system

Abstract

The system according to the embodiment aims to automatically change the air conditioning settings based on the speaker's location. [Solution] A system according to an embodiment includes a voice recognition unit, a position identification unit, an air conditioning control unit, and a security unit. The voice recognition unit recognizes voice. The position identification unit identifies the position of a speaker based on voice data detected by the voice recognition unit. The air conditioning control unit changes air conditioning settings based on the position information identified by the position identification unit. The security unit encrypts the voice data or anonymizes the position information.

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 technology has the problem that it is not possible to automatically change air conditioning settings based on the speaker's position, making it difficult to adjust the temperature to a comfortable level.

[0005] The system according to the embodiment aims to automatically change the air conditioning settings based on the speaker's location. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice recognition unit, a position identification unit, an air conditioning control unit, and a security unit. The voice recognition unit recognizes voice. The position identification unit identifies the position of the speaker based on voice data detected by the voice recognition unit. The air conditioning control unit changes the air conditioning settings based on the position information identified by the position identification unit. The security unit encrypts the voice data or anonymizes the position information. [Effects of the Invention]

[0007] Embodiments of the system can automatically change air conditioning settings based on the speaker's location. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An air conditioning control system according to an embodiment of the present invention uses sensors installed in a company to listen for voices such as "cold" or "hot," identify the speaker's location, and automatically adjust the air conditioning settings to a comfortable temperature. When an employee utters "cold" or "hot," the system detects the voice, identifies the speaker's location, and automatically adjusts the air conditioning settings based on that location. For example, if an employee utters "cold," the air conditioning setting in that area is changed to a warmer temperature. Conversely, if an employee utters "hot," the air conditioning setting is changed to a cooler temperature. This mechanism allows employees to easily adjust the air conditioning settings according to their own perceived temperature. For example, if an employee feels "cold" in a conference room, they can simply say "cold" and the air conditioning will be automatically adjusted. This improves employee comfort and improves work efficiency. The air conditioning control system thus improves employee comfort and work efficiency.

[0029] An air conditioning control system according to an embodiment includes a voice recognition unit, a location determination unit, an air conditioning control unit, and a security unit. The voice recognition unit detects speech such as "cold" or "hot" spoken by an employee. The voice recognition unit, for example, has a function for detecting specific keywords. The voice recognition unit, for example, uses a voice recognition algorithm to analyze the content of the speech. The voice recognition unit may also have a function for understanding context. For example, the voice recognition unit may analyze the context of the speech using natural language processing technology. The location determination unit determines the location of the speaker based on the voice data detected by the voice recognition unit. The location determination unit, for example, uses Wi-Fi connection information to determine the location of the speaker. The location determination unit can also determine the location of the speaker using, for example, a Bluetooth (registered product) beacon or RFID. The air conditioning control unit changes the air conditioning settings based on the location information determined by the location determination unit. The air conditioning control unit, for example, has an algorithm for estimating the optimal temperature based on the user's past setting history. The air conditioning control unit can also set the optimal temperature by integrating temperature sensor information, for example. The security unit encrypts voice data or anonymizes location information. The security unit, for example, encrypts voice data. The security unit can also anonymize location information, for example. This allows the air conditioning control system according to the embodiment to improve employee comfort.

[0030] The speech recognition unit may have a function for detecting specific keywords. The speech recognition unit uses, for example, a keyword list to detect specific keywords. For example, the speech recognition unit registers keywords such as "cold" and "hot" in a list and detects these keywords when they are spoken. The speech recognition unit can also detect specific keywords using speech pattern matching technology. For example, the speech recognition unit analyzes the pattern of spoken speech and determines whether it matches a keyword. This allows the detection of specific keywords, thereby improving the accuracy of speech recognition.

[0031] The speech recognition unit may have a function for understanding context. For example, the speech recognition unit uses natural language processing technology to understand the context. For example, the speech recognition unit analyzes the context of the speech content to understand the context. The speech recognition unit may also use a context analysis algorithm to understand the intention of the speech. For example, the speech recognition unit analyzes the context of the speech and infers the intention of the speaker. This understanding of the context improves the accuracy of speech recognition.

[0032] The location determination unit can determine the location of the speaker using Wi-Fi connection information. The location determination unit determines the location of the speaker using, for example, Wi-Fi connection information. For example, the location determination unit analyzes the SSID and signal strength of the Wi-Fi to which the speaker's device is connected to determine the speaker's location. The location determination unit can also determine the speaker's location using Wi-Fi connection history. For example, the location determination unit estimates the speaker's current location based on information about Wi-Fi to which the speaker's device has previously connected. As a result, using the Wi-Fi connection information improves the accuracy of determining the speaker's location.

[0033] The location identification unit can identify the location of the speaker using a Bluetooth beacon or RFID. The location identification unit identifies the location of the speaker using, for example, a Bluetooth beacon. For example, the location identification unit analyzes the signal strength of a Bluetooth beacon received by the speaker's device to identify the speaker's location. The location identification unit can also identify the location of the speaker using RFID. For example, the location identification unit identifies the speaker's location based on information from an RFID tag carried by the speaker. As a result, using a Bluetooth beacon or RFID improves the accuracy of identifying the speaker's location.

[0034] The air conditioning control unit can be equipped with an algorithm that estimates an appropriate temperature based on the user's past setting history. The air conditioning control unit, for example, is equipped with an algorithm that estimates an appropriate temperature based on the user's past setting history. For example, the air conditioning control unit analyzes historical data of temperatures previously set by the user and estimates the optimal temperature for the current situation. The air conditioning control unit can also estimate a temperature according to a specific time period or situation based on the user's setting history. For example, the air conditioning control unit estimates the optimal temperature for the current time period based on the temperature previously set by the user for a specific time period. This improves user comfort by estimating the optimal temperature based on the past setting history.

[0035] The security unit can encrypt the audio data. For example, the security unit encrypts the audio data using an encryption algorithm such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). The security unit can also encrypt the audio data when it is saved or transmitted. For example, the security unit encrypts the audio data when it is saved to prevent it from leaking to the outside. In this way, data security is improved by encrypting the audio data.

[0036] The security unit can anonymize the location information. For example, the security unit anonymizes the location information. For example, the security unit uses a technique for blurring the location information to prevent the speaker's exact location from being identified. The security unit can also generate pseudo-location information to anonymize the speaker's location. For example, the security unit randomly changes the speaker's location information to prevent the speaker's exact location from being identified. In this way, privacy is protected by anonymizing the location information.

[0037] The speech recognition unit can add a filtering function that automatically removes background noise during speech recognition. The speech recognition unit uses, for example, a noise filtering algorithm to automatically remove background noise. For example, the speech recognition unit analyzes noise in an office in real time and removes it during speech recognition. The speech recognition unit can also filter noise during a meeting and recognize only the speaker's voice. For example, the speech recognition unit can remove the operating sounds of an air conditioner or printer to clearly recognize speech. This removes background noise and improves the accuracy of speech recognition.

[0038] The speech recognition unit can add a function to analyze the tone and strength of the speaker's voice during speech recognition to determine the urgency of the speech. The speech recognition unit uses, for example, a speech analysis algorithm to analyze the tone and strength of the speaker's voice. For example, if the speaker's voice is high-pitched and strong, the speech recognition unit may determine the urgency to be high and take immediate action. Alternatively, if the speaker's voice is low and calm, the speech recognition unit may determine the urgency to be low and take a normal action. For example, if the speaker's tone of voice changes, the speech recognition unit may reevaluate the urgency. This allows for a prompt response by determining the urgency of the speech.

[0039] The speech recognition unit can be added with a function for switching the recognition algorithm depending on the language or dialect of the speaker during speech recognition. The speech recognition unit uses, for example, a language model or a dialect dictionary to switch the recognition algorithm depending on the language or dialect of the speaker. For example, the speech recognition unit switches to an English recognition algorithm when the speaker speaks English. The speech recognition unit can also switch to a Kansai dialect recognition algorithm when the speaker speaks Kansai dialect. Furthermore, the speech recognition unit can switch to an appropriate algorithm using an automatic language detection function when the speaker speaks multiple languages. For example, the speech recognition unit automatically detects the language of the speaker and switches to an appropriate recognition algorithm. This improves recognition accuracy by switching the recognition algorithm depending on the language or dialect of the speaker.

[0040] The speech recognition unit can add a function to improve recognition accuracy by referring to the speaker's past speech history during speech recognition. The speech recognition unit, for example, stores and analyzes history data to refer to the speaker's past speech history. For example, the speech recognition unit can improve recognition accuracy based on keywords used by the speaker in the past. The speech recognition unit can also analyze the speaker's past speech patterns and optimize the recognition algorithm. Furthermore, the speech recognition unit can preferentially recognize specific phrases from the speaker's past speech history. For example, the speech recognition unit preferentially recognizes phrases that the speaker has frequently used in the past. In this way, recognition accuracy is improved by referring to the speaker's past speech history.

[0041] The location determination unit may add a function for integrating multiple sensor information to improve the accuracy of location determination during location determination. The location determination unit may use, for example, an integration algorithm to integrate multiple sensor information. For example, the location determination unit may integrate Wi-Fi connection information and Bluetooth beacon information to improve the accuracy of location determination. The location determination unit may also integrate RFID tag and audio sensor information to improve the accuracy of location determination. Furthermore, the location determination unit may also integrate GPS information and indoor sensor information to improve the accuracy of location determination. For example, the location determination unit may integrate data obtained from multiple sensors to more accurately identify the speaker's location. In this way, the accuracy of location determination is improved by integrating multiple sensor information.

[0042] The position identification unit can add a function of analyzing the movement pattern of the speaker at the time of position identification to improve the prediction accuracy of position identification. The position identification unit uses, for example, a pattern recognition algorithm to analyze the movement pattern of the speaker. For example, the position identification unit analyzes the speaker's past movement pattern to improve the prediction accuracy of position identification. The position identification unit can also improve the prediction accuracy of position identification by taking into account the speaker's current movement speed. Furthermore, the position identification unit can analyze the movement direction of the speaker to improve the prediction accuracy of position identification. For example, the position identification unit predicts the speaker's current location based on the speaker's movement history. In this way, the prediction accuracy of position identification is improved by analyzing the speaker's movement pattern.

[0043] The location determination unit can add a function to improve the accuracy of location determination by referring to the speaker's device information when determining the speaker's location. The location determination unit uses, for example, a device ID or connection history to refer to the speaker's device information. For example, the location determination unit can improve the accuracy of location determination by referring to the Wi-Fi connection information of the speaker's smartphone. The location determination unit can also improve the accuracy of location determination by referring to the speaker's Bluetooth device information. Furthermore, the location determination unit can improve the accuracy of location determination by referring to the connection information of the speaker's business terminal. For example, the location determination unit determines the speaker's location based on information about the Wi-Fi or Bluetooth to which the speaker's device is connected. In this way, the accuracy of location determination is improved by referring to the speaker's device information.

[0044] The location identification unit can add a function to improve the accuracy of location identification by referring to the speaker's past location history when identifying the speaker's location. The location identification unit, for example, stores and analyzes history data to refer to the speaker's past location history. For example, the location identification unit identifies the speaker's current location based on the speaker's past location history. The location identification unit can also increase the possibility that the speaker is in a specific area based on the speaker's past location history. Furthermore, the location identification unit can analyze the speaker's past location history to improve the accuracy of location identification. For example, the location identification unit estimates the speaker's current location based on places that the speaker has frequently visited in the past. In this way, the accuracy of location identification is improved by referring to the speaker's past location history.

[0045] The air conditioning control unit can add a function to integrate indoor temperature sensor information and set an appropriate temperature when controlling the air conditioning. For example, the air conditioning control unit uses sensor placement and data acquisition methods to integrate indoor temperature sensor information. For example, the air conditioning control unit sets an optimal temperature based on indoor temperature sensor information. The air conditioning control unit can also integrate information from multiple indoor temperature sensors to set a uniform temperature. Furthermore, the air conditioning control unit can update indoor temperature sensor information in real time to maintain an optimal temperature. For example, the air conditioning control unit monitors the current temperature in real time based on indoor temperature sensor information and adjusts the temperature setting as necessary. In this way, the optimal temperature can be set by integrating indoor temperature sensor information.

[0046] The air conditioning control unit can add a function to estimate an appropriate temperature by referring to the speaker's past temperature setting history when controlling the air conditioning. The air conditioning control unit, for example, saves and analyzes history data to refer to the speaker's past temperature setting history. For example, the air conditioning control unit estimates the optimal temperature for the current situation based on the speaker's past temperature setting history. The air conditioning control unit can also analyze the speaker's past temperature setting patterns to estimate the optimal temperature. Furthermore, the air conditioning control unit can estimate the optimal temperature for a specific time period based on the speaker's past temperature setting history. For example, the air conditioning control unit estimates the optimal temperature for the current time period based on the temperature the speaker set for a specific time period in the past. In this way, the optimal temperature can be estimated by referring to the speaker's past temperature setting history.

[0047] The air conditioning control unit can add a function to integrate indoor humidity sensor information and set an appropriate humidity level during air conditioning control. The air conditioning control unit uses, for example, sensor placement and data acquisition methods to integrate indoor humidity sensor information. For example, the air conditioning control unit sets an optimal humidity level based on indoor humidity sensor information. The air conditioning control unit can also integrate information from multiple indoor humidity sensors to set a uniform humidity level. Furthermore, the air conditioning control unit can update indoor humidity sensor information in real time to maintain an optimal humidity level. For example, the air conditioning control unit monitors the current humidity level in real time based on indoor humidity sensor information and adjusts the humidity setting as necessary. In this way, the optimal humidity level can be set by integrating indoor humidity sensor information.

[0048] The air conditioning control unit can add a function to set an appropriate temperature by referring to the speaker's current activity status when controlling the air conditioning. The air conditioning control unit, for example, acquires and analyzes activity data to refer to the speaker's current activity status. For example, the air conditioning control unit sets a moderate temperature when the speaker is doing desk work. The air conditioning control unit can also set a comfortable temperature when the speaker is in a meeting. Furthermore, the air conditioning control unit can set a cool temperature when the speaker is exercising. For example, the air conditioning control unit adjusts the current temperature based on the speaker's activity status. In this way, the optimal temperature can be set by referring to the speaker's current activity status.

[0049] The security unit can add a real-time encryption function for audio data. The security unit uses, for example, an encryption algorithm to perform real-time encryption of audio data. For example, the security unit encrypts audio data in real time to prevent leakage to the outside. The security unit can also encrypt audio data to strengthen security during storage. Furthermore, the security unit can encrypt audio data to strengthen security during communication. For example, the security unit encrypts audio data using an encryption algorithm such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). This improves data security by performing real-time encryption of audio data.

[0050] The security unit can add a real-time anonymization function for location information. The security unit, for example, uses an anonymization algorithm to perform real-time anonymization of location information. For example, the security unit anonymizes location information in real time to prevent leakage to the outside. The security unit can also anonymize location information to strengthen security during storage. Furthermore, the security unit can anonymize location information to strengthen security during communication. For example, the security unit anonymizes location information using a location information blurring technique or generation of pseudo location information. In this way, privacy is protected by performing real-time anonymization of location information.

[0051] The security unit can add a function to dynamically adjust the storage period for audio data. The security unit, for example, uses storage period setting criteria and the importance of the data to dynamically adjust the storage period for audio data. For example, the security unit dynamically adjusts the storage period according to the importance of the audio data. The security unit can also change the storage period for audio data based on user instructions. Furthermore, the security unit can automatically set the storage period for audio data based on a security policy. For example, the security unit evaluates the importance of audio data and dynamically adjusts the storage period. This makes data management more efficient by dynamically adjusting the storage period for audio data.

[0052] The security unit can add a function to dynamically adjust the storage period of location information. For example, the security unit uses a storage period setting standard or the importance of data to dynamically adjust the storage period of location information. For example, the security unit dynamically adjusts the storage period according to the importance of the location information. The security unit can also change the storage period of location information based on a user instruction. Furthermore, the security unit can automatically set the storage period of location information based on a security policy. For example, the security unit evaluates the importance of location information and dynamically adjusts the storage period. This makes data management more efficient by dynamically adjusting the storage period of location information.

[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 air conditioning control system may further include a health management unit that monitors the user's health condition. The health management unit may, for example, measure the user's heart rate and body temperature and evaluate the user's health condition. For example, if the user's heart rate is high, the air conditioning control unit may set the temperature to a cooler level. Alternatively, if the user's body temperature is low, the air conditioning control unit may set the temperature to a warmer level. Furthermore, the health management unit may adjust the air conditioning settings based on the user's health condition. For example, if the user has a cold, the air conditioning control unit may maintain an appropriate humidity level. This allows a comfortable environment to be provided by adjusting the air conditioning settings based on the user's health condition.

[0055] The air conditioning control system may further include an activity management unit that monitors the user's activity level. The activity management unit, for example, measures the user's number of steps and amount of exercise and evaluates the user's activity level. For example, if the user records a large number of steps, the air conditioning control unit may set a cool temperature. Also, if the user is exercising, the air conditioning control unit may maintain an appropriate temperature. Furthermore, the activity management unit may adjust the air conditioning settings based on the user's activity level. For example, if the user is doing desk work, the air conditioning control unit may set an appropriate temperature. This allows the user to provide a comfortable environment by adjusting the air conditioning settings based on the user's activity level.

[0056] The air conditioning control system can further include a schedule management unit that manages the user's schedule. The schedule management unit, for example, acquires the user's calendar information and manages the schedule. For example, if the user has a meeting scheduled, the air conditioning control unit can appropriately set the temperature in the conference room. Also, if the user plans to go out, the air conditioning control unit can turn off the air conditioning. Furthermore, the schedule management unit can adjust the air conditioning settings based on the user's schedule. For example, it can turn on the air conditioning to coincide with the time the user returns home. This allows for efficient air conditioning management by adjusting the air conditioning settings based on the user's schedule.

[0057] The air conditioning control system can further include a learning unit that learns user preferences. The learning unit, for example, analyzes the user's past air conditioning setting history and learns the user's preferences. For example, if the user prefers a specific temperature, the air conditioning control unit can set that temperature. Also, if the user prefers a specific temperature during a specific time period, the air conditioning control unit can set the temperature according to that time period. Furthermore, the learning unit can optimize the air conditioning settings based on user feedback. For example, the temperature that the user finds "comfortable" can be recorded and reflected in future settings. This makes it possible to provide a comfortable environment by adjusting the air conditioning settings based on the user's preferences.

[0058] The air conditioning control system can further include an energy management unit that optimizes energy consumption. The energy management unit, for example, monitors and optimizes the energy consumption of the air conditioning. For example, the energy management unit can analyze the usage status of the air conditioning and set it to improve energy efficiency. The energy management unit can also adjust the air conditioning settings to reduce energy consumption during peak hours. Furthermore, the energy management unit can adjust the air conditioning settings to promote the use of renewable energy. For example, the air conditioning can be operated according to the time period when solar power generation is possible. This optimizes energy consumption, enabling environmentally friendly air conditioning management.

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

[0060] Step 1: The speech recognition unit detects words such as "cold" or "hot" spoken by an employee. The speech recognition unit has the ability to detect specific keywords and analyzes the content of the speech using a speech recognition algorithm. The speech recognition unit can also analyze the context of the speech using natural language processing technology. Step 2: The location determination unit determines the location of the speaker based on the voice data detected by the voice recognition unit. The location determination unit can determine the location of the speaker using Wi-Fi connection information, Bluetooth beacons, RFID, etc. Step 3: The air conditioning control unit changes the air conditioning settings based on the location information identified by the location identification unit. The air conditioning control unit can set the optimal temperature by integrating an algorithm that estimates the optimal temperature based on the user's past setting history and temperature sensor information. Step 4: The security unit encrypts the voice data or anonymizes the location information. The security unit can encrypt the voice data or anonymize the location information.

[0061] (Example 2) An air conditioning control system according to an embodiment of the present invention uses sensors installed in a company to listen for voices such as "cold" or "hot," identify the speaker's location, and automatically adjust the air conditioning settings to a comfortable temperature. When an employee utters "cold" or "hot," the system detects the voice, identifies the speaker's location, and automatically adjusts the air conditioning settings based on that location. For example, if an employee utters "cold," the air conditioning setting in that area is changed to a warmer temperature. Conversely, if an employee utters "hot," the air conditioning setting is changed to a cooler temperature. This mechanism allows employees to easily adjust the air conditioning settings according to their own perceived temperature. For example, if an employee feels "cold" in a conference room, they can simply say "cold" and the air conditioning will be automatically adjusted. This improves employee comfort and improves work efficiency. The air conditioning control system thus improves employee comfort and work efficiency.

[0062] An air conditioning control system according to an embodiment includes a voice recognition unit, a location identification unit, an air conditioning control unit, and a security unit. The voice recognition unit detects speech such as "cold" or "hot" spoken by an employee. The voice recognition unit, for example, has a function for detecting specific keywords. The voice recognition unit, for example, uses a voice recognition algorithm to analyze the content of the speech. The voice recognition unit may also have a function for understanding context. For example, the voice recognition unit may analyze the context of the speech using natural language processing technology. The location identification unit identifies the location of the speaker based on the voice data detected by the voice recognition unit. The location identification unit, for example, uses Wi-Fi connection information to identify the location of the speaker. The location identification unit can also identify the location of the speaker using, for example, a Bluetooth beacon or RFID. The air conditioning control unit changes the air conditioning settings based on the location information identified by the location identification unit. The air conditioning control unit, for example, has an algorithm for estimating the optimal temperature based on the user's past setting history. The air conditioning control unit can also set the optimal temperature by integrating temperature sensor information, for example. The security unit encrypts voice data or anonymizes location information. The security unit, for example, encrypts voice data. The security unit can also anonymize location information, for example. This allows the air conditioning control system according to the embodiment to improve employee comfort.

[0063] The speech recognition unit may have a function for detecting specific keywords. The speech recognition unit uses, for example, a keyword list to detect specific keywords. For example, the speech recognition unit registers keywords such as "cold" and "hot" in a list and detects these keywords when they are spoken. The speech recognition unit can also detect specific keywords using speech pattern matching technology. For example, the speech recognition unit analyzes the pattern of spoken speech and determines whether it matches a keyword. This allows the detection of specific keywords, thereby improving the accuracy of speech recognition.

[0064] The speech recognition unit may have a function for understanding context. For example, the speech recognition unit uses natural language processing technology to understand the context. For example, the speech recognition unit analyzes the context of the speech content to understand the context. The speech recognition unit may also use a context analysis algorithm to understand the intention of the speech. For example, the speech recognition unit analyzes the context of the speech and infers the intention of the speaker. This understanding of the context improves the accuracy of speech recognition.

[0065] The location determination unit can determine the location of the speaker using Wi-Fi connection information. The location determination unit determines the location of the speaker using, for example, Wi-Fi connection information. For example, the location determination unit analyzes the SSID and signal strength of the Wi-Fi to which the speaker's device is connected to determine the speaker's location. The location determination unit can also determine the speaker's location using Wi-Fi connection history. For example, the location determination unit estimates the speaker's current location based on information about Wi-Fi to which the speaker's device has previously connected. As a result, using the Wi-Fi connection information improves the accuracy of determining the speaker's location.

[0066] The location identification unit can identify the location of the speaker using a Bluetooth beacon or RFID. The location identification unit identifies the location of the speaker using, for example, a Bluetooth beacon. For example, the location identification unit analyzes the signal strength of a Bluetooth beacon received by the speaker's device to identify the speaker's location. The location identification unit can also identify the location of the speaker using RFID. For example, the location identification unit identifies the speaker's location based on information from an RFID tag carried by the speaker. As a result, using a Bluetooth beacon or RFID improves the accuracy of identifying the speaker's location.

[0067] The air conditioning control unit can be equipped with an algorithm that estimates an appropriate temperature based on the user's past setting history. The air conditioning control unit, for example, is equipped with an algorithm that estimates an appropriate temperature based on the user's past setting history. For example, the air conditioning control unit analyzes historical data of temperatures previously set by the user and estimates the optimal temperature for the current situation. The air conditioning control unit can also estimate a temperature according to a specific time period or situation based on the user's setting history. For example, the air conditioning control unit estimates the optimal temperature for the current time period based on the temperature previously set by the user for a specific time period. This improves user comfort by estimating the optimal temperature based on the past setting history.

[0068] The security unit can encrypt the audio data. For example, the security unit encrypts the audio data using an encryption algorithm such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). The security unit can also encrypt the audio data when it is saved or transmitted. For example, the security unit encrypts the audio data when it is saved to prevent it from leaking to the outside. In this way, data security is improved by encrypting the audio data.

[0069] The security unit can anonymize the location information. For example, the security unit anonymizes the location information. For example, the security unit uses a technique for blurring the location information to prevent the speaker's exact location from being identified. The security unit can also generate pseudo-location information to anonymize the speaker's location. For example, the security unit randomly changes the speaker's location information to prevent the speaker's exact location from being identified. In this way, privacy is protected by anonymizing the location information.

[0070] The voice recognition unit can estimate the user's emotions and adjust the accuracy of voice recognition based on the estimated user's emotions. The voice recognition unit uses, for example, voice tone analysis and facial expression recognition technology to estimate the user's emotions. For example, the voice recognition unit analyzes the tone and strength of the speaker's voice to estimate the user's emotions. The voice recognition unit can also analyze the speaker's facial expressions using a camera to estimate the user's emotions. For example, the voice recognition unit estimates the speaker's emotions based on changes in the speaker's facial expressions. The voice recognition unit adjusts the accuracy of voice recognition based on the estimated user's emotions. For example, if the user is feeling stressed, the voice recognition sensitivity can be increased to recognize the speech more accurately. Also, if the user is relaxed, the voice recognition sensitivity can be set to normal to recognize natural speech. In this way, the recognition accuracy can be improved by adjusting the accuracy of voice recognition based on the user's emotions.

[0071] The speech recognition unit can add a filtering function that automatically removes background noise during speech recognition. The speech recognition unit uses, for example, a noise filtering algorithm to automatically remove background noise. For example, the speech recognition unit analyzes noise in an office in real time and removes it during speech recognition. The speech recognition unit can also filter noise during a meeting and recognize only the speaker's voice. For example, the speech recognition unit can remove the operating sounds of an air conditioner or printer to clearly recognize speech. This removes background noise and improves the accuracy of speech recognition.

[0072] The speech recognition unit can add a function to analyze the tone and strength of the speaker's voice during speech recognition to determine the urgency of the speech. The speech recognition unit uses, for example, a speech analysis algorithm to analyze the tone and strength of the speaker's voice. For example, if the speaker's voice is high-pitched and strong, the speech recognition unit may determine the urgency to be high and take immediate action. Alternatively, if the speaker's voice is low and calm, the speech recognition unit may determine the urgency to be low and take a normal action. For example, if the speaker's tone of voice changes, the speech recognition unit may reevaluate the urgency. This allows for a prompt response by determining the urgency of the speech.

[0073] The voice recognition unit can estimate the user's emotions and determine the priority of voice recognition based on the estimated user's emotions. The voice recognition unit uses, for example, voice tone analysis and facial expression recognition technology to estimate the user's emotions. For example, the voice recognition unit analyzes the tone and strength of the speaker's voice to estimate the user's emotions. The voice recognition unit can also estimate the user's emotions by analyzing the speaker's facial expressions using a camera. For example, the voice recognition unit estimates the emotion based on changes in the speaker's facial expressions. The voice recognition unit determines the priority of voice recognition based on the estimated user's emotions. For example, if the user is feeling stressed, the voice recognition unit can increase the priority of voice recognition and respond quickly. Also, if the user is relaxed, the voice recognition priority can be set to normal. In this way, by determining the priority of voice recognition based on the user's emotions, important utterances can be recognized preferentially.

[0074] The speech recognition unit can be added with a function for switching the recognition algorithm depending on the language or dialect of the speaker during speech recognition. The speech recognition unit uses, for example, a language model or a dialect dictionary to switch the recognition algorithm depending on the language or dialect of the speaker. For example, the speech recognition unit switches to an English recognition algorithm when the speaker speaks English. The speech recognition unit can also switch to a Kansai dialect recognition algorithm when the speaker speaks Kansai dialect. Furthermore, the speech recognition unit can switch to an appropriate algorithm using an automatic language detection function when the speaker speaks multiple languages. For example, the speech recognition unit automatically detects the language of the speaker and switches to an appropriate recognition algorithm. This improves recognition accuracy by switching the recognition algorithm depending on the language or dialect of the speaker.

[0075] The speech recognition unit can add a function to improve recognition accuracy by referring to the speaker's past speech history during speech recognition. The speech recognition unit, for example, stores and analyzes history data to refer to the speaker's past speech history. For example, the speech recognition unit can improve recognition accuracy based on keywords used by the speaker in the past. The speech recognition unit can also analyze the speaker's past speech patterns and optimize the recognition algorithm. Furthermore, the speech recognition unit can preferentially recognize specific phrases from the speaker's past speech history. For example, the speech recognition unit preferentially recognizes phrases that the speaker has frequently used in the past. In this way, recognition accuracy is improved by referring to the speaker's past speech history.

[0076] The location identification unit can estimate the user's emotions and adjust the accuracy of location identification based on the estimated user's emotions. The location identification unit uses, for example, voice tone analysis or facial expression recognition technology to estimate the user's emotions. For example, the location identification unit analyzes the tone and strength of the speaker's voice to estimate the user's emotions. The location identification unit can also estimate the user's emotions by analyzing the speaker's facial expressions using a camera. For example, the location identification unit estimates the emotions based on changes in the speaker's facial expressions. The location identification unit adjusts the accuracy of location identification based on the estimated user's emotions. For example, if the user is feeling stressed, the accuracy of location identification can be increased to respond quickly. Also, if the user is relaxed, the accuracy of location identification can be set to normal. This allows for a quick response by adjusting the accuracy of location identification based on the user's emotions.

[0077] The location determination unit may add a function for integrating multiple sensor information to improve the accuracy of location determination during location determination. The location determination unit may use, for example, an integration algorithm to integrate multiple sensor information. For example, the location determination unit may integrate Wi-Fi connection information and Bluetooth beacon information to improve the accuracy of location determination. The location determination unit may also integrate RFID tag and audio sensor information to improve the accuracy of location determination. Furthermore, the location determination unit may also integrate GPS information and indoor sensor information to improve the accuracy of location determination. For example, the location determination unit may integrate data obtained from multiple sensors to more accurately identify the speaker's location. In this way, the accuracy of location determination is improved by integrating multiple sensor information.

[0078] The position identification unit can add a function of analyzing the movement pattern of the speaker at the time of position identification to improve the prediction accuracy of position identification. The position identification unit uses, for example, a pattern recognition algorithm to analyze the movement pattern of the speaker. For example, the position identification unit analyzes the speaker's past movement pattern to improve the prediction accuracy of position identification. The position identification unit can also improve the prediction accuracy of position identification by taking into account the speaker's current movement speed. Furthermore, the position identification unit can analyze the movement direction of the speaker to improve the prediction accuracy of position identification. For example, the position identification unit predicts the speaker's current location based on the speaker's movement history. In this way, the prediction accuracy of position identification is improved by analyzing the speaker's movement pattern.

[0079] The location identification unit can estimate the user's emotions and determine the priority of location identification based on the estimated user's emotions. The location identification unit uses, for example, voice tone analysis or facial expression recognition technology to estimate the user's emotions. For example, the location identification unit analyzes the tone and strength of the speaker's voice to estimate the user's emotions. The location identification unit can also estimate the user's emotions by analyzing the speaker's facial expressions using a camera. For example, the location identification unit estimates the emotions based on changes in the speaker's facial expressions. The location identification unit determines the priority of location identification based on the estimated user's emotions. For example, if the user is feeling stressed, the priority of location identification can be increased and a quick response can be made. Also, if the user is relaxed, the priority of location identification can be set to normal. In this way, by determining the priority of location identification based on the user's emotions, important location identification can be prioritized.

[0080] The location determination unit can add a function to improve the accuracy of location determination by referring to the speaker's device information when determining the speaker's location. The location determination unit uses, for example, a device ID or connection history to refer to the speaker's device information. For example, the location determination unit can improve the accuracy of location determination by referring to the Wi-Fi connection information of the speaker's smartphone. The location determination unit can also improve the accuracy of location determination by referring to the speaker's Bluetooth device information. Furthermore, the location determination unit can improve the accuracy of location determination by referring to the connection information of the speaker's business terminal. For example, the location determination unit determines the speaker's location based on information about the Wi-Fi or Bluetooth to which the speaker's device is connected. In this way, the accuracy of location determination is improved by referring to the speaker's device information.

[0081] The location identification unit can add a function to improve the accuracy of location identification by referring to the speaker's past location history when identifying the speaker's location. The location identification unit, for example, stores and analyzes history data to refer to the speaker's past location history. For example, the location identification unit identifies the speaker's current location based on the speaker's past location history. The location identification unit can also increase the possibility that the speaker is in a specific area based on the speaker's past location history. Furthermore, the location identification unit can analyze the speaker's past location history to improve the accuracy of location identification. For example, the location identification unit estimates the speaker's current location based on places that the speaker has frequently visited in the past. In this way, the accuracy of location identification is improved by referring to the speaker's past location history.

[0082] The air conditioning control unit can estimate the user's emotions and adjust the air conditioning settings based on the estimated user's emotions. The air conditioning control unit uses, for example, voice tone analysis and facial expression recognition technology to estimate the user's emotions. For example, the air conditioning control unit analyzes the tone and strength of a speaker's voice to estimate the user's emotions. The air conditioning control unit can also use a camera to analyze the speaker's facial expressions to estimate the user's emotions. For example, the air conditioning control unit estimates emotions based on changes in the speaker's facial expressions. The air conditioning control unit adjusts the air conditioning settings based on the estimated user's emotions. For example, if the user feels cold, the air conditioning setting can be changed to a warmer temperature. Also, if the user feels hot, the air conditioning setting can be changed to a cooler temperature. This makes it possible to provide a comfortable environment by adjusting the air conditioning settings based on the user's emotions.

[0083] The air conditioning control unit can add a function to integrate indoor temperature sensor information and set an appropriate temperature when controlling the air conditioning. For example, the air conditioning control unit uses sensor placement and data acquisition methods to integrate indoor temperature sensor information. For example, the air conditioning control unit sets an optimal temperature based on indoor temperature sensor information. The air conditioning control unit can also integrate information from multiple indoor temperature sensors to set a uniform temperature. Furthermore, the air conditioning control unit can update indoor temperature sensor information in real time to maintain an optimal temperature. For example, the air conditioning control unit monitors the current temperature in real time based on indoor temperature sensor information and adjusts the temperature setting as necessary. In this way, the optimal temperature can be set by integrating indoor temperature sensor information.

[0084] The air conditioning control unit can add a function to estimate an appropriate temperature by referring to the speaker's past temperature setting history when controlling the air conditioning. The air conditioning control unit, for example, saves and analyzes history data to refer to the speaker's past temperature setting history. For example, the air conditioning control unit estimates the optimal temperature for the current situation based on the speaker's past temperature setting history. The air conditioning control unit can also analyze the speaker's past temperature setting patterns to estimate the optimal temperature. Furthermore, the air conditioning control unit can estimate the optimal temperature for a specific time period based on the speaker's past temperature setting history. For example, the air conditioning control unit estimates the optimal temperature for the current time period based on the temperature the speaker set for a specific time period in the past. In this way, the optimal temperature can be estimated by referring to the speaker's past temperature setting history.

[0085] The air conditioning control unit can estimate the user's emotions and determine the priority of air conditioning settings based on the estimated user's emotions. The air conditioning control unit uses, for example, voice tone analysis and facial expression recognition technology to estimate the user's emotions. For example, the air conditioning control unit analyzes the tone and strength of the speaker's voice to estimate the user's emotions. The air conditioning control unit can also use a camera to analyze the speaker's facial expression to estimate the user's emotions. For example, the air conditioning control unit estimates the emotions based on changes in the speaker's facial expression. The air conditioning control unit determines the priority of air conditioning settings based on the estimated user's emotions. For example, if the user feels cold, the air conditioning control unit can increase the priority of air conditioning settings and respond quickly. Also, if the user feels hot, the air conditioning control unit can increase the priority of air conditioning settings and respond quickly. In this way, by determining the priority of air conditioning settings based on the user's emotions, important air conditioning settings can be prioritized.

[0086] The air conditioning control unit can add a function to integrate indoor humidity sensor information and set an appropriate humidity level during air conditioning control. The air conditioning control unit uses, for example, sensor placement and data acquisition methods to integrate indoor humidity sensor information. For example, the air conditioning control unit sets an optimal humidity level based on indoor humidity sensor information. The air conditioning control unit can also integrate information from multiple indoor humidity sensors to set a uniform humidity level. Furthermore, the air conditioning control unit can update indoor humidity sensor information in real time to maintain an optimal humidity level. For example, the air conditioning control unit monitors the current humidity level in real time based on indoor humidity sensor information and adjusts the humidity setting as necessary. In this way, the optimal humidity level can be set by integrating indoor humidity sensor information.

[0087] The air conditioning control unit can add a function to set an appropriate temperature by referring to the speaker's current activity status when controlling the air conditioning. The air conditioning control unit, for example, acquires and analyzes activity data to refer to the speaker's current activity status. For example, the air conditioning control unit sets a moderate temperature when the speaker is doing desk work. The air conditioning control unit can also set a comfortable temperature when the speaker is in a meeting. Furthermore, the air conditioning control unit can set a cool temperature when the speaker is exercising. For example, the air conditioning control unit adjusts the current temperature based on the speaker's activity status. In this way, the optimal temperature can be set by referring to the speaker's current activity status.

[0088] The security unit can estimate the user's emotions and adjust the security level based on the estimated user's emotions. The security unit uses, for example, voice tone analysis and facial expression recognition technology to estimate the user's emotions. For example, the security unit analyzes the tone and strength of the speaker's voice to estimate the user's emotions. The security unit can also use a camera to analyze the speaker's facial expressions to estimate the user's emotions. For example, the security unit estimates the emotions based on changes in the speaker's facial expressions. The security unit adjusts the security level based on the estimated user's emotions. For example, if the user feels anxious, the security level can be increased. Also, if the user is relaxed, the security level can be set to normal. In this way, appropriate security is provided by adjusting the security level based on the user's emotions.

[0089] The security unit can add a real-time encryption function for audio data. The security unit uses, for example, an encryption algorithm to perform real-time encryption of audio data. For example, the security unit encrypts audio data in real time to prevent leakage to the outside. The security unit can also encrypt audio data to strengthen security during storage. Furthermore, the security unit can encrypt audio data to strengthen security during communication. For example, the security unit encrypts audio data using an encryption algorithm such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). This improves data security by performing real-time encryption of audio data.

[0090] The security unit can add a real-time anonymization function for location information. The security unit, for example, uses an anonymization algorithm to perform real-time anonymization of location information. For example, the security unit anonymizes location information in real time to prevent leakage to the outside. The security unit can also anonymize location information to strengthen security during storage. Furthermore, the security unit can anonymize location information to strengthen security during communication. For example, the security unit anonymizes location information using a location information blurring technique or generation of pseudo location information. In this way, privacy is protected by performing real-time anonymization of location information.

[0091] The security unit can estimate the user's emotions and determine the priority of security settings based on the estimated user's emotions. The security unit uses, for example, voice tone analysis or facial expression recognition technology to estimate the user's emotions. For example, the security unit analyzes the tone and strength of the speaker's voice to estimate the user's emotions. The security unit can also use a camera to analyze the speaker's facial expressions to estimate the user's emotions. For example, the security unit estimates emotions based on changes in the speaker's facial expressions. The security unit determines the priority of security settings based on the estimated user's emotions. For example, if the user feels anxious, the security unit can increase the priority of security settings. Also, if the user is relaxed, the security unit can set the priority of security settings to normal. In this way, by determining the priority of security settings based on the user's emotions, important security settings can be prioritized.

[0092] The security unit can add a function to dynamically adjust the storage period for audio data. The security unit, for example, uses storage period setting criteria and the importance of the data to dynamically adjust the storage period for audio data. For example, the security unit dynamically adjusts the storage period according to the importance of the audio data. The security unit can also change the storage period for audio data based on user instructions. Furthermore, the security unit can automatically set the storage period for audio data based on a security policy. For example, the security unit evaluates the importance of audio data and dynamically adjusts the storage period. This makes data management more efficient by dynamically adjusting the storage period for audio data.

[0093] The security unit can add a function to dynamically adjust the storage period of location information. For example, the security unit uses a storage period setting standard or the importance of data to dynamically adjust the storage period of location information. For example, the security unit dynamically adjusts the storage period according to the importance of the location information. The security unit can also change the storage period of location information based on a user instruction. Furthermore, the security unit can automatically set the storage period of location information based on a security policy. For example, the security unit evaluates the importance of location information and dynamically adjusts the storage period. This makes data management more efficient by dynamically adjusting the storage period of location information. === Hard Collateral 1-1 === Each of the multiple elements, including the voice recognition unit, location identification unit, air conditioning control unit, and security unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice recognition unit is implemented by the microphone 38B and control unit 46A of the smart device 14, and detects the employee's speech and analyzes the voice data. The location identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and identifies the speaker's location based on the voice data. The air conditioning control unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and changes the air conditioning settings based on the identified location information. The security unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and encrypts the voice data and anonymizes the location information. === Hard Collateral 1-2 === Each of the multiple elements, including the voice recognition unit, location identification unit, air conditioning control unit, and security unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice recognition unit is implemented by the microphone 238 and the control unit 46A of the smart glasses 214, and detects the employee's speech and analyzes the voice data. The location identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and identifies the speaker's location based on the voice data. The air conditioning control unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and changes the air conditioning settings based on the identified location information. The security unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and encrypts the voice data and anonymizes the location information. === Hard Collateral 1-3 === Each of the multiple elements, including the voice recognition unit, location identification unit, air conditioning control unit, and security unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the voice recognition unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314, detects the employee's speech, and analyzes the voice data. The location identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and identifies the speaker's location based on the voice data. The air conditioning control unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and changes the air conditioning settings based on the identified location information. The security unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and encrypts the voice data and anonymizes the location information. === Hard Collateral 1-4 === Each of the multiple elements, including the voice recognition unit, location identification unit, air conditioning control unit, and security unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice recognition unit is realized by the microphone 238 and control unit 46A of the robot 414, detects the employee's speech, and analyzes the voice data. The location identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the speaker's location based on the voice data. The air conditioning control unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and changes the air conditioning settings based on the identified location information. The security unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and encrypts the voice data and anonymizes the location information.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The air conditioning control system may further include a health management unit that monitors the user's health condition. The health management unit may, for example, measure the user's heart rate and body temperature and evaluate the user's health condition. For example, if the user's heart rate is high, the air conditioning control unit may set the temperature to a cooler level. Alternatively, if the user's body temperature is low, the air conditioning control unit may set the temperature to a warmer level. Furthermore, the health management unit may adjust the air conditioning settings based on the user's health condition. For example, if the user has a cold, the air conditioning control unit may maintain an appropriate humidity level. This allows a comfortable environment to be provided by adjusting the air conditioning settings based on the user's health condition.

[0096] The air conditioning control system may further include an activity management unit that monitors the user's activity level. The activity management unit, for example, measures the user's number of steps and amount of exercise and evaluates the user's activity level. For example, if the user records a large number of steps, the air conditioning control unit may set a cool temperature. Also, if the user is exercising, the air conditioning control unit may maintain an appropriate temperature. Furthermore, the activity management unit may adjust the air conditioning settings based on the user's activity level. For example, if the user is doing desk work, the air conditioning control unit may set an appropriate temperature. This allows the user to provide a comfortable environment by adjusting the air conditioning settings based on the user's activity level.

[0097] The air conditioning control system can further include a schedule management unit that manages the user's schedule. The schedule management unit, for example, acquires the user's calendar information and manages the schedule. For example, if the user has a meeting scheduled, the air conditioning control unit can appropriately set the temperature in the conference room. Also, if the user plans to go out, the air conditioning control unit can turn off the air conditioning. Furthermore, the schedule management unit can adjust the air conditioning settings based on the user's schedule. For example, it can turn on the air conditioning to coincide with the time the user returns home. This allows for efficient air conditioning management by adjusting the air conditioning settings based on the user's schedule.

[0098] The air conditioning control system can further include a learning unit that learns user preferences. The learning unit, for example, analyzes the user's past air conditioning setting history and learns the user's preferences. For example, if the user prefers a specific temperature, the air conditioning control unit can set that temperature. Also, if the user prefers a specific temperature during a specific time period, the air conditioning control unit can set the temperature according to that time period. Furthermore, the learning unit can optimize the air conditioning settings based on user feedback. For example, the temperature that the user finds "comfortable" can be recorded and reflected in future settings. This makes it possible to provide a comfortable environment by adjusting the air conditioning settings based on the user's preferences.

[0099] The air conditioning control system can further include an energy management unit that optimizes energy consumption. The energy management unit, for example, monitors and optimizes the energy consumption of the air conditioning. For example, the energy management unit can analyze the usage status of the air conditioning and set it to improve energy efficiency. The energy management unit can also adjust the air conditioning settings to reduce energy consumption during peak hours. Furthermore, the energy management unit can adjust the air conditioning settings to promote the use of renewable energy. For example, the air conditioning can be operated according to the time period when solar power generation is possible. This optimizes energy consumption, enabling environmentally friendly air conditioning management.

[0100] The air conditioning control system can further estimate the user's emotions and adjust the air conditioning settings based on the estimated user's emotions. For example, if the user is feeling stressed, the air conditioning control unit can set the temperature to a relaxing level. If the user is relaxed, the air conditioning control unit can also set the temperature to a normal level. Furthermore, if the user is excited, the air conditioning control unit can set the temperature to a cool level. This makes it possible to provide a comfortable environment by adjusting the air conditioning settings based on the user's emotions.

[0101] The air conditioning control system can further estimate the user's emotions and determine the priority of air conditioning settings based on the estimated user's emotions. For example, if the user is feeling stressed, the priority of the air conditioning settings can be increased, allowing for a quick response. Also, if the user is relaxed, the priority of the air conditioning settings can be set to normal. Furthermore, if the user is excited, the priority of the air conditioning settings can be increased, allowing for a quick response. In this way, by determining the priority of air conditioning settings based on the user's emotions, important air conditioning settings can be prioritized.

[0102] The air conditioning control system can further estimate the user's emotions and adjust the accuracy of the air conditioning settings based on the estimated user emotions. For example, if the user is feeling stressed, the accuracy of the air conditioning settings can be increased and more precise adjustments can be made. Also, if the user is relaxed, the accuracy of the air conditioning settings can be set to normal. Furthermore, if the user is excited, the accuracy of the air conditioning settings can be increased and a quick response can be made. In this way, a comfortable environment can be provided by adjusting the accuracy of the air conditioning settings based on the user's emotions.

[0103] The air conditioning control system can further estimate the user's emotions and adjust the frequency of the air conditioning settings based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of the air conditioning settings can be increased to respond quickly. Also, if the user is relaxed, the frequency of the air conditioning settings can be set to normal. Furthermore, if the user is excited, the frequency of the air conditioning settings can be increased to respond quickly. In this way, a comfortable environment can be provided by adjusting the frequency of the air conditioning settings based on the user's emotions.

[0104] The air conditioning control system can further estimate the user's emotions and adjust the range of air conditioning settings based on the estimated user's emotions. For example, if the user is feeling stressed, the air conditioning setting range can be widened to set a more comfortable temperature. If the user is relaxed, the air conditioning setting range can be set to normal. Furthermore, if the user is excited, the air conditioning setting range can be widened to respond quickly. In this way, a comfortable environment can be provided by adjusting the air conditioning setting range based on the user's emotions.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The speech recognition unit detects words such as "cold" or "hot" spoken by an employee. The speech recognition unit has the ability to detect specific keywords and analyzes the content of the speech using a speech recognition algorithm. The speech recognition unit can also analyze the context of the speech using natural language processing technology. Step 2: The location determination unit determines the location of the speaker based on the voice data detected by the voice recognition unit. The location determination unit can determine the location of the speaker using Wi-Fi connection information, Bluetooth beacons, RFID, etc. Step 3: The air conditioning control unit changes the air conditioning settings based on the location information identified by the location identification unit. The air conditioning control unit can set the optimal temperature by integrating an algorithm that estimates the optimal temperature based on the user's past setting history and temperature sensor information. Step 4: The security unit encrypts the voice data or anonymizes the location information. The security unit can encrypt the voice data or anonymize the location information.

[0107] 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.

[0108] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 speech recognition unit that recognizes speech; a position identification unit that identifies the position of a speaker based on the voice data detected by the voice recognition unit; an air conditioning control unit that changes air conditioning settings based on the position information identified by the position identifying unit; a security unit that encrypts voice data or anonymizes location information. A system characterized by:

2. The voice recognition unit Has the ability to detect specific keywords 2. The system of claim 1.

3. The voice recognition unit Contextual understanding 2. The system of claim 1.

4. The position identification unit Identifying the speaker's location using Wi-Fi connection information 2. The system of claim 1.

5. The position identification unit Locate the speaker using Bluetooth beacons or RFID 2. The system of claim 1.

6. The air conditioning control unit Equipped with an algorithm that estimates the appropriate temperature based on the user's past setting history 2. The system of claim 1.

7. The security unit Encrypting audio data 2. The system of claim 1.

8. The security unit Anonymize location information 2. The system of claim 1.

9. The voice recognition unit Estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated user emotions.

2. The system of claim 1.

10. The voice recognition unit Add a filtering function to automatically remove background noise during voice recognition.

2. The system of claim 1.

Images