system

Abstract

The system according to the embodiment aims to analyze environmental data and provide an optimal living environment for the user. According to an embodiment, the system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects environmental data. The analysis unit analyzes the data collected by the collection unit. The proposal unit generates proposals based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately provided users with an optimal living environment based on environmental data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze environmental data and provide an optimal living environment for the user. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects environmental data. The analysis unit analyzes the data collected by the collection unit. The proposal unit generates proposals based on analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze environmental data and provide an optimal living environment for the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is an AI-powered system that provides users with an optimal living environment based on various environmental data. This system uses a generating AI to collect data such as carbon dioxide concentration, humidity, and temperature, and analyzes each user's environment based on that data. It then uses that information to present specific adjustment suggestions to optimize the user's living and working environment. For example, the generating AI collects environmental data such as carbon dioxide concentration, humidity, and temperature. For example, if the carbon dioxide concentration in a room is too high, the generating AI suggests opening a window or installing a ventilation system. In addition, in situations where the room is humid, the generating AI recommends using a dehumidifier or ventilating at an appropriate time. Furthermore, in high-temperature environments, the generating AI suggests adjusting the air conditioner's temperature setting or installing insulation. In this way, the generating AI provides reasonable suggestions based on the collected data. This allows the system to provide users with a comfortable living environment. For example, by following the adjustment suggestions proposed by the generating AI, users can sleep comfortably. Furthermore, maintaining appropriate humidity and temperature contributes to a healthy lifestyle. Furthermore, because the generating AI provides reasonable suggestions based on numerical values, users can follow the suggestions with confidence.

[0029] The environmental analysis system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects environmental data. The environmental data includes, but is not limited to, temperature, humidity, and carbon dioxide concentration. The collection unit collects the environmental data using, for example, a sensor. The collection unit can also set the frequency of data collection. For example, the collection unit can collect data at regular intervals. The collection unit can also collect data when a specific event occurs. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, but not limited to, an algorithm used and a purpose of the analysis. For example, the analysis unit can analyze data using a machine learning algorithm. The analysis unit can also analyze data trends. The analysis unit can also detect outliers. The proposal unit generates proposals based on the analysis results obtained by the analysis unit. The proposals are performed based on, for example, but not limited to, a proposal format and criteria. For example, the proposal unit generates proposals using a generation AI. The proposal unit can also present specific adjustment proposals to the user. The proposal unit can also set a priority order for the proposals. As a result, the environmental analysis system according to the embodiment can provide an optimal living environment to the user. For example, the suggestion unit can generate a suggestion for ventilation measures when the carbon dioxide concentration exceeds a reference value. Furthermore, the suggestion unit can generate a suggestion for humidity adjustment measures when the humidity exceeds a reference value. Furthermore, the suggestion unit can generate a suggestion for temperature adjustment measures when the temperature exceeds a reference value. As a result, the user can maintain a comfortable living environment.

[0030] The suggestion unit can generate a ventilation method suggestion when the carbon dioxide concentration exceeds a reference value. The reference value includes, for example, a specific threshold value for carbon dioxide concentration, but is not limited to such an example. The suggestion unit can, for example, suggest opening a window. The suggestion unit can also suggest using a ventilation fan. The suggestion unit can also suggest installing a ventilation system. This can improve the user's living environment by suggesting appropriate ventilation methods when the carbon dioxide concentration is high. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input carbon dioxide concentration data into the generation AI and cause the generation AI to suggest ventilation methods.

[0031] The suggestion unit can generate a suggestion for a humidity adjustment method when the humidity exceeds a reference value. The reference value includes, for example, a specific humidity threshold value, but is not limited to such an example. The suggestion unit can, for example, suggest the use of a dehumidifier. The suggestion unit can also suggest ventilating at an appropriate time. The suggestion unit can also suggest the use of a humidifier. This makes it possible to improve the user's living environment by suggesting an appropriate humidity adjustment method when humidity is high. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input humidity data into the generation AI and cause the generation AI to suggest a humidity adjustment method.

[0032] The suggestion unit can generate a suggestion for a temperature adjustment measure when the temperature exceeds a reference value. The reference value includes, for example, a specific temperature threshold, but is not limited to such an example. The suggestion unit can, for example, suggest reviewing the set temperature of an air conditioner. The suggestion unit can also suggest installing insulation. The suggestion unit can also suggest using a fan heater. This can improve the user's living environment by suggesting an appropriate temperature adjustment measure when the temperature is high. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input temperature data into the generation AI and cause the generation AI to execute a suggestion for a temperature adjustment measure.

[0033] The collection unit can collect environmental data such as carbon dioxide concentration, humidity, and temperature. Examples of environmental data include, but are not limited to, temperature, humidity, and carbon dioxide concentration. The collection unit collects environmental data using, for example, a sensor. The collection unit can also set the frequency of data collection. For example, the collection unit can collect data at regular intervals. The collection unit can also collect data when a specific event occurs. By collecting environmental data, the user's living environment can be analyzed and optimal suggestions can be made. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input environmental data acquired by a sensor into a generation AI and have the generation AI analyze the data.

[0034] The analysis unit can analyze the collected environmental data and generate information for indicating an optimal living environment for the user. An optimal living environment includes, but is not limited to, a comfortable temperature range, an appropriate humidity level, and the like. The analysis unit can analyze the data using, for example, a machine learning algorithm. The analysis unit can also analyze trends in the data. The analysis unit can also detect outliers. By analyzing the collected environmental data, information for providing an optimal living environment for the user can be generated. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected environmental data to a generation AI and have the generation AI analyze the data.

[0035] The collection unit can analyze the user's past environmental data collection history and select the optimal collection method. The collection history includes, for example, but is not limited to, the date and time of past data collection and the type of collected data. The optimal collection method includes, for example, but is not limited to, the placement of sensors and the frequency of data collection. For example, the collection unit prioritizes collecting data that the user collected frequently in the past. The collection unit can also increase the collection frequency during specific time periods based on the user's past collection history. The collection unit can also optimize the collection method under specific environmental conditions based on the user's past collection history. This allows the user's past collection history to be analyzed to select the optimal collection method and enable efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input past collection history data into a generation AI and have the generation AI select the optimal collection method.

[0036] When collecting environmental data, the collection unit can filter the environmental data based on the user's current activity status and lifestyle rhythm. Examples of activity status include, but are not limited to, when exercising or resting. Examples of lifestyle rhythm include, but are not limited to, when sleeping and eating. For example, when the user is working, the collection unit filters the collected data and prioritizes work-related data. Furthermore, when the user is exercising, the collection unit can prioritize collecting environmental data related to exercise. Furthermore, when the user is resting, the collection unit can prioritize collecting environmental data related to rest. By filtering data based on the user's activity status and lifestyle rhythm, more relevant data can be collected. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's activity status data to a generation AI and have the generation AI perform filtering.

[0037] When collecting environmental data, the collection unit can select the optimal collection means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of collection means include, but are not limited to, the type of sensor and the data collection method. For example, when the user uses voice input, the collection unit prioritizes collecting voice data. Furthermore, when the user uses text input, the collection unit can also prioritize collecting text data. Furthermore, when the user uses image input, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0038] When collecting environmental data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of highly relevant data include, but are not limited to, environmental data related to the user's current location. For example, when the user is at home, the collection unit prioritizes collecting home environmental data. Also, when the user is in the office, the collection unit can prioritize collecting office environmental data. Also, when the user is out, the collection unit can prioritize collecting destination environmental data. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0039] When collecting environmental data, the collection unit may analyze the user's social media activity and collect related data. Social media activity may include, but is not limited to, for example, the content of posts and the number of likes. Related data may include, but is not limited to, environmental data related to the content of social media posts. For example, the collection unit may collect environmental data of locations where the user has checked in on social media. The collection unit may also analyze the content of the user's social media posts and collect related environmental data. The collection unit may also collect related environmental data by referring to the activities of the user's friends on social media. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0040] When collecting environmental data, the collection unit can customize the collection method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, the user's opinions and past evaluations. Customizing the collection method can include, but are not limited to, sensor placement and data collection frequency. The collection unit, for example, optimizes the collection method based on feedback provided by the user in the past. The collection unit can also prioritize collection of specific environmental data based on the user's past feedback. The collection unit can also adjust the collection frequency based on the user's past feedback. This allows the collection method to be optimized by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the environmental data. The importance level includes, but is not limited to, the impact and urgency of the data. Adjustments to the level of detail can include, but are not limited to, the depth of the analysis and the level of detail of the information. For example, when the carbon dioxide concentration is high, the analysis unit performs a detailed analysis and proposes specific countermeasures. Furthermore, when the humidity is high, the analysis unit can perform a detailed analysis and propose appropriate humidity adjustment measures. Furthermore, when the temperature is high, the analysis unit can perform a detailed analysis and propose temperature adjustment measures. By adjusting the level of detail of the analysis based on the importance of the environmental data, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input environmental data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of environmental data. Examples of categories include, but are not limited to, temperature data and humidity data. Examples of analysis algorithms include, but are not limited to, regression analysis and clustering. For example, the analysis unit applies an air quality analysis algorithm to carbon dioxide concentration. The analysis unit can also apply a humidity analysis algorithm to humidity. The analysis unit can also apply a temperature analysis algorithm to temperature. By applying different analysis algorithms depending on the category of environmental data, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input environmental data to a generation AI and cause the generation AI to apply an analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, for example, past data patterns and evaluations of analysis results, but are not limited to these examples. Improving the analysis accuracy can include, for example, improving data accuracy and algorithms, but are not limited to these examples. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the analysis accuracy under specific environmental conditions based on the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. This allows the analysis accuracy to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0044] During analysis, the analysis unit can determine analysis priorities based on the collection time of the environmental data. Examples of collection time include, but are not limited to, seasons and time periods. Examples of priorities include, but are not limited to, importance and urgency. For example, the analysis unit prioritizes analysis of the most recent environmental data. The analysis unit can also complement current analysis results by referring to past environmental data. The analysis unit can also prioritize analysis of data collected during a specific time period. This allows for more appropriate analysis results to be provided by determining analysis priorities based on the collection time of the environmental data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input collection time data to the generation AI and have the generation AI determine the analysis priorities.

[0045] During analysis, the analysis unit can adjust the analysis order based on the relevance of the environmental data. Relevance includes, for example, but is not limited to, data correlation and influence. Adjusting the order can include, for example, but is not limited to, analysis priority and data importance. The analysis unit adjusts the analysis order, for example, taking into account the relevance between carbon dioxide concentration and humidity. The analysis unit can also adjust the analysis order taking into account the relevance between temperature and humidity. The analysis unit can also adjust the analysis order taking into account the relevance between carbon dioxide concentration and temperature. By adjusting the analysis order based on the relevance of the environmental data, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input environmental data to a generation AI and have the generation AI adjust the analysis order.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past experience. Examples of adjusting the use of technical terms include, but are not limited to, the definition and frequency of use of technical terms. For example, the analysis unit uses detailed technical terms when the user has technical expertise. Alternatively, the analysis unit can use concise and easy-to-understand terms when the user does not have technical expertise. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the environmental data. Examples of the importance include, but are not limited to, the impact and urgency of the data. Examples of ways to adjust the level of detail include, but are not limited to, the depth of the proposal and the level of detail of the information. For example, the suggestion unit provides a detailed proposal when the carbon dioxide concentration is high. The suggestion unit can also provide a detailed proposal when the humidity is high. The suggestion unit can also provide a detailed proposal when the temperature is high. By adjusting the level of detail of the proposal based on the importance of the environmental data, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input environmental data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of environmental data. Examples of categories include, but are not limited to, temperature data and humidity data. Examples of suggestion algorithms include, but are not limited to, recommendation systems and rule-based suggestions. For example, the suggestion unit applies a suggestion algorithm for ventilation measures to carbon dioxide concentration. The suggestion unit can also apply a suggestion algorithm for humidity adjustment measures to humidity. The suggestion unit can also apply a suggestion algorithm for temperature adjustment measures to temperature. By applying different suggestion algorithms depending on the category of environmental data, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input environmental data to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0049] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. Past suggestion results include, for example, the success rate of past suggestions and user feedback, but are not limited to these examples. Improving the accuracy of the suggestion can include, for example, improving the accuracy of data and algorithms, but are not limited to these examples. The suggestion unit, for example, optimizes the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion under specific environmental conditions based on the user's past suggestion results. The suggestion unit can also adjust the level of detail of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0050] When making a proposal, the suggestion unit may determine the priority of the proposal based on the collection time of the environmental data. Examples of the collection time include, but are not limited to, the season and the time of day. Examples of the priority include, but are not limited to, importance and urgency. For example, the suggestion unit may prioritize the most important proposal based on the latest environmental data. The suggestion unit may also supplement the current proposal with reference to past environmental data. The suggestion unit may also determine the priority based on data collected during a specific time period. This allows for more appropriate proposals to be provided by determining the priority of the proposal based on the collection time of the environmental data. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input collection time data to the generation AI and cause the generation AI to determine the priority of the proposals.

[0051] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the environmental data. Examples of relevance include, but are not limited to, data correlation and influence. Examples of ways to adjust the order include, but are not limited to, proposal priority and data importance. The suggestion unit can adjust the order of proposals by taking into account, for example, the relevance between carbon dioxide concentration and humidity. The suggestion unit can also adjust the order of proposals by taking into account the relevance between temperature and humidity. The suggestion unit can also adjust the order of proposals by taking into account the relevance between carbon dioxide concentration and temperature. By adjusting the order of proposals based on the relevance of the environmental data, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input environmental data to a generation AI and cause the generation AI to adjust the order of proposals.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past experience. Examples of adjusting the use of technical terms include, but are not limited to, the definition and frequency of use of technical terms. For example, the suggestion unit uses detailed technical terms when the user has technical expertise. Alternatively, the suggestion unit can use concise and easy-to-understand terms when the user does not have technical expertise. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This allows for more appropriate proposals to be provided by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

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

[0054] The collection unit can analyze the user's past behavioral patterns and determine the optimal timing for data collection. For example, if the user was active during a specific time period in the past, data collection can be performed during that time period. Also, if the user previously collected data during a specific event, data collection can be performed during that event. Furthermore, if the user previously collected data under specific environmental conditions, data collection can be performed under those environmental conditions. By analyzing the user's past behavioral patterns, the optimal data collection timing can be determined, enabling efficient data collection. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past behavioral pattern data into a generation AI and have the generation AI determine the optimal data collection timing.

[0055] The analysis unit can adjust the level of detail of the analysis based on the collection frequency of the environmental data. For example, a detailed analysis can be performed on frequently collected data, and a simplified analysis can be performed on infrequently collected data. A detailed analysis can also be performed on data collected during a specific event. A detailed analysis can also be performed on data collected during a specific time period. By adjusting the level of detail of the analysis based on the collection frequency of the environmental data, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collection frequency data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0056] The suggestion unit can analyze the user's past proposal history and select the optimal proposal method. For example, it can prioritize proposals that the user has accepted in the past. It can also avoid proposals that the user has rejected in the past. It can also prioritize proposals that the user has given a high rating to. This allows the optimal proposal method to be selected by analyzing the user's past proposal history, enabling efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past proposal history data into a generation AI and cause the generation AI to select the optimal proposal method.

[0057] The collection unit can prioritize collecting environmental data related to a specific region by taking into account the user's geographical location information. For example, if the user is in an urban area, environmental data for the urban area can be prioritized. Also, if the user is in a suburban area, environmental data for the suburban area can be prioritized. Furthermore, if the user is traveling, environmental data for the travel destination can be prioritized. In this way, environmental data related to a specific region can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to collect related environmental data.

[0058] The analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, if the user has expertise, it can provide detailed analysis results. If the user does not have expertise, it can provide concise and easy-to-understand analysis results. Furthermore, if the user has intermediate level expertise, it can provide analysis results with an appropriate level of detail. By adjusting the level of detail of the analysis results according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input expertise level data to the generation AI and cause the generation AI to adjust the level of detail of the analysis results.

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

[0060] Step 1: The collection unit collects environmental data. Examples of environmental data include, but are not limited to, temperature, humidity, and carbon dioxide concentration. The collection unit collects environmental data using, for example, a sensor. The collection unit can also set the frequency of data collection. For example, the collection unit can collect data at regular intervals. The collection unit can also collect data when a specific event occurs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, the algorithm used and the purpose of the analysis, but is not limited to these examples. For example, the analysis unit analyzes the data using a machine learning algorithm. The analysis unit can also analyze trends in the data. The analysis unit can also detect outliers. Step 3: The suggestion unit generates a suggestion based on the analysis results obtained by the analysis unit. The suggestion is made based on, for example, a suggestion format or criteria, but is not limited to such examples. For example, the suggestion unit generates a suggestion using a generation AI. The suggestion unit can also present specific adjustment suggestions to the user. The suggestion unit can also set a priority order for the suggestions. This allows the environmental analysis system according to the embodiment to provide an optimal living environment for the user. For example, the suggestion unit can generate a suggestion for ventilation measures when the carbon dioxide concentration exceeds a reference value. The suggestion unit can also generate a suggestion for humidity adjustment measures when the humidity exceeds a reference value. The suggestion unit can also generate a suggestion for temperature adjustment measures when the temperature exceeds a reference value. This allows the user to maintain a comfortable living environment.

[0061] (Example 2) A system according to an embodiment of the present invention is an AI-powered system that provides users with an optimal living environment based on various environmental data. This system uses a generating AI to collect data such as carbon dioxide concentration, humidity, and temperature, and analyzes each user's environment based on that data. It then uses that information to present specific adjustment suggestions to optimize the user's living and working environment. For example, the generating AI collects environmental data such as carbon dioxide concentration, humidity, and temperature. For example, if the carbon dioxide concentration in a room is too high, the generating AI suggests opening a window or installing a ventilation system. In addition, in situations where the room is humid, the generating AI recommends using a dehumidifier or ventilating at an appropriate time. Furthermore, in high-temperature environments, the generating AI suggests adjusting the air conditioner's temperature setting or installing insulation. In this way, the generating AI provides reasonable suggestions based on the collected data. This allows the system to provide users with a comfortable living environment. For example, by following the adjustment suggestions proposed by the generating AI, users can sleep comfortably. Furthermore, maintaining appropriate humidity and temperature contributes to a healthy lifestyle. Furthermore, because the generating AI provides reasonable suggestions based on numerical values, users can follow the suggestions with confidence.

[0062] The environmental analysis system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects environmental data. The environmental data includes, but is not limited to, temperature, humidity, and carbon dioxide concentration. The collection unit collects the environmental data using, for example, a sensor. The collection unit can also set the frequency of data collection. For example, the collection unit can collect data at regular intervals. The collection unit can also collect data when a specific event occurs. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, but not limited to, an algorithm used and a purpose of the analysis. For example, the analysis unit can analyze data using a machine learning algorithm. The analysis unit can also analyze data trends. The analysis unit can also detect outliers. The proposal unit generates proposals based on the analysis results obtained by the analysis unit. The proposals are performed based on, for example, but not limited to, a proposal format and criteria. For example, the proposal unit generates proposals using a generation AI. The proposal unit can also present specific adjustment proposals to the user. The proposal unit can also set a priority order for the proposals. As a result, the environmental analysis system according to the embodiment can provide an optimal living environment to the user. For example, the suggestion unit can generate a suggestion for ventilation measures when the carbon dioxide concentration exceeds a reference value. Furthermore, the suggestion unit can generate a suggestion for humidity adjustment measures when the humidity exceeds a reference value. Furthermore, the suggestion unit can generate a suggestion for temperature adjustment measures when the temperature exceeds a reference value. As a result, the user can maintain a comfortable living environment.

[0063] The suggestion unit can generate a ventilation method suggestion when the carbon dioxide concentration exceeds a reference value. The reference value includes, for example, a specific threshold value for carbon dioxide concentration, but is not limited to such an example. The suggestion unit can, for example, suggest opening a window. The suggestion unit can also suggest using a ventilation fan. The suggestion unit can also suggest installing a ventilation system. This can improve the user's living environment by suggesting appropriate ventilation methods when the carbon dioxide concentration is high. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input carbon dioxide concentration data into the generation AI and cause the generation AI to suggest ventilation methods.

[0064] The suggestion unit can generate a suggestion for a humidity adjustment method when the humidity exceeds a reference value. The reference value includes, for example, a specific humidity threshold value, but is not limited to such an example. The suggestion unit can, for example, suggest the use of a dehumidifier. The suggestion unit can also suggest ventilating at an appropriate time. The suggestion unit can also suggest the use of a humidifier. This makes it possible to improve the user's living environment by suggesting an appropriate humidity adjustment method when humidity is high. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input humidity data into the generation AI and cause the generation AI to suggest a humidity adjustment method.

[0065] The suggestion unit can generate a suggestion for a temperature adjustment measure when the temperature exceeds a reference value. The reference value includes, for example, a specific temperature threshold, but is not limited to such an example. The suggestion unit can, for example, suggest reviewing the set temperature of an air conditioner. The suggestion unit can also suggest installing insulation. The suggestion unit can also suggest using a fan heater. This can improve the user's living environment by suggesting an appropriate temperature adjustment measure when the temperature is high. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input temperature data into the generation AI and cause the generation AI to execute a suggestion for a temperature adjustment measure.

[0066] The collection unit can collect environmental data such as carbon dioxide concentration, humidity, and temperature. Examples of environmental data include, but are not limited to, temperature, humidity, and carbon dioxide concentration. The collection unit collects environmental data using, for example, a sensor. The collection unit can also set the frequency of data collection. For example, the collection unit can collect data at regular intervals. The collection unit can also collect data when a specific event occurs. By collecting environmental data, the user's living environment can be analyzed and optimal suggestions can be made. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input environmental data acquired by a sensor into a generation AI and have the generation AI analyze the data.

[0067] The analysis unit can analyze the collected environmental data and generate information for indicating an optimal living environment for the user. An optimal living environment includes, but is not limited to, a comfortable temperature range, an appropriate humidity level, and the like. The analysis unit can analyze the data using, for example, a machine learning algorithm. The analysis unit can also analyze trends in the data. The analysis unit can also detect outliers. By analyzing the collected environmental data, information for providing an optimal living environment for the user can be generated. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected environmental data to a generation AI and have the generation AI analyze the data.

[0068] The collection unit can estimate the user's emotions and adjust the timing of collecting environmental data based on the estimated user emotions. Technologies such as, but not limited to, facial expression recognition and voice analysis are used to estimate emotions. Examples of collection timing include, but are not limited to, at regular intervals or when a specific event occurs. For example, when the user is relaxed, the collection unit can set the collection frequency low so as not to interfere with the user's activities. Furthermore, when the user is stressed, the collection unit can set the collection frequency high so as to quickly suggest environmental improvements. Furthermore, when the user is sleeping, the collection unit can set the collection frequency low so as not to disturb the user's sleep. This enables more appropriate data collection by adjusting the timing of collecting environmental data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotional data into the generation AI and have the generation AI adjust the collection timing.

[0069] The collection unit can analyze the user's past environmental data collection history and select the optimal collection method. The collection history includes, for example, but is not limited to, the date and time of past data collection and the type of collected data. The optimal collection method includes, for example, but is not limited to, the placement of sensors and the frequency of data collection. For example, the collection unit prioritizes collecting data that the user collected frequently in the past. The collection unit can also increase the collection frequency during specific time periods based on the user's past collection history. The collection unit can also optimize the collection method under specific environmental conditions based on the user's past collection history. This allows the user's past collection history to be analyzed to select the optimal collection method and enable efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input past collection history data into a generation AI and have the generation AI select the optimal collection method.

[0070] When collecting environmental data, the collection unit can filter the environmental data based on the user's current activity status and lifestyle rhythm. Examples of activity status include, but are not limited to, when exercising or resting. Examples of lifestyle rhythm include, but are not limited to, when sleeping and eating. For example, when the user is working, the collection unit filters the collected data and prioritizes work-related data. Furthermore, when the user is exercising, the collection unit can prioritize collecting environmental data related to exercise. Furthermore, when the user is resting, the collection unit can prioritize collecting environmental data related to rest. By filtering data based on the user's activity status and lifestyle rhythm, more relevant data can be collected. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's activity status data to a generation AI and have the generation AI perform filtering.

[0071] When collecting environmental data, the collection unit can select the optimal collection means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of collection means include, but are not limited to, the type of sensor and the data collection method. For example, when the user uses voice input, the collection unit prioritizes collecting voice data. Furthermore, when the user uses text input, the collection unit can also prioritize collecting text data. Furthermore, when the user uses image input, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0072] The collection unit can estimate the user's emotions and determine the priority of environmental data to be collected based on the estimated user emotions. Technologies such as, but not limited to, facial expression recognition and voice analysis can be used to estimate emotions. Priorities can include, but are not limited to, importance and urgency. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to stress reduction. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data for maintaining comfort. Furthermore, if the user is tired, the collection unit can prioritize collecting data related to rest. This enables more appropriate data collection by determining the priority of data to be collected based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI determine the priority of the collected data.

[0073] When collecting environmental data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of highly relevant data include, but are not limited to, environmental data related to the user's current location. For example, when the user is at home, the collection unit prioritizes collecting home environmental data. Also, when the user is in the office, the collection unit can prioritize collecting office environmental data. Also, when the user is out, the collection unit can prioritize collecting destination environmental data. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0074] When collecting environmental data, the collection unit may analyze the user's social media activity and collect related data. Social media activity may include, but is not limited to, for example, the content of posts and the number of likes. Related data may include, but is not limited to, environmental data related to the content of social media posts. For example, the collection unit may collect environmental data of locations where the user has checked in on social media. The collection unit may also analyze the content of the user's social media posts and collect related environmental data. The collection unit may also collect related environmental data by referring to the activities of the user's friends on social media. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0075] When collecting environmental data, the collection unit can customize the collection method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, the user's opinions and past evaluations. Customizing the collection method can include, but are not limited to, sensor placement and data collection frequency. The collection unit, for example, optimizes the collection method based on feedback provided by the user in the past. The collection unit can also prioritize collection of specific environmental data based on the user's past feedback. The collection unit can also adjust the collection frequency based on the user's past feedback. This allows the collection method to be optimized by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. Techniques such as, but not limited to, facial expression recognition and voice analysis are used to estimate emotions. Examples of techniques for adjusting the presentation method include, but are not limited to, wording and level of detail. For example, the analysis unit can provide detailed analysis results when the user is relaxed. For example, the analysis unit can provide concise and concise analysis results when the user is stressed. For example, the analysis unit can provide analysis results in a format that is easy to understand when the user is in a hurry. By adjusting the presentation method of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the environmental data. The importance level includes, but is not limited to, the impact and urgency of the data. Adjustments to the level of detail can include, but are not limited to, the depth of the analysis and the level of detail of the information. For example, when the carbon dioxide concentration is high, the analysis unit performs a detailed analysis and proposes specific countermeasures. Furthermore, when the humidity is high, the analysis unit can perform a detailed analysis and propose appropriate humidity adjustment measures. Furthermore, when the temperature is high, the analysis unit can perform a detailed analysis and propose temperature adjustment measures. By adjusting the level of detail of the analysis based on the importance of the environmental data, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input environmental data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of environmental data. Examples of categories include, but are not limited to, temperature data and humidity data. Examples of analysis algorithms include, but are not limited to, regression analysis and clustering. For example, the analysis unit applies an air quality analysis algorithm to carbon dioxide concentration. The analysis unit can also apply a humidity analysis algorithm to humidity. The analysis unit can also apply a temperature analysis algorithm to temperature. By applying different analysis algorithms depending on the category of environmental data, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input environmental data to a generation AI and cause the generation AI to apply an analysis algorithm.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, for example, past data patterns and evaluations of analysis results, but are not limited to these examples. Improving the analysis accuracy can include, for example, improving data accuracy and algorithms, but are not limited to these examples. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the analysis accuracy under specific environmental conditions based on the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. This allows the analysis accuracy to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Emotion estimation can be achieved using, for example, facial expression recognition or voice analysis, but is not limited to these examples. Adjusting the length of the analysis can be achieved by, for example, determining the level of detail in the analysis or the amount of information provided, but is not limited to these examples. For example, the analysis unit can provide detailed analysis results when the user is relaxed. For example, the analysis unit can provide concise, concise analysis results when the user is stressed. For example, the analysis unit can provide analysis results in a format that is easy to understand when the user is in a hurry. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis based on the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the analysis.

[0081] During analysis, the analysis unit can determine analysis priorities based on the collection time of the environmental data. Examples of collection time include, but are not limited to, seasons and time periods. Examples of priorities include, but are not limited to, importance and urgency. For example, the analysis unit prioritizes analysis of the most recent environmental data. The analysis unit can also complement current analysis results by referring to past environmental data. The analysis unit can also prioritize analysis of data collected during a specific time period. This allows for more appropriate analysis results to be provided by determining analysis priorities based on the collection time of the environmental data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input collection time data to the generation AI and have the generation AI determine the analysis priorities.

[0082] During analysis, the analysis unit can adjust the analysis order based on the relevance of the environmental data. Relevance includes, for example, but is not limited to, data correlation and influence. Adjusting the order can include, for example, but is not limited to, analysis priority and data importance. The analysis unit adjusts the analysis order, for example, taking into account the relevance between carbon dioxide concentration and humidity. The analysis unit can also adjust the analysis order taking into account the relevance between temperature and humidity. The analysis unit can also adjust the analysis order taking into account the relevance between carbon dioxide concentration and temperature. By adjusting the analysis order based on the relevance of the environmental data, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input environmental data to a generation AI and have the generation AI adjust the analysis order.

[0083] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past experience. Examples of adjusting the use of technical terms include, but are not limited to, the definition and frequency of use of technical terms. For example, the analysis unit uses detailed technical terms when the user has technical expertise. Alternatively, the analysis unit can use concise and easy-to-understand terms when the user does not have technical expertise. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0084] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. Techniques such as, but not limited to, facial expression recognition and voice analysis are used to estimate emotions. Techniques for adjusting the way suggestions are expressed include, but are not limited to, wording and level of detail. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is stressed, the suggestion unit can provide concise and concise suggestions. For example, if the user is in a hurry, the suggestion unit can provide suggestions in a format that can be quickly understood. By adjusting the way suggestions are expressed based on the user's emotions, more appropriate suggestions can be provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the suggestion is expressed.

[0085] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the environmental data. Examples of the importance include, but are not limited to, the impact and urgency of the data. Examples of ways to adjust the level of detail include, but are not limited to, the depth of the proposal and the level of detail of the information. For example, the suggestion unit provides a detailed proposal when the carbon dioxide concentration is high. The suggestion unit can also provide a detailed proposal when the humidity is high. The suggestion unit can also provide a detailed proposal when the temperature is high. By adjusting the level of detail of the proposal based on the importance of the environmental data, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input environmental data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0086] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of environmental data. Examples of categories include, but are not limited to, temperature data and humidity data. Examples of suggestion algorithms include, but are not limited to, recommendation systems and rule-based suggestions. For example, the suggestion unit applies a suggestion algorithm for ventilation measures to carbon dioxide concentration. The suggestion unit can also apply a suggestion algorithm for humidity adjustment measures to humidity. The suggestion unit can also apply a suggestion algorithm for temperature adjustment measures to temperature. By applying different suggestion algorithms depending on the category of environmental data, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input environmental data to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0087] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. Past suggestion results include, for example, the success rate of past suggestions and user feedback, but are not limited to these examples. Improving the accuracy of the suggestion can include, for example, improving the accuracy of data and algorithms, but are not limited to these examples. The suggestion unit, for example, optimizes the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion under specific environmental conditions based on the user's past suggestion results. The suggestion unit can also adjust the level of detail of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0088] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. Techniques such as, but not limited to, facial expression recognition and voice analysis can be used to estimate the emotion. Techniques for adjusting the length of the suggestion can include, but are not limited to, the level of detail and the amount of information in the suggestion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise and to-the-point suggestions when the user is stressed. The suggestion unit can also provide suggestions in a format that is quickly understandable when the user is in a hurry. This allows for more appropriate suggestions to be provided by adjusting the length of the suggestion based on the user's emotion. The emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0089] When making a proposal, the suggestion unit may determine the priority of the proposal based on the collection time of the environmental data. Examples of the collection time include, but are not limited to, the season and the time of day. Examples of the priority include, but are not limited to, importance and urgency. For example, the suggestion unit may prioritize the most important proposal based on the latest environmental data. The suggestion unit may also supplement the current proposal with reference to past environmental data. The suggestion unit may also determine the priority based on data collected during a specific time period. This allows for more appropriate proposals to be provided by determining the priority of the proposal based on the collection time of the environmental data. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input collection time data to the generation AI and cause the generation AI to determine the priority of the proposals.

[0090] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the environmental data. Examples of relevance include, but are not limited to, data correlation and influence. Examples of ways to adjust the order include, but are not limited to, proposal priority and data importance. The suggestion unit can adjust the order of proposals by taking into account, for example, the relevance between carbon dioxide concentration and humidity. The suggestion unit can also adjust the order of proposals by taking into account the relevance between temperature and humidity. The suggestion unit can also adjust the order of proposals by taking into account the relevance between carbon dioxide concentration and temperature. By adjusting the order of proposals based on the relevance of the environmental data, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input environmental data to a generation AI and cause the generation AI to adjust the order of proposals.

[0091] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past experience. Examples of adjusting the use of technical terms include, but are not limited to, the definition and frequency of use of technical terms. For example, the suggestion unit uses detailed technical terms when the user has technical expertise. Alternatively, the suggestion unit can use concise and easy-to-understand terms when the user does not have technical expertise. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This allows for more appropriate proposals to be provided by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect environmental data using a sensor of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates specific adjustment suggestions for the user based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect environmental data using a sensor of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates specific adjustment suggestions for the user based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect environmental data using a sensor of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates specific adjustment suggestions for the user based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect environmental data using a sensor of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a specific adjustment proposal for the user based on the analysis result. The suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

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

[0093] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, data related to stress reduction can be analyzed preferentially. Furthermore, if the user is relaxed, data for maintaining comfort can be analyzed preferentially. Furthermore, if the user is tired, data related to rest can be analyzed preferentially. By determining the analysis priority based on the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priority.

[0094] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion timing can be delayed. Also, if the user is stressed, the suggestion timing can be made quickly. Furthermore, if the user is concentrating, the suggestion timing can be adjusted so as not to disturb the user's concentration. In this way, by adjusting the suggestion timing based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of suggestions.

[0095] The collection unit can estimate the user's emotions and determine the type of data to collect based on the estimated user's emotions. For example, if the user is feeling stressed, data related to stress reduction can be collected preferentially. Furthermore, if the user is relaxed, data for maintaining comfort can be collected preferentially. Furthermore, if the user is tired, data related to rest can be collected preferentially. This enables more appropriate data collection by determining the type of data to collect based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the type of data to collect.

[0096] The analysis unit can estimate the user's emotions and adjust the analysis feedback method based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is stressed, concise and to the point feedback can be provided. Furthermore, if the user is in a hurry, feedback can be provided in a format that is quickly understandable. This allows for more appropriate feedback to be provided by adjusting the analysis feedback method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the feedback method.

[0097] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated user emotions. For example, if the user is relaxed, detailed suggestions can be provided. If the user is stressed, concise and to the point suggestions can be provided. Furthermore, if the user is in a hurry, suggestions can be provided in a format that is quickly understandable. This allows for adjusting the content of the suggestions based on the user's emotions to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content of the suggestions.

[0098] The collection unit can analyze the user's past behavioral patterns and determine the optimal timing for data collection. For example, if the user was active during a specific time period in the past, data collection can be performed during that time period. Also, if the user previously collected data during a specific event, data collection can be performed during that event. Furthermore, if the user previously collected data under specific environmental conditions, data collection can be performed under those environmental conditions. By analyzing the user's past behavioral patterns, the optimal data collection timing can be determined, enabling efficient data collection. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past behavioral pattern data into a generation AI and have the generation AI determine the optimal data collection timing.

[0099] The analysis unit can adjust the level of detail of the analysis based on the collection frequency of the environmental data. For example, a detailed analysis can be performed on frequently collected data, and a simplified analysis can be performed on infrequently collected data. A detailed analysis can also be performed on data collected during a specific event. A detailed analysis can also be performed on data collected during a specific time period. By adjusting the level of detail of the analysis based on the collection frequency of the environmental data, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collection frequency data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0100] The suggestion unit can analyze the user's past proposal history and select the optimal proposal method. For example, it can prioritize proposals that the user has accepted in the past. It can also avoid proposals that the user has rejected in the past. It can also prioritize proposals that the user has given a high rating to. This allows the optimal proposal method to be selected by analyzing the user's past proposal history, enabling efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past proposal history data into a generation AI and cause the generation AI to select the optimal proposal method.

[0101] The collection unit can prioritize collecting environmental data related to a specific region by taking into account the user's geographical location information. For example, if the user is in an urban area, environmental data for the urban area can be prioritized. Also, if the user is in a suburban area, environmental data for the suburban area can be prioritized. Furthermore, if the user is traveling, environmental data for the travel destination can be prioritized. In this way, environmental data related to a specific region can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to collect related environmental data.

[0102] The analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, if the user has expertise, it can provide detailed analysis results. If the user does not have expertise, it can provide concise and easy-to-understand analysis results. Furthermore, if the user has intermediate level expertise, it can provide analysis results with an appropriate level of detail. By adjusting the level of detail of the analysis results according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input expertise level data to the generation AI and cause the generation AI to adjust the level of detail of the analysis results.

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

[0104] Step 1: The collection unit collects environmental data. Examples of environmental data include, but are not limited to, temperature, humidity, and carbon dioxide concentration. The collection unit collects environmental data using, for example, a sensor. The collection unit can also set the frequency of data collection. For example, the collection unit can collect data at regular intervals. The collection unit can also collect data when a specific event occurs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, the algorithm used and the purpose of the analysis, but is not limited to these examples. For example, the analysis unit analyzes the data using a machine learning algorithm. The analysis unit can also analyze trends in the data. The analysis unit can also detect outliers. Step 3: The suggestion unit generates a suggestion based on the analysis results obtained by the analysis unit. The suggestion is made based on, for example, a suggestion format or criteria, but is not limited to such examples. For example, the suggestion unit generates a suggestion using a generation AI. The suggestion unit can also present specific adjustment suggestions to the user. The suggestion unit can also set a priority order for the suggestions. This allows the environmental analysis system according to the embodiment to provide an optimal living environment for the user. For example, the suggestion unit can generate a suggestion for ventilation measures when the carbon dioxide concentration exceeds a reference value. The suggestion unit can also generate a suggestion for humidity adjustment measures when the humidity exceeds a reference value. The suggestion unit can also generate a suggestion for temperature adjustment measures when the temperature exceeds a reference value. This allows the user to maintain a comfortable living environment.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

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

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

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

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

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

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

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

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

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

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

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

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

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

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

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

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

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

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

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

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

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

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

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

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

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

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

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

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

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

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

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

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

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

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

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

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

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

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

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

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

[0176] [Explanation of symbols]

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

Claims

1. a collection unit that collects environmental data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that generates a proposal based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. The proposal unit Generate ventilation suggestions when carbon dioxide levels exceed thresholds 2. The system of claim 1.

3. The proposal unit Generate humidity control suggestions when humidity exceeds a threshold 2. The system of claim 1.

4. The proposal unit Generates temperature adjustment suggestions when temperatures exceed thresholds 2. The system of claim 1.

5. The collecting unit Collect environmental data on carbon dioxide concentration, humidity, and temperature 2. The system of claim 1.

6. The analysis unit Analyze collected environmental data and generate information to show users the optimal living environment 2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting environmental data based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past environmental data collection history and select the optimal collection method 2. The system of claim 1.

9. The collecting unit When collecting environmental data, filtering is performed based on the user's current activity status and lifestyle.

2. The system of claim 1.

10. The collecting unit When collecting environmental data, select the optimal collection method according to the user's input method.

2. The system of claim 1.

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