system

Abstract

The system according to this embodiment aims to provide optimal energy-saving measures based on the user's lifestyle and local climate. [Solution] The system according to the embodiment comprises a learning unit, a suggestion unit, an update unit, and a visualization unit. The learning unit learns the user's lifestyle habits related to energy consumption. The suggestion unit presents optimal energy saving measures based on the information learned by the learning unit. The update unit periodically improves and updates the saving measures presented by the suggestion unit based on the season and local climate. The visualization unit visualizes the user's degree of energy saving achievement.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to provide an optimal energy saving measure based on the user's living habits and the climate of the region, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal energy saving measure based on the user's living habits and the climate of the region.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a suggestion unit, an update unit, and a visualization unit. The learning unit learns the user's lifestyle habits related to energy consumption. The suggestion unit presents optimal energy saving measures based on the information learned by the learning unit. The update unit periodically improves and updates the energy saving measures presented by the suggestion unit based on the season and local climate. The visualization unit visualizes the user's energy saving achievement. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal energy-saving measures based on the user's lifestyle and local climate. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The energy-saving suggestion system according to an embodiment of the present invention is a system that uses generative AI to suggest optimal energy-saving measures based on the user's lifestyle. This energy-saving suggestion system learns the user's lifestyle habits related to energy consumption and presents optimal energy-saving measures. Furthermore, the energy-saving suggestion system takes into account the season and local climate and periodically improves and updates the saving measures. For example, the energy-saving suggestion system learns the user's lifestyle habits related to energy consumption. In this process, it collects detailed data such as what actions the user takes and at what times of day they consume a lot of energy. For example, if a user uses air conditioning a lot at night, this data is collected and analyzed by the generative AI. Next, the generative AI presents optimal energy-saving measures based on the collected data. For example, for a user who uses air conditioning a lot at night, it suggests slightly raising the air conditioner's temperature setting or utilizing the timer function. This reduces wasted energy and enables effective saving. Furthermore, in addition to the user's lifestyle habits, the generative AI takes into account the season and local climate and periodically improves and updates the saving measures. For example, since the use of air conditioning increases in the summer, the generative AI suggests efficient ways to use air conditioning. Furthermore, the system can optimize heating and cooling usage according to the local climate. Finally, the energy saving suggestion system provides a function to visualize the user's energy saving achievements. This allows users to easily check whether their energy use is successful. For example, the energy saving suggestion system visually displays changes in the user's energy usage using graphs and charts. This allows users to feel the results of their savings and maintain motivation. This energy saving suggestion system targets general households and small businesses. It is an ideal tool for people who want to control and optimize their energy usage patterns and energy consumption. To address challenges such as the complexity of energy management, visualization of saving achievements, and seasonal and regional optimization, a personalized energy monitoring app utilizing generative AI is provided. This allows the energy saving suggestion system to effectively manage the user's energy usage and achieve energy savings.

[0029] The energy-saving suggestion system according to this embodiment comprises a learning unit, a suggestion unit, an update unit, and a visualization unit. The learning unit learns the user's lifestyle habits related to energy consumption. The learning unit collects detailed data, such as what actions the user takes and at what times of day they consume a lot of energy. For example, if a user uses the air conditioner a lot at night, the learning unit collects that data, and the generating AI analyzes it. The suggestion unit presents optimal energy-saving measures based on the information learned by the learning unit. For example, the suggestion unit suggests to a user who uses the air conditioner a lot at night that they slightly raise the air conditioner's temperature setting or utilize the timer function. This reduces wasted energy and enables effective saving. The update unit periodically improves and updates the saving measures presented by the suggestion unit based on the season and local climate. For example, the update unit suggests efficient ways to use air conditioning in the summer, as air conditioning use increases during that time. The update unit can also optimize heating and cooling usage methods according to the local climate. The visualization unit visualizes the user's degree of energy saving achievement. The visualization unit visually displays changes in the user's energy consumption, for example, using graphs and charts. This allows the user to feel the results of their energy saving efforts and maintain their motivation. As a result, the energy saving suggestion system according to this embodiment can effectively manage the user's energy consumption and achieve energy savings.

[0030] The learning unit learns about users' lifestyle habits related to energy consumption. Specifically, the learning unit collects detailed data such as what actions users take and when they consume the most energy. For example, if a user uses their air conditioner frequently at night, this data is collected and analyzed by the generative AI. The generative AI uses machine learning algorithms to analyze user behavior patterns and understand energy consumption trends. For example, the generative AI analyzes the user's air conditioner usage data to identify the frequency of use and consumption under specific time periods and temperature conditions. Furthermore, the generative AI learns the user's lifestyle habits and behavior patterns to identify peak energy consumption times and when wasteful consumption occurs. As a result, the learning unit can collect detailed data on users' energy consumption and analyze it using the generative AI to understand energy consumption trends based on users' lifestyle habits. In addition, the learning unit can store the collected data on a cloud server and link it with other systems and departments. For example, the learning unit can provide the collected data to the proposal and update departments to be used for proposing and updating energy saving measures. Furthermore, the learning unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the learning unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The suggestion unit presents optimal energy-saving measures based on information learned by the learning unit. Specifically, the suggestion unit suggests to users who frequently use air conditioners at night that they slightly increase the air conditioner's temperature setting or utilize the timer function. The generation AI analyzes the user's energy consumption data and generates optimal energy-saving measures. For example, based on the user's air conditioner usage data, the generation AI simulates how much energy can be saved by raising the temperature setting by 1 degree. The generation AI also suggests ways to reduce unnecessary air conditioner operation time and lower energy consumption by utilizing the timer function. Furthermore, the suggestion unit can present individually customized energy-saving measures based on the user's lifestyle and behavioral patterns. For example, if a user frequently uses their air conditioner at night, the suggestion unit provides specific advice on optimizing nighttime air conditioner use. This allows the suggestion unit to propose effective energy-saving measures based on detailed data on the user's energy consumption, thereby reducing energy waste. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can provide feedback on the results of users implementing the suggested energy-saving measures and revise the suggestions based on that data. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also email and in-app messages. This allows the proposal department to provide users with energy-saving measures quickly and reliably, minimizing energy waste.

[0032] The update unit regularly improves and updates the energy-saving measures presented by the proposal unit based on seasonal and regional climate. Specifically, since air conditioning use increases in the summer, it proposes efficient ways to use air conditioning. The update unit can also optimize heating and air conditioning usage according to the regional climate. The generation AI analyzes seasonal and regional climate data to generate optimal energy-saving measures. For example, based on summer temperature data, the generation AI proposes ways to optimize air conditioning settings and operating times. The generation AI also provides specific advice for optimizing heating and air conditioning usage based on regional climate data. Furthermore, the update unit can regularly review and update energy-saving measures according to seasonal and regional climate based on the user's energy consumption data. For example, if a user uses air conditioning frequently in the summer, the update unit will propose efficient ways to use air conditioning to reduce energy consumption. The update unit can also collect user feedback and continuously improve the accuracy and effectiveness of the energy-saving measures. For example, it can provide feedback on the results of users implementing the proposed energy-saving measures and revise the measures based on that data. This allows the update unit to provide optimal energy-saving measures tailored to the season and local climate, minimizing energy waste. Furthermore, the update unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also email and in-app messages. This enables the update unit to provide users with energy-saving measures quickly and reliably, minimizing energy waste.

[0033] The visualization unit visualizes the user's energy saving achievements. Specifically, it uses graphs and charts to visually display changes in the user's energy consumption. For example, the visualization unit graphs the user's energy consumption on a daily, weekly, and monthly basis, allowing users to see their saving results at a glance. The generation AI analyzes the user's energy consumption data and generates data for visualization. For example, the generation AI analyzes changes in the user's energy consumption and generates graphs and charts showing the saving results. The visualization unit can also evaluate the user's energy saving achievements and provide rewards and incentives according to the level of achievement. For example, if a user achieves a certain saving goal, the visualization unit can award badges or points to indicate the achievement and increase the user's motivation. Furthermore, the visualization unit can compare the user's energy consumption with other users and display it in a ranking format. This allows users to enjoy competing with other users while working to save energy. The visualization unit can collect user feedback and continuously improve the accuracy and effectiveness of the visualizations. For example, users can provide feedback based on the visualized data, and the visualization method can be reviewed based on that data. Furthermore, the visualization unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through email and in-app messages. This allows the visualization unit to quickly and reliably display changes in energy usage to the user visually, enabling them to feel the results of their energy savings.

[0034] The learning unit can analyze the user's past energy consumption data and select the optimal learning algorithm. For example, the learning unit can obtain the user's past energy consumption data from the cloud, and the generating AI will analyze it. The learning unit can also analyze the user's energy consumption patterns, and the generating AI can select the optimal algorithm. Furthermore, the learning unit can classify the user's energy consumption data by time of day, and the generating AI can adjust the learning algorithm based on this classification. In this way, the optimal learning algorithm can be selected by analyzing past energy consumption data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past energy consumption data into the generating AI and have the generating AI select the optimal learning algorithm.

[0035] The learning unit can perform learning while taking into account the user's daily routine and specific events. For example, the learning unit can acquire the user's calendar information, and the generating AI can adjust the learning based on specific events. The learning unit can also analyze the user's daily routine, and the generating AI can set a learning schedule based on that. Furthermore, the learning unit can adjust the learning content to match the user's holidays and special events. This allows for more accurate learning by taking into account the user's daily routine and specific events. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's calendar information into the generating AI and have the generating AI perform learning adjustments based on specific events.

[0036] The learning unit can prioritize learning highly relevant data by considering the user's geographical location during the learning process. For example, the learning unit can obtain the user's current location, and the generating AI will prioritize learning energy consumption data for that region. The learning unit can also analyze the user's past travel history, allowing the generating AI to select highly relevant data. Furthermore, the learning unit can enable the generating AI to learn region-specific energy consumption patterns based on the user's geographical location. This allows the learning unit to prioritize learning highly relevant data by considering the user's geographical location. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into the generating AI and have the generating AI select highly relevant data.

[0037] The learning unit can analyze the user's social media activity and learn relevant lifestyle data during the learning process. For example, the learning unit can analyze the user's social media posts, and the generating AI can extract information related to energy consumption. The learning unit can also collect and learn lifestyle data from the user's social media activity. Furthermore, the learning unit can analyze the user's social media friendships, and the generating AI can learn relevant data. In this way, relevant lifestyle data can be learned by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media data into the generating AI and have the generating AI perform the learning of relevant lifestyle data.

[0038] The proposal unit can adjust the level of detail of its proposals based on the importance of energy consumption. For example, the proposal unit's generating AI can propose detailed energy-saving measures for high-importance energy consumption, while the AI ​​can propose concise energy-saving measures for low-importance energy consumption. Furthermore, the proposal unit can dynamically adjust the level of detail of its proposals according to the importance of energy consumption. This allows for appropriate proposals by adjusting the level of detail based on the importance of energy consumption. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input energy consumption data into the generating AI and have the generating AI adjust the level of detail of the proposals based on importance.

[0039] The proposal unit can apply different proposal algorithms depending on the energy consumption category when making a proposal. For example, the proposal unit's generating AI can propose specific energy-saving measures for the energy consumption of home appliances. Similarly, the proposal unit's generating AI can propose different energy-saving measures for the energy consumption of lighting. Furthermore, the proposal unit's generating AI can select the optimal proposal algorithm depending on the energy consumption category. This allows for optimal proposals by applying different proposal algorithms depending on the energy consumption category. Some or all of the above processing in the proposal unit may be performed using AI, or without AI. For example, the proposal unit can input energy consumption category data into the generating AI and have the generating AI apply a category-based proposal algorithm.

[0040] The proposal unit can determine the priority of proposals based on the timing of energy consumption. For example, the proposal unit may prioritize proposals for heating energy saving measures in winter, and for cooling energy saving measures in summer. Furthermore, the generation AI can dynamically adjust the priority of proposals according to the timing of energy consumption. This enables appropriate proposals by determining the priority of proposals based on the timing of energy consumption. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input energy consumption timing data into the generation AI and have the generation AI perform the determination of proposal priorities based on the timing.

[0041] The proposal unit can adjust the order of proposals based on the relevance of energy consumption. For example, the generation AI can make proposals first for high-priority energy consumption. Conversely, the proposal unit can have the generation AI postpone proposals for low-priority energy consumption. Furthermore, the proposal unit can dynamically adjust the order of proposals according to the relevance of energy consumption. This allows for appropriate proposals by adjusting the order of proposals based on the relevance of energy consumption. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input energy consumption relevance data into the generation AI and have the generation AI perform the adjustment of the order of proposals based on relevance.

[0042] The update unit can analyze the effectiveness of past energy-saving measures and select the optimal update algorithm during the update process. For example, the update unit can obtain the effectiveness of past energy-saving measures from the cloud, and the generating AI can analyze it. The update unit can also classify the effectiveness of past energy-saving measures by time period, and the generating AI can select the optimal update algorithm. Furthermore, the update unit can analyze the effectiveness of past energy-saving measures, and the generating AI can adjust the update algorithm based on that analysis. In this way, the optimal update algorithm can be selected by analyzing the effectiveness of past energy-saving measures. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input data on the effectiveness of past energy-saving measures into the generating AI and have the generating AI select the optimal update algorithm.

[0043] The update unit can perform updates while taking into account the user's daily routine and specific events. For example, the update unit can acquire the user's calendar information, and the generating AI can adjust the update based on specific events. The update unit can also analyze the user's daily routine, and the generating AI can set the update schedule based on that. Furthermore, the update unit can adjust the update content according to the user's seasonal changes. This makes it possible to perform appropriate updates by taking into account the user's daily routine and specific events. Some or all of the above processes in the update unit may be performed using AI, or they may not be performed using AI. For example, the update unit can input the user's calendar information into the generating AI and have the generating AI perform update adjustments based on specific events.

[0044] The update unit can prioritize updating highly relevant savings strategies by considering the user's geographical location information during the update process. For example, the update unit can obtain the user's current location, and the generating AI can prioritize updating savings strategies for that region. The update unit can also analyze the user's past travel history, and the generating AI can select highly relevant savings strategies. Furthermore, the update unit can update region-specific savings strategies based on the user's geographical location information. This allows for the prioritization of highly relevant savings strategies by considering the user's geographical location information. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location information into the generating AI and have the generating AI select highly relevant savings strategies.

[0045] The update unit can analyze the user's social media activity and update relevant savings strategies during the update process. For example, the update unit can analyze the user's social media posts, and the generating AI can extract information related to savings strategies. The update unit can also collect and update savings strategies from the user's social media activity using the generating AI. Furthermore, the update unit can analyze the user's social media friendships, and the generating AI can update relevant savings strategies. In this way, relevant savings strategies can be updated by analyzing the user's social media activity. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's social media data into the generating AI and have the generating AI perform the update of relevant savings strategies.

[0046] The visualization unit can select the optimal visualization method by referring to the user's past energy consumption data during visualization. For example, the visualization unit can obtain the user's past energy consumption data from the cloud, and the generating AI can select the optimal visualization method. The visualization unit can also analyze the user's energy consumption patterns, and the generating AI can adjust the visualization method based on that. Furthermore, the visualization unit can classify the user's energy consumption data by time of day, and the generating AI can select a visualization method based on that classification. In this way, the optimal visualization method can be selected by referring to the user's past energy consumption data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past energy consumption data into the generating AI and have the generating AI select the optimal visualization method.

[0047] The visualization unit can perform visualizations while considering the user's daily routine and specific events. For example, the visualization unit can acquire the user's calendar information, and the generating AI can adjust the visualization based on specific events. The visualization unit can also analyze the user's daily routine, and the generating AI can set a visualization schedule based on that. Furthermore, the visualization unit can adjust the visualization content to match the user's holidays and special events. This makes it possible to perform appropriate visualizations by considering the user's daily routine and specific events. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's calendar information into the generating AI and have the generating AI perform visualization adjustments based on specific events.

[0048] The visualization unit can prioritize the visualization of highly relevant data by considering the user's geographical location information during the visualization process. For example, the visualization unit can obtain the user's current location, and the generating AI can prioritize the visualization of energy consumption data for that region. The visualization unit can also analyze the user's past travel history, and the generating AI can select highly relevant data. Furthermore, based on the user's geographical location information, the visualization unit can have the generating AI visualize region-specific energy consumption patterns. In this way, by considering the user's geographical location information, highly relevant data can be prioritized for visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's geographical location information into the generating AI and have the generating AI perform the visualization of highly relevant data.

[0049] The visualization unit can analyze the user's social media activity and visualize the relevant data during visualization. For example, the visualization unit can analyze the user's social media posts, and the generating AI can extract and visualize information related to energy consumption. The visualization unit can also collect and visualize lifestyle data from the user's social media activity using the generating AI. Furthermore, the visualization unit can analyze the user's social media friendships, and the generating AI can visualize the relevant data. In this way, relevant data can be visualized by analyzing the user's social media activity. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's social media data into the generating AI and have the generating AI perform the visualization of the relevant data.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The energy-saving suggestion system can also propose energy-saving measures by taking the user's health data into consideration. For example, it can monitor the user's heart rate and sleep patterns and suggest energy-saving measures tailored to their health condition. If the heart rate is high, it can suggest adjusting the lighting to provide a relaxing environment. It can also suggest optimizing nighttime energy consumption based on sleep patterns. This allows the system to provide energy-saving measures that are tailored to the user's health condition.

[0052] The energy-saving suggestion system can further propose energy-saving measures by taking into account the user's hobbies and interests. For example, if the user enjoys watching movies, it can suggest ways to optimize energy consumption during movie viewing. Similarly, if the user enjoys cooking, it can suggest ways to optimize energy consumption during cooking. Furthermore, if the user enjoys sports, it can suggest ways to optimize energy consumption during sports activities. This allows the system to provide energy-saving measures tailored to the user's hobbies and interests.

[0053] The energy-saving suggestion system can further propose energy-saving measures considering the user's family structure. For example, in households with children, it can suggest energy-saving measures that prioritize safety. In households with elderly people, it can suggest energy-saving measures that prioritize comfort. Furthermore, in households with pets, it can suggest energy-saving measures that take into account the comfort of the pets. In this way, it can provide energy-saving measures tailored to the family structure.

[0054] The energy-saving suggestion system can also set energy consumption targets for the user and propose energy-saving measures based on the degree of achievement. For example, if the user sets a monthly energy consumption target, the system can propose energy-saving measures based on that target. Furthermore, if the user sets a specific energy consumption reduction target, the system can propose energy-saving measures based on that target. In addition, if the user sets a long-term energy consumption target, the system can propose energy-saving measures based on that target. This allows the system to provide energy-saving measures tailored to the user's goals.

[0055] The energy saving suggestion system can further analyze past trends in the user's energy consumption and propose energy saving measures based on those trends. For example, it can analyze past energy consumption data to identify seasonal energy consumption patterns. It can also analyze past energy consumption data by time of day to understand energy consumption trends during specific time periods. Furthermore, it can analyze past energy consumption data by region to identify regional energy consumption patterns. This allows the system to provide energy saving measures based on past trends.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The learning unit learns about the user's lifestyle habits related to energy consumption. For example, it collects detailed data such as what actions the user takes and at what times of day they consume the most energy. Specifically, if a user uses the air conditioner a lot at night, this data is collected and analyzed by the generating AI. Step 2: The suggestion unit presents optimal energy-saving measures based on the information learned by the learning unit. For example, for users who use their air conditioner frequently at night, it might suggest slightly raising the air conditioner's temperature setting or utilizing the timer function. This reduces wasted energy and enables effective savings. Step 3: The update department regularly improves and updates the energy-saving measures presented by the proposal department based on the season and local climate. For example, since air conditioning use increases in the summer, they propose ways to use air conditioning efficiently. They can also optimize heating and cooling usage according to the local climate. Step 4: The visualization section visualizes the user's savings achievements. For example, graphs and charts are used to visually display changes in the user's energy consumption. This allows the user to feel the results of their savings and maintain their motivation.

[0058] (Example of form 2) The energy-saving suggestion system according to an embodiment of the present invention is a system that uses generative AI to suggest optimal energy-saving measures based on the user's lifestyle. This energy-saving suggestion system learns the user's lifestyle habits related to energy consumption and presents optimal energy-saving measures. Furthermore, the energy-saving suggestion system takes into account the season and local climate and periodically improves and updates the saving measures. For example, the energy-saving suggestion system learns the user's lifestyle habits related to energy consumption. In this process, it collects detailed data such as what actions the user takes and at what times of day they consume a lot of energy. For example, if a user uses air conditioning a lot at night, this data is collected and analyzed by the generative AI. Next, the generative AI presents optimal energy-saving measures based on the collected data. For example, for a user who uses air conditioning a lot at night, it suggests slightly raising the air conditioner's temperature setting or utilizing the timer function. This reduces wasted energy and enables effective saving. Furthermore, in addition to the user's lifestyle habits, the generative AI takes into account the season and local climate and periodically improves and updates the saving measures. For example, since the use of air conditioning increases in the summer, the generative AI suggests efficient ways to use air conditioning. Furthermore, the system can optimize heating and cooling usage according to the local climate. Finally, the energy saving suggestion system provides a function to visualize the user's energy saving achievements. This allows users to easily check whether their energy use is successful. For example, the energy saving suggestion system visually displays changes in the user's energy usage using graphs and charts. This allows users to feel the results of their savings and maintain motivation. This energy saving suggestion system targets general households and small businesses. It is an ideal tool for people who want to control and optimize their energy usage patterns and energy consumption. To address challenges such as the complexity of energy management, visualization of saving achievements, and seasonal and regional optimization, a personalized energy monitoring app utilizing generative AI is provided. This allows the energy saving suggestion system to effectively manage the user's energy usage and achieve energy savings.

[0059] The energy-saving suggestion system according to this embodiment comprises a learning unit, a suggestion unit, an update unit, and a visualization unit. The learning unit learns the user's lifestyle habits related to energy consumption. The learning unit collects detailed data, such as what actions the user takes and at what times of day they consume a lot of energy. For example, if a user uses the air conditioner a lot at night, the learning unit collects that data, and the generating AI analyzes it. The suggestion unit presents optimal energy-saving measures based on the information learned by the learning unit. For example, the suggestion unit suggests to a user who uses the air conditioner a lot at night that they slightly raise the air conditioner's temperature setting or utilize the timer function. This reduces wasted energy and enables effective saving. The update unit periodically improves and updates the saving measures presented by the suggestion unit based on the season and local climate. For example, the update unit suggests efficient ways to use air conditioning in the summer, as air conditioning use increases during that time. The update unit can also optimize heating and cooling usage methods according to the local climate. The visualization unit visualizes the user's degree of energy saving achievement. The visualization unit visually displays changes in the user's energy consumption, for example, using graphs and charts. This allows the user to feel the results of their energy saving efforts and maintain their motivation. As a result, the energy saving suggestion system according to this embodiment can effectively manage the user's energy consumption and achieve energy savings.

[0060] The learning unit learns about users' lifestyle habits related to energy consumption. Specifically, the learning unit collects detailed data such as what actions users take and when they consume the most energy. For example, if a user uses their air conditioner frequently at night, this data is collected and analyzed by the generative AI. The generative AI uses machine learning algorithms to analyze user behavior patterns and understand energy consumption trends. For example, the generative AI analyzes the user's air conditioner usage data to identify the frequency of use and consumption under specific time periods and temperature conditions. Furthermore, the generative AI learns the user's lifestyle habits and behavior patterns to identify peak energy consumption times and when wasteful consumption occurs. As a result, the learning unit can collect detailed data on users' energy consumption and analyze it using the generative AI to understand energy consumption trends based on users' lifestyle habits. In addition, the learning unit can store the collected data on a cloud server and link it with other systems and departments. For example, the learning unit can provide the collected data to the proposal and update departments to be used for proposing and updating energy saving measures. Furthermore, the learning unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the learning unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The suggestion unit presents optimal energy-saving measures based on information learned by the learning unit. Specifically, the suggestion unit suggests to users who frequently use air conditioners at night that they slightly increase the air conditioner's temperature setting or utilize the timer function. The generation AI analyzes the user's energy consumption data and generates optimal energy-saving measures. For example, based on the user's air conditioner usage data, the generation AI simulates how much energy can be saved by raising the temperature setting by 1 degree. The generation AI also suggests ways to reduce unnecessary air conditioner operation time and lower energy consumption by utilizing the timer function. Furthermore, the suggestion unit can present individually customized energy-saving measures based on the user's lifestyle and behavioral patterns. For example, if a user frequently uses their air conditioner at night, the suggestion unit provides specific advice on optimizing nighttime air conditioner use. This allows the suggestion unit to propose effective energy-saving measures based on detailed data on the user's energy consumption, thereby reducing energy waste. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can provide feedback on the results of users implementing the suggested energy-saving measures and revise the suggestions based on that data. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also email and in-app messages. This allows the proposal department to provide users with energy-saving measures quickly and reliably, minimizing energy waste.

[0062] The update unit regularly improves and updates the energy-saving measures presented by the proposal unit based on seasonal and regional climate. Specifically, since air conditioning use increases in the summer, it proposes efficient ways to use air conditioning. The update unit can also optimize heating and air conditioning usage according to the regional climate. The generation AI analyzes seasonal and regional climate data to generate optimal energy-saving measures. For example, based on summer temperature data, the generation AI proposes ways to optimize air conditioning settings and operating times. The generation AI also provides specific advice for optimizing heating and air conditioning usage based on regional climate data. Furthermore, the update unit can regularly review and update energy-saving measures according to seasonal and regional climate based on the user's energy consumption data. For example, if a user uses air conditioning frequently in the summer, the update unit will propose efficient ways to use air conditioning to reduce energy consumption. The update unit can also collect user feedback and continuously improve the accuracy and effectiveness of the energy-saving measures. For example, it can provide feedback on the results of users implementing the proposed energy-saving measures and revise the measures based on that data. This allows the update unit to provide optimal energy-saving measures tailored to the season and local climate, minimizing energy waste. Furthermore, the update unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also email and in-app messages. This enables the update unit to provide users with energy-saving measures quickly and reliably, minimizing energy waste.

[0063] The visualization unit visualizes the user's energy saving achievements. Specifically, it uses graphs and charts to visually display changes in the user's energy consumption. For example, the visualization unit graphs the user's energy consumption on a daily, weekly, and monthly basis, allowing users to see their saving results at a glance. The generation AI analyzes the user's energy consumption data and generates data for visualization. For example, the generation AI analyzes changes in the user's energy consumption and generates graphs and charts showing the saving results. The visualization unit can also evaluate the user's energy saving achievements and provide rewards and incentives according to the level of achievement. For example, if a user achieves a certain saving goal, the visualization unit can award badges or points to indicate the achievement and increase the user's motivation. Furthermore, the visualization unit can compare the user's energy consumption with other users and display it in a ranking format. This allows users to enjoy competing with other users while working to save energy. The visualization unit can collect user feedback and continuously improve the accuracy and effectiveness of the visualizations. For example, users can provide feedback based on the visualized data, and the visualization method can be reviewed based on that data. Furthermore, the visualization unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through email and in-app messages. This allows the visualization unit to quickly and reliably display changes in energy usage to the user visually, enabling them to feel the results of their energy savings.

[0064] The learning unit can estimate the user's emotions and adjust the learning method for lifestyle habits related to energy consumption based on the estimated emotions. For example, if the user is stressed, the generating AI can reduce the user's burden by using a simple data collection method. If the user is relaxed, the generating AI can collect detailed data and perform more accurate learning. If the user is in a hurry, the generating AI can quickly collect data and start learning immediately. This allows for the collection of more appropriate energy consumption data by adjusting the learning method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input the user's emotion data into the generating AI and have the generating AI perform adjustments to the learning method based on emotions.

[0065] The learning unit can analyze the user's past energy consumption data and select the optimal learning algorithm. For example, the learning unit can obtain the user's past energy consumption data from the cloud, and the generating AI will analyze it. The learning unit can also analyze the user's energy consumption patterns, and the generating AI can select the optimal algorithm. Furthermore, the learning unit can classify the user's energy consumption data by time of day, and the generating AI can adjust the learning algorithm based on this classification. In this way, the optimal learning algorithm can be selected by analyzing past energy consumption data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past energy consumption data into the generating AI and have the generating AI select the optimal learning algorithm.

[0066] The learning unit can perform learning while taking into account the user's daily routine and specific events. For example, the learning unit can acquire the user's calendar information, and the generating AI can adjust the learning based on specific events. The learning unit can also analyze the user's daily routine, and the generating AI can set a learning schedule based on that. Furthermore, the learning unit can adjust the learning content to match the user's holidays and special events. This allows for more accurate learning by taking into account the user's daily routine and specific events. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's calendar information into the generating AI and have the generating AI perform learning adjustments based on specific events.

[0067] The learning unit can estimate the user's emotions and determine the priority of data to learn based on the estimated emotions. For example, if the user is stressed, the generating AI will prioritize learning data of low importance. If the user is relaxed, the generating AI can prioritize learning data of high importance. If the user is in a hurry, the generating AI can prioritize data that can be learned quickly. This enables efficient learning by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into the generating AI and have the generating AI perform the determination of data priority based on emotions.

[0068] The learning unit can prioritize learning highly relevant data by considering the user's geographical location during the learning process. For example, the learning unit can obtain the user's current location, and the generating AI will prioritize learning energy consumption data for that region. The learning unit can also analyze the user's past travel history, allowing the generating AI to select highly relevant data. Furthermore, the learning unit can enable the generating AI to learn region-specific energy consumption patterns based on the user's geographical location. This allows the learning unit to prioritize learning highly relevant data by considering the user's geographical location. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into the generating AI and have the generating AI select highly relevant data.

[0069] The learning unit can analyze the user's social media activity and learn relevant lifestyle data during the learning process. For example, the learning unit can analyze the user's social media posts, and the generating AI can extract information related to energy consumption. The learning unit can also collect and learn lifestyle data from the user's social media activity. Furthermore, the learning unit can analyze the user's social media friendships, and the generating AI can learn relevant data. In this way, relevant lifestyle data can be learned by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media data into the generating AI and have the generating AI perform the learning of relevant lifestyle data.

[0070] The suggestion unit can estimate the user's emotions and adjust the way energy-saving measures are presented based on those emotions. For example, if the user is stressed, the suggestion unit's generating AI will suggest simple and easy-to-understand energy-saving measures. If the user is relaxed, the suggestion unit's generating AI can suggest energy-saving measures with detailed explanations. If the user is in a hurry, the suggestion unit's generating AI can suggest energy-saving measures that can be implemented quickly. By adjusting the way energy-saving measures are presented according to the user's emotions, more effective suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generating AI and have the generating AI adjust the presentation based on those emotions.

[0071] The proposal unit can adjust the level of detail of its proposals based on the importance of energy consumption. For example, the proposal unit's generating AI can propose detailed energy-saving measures for high-importance energy consumption, while the AI ​​can propose concise energy-saving measures for low-importance energy consumption. Furthermore, the proposal unit can dynamically adjust the level of detail of its proposals according to the importance of energy consumption. This allows for appropriate proposals by adjusting the level of detail based on the importance of energy consumption. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input energy consumption data into the generating AI and have the generating AI adjust the level of detail of the proposals based on importance.

[0072] The proposal unit can apply different proposal algorithms depending on the energy consumption category when making a proposal. For example, the proposal unit's generating AI can propose specific energy-saving measures for the energy consumption of home appliances. Similarly, the proposal unit's generating AI can propose different energy-saving measures for the energy consumption of lighting. Furthermore, the proposal unit's generating AI can select the optimal proposal algorithm depending on the energy consumption category. This allows for optimal proposals by applying different proposal algorithms depending on the energy consumption category. Some or all of the above processing in the proposal unit may be performed using AI, or without AI. For example, the proposal unit can input energy consumption category data into the generating AI and have the generating AI apply a category-based proposal algorithm.

[0073] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit's generating AI can provide short, concise suggestions. If the user is relaxed, the generating AI can provide longer suggestions with more detailed explanations. If the user is in a hurry, the generating AI can provide short, actionable suggestions. By adjusting the length of suggestions according to the user's emotions, more effective suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generating AI and have the generating AI adjust the length of suggestions based on those emotions.

[0074] The proposal unit can determine the priority of proposals based on the timing of energy consumption. For example, the proposal unit may prioritize proposals for heating energy saving measures in winter, and for cooling energy saving measures in summer. Furthermore, the generation AI can dynamically adjust the priority of proposals according to the timing of energy consumption. This enables appropriate proposals by determining the priority of proposals based on the timing of energy consumption. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input energy consumption timing data into the generation AI and have the generation AI perform the determination of proposal priorities based on the timing.

[0075] The proposal unit can adjust the order of proposals based on the relevance of energy consumption. For example, the generation AI can make proposals first for high-priority energy consumption. Conversely, the proposal unit can have the generation AI postpone proposals for low-priority energy consumption. Furthermore, the proposal unit can dynamically adjust the order of proposals according to the relevance of energy consumption. This allows for appropriate proposals by adjusting the order of proposals based on the relevance of energy consumption. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input energy consumption relevance data into the generation AI and have the generation AI perform the adjustment of the order of proposals based on relevance.

[0076] The update unit can estimate the user's emotions and adjust the update frequency of the savings strategy based on the estimated emotions. For example, if the user is stressed, the generation AI will set a lower update frequency. If the user is relaxed, the generation AI will set a higher update frequency. If the user is in a hurry, the generation AI will perform updates quickly. This allows for appropriate updates by adjusting the update frequency of the savings strategy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, or not using AI. For example, the update unit can input user emotion data into the generation AI and have the generation AI adjust the update frequency based on emotions.

[0077] The update unit can analyze the effectiveness of past energy-saving measures and select the optimal update algorithm during the update process. For example, the update unit can obtain the effectiveness of past energy-saving measures from the cloud, and the generating AI can analyze it. The update unit can also classify the effectiveness of past energy-saving measures by time period, and the generating AI can select the optimal update algorithm. Furthermore, the update unit can analyze the effectiveness of past energy-saving measures, and the generating AI can adjust the update algorithm based on that analysis. In this way, the optimal update algorithm can be selected by analyzing the effectiveness of past energy-saving measures. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input data on the effectiveness of past energy-saving measures into the generating AI and have the generating AI select the optimal update algorithm.

[0078] The update unit can perform updates while taking into account the user's daily routine and specific events. For example, the update unit can acquire the user's calendar information, and the generating AI can adjust the update based on specific events. The update unit can also analyze the user's daily routine, and the generating AI can set the update schedule based on that. Furthermore, the update unit can adjust the update content according to the user's seasonal changes. This makes it possible to perform appropriate updates by taking into account the user's daily routine and specific events. Some or all of the above processes in the update unit may be performed using AI, or they may not be performed using AI. For example, the update unit can input the user's calendar information into the generating AI and have the generating AI perform update adjustments based on specific events.

[0079] The update unit can estimate the user's emotions and determine the priority of savings to update based on the estimated emotions. For example, if the user is stressed, the generating AI will prioritize updating savings of lower importance. If the user is relaxed, the generating AI can prioritize updating savings of higher importance. If the user is in a hurry, the generating AI can prioritize selecting savings that can be updated quickly. This allows for appropriate updates by determining the priority of savings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input user emotion data into the generating AI and have the generating AI determine the priority of savings based on emotions.

[0080] The update unit can prioritize updating highly relevant savings strategies by considering the user's geographical location information during the update process. For example, the update unit can obtain the user's current location, and the generating AI can prioritize updating savings strategies for that region. The update unit can also analyze the user's past travel history, and the generating AI can select highly relevant savings strategies. Furthermore, the update unit can update region-specific savings strategies based on the user's geographical location information. This allows for the prioritization of highly relevant savings strategies by considering the user's geographical location information. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location information into the generating AI and have the generating AI select highly relevant savings strategies.

[0081] The update unit can analyze the user's social media activity and update relevant savings strategies during the update process. For example, the update unit can analyze the user's social media posts, and the generating AI can extract information related to savings strategies. The update unit can also collect and update savings strategies from the user's social media activity using the generating AI. Furthermore, the update unit can analyze the user's social media friendships, and the generating AI can update relevant savings strategies. In this way, relevant savings strategies can be updated by analyzing the user's social media activity. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's social media data into the generating AI and have the generating AI perform the update of relevant savings strategies.

[0082] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is stressed, the generating AI can provide a simple and highly visual graph. If the user is relaxed, the generating AI can provide a visualization that includes detailed data. If the user is in a hurry, the generating AI can provide a visualization that can be quickly understood. This allows for more effective visualization by adjusting the visualization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input user emotion data into the generating AI and have the generating AI adjust the visualization method based on the emotions.

[0083] The visualization unit can select the optimal visualization method by referring to the user's past energy consumption data during visualization. For example, the visualization unit can obtain the user's past energy consumption data from the cloud, and the generating AI can select the optimal visualization method. The visualization unit can also analyze the user's energy consumption patterns, and the generating AI can adjust the visualization method based on that. Furthermore, the visualization unit can classify the user's energy consumption data by time of day, and the generating AI can select a visualization method based on that classification. In this way, the optimal visualization method can be selected by referring to the user's past energy consumption data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past energy consumption data into the generating AI and have the generating AI select the optimal visualization method.

[0084] The visualization unit can perform visualizations while considering the user's daily routine and specific events. For example, the visualization unit can acquire the user's calendar information, and the generating AI can adjust the visualization based on specific events. The visualization unit can also analyze the user's daily routine, and the generating AI can set a visualization schedule based on that. Furthermore, the visualization unit can adjust the visualization content to match the user's holidays and special events. This makes it possible to perform appropriate visualizations by considering the user's daily routine and specific events. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's calendar information into the generating AI and have the generating AI perform visualization adjustments based on specific events.

[0085] The visualization unit can estimate the user's emotions and determine the priority of data to visualize based on the estimated emotions. For example, if the user is stressed, the generation AI in the visualization unit can prioritize visualizing data of lower importance. If the user is relaxed, the generation AI can prioritize visualizing data of higher importance. If the user is in a hurry, the generation AI can prioritize selecting data that can be visualized quickly. This enables efficient visualization by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, or not using AI. For example, the visualization unit can input user emotion data into the generation AI and have the generation AI perform the determination of data priority based on emotions.

[0086] The visualization unit can prioritize the visualization of highly relevant data by considering the user's geographical location information during the visualization process. For example, the visualization unit can obtain the user's current location, and the generating AI can prioritize the visualization of energy consumption data for that region. The visualization unit can also analyze the user's past travel history, and the generating AI can select highly relevant data. Furthermore, based on the user's geographical location information, the visualization unit can have the generating AI visualize region-specific energy consumption patterns. In this way, by considering the user's geographical location information, highly relevant data can be prioritized for visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's geographical location information into the generating AI and have the generating AI perform the visualization of highly relevant data.

[0087] The visualization unit can analyze the user's social media activity and visualize the relevant data during visualization. For example, the visualization unit can analyze the user's social media posts, and the generating AI can extract and visualize information related to energy consumption. The visualization unit can also collect and visualize lifestyle data from the user's social media activity using the generating AI. Furthermore, the visualization unit can analyze the user's social media friendships, and the generating AI can visualize the relevant data. In this way, relevant data can be visualized by analyzing the user's social media activity. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's social media data into the generating AI and have the generating AI perform the visualization of the relevant data.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The energy-saving suggestion system can also propose energy-saving measures by taking the user's health data into consideration. For example, it can monitor the user's heart rate and sleep patterns and suggest energy-saving measures tailored to their health condition. If the heart rate is high, it can suggest adjusting the lighting to provide a relaxing environment. It can also suggest optimizing nighttime energy consumption based on sleep patterns. This allows the system to provide energy-saving measures that are tailored to the user's health condition.

[0090] The energy-saving suggestion system can estimate the user's emotions and adjust the timing of energy-saving measures based on those emotions. For example, if the user is stressed, the generating AI can delay the implementation of energy-saving measures. Conversely, if the user is relaxed, the generating AI can implement energy-saving measures immediately. Furthermore, if the user is in a hurry, the generating AI can select energy-saving measures that can be implemented quickly. By adjusting the timing of energy-saving measures according to the user's emotions, more effective energy saving becomes possible.

[0091] The energy-saving suggestion system can further propose energy-saving measures by taking into account the user's hobbies and interests. For example, if the user enjoys watching movies, it can suggest ways to optimize energy consumption during movie viewing. Similarly, if the user enjoys cooking, it can suggest ways to optimize energy consumption during cooking. Furthermore, if the user enjoys sports, it can suggest ways to optimize energy consumption during sports activities. This allows the system to provide energy-saving measures tailored to the user's hobbies and interests.

[0092] The energy-saving suggestion system can estimate the user's emotions and adjust the feedback method for energy-saving measures based on those emotions. For example, if the user is stressed, the generating AI can provide positive feedback. If the user is relaxed, the generating AI can provide detailed feedback. Furthermore, if the user is in a hurry, the generating AI can provide concise feedback. By adjusting the feedback method according to the user's emotions, more effective energy saving becomes possible.

[0093] The energy-saving suggestion system can further propose energy-saving measures considering the user's family structure. For example, in households with children, it can suggest energy-saving measures that prioritize safety. In households with elderly people, it can suggest energy-saving measures that prioritize comfort. Furthermore, in households with pets, it can suggest energy-saving measures that take into account the comfort of the pets. In this way, it can provide energy-saving measures tailored to the family structure.

[0094] The energy-saving suggestion system can estimate the user's emotions and adjust the notification method for energy-saving measures based on those emotions. For example, if the user is stressed, the generating AI can provide less intense notifications. If the user is relaxed, the generating AI can provide detailed notifications. Furthermore, if the user is in a hurry, the generating AI can provide concise notifications. By adjusting the notification method according to the user's emotions, more effective energy saving becomes possible.

[0095] The energy-saving suggestion system can also set energy consumption targets for the user and propose energy-saving measures based on the degree of achievement. For example, if the user sets a monthly energy consumption target, the system can propose energy-saving measures based on that target. Furthermore, if the user sets a specific energy consumption reduction target, the system can propose energy-saving measures based on that target. In addition, if the user sets a long-term energy consumption target, the system can propose energy-saving measures based on that target. This allows the system to provide energy-saving measures tailored to the user's goals.

[0096] The energy-saving suggestion system can estimate the user's emotions and adjust the priority of energy-saving measures based on those emotions. For example, if the user is stressed, the generating AI can prioritize suggesting energy-saving measures that are easy to implement. If the user is relaxed, the generating AI can prioritize suggesting energy-saving measures that are highly effective. Furthermore, if the user is in a hurry, the generating AI can prioritize suggesting energy-saving measures that can be implemented quickly. By adjusting the priority of energy-saving measures according to the user's emotions, more effective energy saving becomes possible.

[0097] The energy saving suggestion system can further analyze past trends in the user's energy consumption and propose energy saving measures based on those trends. For example, it can analyze past energy consumption data to identify seasonal energy consumption patterns. It can also analyze past energy consumption data by time of day to understand energy consumption trends during specific time periods. Furthermore, it can analyze past energy consumption data by region to identify regional energy consumption patterns. This allows the system to provide energy saving measures based on past trends.

[0098] The energy-saving suggestion system can estimate the user's emotions and adjust how it visualizes the effectiveness of energy-saving measures based on those emotions. For example, if the user is stressed, the generating AI can provide a simple and easy-to-understand graph. If the user is relaxed, the generating AI can provide a visualization with detailed data. Furthermore, if the user is in a hurry, the generating AI can provide a visualization that can be quickly understood. By adjusting the visualization method according to the user's emotions, more effective energy saving becomes possible.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The learning unit learns about the user's lifestyle habits related to energy consumption. For example, it collects detailed data such as what actions the user takes and at what times of day they consume the most energy. Specifically, if a user uses the air conditioner a lot at night, this data is collected and analyzed by the generating AI. Step 2: The suggestion unit presents optimal energy-saving measures based on the information learned by the learning unit. For example, for users who use their air conditioner frequently at night, it might suggest slightly raising the air conditioner's temperature setting or utilizing the timer function. This reduces wasted energy and enables effective savings. Step 3: The update department regularly improves and updates the energy-saving measures presented by the proposal department based on the season and local climate. For example, since air conditioning use increases in the summer, they propose ways to use air conditioning efficiently. They can also optimize heating and cooling usage according to the local climate. Step 4: The visualization section visualizes the user's savings achievements. For example, graphs and charts are used to visually display changes in the user's energy consumption. This allows the user to feel the results of their savings and maintain their motivation.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0104] Each of the multiple elements described above, including the learning unit, proposal unit, update unit, and visualization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit collects user behavior data using the camera 42 and microphone 38B of the smart device 14 and analyzes it with the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates optimal energy saving measures based on the data from the learning unit. The update unit is implemented in the specific processing unit 290 of the data processing unit 12 and periodically improves and updates the energy saving measures based on the season and local climate. The visualization unit displays changes in the user's energy consumption in graphs and charts using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the learning unit, suggestion unit, update unit, and visualization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit collects user behavior data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes it with the control unit 46A. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates optimal energy saving measures based on the data from the learning unit. The update unit is implemented in the specific processing unit 290 of the data processing unit 12 and periodically improves and updates the energy saving measures based on the season and local climate. The visualization unit displays changes in the user's energy consumption in graphs and charts using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the learning unit, suggestion unit, update unit, and visualization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit collects user behavior data using the camera 42 and microphone 238 of the headset terminal 314 and analyzes it with the control unit 46A. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates optimal energy saving measures based on the data from the learning unit. The update unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and periodically improves and updates the energy saving measures based on the season and local climate. The visualization unit displays changes in the user's energy consumption in graphs and charts using the display 343 of the headset terminal 314. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the learning unit, proposal unit, update unit, and visualization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning unit collects user behavior data using the camera 42 and microphone 238 of the robot 414 and analyzes it with the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates optimal energy saving measures based on the data from the learning unit. The update unit is implemented in the specific processing unit 290 of the data processing unit 12 and periodically improves and updates the energy saving measures based on the season and local climate. The visualization unit displays changes in the user's energy consumption in graphs and charts using the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A learning unit that learns about the user's lifestyle habits related to energy consumption, A proposal unit that presents the optimal energy saving measures based on the information learned by the learning unit, The update unit periodically improves and updates the energy-saving measures presented by the aforementioned proposal unit based on the season and local climate, It includes a visualization unit that visualizes the user's savings achievement. A system characterized by the following features. (Note 2) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning method for lifestyle habits related to energy consumption based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, The system analyzes the user's past energy consumption data and selects the optimal learning algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, During the learning process, the system takes into account the user's daily routine and specific events. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, It estimates the user's emotions and determines the priority of data to be used for learning based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, During training, the system prioritizes learning highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, During training, the system analyzes users' social media activity and learns related lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, It estimates the user's emotions and adjusts how energy-saving measures are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of energy consumption. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the energy consumption category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making proposals, prioritize them based on the timing of energy consumption. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to energy consumption. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned update unit is, It estimates the user's emotions and adjusts the frequency of savings updates based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned update unit is, During updates, the effectiveness of past cost-saving measures is analyzed to select the optimal update algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned update unit is, Updates are performed taking into account the user's daily routine and specific events. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned update unit is, It estimates the user's emotions and determines the priority of savings to update based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned update unit is, During updates, the system prioritizes updating relevant energy savings based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned update unit is, During updates, we analyze users' social media activity and update relevant savings strategies. The system described in Appendix 1, characterized by the features described herein. (Note 20) The visualization unit is, It estimates the user's emotions and adjusts the visualization method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The visualization unit is, During visualization, the system selects the optimal visualization method by referring to the user's past energy consumption data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The visualization unit is, When creating visualizations, consider the user's daily routine and specific events. The system described in Appendix 1, characterized by the features described herein. (Note 23) The visualization unit is, It estimates the user's emotions and prioritizes the data to visualize based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The visualization unit is, When visualizing data, the system prioritizes the visualization of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The visualization unit is, During visualization, analyze users' social media activity and visualize relevant data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A learning unit that learns about the user's lifestyle habits related to energy consumption, A proposal unit that presents the optimal energy saving measures based on the information learned by the learning unit, The update unit periodically improves and updates the energy-saving measures presented by the aforementioned proposal unit based on the season and local climate, It includes a visualization unit that visualizes the user's savings achievement. A system characterized by the following features.

2. The aforementioned learning unit, It estimates the user's emotions and adjusts the learning method for lifestyle habits related to energy consumption based on the estimated emotions. The system according to feature 1.

3. The aforementioned learning unit, The system analyzes the user's past energy consumption data and selects the optimal learning algorithm. The system according to feature 1.

4. The aforementioned learning unit, During the learning process, the system takes into account the user's daily routine and specific events. The system according to feature 1.

5. The aforementioned learning unit, It estimates the user's emotions and determines the priority of data to be used for learning based on those estimated emotions. The system according to feature 1.

6. The aforementioned learning unit, During training, the system prioritizes learning highly relevant data, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned learning unit, During training, the system analyzes users' social media activity and learns related lifestyle data. The system according to feature 1.

8. The aforementioned proposal section is, It estimates the user's emotions and adjusts how energy-saving measures are presented based on those estimated emotions. The system according to feature 1.

9. The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of energy consumption. The system according to feature 1.

10. The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the energy consumption category. The system according to feature 1.

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