System

Abstract

An object of a system according to an embodiment is to propose an optimal power plan to each household and to easily update a contract.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, and a contract renewal unit. The collection unit collects power consumption data and climate change data of each household. The analysis unit analyzes the data collected by the collection unit and proposes a power plan suitable for each household. The contract renewal unit renews the contract based on the power plan proposed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately propose optimal power plans for each household or facilitate contract renewals, so there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal power plan for each household and to facilitate contract renewal. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a contract renewal unit. The collection unit collects electricity consumption data and climate change data for each household. The analysis unit analyzes the data collected by the collection unit and proposes an electricity plan suitable for each household. The contract renewal unit renews the contract based on the electricity plan proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose the most suitable power plan for each household and can easily renew contracts. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An embodiment of the present invention relates to an energy optimization system that collects and analyzes energy consumption data and climate change data for each household, proposes an optimal energy plan, and handles contract renewals. The energy optimization system collects and analyzes energy consumption data and climate change data for each household, and proposes an optimal energy plan. In doing so, it also considers cost minimization and contribution to clean energy. Furthermore, it provides services that combine the unique features of each energy company, allowing users to easily select contract renewals with a single touch. For example, the energy optimization system collects detailed data such as each household's energy usage patterns and local weather conditions. For example, it collects data such as each household's monthly energy consumption and the local average annual temperature. The energy optimization system then analyzes the collected data and proposes an optimal energy plan for each household. It uses AI for the analysis and calculates the optimal energy plan based on each household's energy consumption patterns and weather conditions. For example, for households whose energy consumption increases in the summer, it proposes a plan with a lower summer energy rate. It also considers contribution to clean energy and prioritizes plans that include a large amount of renewable energy. Furthermore, the energy optimization system provides services that combine the unique features of each energy company. For example, if one power company offers a low rate plan, but another has a high proportion of clean energy, the system will propose a plan that combines the features of each company. Finally, the power optimization system also makes it easy to select contract renewals with just one touch. Users can select the most suitable plan from the proposed plans and renew their contract with just one touch. In this way, the power optimization system can optimize power usage and realize a sustainable lifestyle. In this way, the power optimization system can reduce power costs and increase contributions to clean energy by allowing each household to use the most suitable power plan. In addition, the ease of contract renewal improves user convenience.

[0029] The power optimization system according to the embodiment includes a collection unit, an analysis unit, and a contract renewal unit. The collection unit collects power consumption data and climate change data for each household. For example, the collection unit collects monthly power consumption data for each household. The collection unit can also collect climate change data such as the average annual temperature and precipitation for a region. The collection unit can also collect power usage patterns for each household. For example, the collection unit collects daytime and nighttime usage data. The analysis unit analyzes the data collected by the collection unit and proposes an optimal power plan for each household. AI is used for the analysis to calculate an optimal power plan based on each household's power consumption pattern and climatic conditions. For example, the analysis unit proposes a plan with lower summer electricity rates to a household whose power consumption increases in the summer. The analysis unit can also consider contributions to clean energy and prioritize plans that include a large amount of renewable energy. The contract renewal unit renews the contract based on the power plan proposed by the analysis unit. For example, the contract renewal unit allows the user to select the most suitable plan from the proposed plans and renew the contract with one touch. As a result, the power optimization system according to the embodiment can optimize power usage and realize a sustainable lifestyle.

[0030] The collection unit can collect data on the electricity usage patterns of each household or the climatic conditions of the region. The collection unit, for example, collects the electricity usage patterns of each household. For example, the collection unit collects daytime and nighttime usage amounts. The collection unit can also collect data on the climatic conditions of the region. For example, the collection unit collects data such as the average annual temperature and precipitation. This allows for more accurate analysis by collecting detailed electricity consumption data for each household. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the electricity usage patterns of each household into AI, which then analyzes and collects the data.

[0031] The analysis unit can analyze the collected data and propose an appropriate power plan based on each household's power consumption pattern and weather conditions. The analysis unit, for example, analyzes the collected data and proposes an optimal power plan based on each household's power consumption pattern and weather conditions. For example, the analysis unit can propose a plan with lower summer electricity rates to a household whose power consumption increases in the summer. The analysis unit can also take into account the contribution to clean energy and preferentially propose plans that include a large amount of renewable energy. This makes it possible to optimize power use by proposing an optimal power plan for each household. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which then proposes an optimal power plan.

[0032] The analysis unit can propose an electricity plan based on cost minimization or contribution to clean energy. The analysis unit, for example, proposes an electricity plan based on cost minimization. For example, the analysis unit proposes an optimal plan aimed at reducing electricity bills. The analysis unit can also propose an electricity plan based on contribution to clean energy. For example, the analysis unit preferentially proposes plans with a high proportion of renewable energy usage. This makes it possible to propose an electricity plan that achieves both cost minimization and contribution to clean energy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input cost minimization or contribution to clean energy into AI, which then proposes an optimal electricity plan.

[0033] The contract renewal unit can enable the user to easily renew the contract. The contract renewal unit can, for example, enable the user to renew the contract with one touch. For example, the contract renewal unit can select the most suitable plan from among proposed plans and renew the contract with one touch. The contract renewal unit can also provide an intuitive interface to enable the user to easily renew the contract. This allows the user to easily renew the contract. Some or all of the above-mentioned processing in the contract renewal unit can be performed, for example, using AI or without using AI. For example, the contract renewal unit can input the user's selection into AI, which can then renew the contract.

[0034] The analysis unit can propose a plan that combines the features of each electric power company. The analysis unit, for example, proposes a plan that combines the rate plans and service contents of each electric power company. For example, if one electric power company has a low rate plan but another electric power company has a high proportion of clean energy, the analysis unit proposes a plan that combines the features of each electric power company. This makes it possible to propose an optimal plan that makes use of the features of each electric power company. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the features of each electric power company into AI, which then proposes an optimal plan.

[0035] The collection unit can analyze past electricity consumption data for each household and select the optimal data collection method. For example, the collection unit can identify peak consumption from past electricity consumption data and focus on collecting data during those time periods. For example, the collection unit can analyze seasonal consumption patterns based on past data and select a collection method appropriate for the season. For example, the collection unit can find from past data a tendency for consumption to concentrate on specific days of the week or time periods and collect data during those time periods. This enables efficient data collection by selecting the optimal collection method based on past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past electricity consumption data into AI, which then selects the optimal collection method.

[0036] When collecting electricity consumption data, the collection unit can filter the data taking into account the lifestyle patterns and seasonal variations of each household. For example, the collection unit analyzes the lifestyle patterns of each household and filters and collects nighttime data. For example, the collection unit can also apply different collection methods to summer and winter, taking seasonal variations into account. For example, the collection unit can also set the optimal collection timing by combining lifestyle patterns and seasonal variations. This enables more accurate data collection by taking lifestyle patterns and seasonal variations into account. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the lifestyle patterns and seasonal variations of each household into AI, which can then filter the data.

[0037] When collecting power consumption data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects data using voice recognition technology. For example, if the user uses text input, the collection unit can also collect data using text analysis technology. For example, if the user uses image input, the collection unit can also collect data using image recognition technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into AI, which then selects the optimal collection means.

[0038] When collecting electricity consumption data, the collection unit can prioritize collecting relevant data based on the user's geographical location information. For example, the collection unit prioritizes collecting data related to local climatic conditions based on the user's geographical location information. For example, the collection unit can also prioritize collecting data related to local electricity consumption patterns based on the user's geographical location information. For example, the collection unit can also prioritize collecting data related to local electricity supply conditions based on the user's geographical location information. In this way, by taking the geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, and the AI ​​can prioritize collecting relevant data.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting electricity consumption data. For example, the collection unit can analyze the user's social media posts and collect data related to electricity consumption. For example, the collection unit can also collect data related to electricity consumption based on the user's social media check-in information. For example, the collection unit can also collect data related to electricity consumption by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, which then collects the related data.

[0040] When collecting power consumption data, the collection unit can adjust the collection method by reflecting the user's past feedback. For example, the collection unit can improve the collection method based on the user's past feedback and collect data more efficiently. For example, the collection unit can adjust the collection timing based on the user's past feedback to reduce the burden on the user. For example, the collection unit can select a collection means based on the user's past feedback to improve user convenience. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into AI, which can adjust the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the power consumption data. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit can also adjust the level of detail of the analysis in stages depending on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis depending on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the importance of the power consumption data to AI, which can then adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the type of power consumption data. For example, the analysis unit applies an analysis algorithm specialized for home use to home power consumption data. For example, the analysis unit can also apply an analysis algorithm specialized for commercial use to commercial power consumption data. For example, the analysis unit can also apply an analysis algorithm specialized for industrial use to industrial power consumption data. By applying an analysis algorithm depending on the type of data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the type of power consumption data into AI, which then applies an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results to improve accuracy. For example, the analysis unit can also optimize analysis parameters by referring to the user's past analysis results. For example, the analysis unit can also reduce analysis errors by using the user's past analysis results. In this way, by referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the order of analysis based on the submission time of the power consumption data. For example, the analysis unit prioritizes analysis of data submitted earlier. For example, the analysis unit can also postpone analysis of data submitted later. For example, the analysis unit can also gradually adjust the analysis priority according to the submission time. This enables efficient analysis by determining the analysis priority based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the power consumption data into AI, and the AI ​​can determine the order of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the power consumption data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit can also postpone analysis of less relevant data. For example, the analysis unit can also gradually adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the power consumption data into AI, which can adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of knowledge. For example, if the user has specialized knowledge, the analysis unit can provide analysis results using detailed technical terms. For example, if the user does not have specialized knowledge, the analysis unit can also provide analysis results explained in simple terms. For example, the analysis unit can gradually adjust the use of technical terms in the analysis results according to the user's level of expertise. This allows the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of knowledge into AI, and the AI ​​can adjust the use of technical terms in the analysis results.

[0047] At the time of contract renewal, the contract renewal unit can analyze the user's past contract history and select an appropriate renewal method. The contract renewal unit, for example, proposes an optimal renewal plan based on the user's past contract history. The contract renewal unit can also analyze the user's past contract history and optimize the renewal timing, for example. The contract renewal unit can also simplify the renewal procedure by referring to the user's past contract history, for example. In this way, the optimal renewal method can be selected by analyzing the past contract history. Some or all of the above-mentioned processing in the contract renewal unit may be performed using, for example, AI, or may be performed without using AI. For example, the contract renewal unit can input the user's past contract history into AI, which can select an appropriate renewal method.

[0048] The contract renewal unit can adjust the renewal method based on the user's current living situation when renewing the contract. The contract renewal unit, for example, analyzes the user's current living situation and proposes an optimal renewal plan. The contract renewal unit can also simplify the renewal procedure, for example, depending on the user's current living situation. The contract renewal unit can also optimize the renewal timing, for example, taking into account the user's current living situation. This allows the renewal method to be customized based on the user's current living situation, thereby providing the optimal renewal method for the user. Some or all of the above-mentioned processing in the contract renewal unit can be performed, for example, using AI, or can be performed without using AI. For example, the contract renewal unit can input the user's current living situation into AI, and the AI ​​can adjust the renewal method.

[0049] The contract renewal unit can improve the renewal method by reflecting user feedback at the time of contract renewal. The contract renewal unit can, for example, simplify the renewal procedure based on the user's past feedback. The contract renewal unit can also, for example, improve the method for proposing a renewal plan by reflecting user feedback. The contract renewal unit can also, for example, optimize the renewal timing by referring to user feedback. In this way, the renewal method can be improved by reflecting feedback. Some or all of the above-mentioned processing in the contract renewal unit can be performed, for example, using AI, or can be performed without using AI. For example, the contract renewal unit can input user feedback into AI, which can improve the renewal method.

[0050] The contract renewal unit can select an appropriate renewal method based on the user's geographical location information at the time of contract renewal. The contract renewal unit, for example, proposes an renewal plan based on the local power supply situation based on the user's geographical location information. The contract renewal unit can also propose an renewal plan based on the local climatic conditions based on the user's geographical location information. The contract renewal unit can also propose an renewal plan based on the local power consumption pattern based on the user's geographical location information. This makes it possible to select an optimal renewal method by taking the geographical location information into consideration. Some or all of the above-described processing in the contract renewal unit may be performed using, for example, AI, or may be performed without using AI. For example, the contract renewal unit can input the user's geographical location information into AI, which can then select an appropriate renewal method.

[0051] At the time of contract renewal, the contract renewal unit can analyze the user's social media activity and suggest a renewal method. The contract renewal unit can, for example, analyze the content of the user's social media posts and suggest an optimal renewal plan. The contract renewal unit can also, for example, suggest an optimal renewal plan based on the user's social media check-in information. The contract renewal unit can also, for example, suggest an optimal renewal plan by referring to the activity of the user's friends on social media. In this way, the optimal renewal method can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the contract renewal unit may be performed, for example, using AI, or may be performed without using AI. For example, the contract renewal unit can input the user's social media data into AI, which then suggests the optimal renewal method.

[0052] The contract renewal unit can adjust the renewal method by reflecting the user's past feedback when renewing the contract. The contract renewal unit can, for example, simplify the renewal procedure based on the user's past feedback. The contract renewal unit can also improve the method for proposing a renewal plan by reflecting the user's feedback. The contract renewal unit can also optimize the renewal timing by referring to the user's feedback. This allows the renewal method to be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the contract renewal unit can be performed using, for example, AI, or can be performed without using AI. For example, the contract renewal unit can input the user's past feedback into AI, which can adjust the renewal method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can prioritize collection of relevant data based on the user's geographical location information. For example, data related to local climatic conditions can be prioritized based on the user's geographical location information. Data related to local electricity consumption patterns can also be prioritized based on the user's geographical location information. Data related to local electricity supply conditions can also be prioritized based on the user's geographical location information. In this way, by taking geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, and the AI ​​can prioritize collection of relevant data.

[0055] The analysis unit can apply different analysis algorithms depending on the type of power consumption data. For example, an analysis algorithm specialized for home use can be applied to home power consumption data. An analysis algorithm specialized for commercial use can be applied to commercial power consumption data. An analysis algorithm specialized for industrial use can be applied to industrial power consumption data. By applying an analysis algorithm according to the type of data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the type of power consumption data into AI, which can then apply an appropriate analysis algorithm.

[0056] When renewing a contract, the contract renewal unit can analyze the user's past contract history and select an appropriate renewal method. For example, it can propose an optimal renewal plan based on the user's past contract history. It can also analyze the user's past contract history and optimize the renewal timing. It can also refer to the user's past contract history to simplify the renewal procedure. In this way, the optimal renewal method can be selected by analyzing the past contract history. Some or all of the above-mentioned processing in the contract renewal unit may be performed using, for example, AI, or may be performed without using AI. For example, the contract renewal unit can input the user's past contract history into AI, which can select an appropriate renewal method.

[0057] When collecting electricity consumption data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can analyze the user's social media posts and collect data related to electricity consumption. Data related to electricity consumption can also be collected based on the user's social media check-in information. Data related to electricity consumption can also be collected by referring to the activities of the user's friends on social media. In this way, by analyzing social media activities, related data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, which then collects the related data.

[0058] During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of knowledge. For example, if the user has specialized knowledge, the analysis unit can provide analysis results using detailed technical terms. If the user does not have specialized knowledge, the analysis unit can provide analysis results explained in simple terms. The use of technical terms in the analysis results can also be adjusted in stages according to the user's level of expertise. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of knowledge into AI, which can then adjust the use of technical terms in the analysis results.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects electricity consumption data and climate change data for each household. For example, the collection unit collects monthly electricity consumption data for each household, the average annual temperature and precipitation for the region, and electricity usage patterns for each household (daytime usage and nighttime usage). Step 2: The analysis unit analyzes the data collected by the collection unit and proposes the optimal electricity plan for each household. AI is used for the analysis, and the optimal electricity plan is calculated based on each household's electricity consumption patterns and weather conditions. For example, for households whose electricity consumption increases in the summer, a plan with lower summer electricity rates will be proposed, and plans that include a large amount of renewable energy will be prioritized, taking into account the contribution to clean energy. Step 3: The contract renewal unit renews the contract based on the power plan proposed by the analysis unit. For example, the user can select the most suitable plan from the proposed plans and renew the contract with one touch.

[0061] (Example 2) An embodiment of the present invention relates to an energy optimization system that collects and analyzes energy consumption data and climate change data for each household, proposes an optimal energy plan, and handles contract renewals. The energy optimization system collects and analyzes energy consumption data and climate change data for each household, and proposes an optimal energy plan. In doing so, it also considers cost minimization and contribution to clean energy. Furthermore, it provides services that combine the unique features of each energy company, allowing users to easily select contract renewals with a single touch. For example, the energy optimization system collects detailed data such as each household's energy usage patterns and local weather conditions. For example, it collects data such as each household's monthly energy consumption and the local average annual temperature. The energy optimization system then analyzes the collected data and proposes an optimal energy plan for each household. It uses AI for the analysis and calculates the optimal energy plan based on each household's energy consumption patterns and weather conditions. For example, for households whose energy consumption increases in the summer, it proposes a plan with a lower summer energy rate. It also considers contribution to clean energy and prioritizes plans that include a large amount of renewable energy. Furthermore, the energy optimization system provides services that combine the unique features of each energy company. For example, if one power company offers a low rate plan, but another has a high proportion of clean energy, the system will propose a plan that combines the features of each company. Finally, the power optimization system also makes it easy to select contract renewals with just one touch. Users can select the most suitable plan from the proposed plans and renew their contract with just one touch. In this way, the power optimization system can optimize power usage and realize a sustainable lifestyle. In this way, the power optimization system can reduce power costs and increase contributions to clean energy by allowing each household to use the most suitable power plan. In addition, the ease of contract renewal improves user convenience.

[0062] The power optimization system according to the embodiment includes a collection unit, an analysis unit, and a contract renewal unit. The collection unit collects power consumption data and climate change data for each household. For example, the collection unit collects monthly power consumption data for each household. The collection unit can also collect climate change data such as the average annual temperature and precipitation for a region. The collection unit can also collect power usage patterns for each household. For example, the collection unit collects daytime and nighttime usage data. The analysis unit analyzes the data collected by the collection unit and proposes an optimal power plan for each household. AI is used for the analysis to calculate an optimal power plan based on each household's power consumption pattern and climatic conditions. For example, the analysis unit proposes a plan with lower summer electricity rates to a household whose power consumption increases in the summer. The analysis unit can also consider contributions to clean energy and prioritize plans that include a large amount of renewable energy. The contract renewal unit renews the contract based on the power plan proposed by the analysis unit. For example, the contract renewal unit allows the user to select the most suitable plan from the proposed plans and renew the contract with one touch. As a result, the power optimization system according to the embodiment can optimize power usage and realize a sustainable lifestyle.

[0063] The collection unit can collect data on the electricity usage patterns of each household or the climatic conditions of the region. The collection unit, for example, collects the electricity usage patterns of each household. For example, the collection unit collects daytime and nighttime usage amounts. The collection unit can also collect data on the climatic conditions of the region. For example, the collection unit collects data such as the average annual temperature and precipitation. This allows for more accurate analysis by collecting detailed electricity consumption data for each household. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the electricity usage patterns of each household into AI, which then analyzes and collects the data.

[0064] The analysis unit can analyze the collected data and propose an appropriate power plan based on each household's power consumption pattern and weather conditions. The analysis unit, for example, analyzes the collected data and proposes an optimal power plan based on each household's power consumption pattern and weather conditions. For example, the analysis unit can propose a plan with lower summer electricity rates to a household whose power consumption increases in the summer. The analysis unit can also take into account the contribution to clean energy and preferentially propose plans that include a large amount of renewable energy. This makes it possible to optimize power use by proposing an optimal power plan for each household. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which then proposes an optimal power plan.

[0065] The analysis unit can propose an electricity plan based on cost minimization or contribution to clean energy. The analysis unit, for example, proposes an electricity plan based on cost minimization. For example, the analysis unit proposes an optimal plan aimed at reducing electricity bills. The analysis unit can also propose an electricity plan based on contribution to clean energy. For example, the analysis unit preferentially proposes plans with a high proportion of renewable energy usage. This makes it possible to propose an electricity plan that achieves both cost minimization and contribution to clean energy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input cost minimization or contribution to clean energy into AI, which then proposes an optimal electricity plan.

[0066] The contract renewal unit can enable the user to easily renew the contract. The contract renewal unit can, for example, enable the user to renew the contract with one touch. For example, the contract renewal unit can select the most suitable plan from among proposed plans and renew the contract with one touch. The contract renewal unit can also provide an intuitive interface to enable the user to easily renew the contract. This allows the user to easily renew the contract. Some or all of the above-mentioned processing in the contract renewal unit can be performed, for example, using AI or without using AI. For example, the contract renewal unit can input the user's selection into AI, which can then renew the contract.

[0067] The analysis unit can propose a plan that combines the features of each electric power company. The analysis unit, for example, proposes a plan that combines the rate plans and service contents of each electric power company. For example, if one electric power company has a low rate plan but another electric power company has a high proportion of clean energy, the analysis unit proposes a plan that combines the features of each electric power company. This makes it possible to propose an optimal plan that makes use of the features of each electric power company. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the features of each electric power company into AI, which then proposes an optimal plan.

[0068] The collection unit can estimate the user's emotions and adjust the timing of collecting power consumption data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can set the collection timing to nighttime, thereby reducing the user's burden. For example, if the user is relaxed, the collection unit can set the collection timing to daytime, thereby collecting data in real time. For example, if the user is in a hurry, the collection unit can shorten the collection timing to quickly collect data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI, which can then adjust the collection timing.

[0069] The collection unit can analyze past electricity consumption data for each household and select the optimal data collection method. For example, the collection unit can identify peak consumption from past electricity consumption data and focus on collecting data during those time periods. For example, the collection unit can analyze seasonal consumption patterns based on past data and select a collection method appropriate for the season. For example, the collection unit can find from past data a tendency for consumption to concentrate on specific days of the week or time periods and collect data during those time periods. This enables efficient data collection by selecting the optimal collection method based on past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past electricity consumption data into AI, which then selects the optimal collection method.

[0070] When collecting electricity consumption data, the collection unit can filter the data taking into account the lifestyle patterns and seasonal variations of each household. For example, the collection unit analyzes the lifestyle patterns of each household and filters and collects nighttime data. For example, the collection unit can also apply different collection methods to summer and winter, taking seasonal variations into account. For example, the collection unit can also set the optimal collection timing by combining lifestyle patterns and seasonal variations. This enables more accurate data collection by taking lifestyle patterns and seasonal variations into account. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the lifestyle patterns and seasonal variations of each household into AI, which can then filter the data.

[0071] When collecting power consumption data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects data using voice recognition technology. For example, if the user uses text input, the collection unit can also collect data using text analysis technology. For example, if the user uses image input, the collection unit can also collect data using image recognition technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into AI, which then selects the optimal collection means.

[0072] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting only important data. For example, when the user is relaxed, the collection unit can also prioritize collecting detailed data. For example, when the user is in a hurry, the collection unit can also prioritize collecting data that can be collected quickly. This allows important data to be collected preferentially by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into an AI, which can then prioritize the data.

[0073] When collecting electricity consumption data, the collection unit can prioritize collecting relevant data based on the user's geographical location information. For example, the collection unit prioritizes collecting data related to local climatic conditions based on the user's geographical location information. For example, the collection unit can also prioritize collecting data related to local electricity consumption patterns based on the user's geographical location information. For example, the collection unit can also prioritize collecting data related to local electricity supply conditions based on the user's geographical location information. In this way, by taking the geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, and the AI ​​can prioritize collecting relevant data.

[0074] The collection unit can analyze the user's social media activities and collect related data when collecting electricity consumption data. For example, the collection unit can analyze the user's social media posts and collect data related to electricity consumption. For example, the collection unit can also collect data related to electricity consumption based on the user's social media check-in information. For example, the collection unit can also collect data related to electricity consumption by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, which then collects the related data.

[0075] When collecting power consumption data, the collection unit can adjust the collection method by reflecting the user's past feedback. For example, the collection unit can improve the collection method based on the user's past feedback and collect data more efficiently. For example, the collection unit can adjust the collection timing based on the user's past feedback to reduce the burden on the user. For example, the collection unit can select a collection means based on the user's past feedback to improve user convenience. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into AI, which can adjust the collection method.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide analysis results that are concise. By adjusting the presentation method of the analysis results according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI, which can adjust the presentation method of the analysis results.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the power consumption data. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit can also adjust the level of detail of the analysis in stages depending on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis depending on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the importance of the power consumption data to AI, which can then adjust the level of detail of the analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the type of power consumption data. For example, the analysis unit applies an analysis algorithm specialized for home use to home power consumption data. For example, the analysis unit can also apply an analysis algorithm specialized for commercial use to commercial power consumption data. For example, the analysis unit can also apply an analysis algorithm specialized for industrial use to industrial power consumption data. By applying an analysis algorithm depending on the type of data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the type of power consumption data into AI, which then applies an appropriate analysis algorithm.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results to improve accuracy. For example, the analysis unit can also optimize analysis parameters by referring to the user's past analysis results. For example, the analysis unit can also reduce analysis errors by using the user's past analysis results. In this way, by referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI, which can improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis result based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows the user to receive the optimal amount of information by adjusting the length of the analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI, which can then adjust the length of the analysis result.

[0081] During analysis, the analysis unit can determine the order of analysis based on the submission time of the power consumption data. For example, the analysis unit prioritizes analysis of data submitted earlier. For example, the analysis unit can also postpone analysis of data submitted later. For example, the analysis unit can also gradually adjust the analysis priority according to the submission time. This enables efficient analysis by determining the analysis priority based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the power consumption data into AI, and the AI ​​can determine the order of analysis.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the power consumption data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit can also postpone analysis of less relevant data. For example, the analysis unit can also gradually adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the power consumption data into AI, which can adjust the order of analysis.

[0083] During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of knowledge. For example, if the user has specialized knowledge, the analysis unit can provide analysis results using detailed technical terms. For example, if the user does not have specialized knowledge, the analysis unit can also provide analysis results explained in simple terms. For example, the analysis unit can gradually adjust the use of technical terms in the analysis results according to the user's level of expertise. This allows the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of knowledge into AI, and the AI ​​can adjust the use of technical terms in the analysis results.

[0084] The contract renewal unit can estimate the user's emotions and adjust the contract renewal method based on the estimated user emotions. For example, if the user is nervous, the contract renewal unit can provide a simple and highly visible renewal method. For example, if the user is relaxed, the contract renewal unit can also provide a renewal method that includes detailed information. For example, if the user is in a hurry, the contract renewal unit can also provide a renewal method that focuses on the main points. This allows the contract renewal method to be adjusted according to the user's emotions, thereby providing the optimal renewal method for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the contract renewal unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the contract renewal unit can input the user's emotion data into an AI, which can then adjust the contract renewal method.

[0085] At the time of contract renewal, the contract renewal unit can analyze the user's past contract history and select an appropriate renewal method. The contract renewal unit, for example, proposes an optimal renewal plan based on the user's past contract history. The contract renewal unit can also analyze the user's past contract history and optimize the renewal timing, for example. The contract renewal unit can also simplify the renewal procedure by referring to the user's past contract history, for example. In this way, the optimal renewal method can be selected by analyzing the past contract history. Some or all of the above-mentioned processing in the contract renewal unit may be performed using, for example, AI, or may be performed without using AI. For example, the contract renewal unit can input the user's past contract history into AI, which can select an appropriate renewal method.

[0086] The contract renewal unit can adjust the renewal method based on the user's current living situation when renewing the contract. The contract renewal unit, for example, analyzes the user's current living situation and proposes an optimal renewal plan. The contract renewal unit can also simplify the renewal procedure, for example, depending on the user's current living situation. The contract renewal unit can also optimize the renewal timing, for example, taking into account the user's current living situation. This allows the renewal method to be customized based on the user's current living situation, thereby providing the optimal renewal method for the user. Some or all of the above-mentioned processing in the contract renewal unit can be performed, for example, using AI, or can be performed without using AI. For example, the contract renewal unit can input the user's current living situation into AI, and the AI ​​can adjust the renewal method.

[0087] The contract renewal unit can improve the renewal method by reflecting user feedback at the time of contract renewal. The contract renewal unit can, for example, simplify the renewal procedure based on the user's past feedback. The contract renewal unit can also, for example, improve the method for proposing a renewal plan by reflecting user feedback. The contract renewal unit can also, for example, optimize the renewal timing by referring to user feedback. In this way, the renewal method can be improved by reflecting feedback. Some or all of the above-mentioned processing in the contract renewal unit can be performed, for example, using AI, or can be performed without using AI. For example, the contract renewal unit can input user feedback into AI, which can improve the renewal method.

[0088] The contract renewal unit can estimate the user's emotions and determine the priority of contract renewal based on the estimated user emotions. For example, the contract renewal unit prioritizes contract renewal when the user is stressed. For example, the contract renewal unit can also perform contract renewal with normal priority when the user is relaxed. For example, the contract renewal unit can also perform contract renewal quickly when the user is in a hurry. This allows contract renewal to be performed at the optimal timing for the user by determining the priority of contract renewal according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the contract renewal unit may be performed using an AI, for example, or without an AI. For example, the contract renewal unit can input the user's emotion data into an AI, which can then determine the priority of contract renewal.

[0089] The contract renewal unit can select an appropriate renewal method based on the user's geographical location information at the time of contract renewal. The contract renewal unit, for example, proposes an renewal plan based on the local power supply situation based on the user's geographical location information. The contract renewal unit can also propose an renewal plan based on the local climatic conditions based on the user's geographical location information. The contract renewal unit can also propose an renewal plan based on the local power consumption pattern based on the user's geographical location information. This makes it possible to select an optimal renewal method by taking the geographical location information into consideration. Some or all of the above-described processing in the contract renewal unit may be performed using, for example, AI, or may be performed without using AI. For example, the contract renewal unit can input the user's geographical location information into AI, which can then select an appropriate renewal method.

[0090] At the time of contract renewal, the contract renewal unit can analyze the user's social media activity and suggest a renewal method. The contract renewal unit can, for example, analyze the content of the user's social media posts and suggest an optimal renewal plan. The contract renewal unit can also, for example, suggest an optimal renewal plan based on the user's social media check-in information. The contract renewal unit can also, for example, suggest an optimal renewal plan by referring to the activity of the user's friends on social media. In this way, the optimal renewal method can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the contract renewal unit may be performed, for example, using AI, or may be performed without using AI. For example, the contract renewal unit can input the user's social media data into AI, which then suggests the optimal renewal method.

[0091] The contract renewal unit can adjust the renewal method by reflecting the user's past feedback when renewing the contract. The contract renewal unit can, for example, simplify the renewal procedure based on the user's past feedback. The contract renewal unit can also improve the method for proposing a renewal plan by reflecting the user's feedback. The contract renewal unit can also optimize the renewal timing by referring to the user's feedback. This allows the renewal method to be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the contract renewal unit can be performed using, for example, AI, or can be performed without using AI. For example, the contract renewal unit can input the user's past feedback into AI, which can adjust the renewal method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and contract renewal unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect electricity consumption data and climate change data for each household using the camera 42 and microphone 38B of the smart device 14. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, analyze the collected data, and propose an optimal electricity plan. For example, the contract renewal unit can be realized by the control unit 46A of the smart device 14, allowing the user to renew the contract with one touch. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and collect electricity usage patterns and weather conditions for each household. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and contract renewal unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect electricity consumption data and climate change data for each household using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, analyze the collected data, and propose an optimal electricity plan. For example, the contract renewal unit can be realized by the control unit 46A of the smart glasses 214, allowing the user to renew the contract with one touch. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and collect electricity usage patterns and weather conditions for each household. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and contract renewal unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect electricity consumption data and climate change data for each household using the camera 42 and microphone 238 of the headset terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and proposes an optimal electricity plan. For example, the contract renewal unit is realized by the control unit 46A of the headset terminal 314, and allows the user to renew the contract with a single touch. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and collect electricity usage patterns and weather conditions for each household. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and contract renewal unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect electricity consumption data and climate change data of each household using the camera 42 and microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and proposes an optimal electricity plan. For example, the contract renewal unit is realized by the control unit 46A of the robot 414, and allows the user to renew the contract with one touch. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect electricity usage patterns and weather conditions of each household.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a summary analysis result can be provided. By adjusting the way the analysis results are presented according to the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI, which can then adjust the way the analysis results are presented.

[0094] The collection unit can prioritize collection of relevant data based on the user's geographical location information. For example, data related to local climatic conditions can be prioritized based on the user's geographical location information. Data related to local electricity consumption patterns can also be prioritized based on the user's geographical location information. Data related to local electricity supply conditions can also be prioritized based on the user's geographical location information. In this way, by taking geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, and the AI ​​can prioritize collection of relevant data.

[0095] The contract renewal unit can estimate the user's emotions and adjust the contract renewal method based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible renewal method can be provided. If the user is relaxed, a renewal method including detailed information can be provided. If the user is in a hurry, a renewal method that focuses on the main points can be provided. This allows the contract renewal method to be adjusted according to the user's emotions, thereby providing the optimal renewal method for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the contract renewal unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the contract renewal unit can input the user's emotion data into an AI, which can then adjust the contract renewal method.

[0096] The analysis unit can apply different analysis algorithms depending on the type of power consumption data. For example, an analysis algorithm specialized for home use can be applied to home power consumption data. An analysis algorithm specialized for commercial use can be applied to commercial power consumption data. An analysis algorithm specialized for industrial use can be applied to industrial power consumption data. By applying an analysis algorithm according to the type of data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the type of power consumption data into AI, which can then apply an appropriate analysis algorithm.

[0097] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, only important data can be collected with priority. If the user is relaxed, detailed data can be collected with priority. If the user is in a hurry, data that can be collected quickly can be collected with priority. This allows important data to be collected with priority by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI, which can then prioritize the data.

[0098] When renewing a contract, the contract renewal unit can analyze the user's past contract history and select an appropriate renewal method. For example, it can propose an optimal renewal plan based on the user's past contract history. It can also analyze the user's past contract history and optimize the renewal timing. It can also refer to the user's past contract history to simplify the renewal procedure. In this way, the optimal renewal method can be selected by analyzing the past contract history. Some or all of the above-mentioned processing in the contract renewal unit may be performed using, for example, AI, or may be performed without using AI. For example, the contract renewal unit can input the user's past contract history into AI, which can select an appropriate renewal method.

[0099] The analysis unit can estimate the user's emotions and adjust the length of the analysis results based on the estimated user emotions. For example, if the user is in a hurry, a short and concise analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is excited, an analysis result with a visually stimulating effect can be provided. By adjusting the length of the analysis results according to the user's emotions, the optimal amount of information can be provided to the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI, which can then adjust the length of the analysis results.

[0100] When collecting electricity consumption data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can analyze the user's social media posts and collect data related to electricity consumption. Data related to electricity consumption can also be collected based on the user's social media check-in information. Data related to electricity consumption can also be collected by referring to the activities of the user's friends on social media. In this way, by analyzing social media activities, related data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, which then collects the related data.

[0101] The contract renewal unit can estimate the user's emotions and determine the priority of contract renewal based on the estimated user emotions. For example, if the user is feeling stressed, contract renewal can be prioritized. If the user is relaxed, contract renewal can be performed with normal priority. If the user is in a hurry, contract renewal can be performed quickly. This allows contract renewal to be performed at the optimal timing for the user by determining the priority of contract renewal according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the contract renewal unit can be performed using, for example, an AI, or without using an AI. For example, the contract renewal unit can input the user's emotion data into an AI, which can then determine the priority of contract renewal.

[0102] During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of knowledge. For example, if the user has specialized knowledge, the analysis unit can provide analysis results using detailed technical terms. If the user does not have specialized knowledge, the analysis unit can provide analysis results explained in simple terms. The use of technical terms in the analysis results can also be adjusted in stages according to the user's level of expertise. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of knowledge into AI, which can then adjust the use of technical terms in the analysis results.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects electricity consumption data and climate change data for each household. For example, the collection unit collects monthly electricity consumption data for each household, the average annual temperature and precipitation for the region, and electricity usage patterns for each household (daytime usage and nighttime usage). Step 2: The analysis unit analyzes the data collected by the collection unit and proposes the optimal electricity plan for each household. AI is used for the analysis, and the optimal electricity plan is calculated based on each household's electricity consumption patterns and weather conditions. For example, for households whose electricity consumption increases in the summer, a plan with lower summer electricity rates will be proposed, and plans that include a large amount of renewable energy will be prioritized, taking into account the contribution to clean energy. Step 3: The contract renewal unit renews the contract based on the power plan proposed by the analysis unit. For example, the user can select the most suitable plan from the proposed plans and renew the contract with one touch.

[0105] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects electricity consumption data and climate change data from each household; an analysis unit that analyzes the data collected by the collection unit and proposes an electricity plan suitable for each household; a contract renewal unit that renews the contract based on the power plan proposed by the analysis unit. A system characterized by:

2. The collecting unit Collect data on household electricity usage patterns or local weather conditions 2. The system of claim 1.

3. The analysis unit Analyzes collected data and proposes appropriate electricity plans based on each household's electricity consumption patterns and weather conditions 2. The system of claim 1.

4. The analysis unit Propose electricity plans based on cost minimization or clean energy contribution 2. The system of claim 1.

5. The contract renewal unit Make it easy for users to renew their contracts 2. The system of claim 1.

6. The analysis unit Propose plans that combine the features of each power company 2. The system of claim 1.

7. The collecting unit The method estimates the user's emotions and adjusts the timing of collecting power consumption data based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze each household's past electricity consumption data and select the optimal data collection method 2. The system of claim 1.

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