methods for evaluating specific effects or results based on collected data.

[0361] A "proposal" refers to a report that includes improvement measures and plans for building owners and managers, based on the analyzed data.

[0362] "Emotional analysis methods" are technologies that analyze emotions from a user's voice and facial expressions to evaluate their psychological state.

[0363] "Emotional data" refers to numerical data and information that represents the user's emotions, and is used to adjust the content of the suggestions.

[0364] "Adjustment methods" refer to techniques for adapting or modifying proposals based on analyzed sentiment data.

[0365] This invention is a system that combines an information processing device and emotion analysis technology, and is implemented to promote the conversion of aging buildings to LED lighting. The server collects information on older buildings nationwide and forms a building information database. The collected information includes location, age of the building, occupancy rate, type of existing lighting equipment, and power consumption. The server uses a generative AI model built with Python and TensorFlow to analyze this data and evaluate the power reduction effect of LED conversion. Based on this evaluation, it creates a proposal that includes details on the potential for power reduction, the availability of subsidies, and the necessary capital investment.

[0366] Users (building owners and managers) access this proposal via smartphones or dedicated terminals. To make the proposal easier to understand, the server analyzes the user's emotional data in real time using sentiment analysis tools. Specifically, it uses the smartphone's camera and microphone to collect the user's facial expressions and voice tone. This makes it possible to detect any anxieties or questions the user may have about the proposal. Based on the emotional data, information is provided in a format that is easy for the user to understand. For example, if anxiety is detected, the server may display videos of past success stories or provide graphs and charts to make the proposal easier to understand.

[0367] As a concrete example of implementation, consider a scenario where a user receives a proposal to convert their company building to LED lighting. In this case, if the user has questions, additional information to address those questions is immediately provided, deepening their understanding of the proposal.

[0368] By utilizing generative AI models, suggestions can be dynamically optimized, facilitating user understanding. An example of a prompt might be: "Please enter information about the building. Would you like to know about the power savings from LED lighting? Also, please optimize the suggestions while analyzing my emotions in real time." In this way, better building management and increased economic value become possible.

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The server collects information on older buildings across Japan. It takes location, age, occupancy rate, lighting equipment type, and power consumption from a building database as input, and outputs a structured building information database. This process systematically organizes the information necessary for subsequent analysis based on the collected data.

[0372] Step 2:

[0373] The server analyzes building information using a generative AI model. Using the building information acquired in Step 1 as input, it evaluates the power reduction effects of LED conversion and the feasibility of utilizing subsidies. The evaluation results are included in the proposal as output. Specifically, it performs data pattern matching and simulations to find the most efficient method of LED conversion.

[0374] Step 3:

[0375] The server generates a proposal based on the analysis results. Using the evaluation results from Step 2 as input, it outputs a comprehensive explanation of the potential for power reduction, details of necessary capital investments, and subsidy information. Specifically, it organizes the proposal content in a user-friendly format and includes charts, graphs, and attached documents to aid visual understanding.

[0376] Step 4:

[0377] The user retrieves the proposal via their device and reviews its contents. The input involves providing authentication information to access the proposal data on the device, and the output is the detailed proposal content. Specifically, the device downloads the proposal sent from the server and displays it to the user.

[0378] Step 5:

[0379] The server acquires real-time emotional data from users using emotion analysis tools. It uses the user's facial expressions and voice tone, collected through the device's camera and microphone, as input, and outputs the analysis results. Specifically, it deploys machine learning algorithms to identify the user's psychological state.

[0380] Step 6:

[0381] The server adjusts the suggestions based on emotional data. It uses the emotional analysis results obtained in step 5 as input and outputs optimized suggestions after adjustment. Specifically, it enhances the suggestions by adding success stories and graphs for users who have anxieties or questions.

[0382] Step 7:

[0383] The user gains a deeper understanding of the optimized proposal and makes a final decision. Inputs include receiving the adjusted proposal, and outputs include approving the LED conversion for the building or requesting further information. Specific actions include communication for feedback on the proposal and question-and-answer sessions.

[0384] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0385] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0390] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0391] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0392] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0393] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0394] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0395] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0396] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0397] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0398] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0399] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0400] This invention aims to improve the efficiency of managing aging buildings and enhance their value by using an information processing system. Servers, terminals, and users each play a crucial role in the operation of this system. The system is constructed using an information processing device and functions as follows:

[0401] First, the server collects data on buildings across Japan that are 40 years old or older. This data includes information such as location, building size, current lighting equipment type, power consumption, and occupancy rate. Based on the collected data, the server selects buildings that meet specific criteria.

[0402] Next, based on the data collected by the server, a generative AI model is used to analyze the power reduction effect of converting to LEDs. This analysis calculates the expected reduction in electricity costs for each building and the payback period for the initial costs of converting to LEDs.

[0403] Next, based on the analysis results, the server creates a proposal for building owners to implement LED lighting. This proposal includes details on the economic benefits of implementation and relevant subsidy programs. Furthermore, options for smart office solutions and improvements to the communication network are also considered and presented in the proposal.

[0404] Once the proposal is complete, the server notifies the user (building owner). The user can access the proposal using a terminal and review its contents. If the user has detailed questions about the proposal, they can inquire with the server via a dedicated terminal and receive relevant information immediately.

[0405] As a concrete example, let's say the user is the owner of a 50-year-old building. The server analyzes the building's data and presents a proposal showing that converting to LED lighting is expected to result in a 30% annual power saving. Furthermore, it might include information about the possibility of utilizing subsidies from the national and local governments to cover 50% of the initial investment.

[0406] In this way, the invention functions as a system that enables efficient management and improved operation of aging buildings, thereby bringing economic benefits to building owners.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The server connects to a database and collects data on buildings across Japan that are 40 years old or older. This data includes information such as location, age of construction, building size, current lighting equipment, power consumption, and occupancy rate. The server organizes this data and stores it in the database.

[0410] Step 2:

[0411] The system uses building data stored on the server to activate a generating AI model. The AI ​​model performs analysis to predict the power reduction effect of converting each building to LED lighting. Specifically, it compares current power consumption with predicted consumption after LED installation to calculate the potential cost savings.

[0412] Step 3:

[0413] Based on the analysis results, the server generates a proposal for LED conversion. This proposal includes estimated power cost savings, payback period for initial investment, information on applicable subsidies, and also presents options for smart office implementation and network improvements.

[0414] Step 4:

[0415] The server notifies the building owner (user) of the generated proposal. The notification is made through a portal exclusively for building owners, and the user can access the proposal.

[0416] Step 5:

[0417] Users review the proposal using a dedicated terminal. If users wish to obtain more detailed information based on the proposal, they can query the server via the terminal to receive additional information.

[0418] Step 6:

[0419] After the server accepts user inquiries and approvals, it begins the process of arranging LED conversion work with partner construction companies. Once the construction schedule is finalized, the user will be notified, and construction will begin. As part of after-sales service, support will also be provided if any proposed smart office modifications or network improvements are necessary.

[0420] (Example 1)

[0421] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0422] In aging structures, there is a need to improve efficient management and operation, and to provide economic benefits to the structure owners. To achieve this, effective data analysis and proposal development methods are necessary.

[0423] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0424] In this invention, the server includes means for collecting structural information that meets certain conditions, means for analyzing the effects of converting structures into light-emitting devices using a generated AI model, and means for creating proposals for converting structures into light-emitting devices for the structure owners. This enables effective management and operational improvement of structures.

[0425] An "information processing system" is a collection of computers used for collecting, analyzing, and processing data.

[0426] "Structural information" refers to detailed data about buildings, such as their location, size, energy consumption, and occupancy rate.

[0427] A "generative AI model" is an artificial intelligence model that learns from data and uses that data to make predictions and perform analyses.

[0428] "Light-emitting device conversion" refers to replacing conventional lighting fixtures with light-emitting diodes or similar energy-efficient lighting devices.

[0429] A "proposal" is a document created based on the analysis results, outlining improvement suggestions and economic benefits for the structure owner.

[0430] "Structure owner" refers to an individual or organization that owns a particular building or structure.

[0431] "Grant information" refers to information regarding financial support or subsidies from the government or related organizations.

[0432] "Office automation" refers to the use of technological means to automate tasks and management processes with the aim of improving efficiency.

[0433] "Communication network improvement options" refers to various technologies and methods for improving the efficiency or expanding communication infrastructure.

[0434] This invention functions as an information processing system that provides economic benefits to structure owners by streamlining the management and operation of aging structures and promoting their integration with light-emitting devices. This system is implemented as follows.

[0435] First, the server uses an information processing system to collect information on structures across Japan that are 40 years old or older. The hardware used includes high-performance computer servers for data collection and processing. This includes an API interface that allows access to public databases and datasets provided by local governments via network connectivity.

[0436] Next, the server utilizes a generative AI model to analyze the collected data. This AI model learns from past data and predicts the improvement in energy efficiency through the use of light-emitting devices. Specifically, it analyzes the power consumption patterns of each structure and simulates the reduction effect of using light-emitting diodes. This simulation calculates the payback period for the initial costs of using light-emitting devices and the expected cost reduction effect.

[0437] Based on the analysis results, the server creates a proposal for the structure owner to implement light-emitting devices. This proposal, based on the AI ​​model's analysis results, includes information on the economic benefits of implementation and available subsidies. A text generation program is used to create the proposal. Furthermore, to clearly show the structure owner what improvements they can actually implement, options such as office automation and communication network improvements are also included.

[0438] After the proposal is created, the server notifies the building owner, allowing the user to access the proposal using a dedicated terminal and review the details. If the user has questions about the proposal, they can inquire with the server from the dedicated terminal, and the server will provide an immediate answer. For example, if the user is the owner of a 50-year-old building, the proposal might show a 30% reduction in power consumption through LED lighting and indicate that 50% of the initial investment will be covered by a subsidy program.

[0439] Example of a prompt:

[0440] "Please generate a proposal for efficiency improvements using LEDs, based on electricity consumption data from a 50-year-old building."

[0441] Thus, this invention utilizes data analysis and AI technology to improve the efficiency and cost-effectiveness of structural management.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] The server collects structural information. As input, it queries a database of buildings across Japan that are 40 years old or older, obtaining information such as location, size, current power consumption, and occupancy rate. This information is typically obtained from real estate databases and public datasets via APIs. As output, it organizes this information by structure and generates a dataset to be passed on to the next step.

[0445] Step 2:

[0446] The server analyzes the collected structural information. The dataset generated in step 1 is used as input. The generated AI model uses this dataset to analyze the power consumption patterns of each structure and simulate the effects of incorporating light-emitting devices. Specifically, the AI ​​model uses a machine learning algorithm to learn patterns from past data and predicts the amount of electricity cost reduction and the payback period for initial costs. The output is an analysis result that includes the estimated effect of incorporating light-emitting devices.

[0447] Step 3:

[0448] The server generates a proposal based on the analysis results. The analysis results obtained in Step 2 are used as input. The proposal includes the economic benefits of using light-emitting devices, details of relevant grant programs, and options for office automation and communication network improvements. Specifically, text generation software combines these elements and documents them in a format easily understandable to the building owner. The completed proposal is generated as output.

[0449] Step 4:

[0450] The server notifies the user (structure owner) of the proposal. The proposal created in step 3 is used as input. A notification is sent to the user's device via email or a dedicated app, and an access link is provided. Specifically, the notification system sends the proposal information to the user, making it easy for the user to access the proposal. The output is the notification received by the user and the access link to the proposal.

[0451] Step 5:

[0452] Users can request more detailed information about the proposal. As input, the user's inquiry request is sent to the server via a dedicated terminal. The server searches its database and immediately provides the user with relevant information. Specifically, the server's internal data processing system analyzes the user's request, gathers the necessary information, and responds to the user. The output provides specific information in response to the user's request.

[0453] (Application Example 1)

[0454] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0455] Managing aging buildings can lead to problems such as increased energy consumption and rising operating costs due to a lack of proper maintenance and operation. Furthermore, building owners often lack effective management strategies and renovation proposals, making it difficult to maximize asset value. Therefore, there is a need for efficient means to improve building value and promote energy conservation.

[0456] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0457] In this invention, the server includes means for collecting building information that meets specific conditions using a terminal, means for analyzing the energy-saving effect of converting to light-emitting diodes based on the building information using an information processing device, and means for creating proposals for converting to light-emitting diodes for the constituent elements. This makes it possible to monitor the status of the property in real time, provide effective energy-saving proposals, and further maximize the value of the building and operational efficiency by utilizing subsidy information.

[0458] A "terminal" is a device used for inputting or outputting information, and it has the function of connecting to a computer network and exchanging data.

[0459] "Building information" refers to data about a building, including details such as its location, size, facilities, and energy consumption.

[0460] An "information processing device" is a computer system used to collect, process, and analyze data.

[0461] "Light-emitting diode conversion" refers to replacing conventional lighting equipment with light-emitting diodes (LEDs) to improve power efficiency and reduce energy consumption.

[0462] "Energy-saving effect" refers to the improvement in efficiency and cost reduction achieved by reducing energy consumption.

[0463] "Components" are the individual parts or components necessary to form the whole.

[0464] A "proposal" refers to a document or notice that outlines methods or plans for achieving a specific objective.

[0465] An "operator" is a person whose role is to operate or manage a system or piece of equipment.

[0466] "Support information" refers to data about programs and schemes that provide financial support for specific activities or improvements.

[0467] This invention is a system that uses an information processing system to streamline the management of aging buildings and improve their value. The system functions as follows, with the server, terminal, and user elements working together.

[0468] The server's role is to collect information on buildings across Japan that are above a certain age. This building information includes location, building size, current equipment status, and energy consumption. Based on the collected information, the server selects buildings that meet specific criteria and uses an information processing device to analyze the energy-saving effects of converting to LEDs. A generated AI model is used in this analysis to calculate the power reduction effect and the payback period for initial costs.

[0469] Next, the server generates a proposal for LED conversion for each component based on the analysis results. This proposal includes information on energy-saving effects and subsidies, clearly outlining the economic benefits. Options for smart office implementation and communication network improvements are also presented.

[0470] The user receives and reviews this proposal using their device. Specifically, the proposal presents a plan to reduce energy consumption by 30% annually by installing LED lighting in the user's 50-year-old building. The proposal also includes the possibility of utilizing a subsidy program to cover 50% of the initial investment.

[0471] An example of a prompt message would be: "We are considering introducing a new LED lighting system. It is expected to reduce electricity consumption by 30% annually in our 50-year-old building, and we are also eligible for subsidies from the local government. Please tell us about the specific process and the expected economic effects."

[0472] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0473] Step 1:

[0474] The server collects information on buildings across Japan that are above a certain age. This process collects data such as location, size, equipment status, and energy consumption. It retrieves relevant information through databases and the internet and stores the collected data in the system.

[0475] Step 2:

[0476] The server selects buildings that meet specific criteria based on the collected building information. It uses a complete list of building information as input and filters it to extract information that matches the criteria. The criteria are set based on factors such as the building's age and energy consumption.

[0477] Step 3:

[0478] The server uses an information processing device to analyze the energy-saving effects of converting selected buildings to LED lighting. It uses information about the selected buildings as input and utilizes a generated AI model to evaluate the energy-saving effects. The output includes analysis results such as power reduction effects and initial cost payback periods for each building.

[0479] Step 4:

[0480] Based on the analysis results, the server will create a proposal for LED conversion. This will include economic benefits and information on subsidies. The server will format the necessary data for the proposal and present it in a visually easy-to-understand format using tables and graphs.

[0481] Step 5:

[0482] The server notifies the user of the created proposal. The user accesses the proposal using their terminal and reviews its contents. The notification system sends an alert to the user, guiding them to log in to view the details.

[0483] Step 6:

[0484] The user reviews the presented proposal and, if they have any questions or further requests, contacts the server via their device. The inquiry involves entering prompts generated using a generative AI model, and the server immediately provides additional information.

[0485] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0486] This invention is a system that combines an information processing device and an emotion engine to support the conversion of aging buildings to LEDs, and further provides suggestions that take into account the emotions of users. The embodiments of this system are described in detail below.

[0487] The system consists of a server, terminals, and users. First, the server collects data on buildings across Japan that are 40 years old or older, organizing information such as location, age, occupancy rate, type of lighting equipment, and power consumption, and storing it in a database. Based on this, the server uses a generative AI model to analyze the power reduction effect of converting to LED lighting in each building. Based on the results of this analysis, it creates a proposal document detailing the potential for power reduction and the possibility of utilizing subsidies.

[0488] Next, when the server provides this proposal to a user (building owner) connected to the system, it uses an emotion engine to analyze the user's emotions in real time. This emotion engine captures the user's reaction using voice tone and facial expression analysis, and can adjust the proposal content based on the obtained emotion data. For example, if the server detects that the user is feeling uneasy about the proposal, it will provide additional, easy-to-understand support information.

[0489] After the proposal is presented, the user reviews it. The user can access the proposal using a dedicated terminal and examine its contents in detail. If further information is needed regarding the proposal, the user can query the server via the terminal and receive supplementary information immediately.

[0490] For example, if a user is the owner of their own building and expresses doubt during the explanation of the proposal, the server will provide actual implementation case videos and details of success stories to address their concerns. In this way, the system aims to dynamically optimize the proposal content using an emotion engine to enhance user understanding and acceptance.

[0491] Ultimately, after the server receives approval from the user for the proposal, it arranges for the LED conversion work with a partner construction company. Furthermore, depending on the user's requests, it offers options for smart office implementation and communication network improvements, aiming to enhance the overall value of the building. Through this process, the invention achieves building management with greater economic and functional value.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The server collects data on buildings over 40 years old from real estate databases and public sources. This data includes address, year of construction, occupancy rate, type of lighting fixtures, and current electricity consumption. The server filters the collected information to identify target buildings.

[0495] Step 2:

[0496] The server processes data on identified buildings and uses a generated AI model to analyze the effects of converting to LEDs. Specifically, it compares current power consumption with predicted power consumption after LED conversion to calculate the amount of electricity cost reduction. It also investigates the possibility of subsidy eligibility.

[0497] Step 3:

[0498] Based on the analysis results, the server creates a proposal for each building owner to implement LED lighting. The proposal includes details on potential electricity cost savings, subsidy utilization, implementation costs, and projected cash flow after implementation.

[0499] Step 4:

[0500] The server activates an emotion engine when presenting a proposal to the user. It monitors the user's voice, facial expressions, and reaction speed, and analyzes their emotions in real time. This allows the server to evaluate how the user feels about the proposal.

[0501] Step 5:

[0502] The user receives the proposal via their device and reviews its contents. If the user expresses concerns or doubts, the server adjusts the proposal or provides supplementary materials based on the results of the emotion engine. For example, it might add specific implementation examples or FAQs.

[0503] Step 6:

[0504] If the user approves the proposal or has further questions, they will notify the server using their device. The server will then coordinate the construction schedule with the contractor and provide any necessary additional information, based on the user's instructions. It will also provide detailed information about smart office and network improvement options.

[0505] This system allows users to receive efficient and emotionally sensitive suggestions, enabling a smooth LED conversion process.

[0506] (Example 2)

[0507] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0508] In modern society, there are many older buildings, and there is a need for efficient management and optimization of energy consumption. Furthermore, proposals that take into account not only energy efficiency but also the needs and feelings of users are required. However, the current challenge is that there are not enough systems available that meet these requirements.

[0509] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0510] In this invention, the server includes means for collecting information about buildings that meet specific conditions using an information processing device, means for using an artificial intelligence model to analyze the effects of converting buildings to light-emitting diodes based on the information about the buildings, and means for adjusting suggestions using an emotion recognition system and notifying the user. This makes it possible to provide optimal suggestions that take into account both improved energy efficiency and the user's emotions.

[0511] An "information processing device" refers to a computer system for collecting, organizing, and storing data, and is designed to efficiently process data related to buildings.

[0512] "Information about buildings" refers to data about the characteristics of a building, including its location, age, occupancy rate, type of lighting equipment, and electricity consumption.

[0513] "Light-emitting diode (LED) conversion" refers to replacing conventional lighting equipment with energy-efficient light-emitting diode (LED) lighting, a technology aimed at reducing power consumption.

[0514] An "artificial intelligence model" is a program that uses machine learning algorithms to analyze data and evaluate the effectiveness of improving the energy efficiency of buildings.

[0515] An "emotion recognition system" is a technology that analyzes a user's emotions in real time through methods such as speech recognition and facial expression analysis, making it possible to adjust the suggested content according to the user's emotions.

[0516] "Adjusting a proposal" is the process of modifying or strengthening the content of a proposal based on the user's feelings to provide the user with the most relevant information.

[0517] This invention provides a system that offers efficient suggestions for building management by combining an information processing device and emotion recognition technology.

[0518] The server functions as an information processing device, collecting information on buildings across Japan that are 40 years old or older. This information includes the building's location, age, occupancy rate, type of lighting equipment, and power consumption. The server can use Amazon Web Services (AWS) or Google Cloud Platform (GCP) to organize this data and store it in a database.

[0519] Based on the collected information, the server uses a generative AI model to analyze the power reduction effect of switching to LEDs. Specifically, it uses machine learning algorithms to evaluate the potential for energy efficiency improvement for each building. An example of a prompt message in this case would be, "Calculate the power reduction effect of introducing LEDs in buildings that are 40 years old or older."

[0520] Based on the analysis results, the server will create a proposal that includes power reduction effects and the possibility of subsidy utilization. The proposal will be documented using Microsoft Office or Adobe Acrobat and generated in PDF or Word format.

[0521] Once the proposal is complete, the server uses an emotion recognition system to adjust the proposal based on the user's emotions. Using voice recognition software and facial recognition cameras, it analyzes the user's reactions in real time and provides easy-to-understand information and additional support as needed.

[0522] Ultimately, users can view the proposal via a dedicated terminal. The proposal includes actual implementation examples and success stories as concrete examples. If a user requests additional information, they can query the server through the terminal and receive the necessary supplementary information immediately. Through this process, the invention promotes improvements in building management and enhances user satisfaction.

[0523] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0524] Step 1:

[0525] The server begins collecting information. It accesses real estate databases and official statistical databases to gather information on buildings 40 years old or older across the country. The input includes data on the building's location, age, occupancy rate, type of lighting equipment, and power consumption. This information is converted into a digital format and stored in the database. The output is building data stored in an organized format.

[0526] Step 2:

[0527] The analysis is performed based on building information collected by the server. A generative AI model is used, with the prompt "Calculate the power reduction effect of introducing LEDs in buildings over 40 years old" as input. For data processing, a machine learning algorithm is used to analyze the energy consumption data of each building and evaluate the power reduction effect of switching to LEDs. The output is the predicted energy reduction rate and the possible cost reduction for each building.

[0528] Step 3:

[0529] The server generates a proposal based on the analysis results. The analysis results are incorporated into a template, and a PDF or Word document is generated using Microsoft Office or Adobe Acrobat. This proposal includes power reduction potential, available subsidy information, and estimated implementation costs. The input is the analysis results and relevant subsidy information, and the output is the completed proposal.

[0530] Step 4:

[0531] The server activates an emotion recognition system to provide the proposal to the user. Using speech recognition software and a camera, it monitors the user's facial expressions and voice tone in real time. The input is user reaction data to the proposal. Based on this, the system determines the user's emotions and, if necessary, adds additional information or clearer explanations to the proposal. The output is a proposal optimized for the user.

[0532] Step 5:

[0533] Users access and review proposals via a dedicated terminal. Input consists of the proposal itself and any supplementary information related to it. If further details are needed during the review process, users can send a request for supplementary information to the server via the terminal. The output consists of additional information obtained by the user, supporting decision-making based on the proposal's content.

[0534] (Application Example 2)

[0535] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0536] There is a need for effective proposals to reduce power consumption in aging buildings and promote the adoption of LED lighting. Furthermore, these proposals must be presented to building managers and owners in a way that is easily understandable and convincing. In particular, a key challenge is providing a system that can detect and appropriately address any concerns or questions that recipients may have in real time.

[0537] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0538] In this invention, the server includes means for collecting building information that meets specific conditions using an information processing device, means for analyzing the effects of LED conversion based on the building information using the information processing device, means for creating a proposal for LED conversion for the building, emotion analysis means for analyzing user emotion data, and means for adjusting the proposal content based on the emotion data. This reduces the psychological burden on the recipient of the proposal and enables smoother decision-making regarding LED conversion.

[0539] An "information processing device" is a device used to collect, analyze, and process data, and is used to handle information about buildings.

[0540] "Building information" refers to data about a specific building, such as its location, age, occupancy rate, type of lighting equipment, and electricity consumption.

[0541] "Light-emitting diode conversion" is a technical method that reduces power consumption by replacing existing lighting equipment with light-emitting diode (LED) lighting.

[0542] "Analytical methods" refer to processes and methods for evaluating specific effects or results based on collected data.

[0543] A "proposal" refers to a report that includes improvement measures and plans for building owners and managers, based on the analyzed data.

[0544] "Emotional analysis methods" are technologies that analyze emotions from a user's voice and facial expressions to evaluate their psychological state.

[0545] "Emotional data" refers to numerical data and information that represents the user's emotions, and is used to adjust the content of the suggestions.

[0546] "Adjustment methods" refer to techniques for adapting or modifying proposals based on analyzed sentiment data.

[0547] This invention is a system that combines an information processing device and emotion analysis technology, and is implemented to promote the conversion of aging buildings to LED lighting. The server collects information on older buildings nationwide and forms a building information database. The collected information includes location, age of the building, occupancy rate, type of existing lighting equipment, and power consumption. The server uses a generative AI model built with Python and TensorFlow to analyze this data and evaluate the power reduction effect of LED lighting. Based on this evaluation, it creates a proposal that includes details on the potential for power reduction, the availability of subsidies, and the necessary capital investment.

[0548] Users (building owners and managers) access this proposal via smartphones or dedicated terminals. To make the proposal easier to understand, the server analyzes the user's emotional data in real time using sentiment analysis tools. Specifically, it uses the smartphone's camera and microphone to collect the user's facial expressions and voice tone. This makes it possible to detect any anxieties or questions the user may have about the proposal. Based on the emotional data, information is provided in a format that is easy for the user to understand. For example, if anxiety is detected, the server may display videos of past success stories or provide graphs and charts to make the proposal easier to understand.

[0549] As a concrete example of implementation, consider a scenario where a user receives a proposal to convert their company building to LED lighting. In this case, if the user has questions, additional information to address those questions is immediately provided, deepening their understanding of the proposal.

[0550] By utilizing generative AI models, suggestions can be dynamically optimized, facilitating user understanding. An example of a prompt might be: "Please enter information about the building. Would you like to know about the power savings from LED lighting? Also, please optimize the suggestions while analyzing my emotions in real time." In this way, better building management and increased economic value become possible.

[0551] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0552] Step 1:

[0553] The server collects information on older buildings across Japan. It takes location, age, occupancy rate, lighting equipment type, and power consumption from a building database as input, and outputs a structured building information database. This process systematically organizes the information necessary for subsequent analysis based on the collected data.

[0554] Step 2:

[0555] The server analyzes building information using a generative AI model. Using the building information acquired in Step 1 as input, it evaluates the power reduction effects of LED conversion and the feasibility of utilizing subsidies. The evaluation results are included in the proposal as output. Specifically, it performs data pattern matching and simulations to find the most efficient method of LED conversion.

[0556] Step 3:

[0557] The server generates a proposal based on the analysis results. Using the evaluation results from Step 2 as input, it outputs a comprehensive explanation of the potential for power reduction, details of necessary capital investments, and subsidy information. Specifically, it organizes the proposal content in a user-friendly format and includes charts, graphs, and attached documents to aid visual understanding.

[0558] Step 4:

[0559] The user retrieves the proposal via their device and reviews its contents. The input involves providing authentication information to access the proposal data on the device, and the output is the detailed proposal content. Specifically, the device downloads the proposal sent from the server and displays it to the user.

[0560] Step 5:

[0561] The server acquires real-time emotional data from users using emotion analysis tools. It uses the user's facial expressions and voice tone, collected through the device's camera and microphone, as input, and outputs the analysis results. Specifically, it deploys machine learning algorithms to identify the user's psychological state.

[0562] Step 6:

[0563] The server adjusts the suggestions based on emotional data. It uses the emotional analysis results obtained in step 5 as input and outputs optimized suggestions after adjustment. Specifically, it enhances the suggestions by adding success stories and graphs for users who have anxieties or questions.

[0564] Step 7:

[0565] The user gains a deeper understanding of the optimized proposal and makes a final decision. Inputs include receiving the adjusted proposal, and outputs include approving the LED conversion for the building or requesting further information. Specific actions include communication for feedback on the proposal and question-and-answer sessions.

[0566] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0567] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0568] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0569] [Fourth Embodiment]

[0570] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0571] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0572] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0573] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0574] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0575] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0576] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0577] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0578] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0579] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0580] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0581] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0582] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0583] This invention aims to improve the efficiency of managing aging buildings and enhance their value by using an information processing system. Servers, terminals, and users each play a crucial role in the operation of this system. The system is constructed using an information processing device and functions as follows:

[0584] First, the server collects data on buildings across Japan that are 40 years old or older. This data includes information such as location, building size, current lighting equipment type, power consumption, and occupancy rate. Based on the collected data, the server selects buildings that meet specific criteria.

[0585] Next, based on the data collected by the server, a generative AI model is used to analyze the power reduction effect of converting to LEDs. This analysis calculates the expected reduction in electricity costs for each building and the payback period for the initial costs of converting to LEDs.

[0586] Next, based on the analysis results, the server creates a proposal for building owners to implement LED lighting. This proposal includes details on the economic benefits of implementation and relevant subsidy programs. Furthermore, options for smart office solutions and improvements to the communication network are also considered and presented in the proposal.

[0587] Once the proposal is complete, the server notifies the user (building owner). The user can access the proposal using a terminal and review its contents. If the user has detailed questions about the proposal, they can inquire with the server via a dedicated terminal and receive relevant information immediately.

[0588] As a concrete example, let's say the user is the owner of a 50-year-old building. The server analyzes the building's data and presents a proposal showing that converting to LED lighting is expected to result in a 30% annual power saving. Furthermore, it might include information about the possibility of utilizing subsidies from the national and local governments to cover 50% of the initial investment.

[0589] In this way, the invention functions as a system that enables efficient management and improved operation of aging buildings, thereby bringing economic benefits to building owners.

[0590] The following describes the processing flow.

[0591] Step 1:

[0592] The server connects to a database and collects data on buildings across Japan that are 40 years old or older. This data includes information such as location, age of construction, building size, current lighting equipment, power consumption, and occupancy rate. The server organizes this data and stores it in the database.

[0593] Step 2:

[0594] The system uses building data stored on the server to activate a generating AI model. The AI ​​model performs analysis to predict the power reduction effect of converting each building to LED lighting. Specifically, it compares current power consumption with predicted consumption after LED installation to calculate the potential cost savings.

[0595] Step 3:

[0596] Based on the analysis results, the server generates a proposal for LED conversion. This proposal includes estimated power cost savings, payback period for initial investment, information on applicable subsidies, and also presents options for smart office implementation and network improvements.

[0597] Step 4:

[0598] The server notifies the building owner (user) of the generated proposal. The notification is made through a portal exclusively for building owners, and the user can access the proposal.

[0599] Step 5:

[0600] Users review the proposal using a dedicated terminal. If users wish to obtain more detailed information based on the proposal, they can query the server via the terminal to receive additional information.

[0601] Step 6:

[0602] After the server accepts user inquiries and approvals, it begins the process of arranging LED conversion work with partner construction companies. Once the construction schedule is finalized, the user will be notified, and construction will begin. As part of after-sales service, support will also be provided if any proposed smart office modifications or network improvements are necessary.

[0603] (Example 1)

[0604] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0605] In aging structures, there is a need to improve efficient management and operation, and to provide economic benefits to the structure owners. To achieve this, effective data analysis and proposal development methods are necessary.

[0606] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0607] In this invention, the server includes means for collecting structural information that meets certain conditions, means for analyzing the effects of converting structures into light-emitting devices using a generated AI model, and means for creating proposals for converting structures into light-emitting devices for the structure owners. This enables effective management and operational improvement of structures.

[0608] An "information processing system" is a collection of computers used for collecting, analyzing, and processing data.

[0609] "Structural information" refers to detailed data about buildings, such as their location, size, energy consumption, and occupancy rate.

[0610] A "generative AI model" is an artificial intelligence model that learns from data and uses that data to make predictions and perform analyses.

[0611] "Light-emitting device conversion" refers to replacing conventional lighting fixtures with light-emitting diodes or similar energy-efficient lighting devices.

[0612] A "proposal" is a document created based on the analysis results, outlining improvement suggestions and economic benefits for the structure owner.

[0613] "Structure owner" refers to an individual or organization that owns a particular building or structure.

[0614] "Grant information" refers to information regarding financial support or subsidies from the government or related organizations.

[0615] "Office automation" refers to the use of technological means to automate tasks and management processes with the aim of improving efficiency.

[0616] "Communication network improvement options" refers to various technologies and methods for improving the efficiency or expanding communication infrastructure.

[0617] This invention functions as an information processing system that provides economic benefits to structure owners by streamlining the management and operation of aging structures and promoting their integration with light-emitting devices. This system is implemented as follows.

[0618] First, the server uses an information processing system to collect information on structures across Japan that are 40 years old or older. The hardware used includes high-performance computer servers for data collection and processing. This includes an API interface that allows access to public databases and datasets provided by local governments via network connectivity.

[0619] Next, the server utilizes a generative AI model to analyze the collected data. This AI model learns from past data and predicts the improvement in energy efficiency through the use of light-emitting devices. Specifically, it analyzes the power consumption patterns of each structure and simulates the reduction effect of using light-emitting diodes. This simulation calculates the payback period for the initial costs of using light-emitting devices and the expected cost reduction effect.

[0620] Based on the analysis results, the server creates a proposal for the structure owner to implement light-emitting devices. This proposal, based on the AI ​​model's analysis results, includes information on the economic benefits of implementation and available subsidies. A text generation program is used to create the proposal. Furthermore, to clearly show the structure owner what improvements they can actually implement, options such as office automation and communication network improvements are also included.

[0621] After the proposal is created, the server notifies the building owner, allowing the user to access the proposal using a dedicated terminal and review the details. If the user has questions about the proposal, they can inquire with the server from the dedicated terminal, and the server will provide an immediate answer. For example, if the user is the owner of a 50-year-old building, the proposal might show a 30% reduction in power consumption through LED lighting and indicate that 50% of the initial investment will be covered by a subsidy program.

[0622] Example of a prompt:

[0623] "Please generate a proposal for efficiency improvements using LEDs, based on electricity consumption data from a 50-year-old building."

[0624] Thus, this invention utilizes data analysis and AI technology to improve the efficiency and cost-effectiveness of structural management.

[0625] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0626] Step 1:

[0627] The server collects structural information. As input, it queries a database of buildings across Japan that are 40 years old or older, obtaining information such as location, size, current power consumption, and occupancy rate. This information is typically obtained from real estate databases and public datasets via APIs. As output, it organizes this information by structure and generates a dataset to be passed on to the next step.

[0628] Step 2:

[0629] The server analyzes the collected structural information. The dataset generated in step 1 is used as input. The generated AI model uses this dataset to analyze the power consumption patterns of each structure and simulate the effects of incorporating light-emitting devices. Specifically, the AI ​​model uses a machine learning algorithm to learn patterns from past data and predicts the amount of electricity cost reduction and the payback period for initial costs. The output is an analysis result that includes the estimated effect of incorporating light-emitting devices.

[0630] Step 3:

[0631] The server generates a proposal based on the analysis results. The analysis results obtained in Step 2 are used as input. The proposal includes the economic benefits of using light-emitting devices, details of relevant grant programs, and options for office automation and communication network improvements. Specifically, text generation software combines these elements and documents them in a format easily understandable to the building owner. The completed proposal is generated as output.

[0632] Step 4:

[0633] The server notifies the user (structure owner) of the proposal. The proposal created in step 3 is used as input. A notification is sent to the user's device via email or a dedicated app, and an access link is provided. Specifically, the notification system sends the proposal information to the user, making it easy for the user to access the proposal. The output is the notification received by the user and the access link to the proposal.

[0634] Step 5:

[0635] Users can request more detailed information about the proposal. As input, the user's inquiry request is sent to the server via a dedicated terminal. The server searches its database and immediately provides the user with relevant information. Specifically, the server's internal data processing system analyzes the user's request, gathers the necessary information, and responds to the user. The output provides specific information in response to the user's request.

[0636] (Application Example 1)

[0637] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] Managing aging buildings can lead to problems such as increased energy consumption and rising operating costs due to a lack of proper maintenance and operation. Furthermore, building owners often lack effective management strategies and renovation proposals, making it difficult to maximize asset value. Therefore, there is a need for efficient means to improve building value and promote energy conservation.

[0639] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0640] In this invention, the server includes means for collecting building information that meets specific conditions using a terminal, means for analyzing the energy-saving effect of converting to light-emitting diodes based on the building information using an information processing device, and means for creating proposals for converting to light-emitting diodes for the constituent elements. This makes it possible to monitor the status of the property in real time, provide effective energy-saving proposals, and further maximize the value of the building and operational efficiency by utilizing subsidy information.

[0641] A "terminal" is a device used for inputting or outputting information, and it has the function of connecting to a computer network and exchanging data.

[0642] "Building information" refers to data about a building, including details such as its location, size, facilities, and energy consumption.

[0643] An "information processing device" is a computer system used to collect, process, and analyze data.

[0644] "Light-emitting diode conversion" refers to replacing conventional lighting equipment with light-emitting diodes (LEDs) to improve power efficiency and reduce energy consumption.

[0645] "Energy-saving effect" refers to the improvement in efficiency and cost reduction achieved by reducing energy consumption.

[0646] "Components" are the individual parts or components necessary to form the whole.

[0647] A "proposal" refers to a document or notice that outlines methods or plans for achieving a specific objective.

[0648] An "operator" is a person whose role is to operate or manage a system or piece of equipment.

[0649] "Support information" refers to data about programs and schemes that provide financial support for specific activities or improvements.

[0650] This invention is a system that uses an information processing system to streamline the management of aging buildings and improve their value. The system functions as follows, with the server, terminal, and user elements working together.

[0651] The server's role is to collect information on buildings across Japan that are above a certain age. This building information includes location, building size, current equipment status, and energy consumption. Based on the collected information, the server selects buildings that meet specific criteria and uses an information processing device to analyze the energy-saving effects of converting to LEDs. A generated AI model is used in this analysis to calculate the power reduction effect and the payback period for initial costs.

[0652] Next, the server generates a proposal for LED conversion for each component based on the analysis results. This proposal includes information on energy-saving effects and subsidies, clearly outlining the economic benefits. Options for smart office implementation and communication network improvements are also presented.

[0653] The user receives and reviews this proposal using their device. Specifically, the proposal presents a plan to reduce energy consumption by 30% annually by installing LED lighting in the user's 50-year-old building. The proposal also includes the possibility of utilizing a subsidy program to cover 50% of the initial investment.

[0654] An example of a prompt message would be: "We are considering introducing a new LED lighting system. It is expected to reduce electricity consumption by 30% annually in our 50-year-old building, and we are also eligible for subsidies from the local government. Please tell us about the specific process and the expected economic effects."

[0655] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0656] Step 1:

[0657] The server collects information on buildings across Japan that are above a certain age. This process collects data such as location, size, equipment status, and energy consumption. It retrieves relevant information through databases and the internet and stores the collected data in the system.

[0658] Step 2:

[0659] The server selects buildings that meet specific criteria based on the collected building information. It uses a complete list of building information as input and filters it to extract information that matches the criteria. The criteria are set based on factors such as the building's age and energy consumption.

[0660] Step 3:

[0661] The server uses an information processing device to analyze the energy-saving effects of converting selected buildings to LED lighting. It uses information about the selected buildings as input and utilizes a generated AI model to evaluate the energy-saving effects. The output includes analysis results such as power reduction effects and initial cost payback periods for each building.

[0662] Step 4:

[0663] Based on the analysis results, the server will create a proposal for LED conversion. This will include economic benefits and information on subsidies. The server will format the necessary data for the proposal and present it in a visually easy-to-understand format using tables and graphs.

[0664] Step 5:

[0665] The server notifies the user of the created proposal. The user accesses the proposal using their terminal and reviews its contents. The notification system sends an alert to the user, guiding them to log in to view the details.

[0666] Step 6:

[0667] The user reviews the presented proposal and, if they have any questions or further requests, contacts the server via their device. The inquiry involves entering prompts generated using a generative AI model, and the server immediately provides additional information.

[0668] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0669] This invention is a system that combines an information processing device and an emotion engine to support the conversion of aging buildings to LEDs, and further provides suggestions that take into account the emotions of users. The embodiments of this system are described in detail below.

[0670] The system consists of a server, terminals, and users. First, the server collects data on buildings across Japan that are 40 years old or older, organizing information such as location, age, occupancy rate, type of lighting equipment, and power consumption, and storing it in a database. Based on this, the server uses a generative AI model to analyze the power reduction effect of converting to LED lighting in each building. Based on the results of this analysis, it creates a proposal document detailing the potential for power reduction and the possibility of utilizing subsidies.

[0671] Next, when the server provides this proposal to a user (building owner) connected to the system, it uses an emotion engine to analyze the user's emotions in real time. This emotion engine captures the user's reaction using voice tone and facial expression analysis, and can adjust the proposal content based on the obtained emotion data. For example, if the server detects that the user is feeling uneasy about the proposal, it will provide additional, easy-to-understand support information.

[0672] After the proposal is presented, the user reviews it. The user can access the proposal using a dedicated terminal and examine its contents in detail. If further information is needed regarding the proposal, the user can query the server via the terminal and receive supplementary information immediately.

[0673] For example, if a user is the owner of their own building and expresses doubt during the explanation of the proposal, the server will provide actual implementation case videos and details of success stories to address their concerns. In this way, the system aims to dynamically optimize the proposal content using an emotion engine to enhance user understanding and acceptance.

[0674] Ultimately, after the server receives approval from the user for the proposal, it arranges for the LED conversion work with a partner construction company. Furthermore, depending on the user's requests, it offers options for smart office implementation and communication network improvements, aiming to enhance the overall value of the building. Through this process, the invention achieves building management with greater economic and functional value.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] The server collects data on buildings over 40 years old from real estate databases and public sources. This data includes address, year of construction, occupancy rate, type of lighting fixtures, and current electricity consumption. The server filters the collected information to identify target buildings.

[0678] Step 2:

[0679] The server processes data on identified buildings and uses a generated AI model to analyze the effects of converting to LEDs. Specifically, it compares current power consumption with predicted power consumption after LED conversion to calculate the amount of electricity cost reduction. It also investigates the possibility of subsidy eligibility.

[0680] Step 3:

[0681] Based on the analysis results, the server creates a proposal for each building owner to implement LED lighting. The proposal includes details on potential electricity cost savings, subsidy utilization, implementation costs, and projected cash flow after implementation.

[0682] Step 4:

[0683] The server activates an emotion engine when presenting a proposal to the user. It monitors the user's voice, facial expressions, and reaction speed, and analyzes their emotions in real time. This allows the server to evaluate how the user feels about the proposal.

[0684] Step 5:

[0685] The user receives the proposal via their device and reviews its contents. If the user expresses concerns or doubts, the server adjusts the proposal or provides supplementary materials based on the results of the emotion engine. For example, it might add specific implementation examples or FAQs.

[0686] Step 6:

[0687] If the user approves the proposal or has further questions, they will notify the server using their device. The server will then coordinate the construction schedule with the contractor and provide any necessary additional information, based on the user's instructions. It will also provide detailed information about smart office and network improvement options.

[0688] This system allows users to receive efficient and emotionally sensitive suggestions, enabling a smooth LED conversion process.

[0689] (Example 2)

[0690] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] In modern society, there are many older buildings, and there is a need for efficient management and optimization of energy consumption. Furthermore, proposals that take into account not only energy efficiency but also the needs and feelings of users are required. However, the current challenge is that there are not enough systems available that meet these requirements.

[0692] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0693] In this invention, the server includes means for collecting information about buildings that meet specific conditions using an information processing device, means for using an artificial intelligence model to analyze the effects of converting buildings to light-emitting diodes based on the information about the buildings, and means for adjusting suggestions using an emotion recognition system and notifying the user. This makes it possible to provide optimal suggestions that take into account both improved energy efficiency and the user's emotions.

[0694] An "information processing device" refers to a computer system for collecting, organizing, and storing data, and is designed to efficiently process data related to buildings.

[0695] "Information about buildings" refers to data about the characteristics of a building, including its location, age, occupancy rate, type of lighting equipment, and electricity consumption.

[0696] "Light-emitting diode (LED) conversion" refers to replacing conventional lighting equipment with energy-efficient light-emitting diode (LED) lighting, a technology aimed at reducing power consumption.

[0697] An "artificial intelligence model" is a program that uses machine learning algorithms to analyze data and evaluate the effectiveness of improving the energy efficiency of buildings.

[0698] An "emotion recognition system" is a technology that analyzes a user's emotions in real time through methods such as speech recognition and facial expression analysis, making it possible to adjust the suggested content according to the user's emotions.

[0699] "Adjusting a proposal" is the process of modifying or strengthening the content of a proposal based on the user's feelings to provide the user with the most relevant information.

[0700] This invention provides a system that offers efficient suggestions for building management by combining an information processing device and emotion recognition technology.

[0701] The server functions as an information processing device, collecting information on buildings across Japan that are 40 years old or older. This information includes the building's location, age, occupancy rate, type of lighting equipment, and power consumption. The server can use Amazon Web Services (AWS) or Google Cloud Platform (GCP) to organize this data and store it in a database.

[0702] Based on the collected information, the server uses a generative AI model to analyze the power reduction effect of switching to LEDs. Specifically, it uses machine learning algorithms to evaluate the potential for energy efficiency improvement for each building. An example of a prompt message in this case would be, "Calculate the power reduction effect of introducing LEDs in buildings that are 40 years old or older."

[0703] Based on the analysis results, the server will create a proposal that includes power reduction effects and the possibility of subsidy utilization. The proposal will be documented using Microsoft Office or Adobe Acrobat and generated in PDF or Word format.

[0704] Once the proposal is complete, the server uses an emotion recognition system to adjust the proposal based on the user's emotions. Using voice recognition software and facial recognition cameras, it analyzes the user's reactions in real time and provides easy-to-understand information and additional support as needed.

[0705] Ultimately, users can view the proposal via a dedicated terminal. The proposal includes actual implementation examples and success stories as concrete examples. If a user requests additional information, they can query the server through the terminal and receive the necessary supplementary information immediately. Through this process, the invention promotes improvements in building management and enhances user satisfaction.

[0706] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0707] Step 1:

[0708] The server begins collecting information. It accesses real estate databases and official statistical databases to gather information on buildings 40 years old or older across the country. The input includes data on the building's location, age, occupancy rate, type of lighting equipment, and power consumption. This information is converted into a digital format and stored in the database. The output is building data stored in an organized format.

[0709] Step 2:

[0710] The analysis is performed based on building information collected by the server. A generative AI model is used, with the prompt "Calculate the power reduction effect of introducing LEDs in buildings over 40 years old" as input. For data processing, a machine learning algorithm is used to analyze the energy consumption data of each building and evaluate the power reduction effect of switching to LEDs. The output is the predicted energy reduction rate and the possible cost reduction for each building.

[0711] Step 3:

[0712] The server generates a proposal based on the analysis results. The analysis results are incorporated into a template, and a PDF or Word document is generated using Microsoft Office or Adobe Acrobat. This proposal includes power reduction potential, available subsidy information, and estimated implementation costs. The input is the analysis results and relevant subsidy information, and the output is the completed proposal.

[0713] Step 4:

[0714] The server activates an emotion recognition system to provide the proposal to the user. Using speech recognition software and a camera, it monitors the user's facial expressions and voice tone in real time. The input is user reaction data to the proposal. Based on this, the system determines the user's emotions and, if necessary, adds additional information or clearer explanations to the proposal. The output is a proposal optimized for the user.

[0715] Step 5:

[0716] Users access and review proposals via a dedicated terminal. Input consists of the proposal itself and any supplementary information related to it. If further details are needed during the review process, users can send a request for supplementary information to the server via the terminal. The output consists of additional information obtained by the user, supporting decision-making based on the proposal's content.

[0717] (Application Example 2)

[0718] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] There is a need for effective proposals to reduce power consumption in aging buildings and promote the adoption of LED lighting. Furthermore, these proposals must be presented to building managers and owners in a way that is easily understandable and convincing. In particular, a key challenge is providing a system that can detect and appropriately address any concerns or questions that recipients may have in real time.

[0720] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0721] In this invention, the server includes means for collecting building information that meets specific conditions using an information processing device, means for analyzing the effects of LED conversion based on the building information using the information processing device, means for creating a proposal for LED conversion for the building, emotion analysis means for analyzing user emotion data, and means for adjusting the proposal content based on the emotion data. This reduces the psychological burden on the recipient of the proposal and enables smoother decision-making regarding LED conversion.

[0722] An "information processing device" is a device used to collect, analyze, and process data, and is used to handle information about buildings.

[0723] "Building information" refers to data about a specific building, such as its location, age, occupancy rate, type of lighting equipment, and electricity consumption.

[0724] "Light-emitting diode conversion" is a technical method that reduces power consumption by replacing existing lighting equipment with light-emitting diode (LED) lighting.

[0725] "Analytical methods" refer to processes and methods for evaluating specific effects or results based on collected data.

[0726] A "proposal" refers to a report that includes improvement measures and plans for building owners and managers, based on the analyzed data.

[0727] "Emotional analysis methods" are technologies that analyze emotions from a user's voice and facial expressions to evaluate their psychological state.

[0728] "Emotional data" refers to numerical data and information that represents the user's emotions, and is used to adjust the content of the suggestions.

[0729] "Adjustment methods" refer to techniques for adapting or modifying proposals based on analyzed sentiment data.

[0730] This invention is a system that combines an information processing device and emotion analysis technology, and is implemented to promote the conversion of aging buildings to LED lighting. The server collects information on older buildings nationwide and forms a building information database. The collected information includes location, age of the building, occupancy rate, type of existing lighting equipment, and power consumption. The server uses a generative AI model built with Python and TensorFlow to analyze this data and evaluate the power reduction effect of LED lighting. Based on this evaluation, it creates a proposal that includes details on the potential for power reduction, the availability of subsidies, and the necessary capital investment.

[0731] Users (building owners and managers) access this proposal via smartphones or dedicated terminals. To make the proposal easier to understand, the server analyzes the user's emotional data in real time using sentiment analysis tools. Specifically, it uses the smartphone's camera and microphone to collect the user's facial expressions and voice tone. This makes it possible to detect any anxieties or questions the user may have about the proposal. Based on the emotional data, information is provided in a format that is easy for the user to understand. For example, if anxiety is detected, the server may display videos of past success stories or provide graphs and charts to make the proposal easier to understand.

[0732] As a concrete example of implementation, consider a scenario where a user receives a proposal to convert their company building to LED lighting. In this case, if the user has questions, additional information to address those questions is immediately provided, deepening their understanding of the proposal.

[0733] By utilizing generative AI models, suggestions can be dynamically optimized, facilitating user understanding. An example of a prompt might be: "Please enter information about the building. Would you like to know about the power savings from LED lighting? Also, please optimize the suggestions while analyzing my emotions in real time." In this way, better building management and increased economic value become possible.

[0734] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0735] Step 1:

[0736] The server collects information on older buildings across Japan. It takes location, age, occupancy rate, lighting equipment type, and power consumption from a building database as input, and outputs a structured building information database. This process systematically organizes the information necessary for subsequent analysis based on the collected data.

[0737] Step 2:

[0738] The server analyzes building information using a generative AI model. Using the building information acquired in Step 1 as input, it evaluates the power reduction effects of LED conversion and the feasibility of utilizing subsidies. The evaluation results are included in the proposal as output. Specifically, it performs data pattern matching and simulations to find the most efficient method of LED conversion.

[0739] Step 3:

[0740] The server generates a proposal based on the analysis results. Using the evaluation results from Step 2 as input, it outputs a comprehensive explanation of the potential for power reduction, details of necessary capital investments, and subsidy information. Specifically, it organizes the proposal content in a user-friendly format and includes charts, graphs, and attached documents to aid visual understanding.

[0741] Step 4:

[0742] The user retrieves the proposal via their device and reviews its contents. The input involves providing authentication information to access the proposal data on the device, and the output is the detailed proposal content. Specifically, the device downloads the proposal sent from the server and displays it to the user.

[0743] Step 5:

[0744] The server acquires real-time emotional data from users using emotion analysis tools. It uses the user's facial expressions and voice tone, collected through the device's camera and microphone, as input, and outputs the analysis results. Specifically, it deploys machine learning algorithms to identify the user's psychological state.

[0745] Step 6:

[0746] The server adjusts the suggestions based on emotional data. It uses the emotional analysis results obtained in step 5 as input and outputs optimized suggestions after adjustment. Specifically, it enhances the suggestions by adding success stories and graphs for users who have anxieties or questions.

[0747] Step 7:

[0748] The user gains a deeper understanding of the optimized proposal and makes a final decision. Inputs include receiving the adjusted proposal, and outputs include approving the LED conversion for the building or requesting further information. Specific actions include communication for feedback on the proposal and question-and-answer sessions.

[0749] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0750] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0751] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0752] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0753] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0754] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0755] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0756] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0757] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0758] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0759] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0760] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0761] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0762] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0763] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0764] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0765] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0766] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0767] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0768] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0769] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0770] The following is further disclosed regarding the embodiments described above.

[0771] (Claim 1)

[0772] A means for collecting building data that meets specific conditions using an information processing device,

[0773] Using the aforementioned information processing device, a means for analyzing the effects of converting to light-emitting diodes based on the building data,

[0774] A means for creating a proposal for the conversion of the building to light-emitting diodes,

[0775] Means for notifying users of the aforementioned proposal,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, comprising means for verifying subsidy information related to the conversion to light-emitting diodes and including the results in the proposal.

[0779] (Claim 3)

[0780] The system according to claim 1, comprising means for including smart office and communication network improvement options in the proposal.

[0781] "Example 1"

[0782] (Claim 1)

[0783] A means for collecting structural information that meets certain conditions using an information processing system,

[0784] The aforementioned information processing system provides a means for analyzing the effects of creating a light-emitting device using a generated AI model based on the structural information,

[0785] Based on the aforementioned analysis results, a means for creating a proposal for the construction of a light-emitting device for the structure owner,

[0786] Means for notifying the structure owner of the aforementioned proposal and making it verifiable,

[0787] A means for immediately responding to inquiries from the owner of the aforementioned structure,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, further comprising means for verifying subsidy information related to the development of light-emitting devices and including the results in the proposal.

[0791] (Claim 3)

[0792] The system according to claim 1, comprising means for including options for office automation and communication network improvements in the proposal.

[0793] "Application Example 1"

[0794] (Claim 1)

[0795] A means of collecting building information that meets specific conditions using a terminal,

[0796] A means for analyzing the energy-saving effect of converting to light-emitting diodes based on the building information using the aforementioned information processing device,

[0797] A means for creating the aforementioned proposal for light-emitting diodes for the constituent elements,

[0798] Means for notifying the operator of the above proposal,

[0799] A means of monitoring the property's condition in real time and providing information,

[0800] A means of showing the economic effects and subsidy information related to the aforementioned proposal,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, comprising means for verifying information on subsidies related to the conversion to light-emitting diodes and including the results in the proposal.

[0804] (Claim 3)

[0805] The system according to claim 1, comprising means for including proposals for smart office implementation and improvements to the communication network in the aforementioned proposal.

[0806] "Example 2 of combining an emotion engine"

[0807] (Claim 1)

[0808] A means for collecting information about buildings that meet specific conditions using an information processing device,

[0809] The means of using the aforementioned information processing device to employ an artificial intelligence model for analyzing the effects of converting to light-emitting diodes based on information about the building,

[0810] A means for creating a proposal for the conversion of the building to light-emitting diodes,

[0811] A means of adjusting suggestions using an emotion recognition system and notifying the user,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, comprising means for verifying subsidy information related to the conversion to light-emitting diodes and including the results in the proposal.

[0815] (Claim 3)

[0816] The system according to claim 1, comprising means for including smart building and communication network improvement options in the proposal.

[0817] "Application example 2 when combining with an emotional engine"

[0818] (Claim 1)

[0819] A means for collecting building information that meets specific conditions using an information processing device,

[0820] Using the aforementioned information processing device, a means for analyzing the effects of converting to light-emitting diodes based on the building information,

[0821] A means for creating a proposal for the conversion of the building to light-emitting diodes,

[0822] Means for notifying users of the aforementioned proposal,

[0823] A means of analyzing user emotional data,

[0824] A means for adjusting the proposed content based on the aforementioned sentiment data,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, comprising means for verifying subsidy information related to the conversion to light-emitting diodes and including the results in the proposal.

[0828] (Claim 3)

[0829] The system according to claim 1, comprising means for including smart facility and communication network improvement options in the proposal. [Explanation of symbols]

[0830] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting building information that meets specific conditions using a terminal, A means for analyzing the energy-saving effect of converting to light-emitting diodes based on the building information using the aforementioned information processing device, A means for creating the aforementioned proposal for light-emitting diodes for the constituent elements, Means for notifying the operator of the above proposal, A means of monitoring the property's condition in real time and providing information, A means of showing the economic effects and subsidy information related to the aforementioned proposal, A system that includes this.

2. The system according to claim 1, further comprising means for verifying information on subsidies related to the conversion to light-emitting diodes and including the results in the proposal.

3. The system according to claim 1, comprising means for including proposals for smart office implementation and improvements to the communication network in the aforementioned proposal.

Images