system

Abstract

The system according to the embodiment aims to strengthen home security when you are out. [Solution] A system according to an embodiment includes a voice generation unit, a transmission unit, a lighting control unit, a warning generation unit, and a warning transmission unit. The voice generation unit generates a fake voice message using a voice generation AI. The transmission unit transmits the voice message generated by the voice generation unit to the outside. The lighting control unit automatically turns the room lights on and off using a sensor or a timer. The warning generation unit generates a warning message when the sensor detects suspicious activity. The warning transmission unit transmits the warning message generated by the warning generation unit to the outside.

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 home security has had the problem of not providing sufficient and effective countermeasures against suspicious individuals when you are out.

[0005] The system according to the embodiment aims to strengthen home security when you are out. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice generation unit, a transmission unit, a lighting control unit, a warning generation unit, and a warning transmission unit. The voice generation unit generates a fake voice message using a voice generation AI. The transmission unit transmits the voice message generated by the voice generation unit to the outside. The lighting control unit automatically turns the room lights on and off using a sensor or a timer. The warning generation unit generates a warning message when the sensor detects suspicious movement. The warning transmission unit transmits the warning message generated by the warning generation unit to the outside. [Effects of the Invention]

[0007] The system according to the embodiment can strengthen home security when you are out. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A smart home security system according to an embodiment of the present invention utilizes a voice generation AI to enhance home security while the user is away, making it appear as if someone is present at home. This system has a function in which the voice generation AI generates fake voice messages and transmits them to the outside. For example, the system can mimic family conversations or television sounds, making them audible from outside. The system also has a function to automatically turn room lights on and off using sensors and timers, making it appear as if someone is moving around the room from the outside. Furthermore, if the sensor detects suspicious movement, the voice generation AI generates a warning message and transmits it to the outside. This allows the system to enhance home security even when the user is away, preventing intruders from entering the home. This smart home security system enhances home security even when the user is away, preventing intruders from entering the home.

[0029] A smart home security system according to an embodiment includes a voice generation unit, a transmission unit, a lighting control unit, a warning generation unit, and a warning transmission unit. The voice generation unit generates fake voice messages using a voice generation AI. For example, the voice generation unit generates voice messages that imitate family conversations or television sounds. The voice generation unit inputs prompts such as "Please imitate family conversations" or "Please imitate television sounds" to the generation AI, and the generation AI generates a voice message based on the prompts. The transmission unit transmits the voice message generated by the voice generation unit to the outside. For example, the voice message is transmitted to the outside via a speaker. The transmission unit inputs the generated voice message into a speaker, which then transmits the voice to the outside. The lighting control unit automatically turns on and off room lights using a sensor or a timer. For example, the living room light is automatically turned on in the evening and the bedroom light is turned on at night. The lighting control unit controls the on / off of lights based on the settings of the sensor or timer. The warning generation unit generates a warning message when the sensor detects suspicious activity. For example, the warning message is generated as follows: "A suspicious person has entered. The police have been notified." The warning generation unit receives input from the sensor and inputs a prompt to the generation AI, such as "A suspicious person has intruded. The police have been notified." Based on this, the generation AI generates a warning message. The warning transmission unit transmits the warning message generated by the warning generation unit to the outside. For example, the warning message is transmitted to the outside via a speaker. The warning transmission unit inputs the generated warning message into a speaker, which then transmits the warning message to the outside. In this way, the smart home security system according to the embodiment can strengthen home security and prevent intrusion of suspicious persons even when you are away from home.

[0030] The voice generation unit can generate fake voice messages that imitate family conversations or television sounds. The voice generation unit generates, for example, a voice message that imitates family conversations. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please imitate family conversations," and the generation AI generates a voice message that imitates family conversations based on this. The voice generation unit can also generate voice messages that imitate television sounds. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please imitate television sounds," and the generation AI generates a voice message that imitates television sounds based on this. This makes it possible to generate more natural voice messages by imitating family conversations or television sounds. Some or all of the above-described processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this.

[0031] The lighting control unit can use a sensor or a timer to automatically turn on the living room lights in the evening (e.g., 6 p.m.) and turn on the bedroom lights at night (e.g., 10 p.m.). The lighting control unit can, for example, use a sensor to automatically turn on the living room lights in the evening. For example, the lighting control unit turns on the living room lights when the sensor detects 6 p.m. The lighting control unit can also use a timer to turn on the bedroom lights at night. For example, the lighting control unit turns on the bedroom lights when the timer detects 10 p.m. In this way, by using a sensor or a timer, it can appear as if a person is moving around the room when viewed from outside. Some or all of the above-mentioned processing in the lighting control unit may be performed using AI, or may be performed without AI. For example, the lighting control unit can input data from a sensor or timer into AI, which can then control the on / off of the lights based on this data.

[0032] The warning generation unit can generate a warning message such as "A suspicious person has entered. The police have been notified." when the sensor detects suspicious movement. For example, the warning generation unit generates a warning message such as "A suspicious person has entered. The police have been notified." when the sensor detects suspicious movement. For example, the warning generation unit receives input from the sensor and inputs a prompt such as "A suspicious person has entered. The police have been notified." to the generation AI, and the generation AI generates a warning message based on this. In this way, by generating a warning message when suspicious movement is detected, it is possible to intimidate the suspicious person. Some or all of the above-mentioned processing in the warning generation unit is performed using the generation AI. For example, the warning generation unit inputs a prompt to the generation AI, and the generation AI generates a warning message based on this.

[0033] The warning transmission unit can transmit the warning message generated by the warning generation unit to the outside. The warning transmission unit transmits the warning message generated by the warning generation unit to the outside via a speaker, for example. For example, the warning transmission unit inputs the generated warning message into a speaker, which then transmits the warning message to the outside. In this way, transmitting the warning message to the outside can enhance the deterrent effect against suspicious individuals. Some or all of the above-mentioned processing in the warning transmission unit may be performed using AI, or may be performed without using AI. For example, the warning transmission unit inputs a prompt to the generation AI, which then generates a warning message based on the prompt and transmits the warning message to the outside via a speaker.

[0034] The voice generation unit can generate more natural voice messages by referencing past voice data when generating voice. The voice generation unit, for example, references previously recorded family conversations to generate natural conversational voices. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate natural conversational voices by referring to past family conversations," and the generation AI generates natural conversational voices based on this. The voice generation unit can also generate realistic television voices by referencing the voice of past television programs. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate realistic television voices by referring to the voice of past television programs," and the generation AI generates realistic television voices based on this. The voice generation unit can also generate natural radio voices by referencing the voice of past radio broadcasts. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate natural radio voices by referring to the voice of past radio broadcasts," and the generation AI generates natural radio voices based on this. This allows for more natural voice messages to be generated by referencing past voice data. Some or all of the above-described processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this.

[0035] The voice generation unit can adjust the volume and tone of the voice message it generates when generating the voice. For example, the voice generation unit increases the volume during the day and decreases the volume at night. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate voice messages at a higher volume during the day and at a lower volume at night," and the generation AI generates a voice message with the volume adjusted based on this. The voice generation unit can also generate a warning voice with a higher tone in an emergency. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a warning voice with a higher tone in an emergency," and the generation AI generates a warning voice with a higher tone based on this. The voice generation unit can also generate a voice with a lower tone to create a relaxing atmosphere. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a voice with a lower tone to create a relaxing atmosphere," and the generation AI generates a voice with a lower tone based on this. In this way, by adjusting the volume and tone, it is possible to provide an appropriate voice message according to the situation. Some or all of the above-mentioned processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this.

[0036] The voice generation unit can generate a region-specific voice message by taking into account the user's geographical location information when generating the voice. The voice generation unit, for example, generates a voice message incorporating a regional dialect. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a voice message incorporating a regional dialect," and the generation AI generates a voice message incorporating a regional dialect based on this. The voice generation unit can also generate a voice message including regional event information. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a voice message including regional event information," and the generation AI generates a voice message including regional event information based on this. The voice generation unit can also generate a voice message including regional weather information. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a voice message including regional weather information," and the generation AI generates a voice message including regional weather information based on this. This generates a region-specific voice message, allowing for a more natural and effective voice message. Some or all of the above-described processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this.

[0037] The voice generation unit can analyze the user's social media activity and generate a relevant voice message when generating the voice. The voice generation unit generates the voice message based on, for example, the content the user is discussing on social media. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate a voice message based on the content the user is discussing on social media," and the generation AI generates the relevant voice message based on this. The voice generation unit can also generate a relevant voice message by referring to the content posted on social media by the user. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate a relevant voice message by referring to the content posted on social media by the user," and the generation AI generates the relevant voice message based on this. The voice generation unit can also generate a relevant voice message by taking into account the user's social media friendships. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate a relevant voice message by taking into account the user's social media friendships," and the generation AI generates the relevant voice message based on this. This allows for the provision of more relevant voice messages by analyzing social media activity. Some or all of the above-described processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this.

[0038] When making a call, the sending unit can set multiple destinations for a voice message and send them simultaneously. For example, the sending unit can send a voice message to multiple speakers in a house simultaneously. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message to multiple speakers in the house simultaneously," which the generation AI generates based on the prompt and sends to the multiple speakers simultaneously. The sending unit can also send a voice message to both external and internal speakers in a house simultaneously. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message to external and internal speakers in the house simultaneously," which the generation AI generates based on the prompt and sends to the external and internal speakers simultaneously. The sending unit can also send a voice message to speakers installed in each room in a house simultaneously. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message to speakers installed in each room in a house simultaneously," which the generation AI generates based on the prompt and sends to the speakers in each room simultaneously. This increases the effectiveness of the voice message by simultaneously sending to multiple destinations. Some or all of the above-described processing in the sending unit is performed using the generation AI. For example, the sending unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this and sends it to multiple destinations simultaneously.

[0039] When making a call, the sending unit can select a method for sending a voice message and send it through, for example, a speaker or a smartphone. The sending unit, for example, sends a voice message through a home speaker. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message through the home speaker," which generates a voice message based on the prompt and sends it through the speaker. The sending unit can also send a voice message through the user's smartphone. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message through the user's smartphone," which generates a voice message based on the prompt and sends it through the smartphone. The sending unit can also send a voice message through the home intercom. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message through the home intercom," which generates a voice message based on the prompt and sends it through the intercom. This allows for appropriate calling according to the situation by selecting the calling method. Some or all of the above-mentioned processing in the sending unit is performed using the generation AI. For example, the sending unit inputs a prompt to the generation AI, which generates a voice message based on the prompt and selects the calling method.

[0040] When making a call, the calling unit can select the optimal call destination by taking into account the user's geographical location information. For example, if the user is near their home, the calling unit prioritizes calling from an external speaker in the home. For example, the calling unit inputs a prompt to the generation AI saying, "If the user is near their home, please prioritize calling from an external speaker in the home." The generation AI generates a voice message based on this and prioritizes calling from the external speaker. The calling unit can also prioritize calling from a smartphone when the user is far away. For example, the calling unit inputs a prompt to the generation AI saying, "If the user is far away, please prioritize calling from a smartphone." The generation AI generates a voice message based on this and prioritizes calling from the smartphone. The calling unit can also select the optimal call destination for a specific location when the user is in that location. For example, the calling unit inputs a prompt to the generation AI saying, "If the user is in a specific location, please select the optimal call destination for that location." The generation AI generates a voice message based on this and selects the optimal call destination. This enables more effective calls by taking geographical location information into account. Some or all of the above-mentioned processing in the sending unit is performed using the generation AI. For example, the sending unit inputs a prompt to the generation AI, which then generates a voice message based on the prompt and selects the optimal destination.

[0041] When sending a message, the sending unit can analyze the user's social media activity and select relevant destinations. For example, the sending unit selects destinations related to the content the user is discussing on social media. For example, the sending unit inputs a prompt to the generation AI, such as, "Please select destinations related to the content the user is discussing on social media," and the generation AI generates a voice message and selects relevant destinations based on this. The sending unit can also select relevant destinations by taking into account the user's social media friendships. For example, the sending unit inputs a prompt to the generation AI, such as, "Please select relevant destinations by taking into account the user's social media friendships," and the generation AI generates a voice message and selects relevant destinations based on this. The sending unit can also select relevant destinations by referring to the content of the user's social media posts. For example, the sending unit inputs a prompt to the generation AI, such as, "Please select relevant destinations by referring to the content of the user's social media posts," and the generation AI generates a voice message and selects relevant destinations based on this. This enables more relevant messages to be sent by analyzing social media activity. Some or all of the above-mentioned processing in the sending unit is performed using the generation AI. For example, the sending unit inputs a prompt to the generation AI, which then generates a voice message based on the prompt and selects the relevant destination.

[0042] The lighting control unit can set an optimal lighting pattern by referring to past lighting usage data when controlling the lighting. For example, the lighting control unit sets an optimal lighting pattern based on past lighting usage data. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please set an optimal lighting pattern based on past lighting usage data," and the generation AI sets the optimal lighting pattern based on this. The lighting control unit can also analyze past lighting usage data and set an energy-efficient lighting pattern. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please analyze past lighting usage data and set an energy-efficient lighting pattern," and the generation AI sets an energy-efficient lighting pattern based on this. The lighting control unit can also refer to past lighting usage data and set a lighting pattern tailored to the user's preferences. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please refer to past lighting usage data and set a lighting pattern tailored to the user's preferences," and the generation AI sets a lighting pattern tailored to the user's preferences based on this. This allows the optimal lighting pattern to be set by referring to past lighting usage data. Some or all of the above-described processing in the lighting control unit is performed using the generation AI. For example, the lighting control unit inputs a prompt to the generation AI, and the generation AI sets a lighting pattern based on this.

[0043] The lighting control unit can adjust the brightness and color of the lighting when controlling the lighting. For example, the lighting control unit uses bright lighting during the day and dim lighting at night. For example, the lighting control unit inputs a prompt to the generation AI, such as "Use bright lighting during the day and dim lighting at night," and the generation AI adjusts the brightness of the lighting based on this. The lighting control unit can also use warm-colored lighting to create a relaxing atmosphere. For example, the lighting control unit inputs a prompt to the generation AI, such as "Use warm-colored lighting to create a relaxing atmosphere," and the generation AI adjusts the color of the lighting based on this. The lighting control unit can also use bright white lighting in an emergency. For example, the lighting control unit inputs a prompt to the generation AI, such as "Use bright white lighting in an emergency," and the generation AI adjusts the brightness and color of the lighting based on this. In this way, by adjusting the brightness and color of the lighting, appropriate lighting can be provided according to the situation. Some or all of the above-mentioned processing in the lighting control unit is performed using the generation AI. For example, the lighting control unit inputs a prompt to the generation AI, and the generation AI adjusts the brightness and color of the lighting based on this.

[0044] When controlling lighting, the lighting control unit can set an optimal lighting pattern taking into account the user's geographical location information. For example, if the user is near the house, the lighting control unit prioritizes turning on the exterior lights of the house. For example, the lighting control unit inputs a prompt to the generation AI such as, "When the user is near the house, please turn on the exterior lights of the house first," and the generation AI sets the lighting pattern based on this. The lighting control unit can also prioritize turning on the interior lights of the house if the user is far away. For example, the lighting control unit inputs a prompt to the generation AI such as, "When the user is far away, please turn on the interior lights of the house first," and the generation AI sets the lighting pattern based on this. The lighting control unit can also set an optimal lighting pattern for a specific location if the user is in that location. For example, the lighting control unit inputs a prompt to the generation AI such as, "When the user is in a specific location, please set the optimal lighting pattern for that location," and the generation AI sets the lighting pattern based on this. This enables more effective lighting control by taking into account the geographical location information. Some or all of the above-described processing in the lighting control unit is performed using the generation AI. For example, the lighting control unit inputs a prompt to the generation AI, and the generation AI sets a lighting pattern based on this.

[0045] When controlling the lighting, the lighting control unit can analyze the user's social media activity and set a related lighting pattern. For example, the lighting control unit sets a lighting pattern related to the content the user is talking about on social media. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please set a lighting pattern related to the content the user is talking about on social media," and the generation AI sets a lighting pattern based on this. The lighting control unit can also reference the user's social media posts and set a related lighting pattern. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please reference the user's social media posts and set a related lighting pattern," and the generation AI sets a lighting pattern based on this. The lighting control unit can also set a related lighting pattern taking into account the user's social media friendships. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please set a related lighting pattern taking into account the user's social media friendships," and the generation AI sets a lighting pattern based on this. This allows for more relevant lighting patterns to be provided by analyzing social media activity. Some or all of the above-described processing in the lighting control unit is performed using the generation AI. For example, the lighting control unit inputs a prompt to the generation AI, and the generation AI sets a lighting pattern based on this.

[0046] When generating a warning, the warning generation unit can refer to past warning data to generate a more effective warning message. The warning generation unit, for example, generates an optimal warning message based on past warning data. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate an optimal warning message based on past warning data," and the generation AI generates an optimal warning message based on this. The warning generation unit can also analyze past warning data to generate an effective warning message. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please analyze past warning data and generate an effective warning message," and the generation AI generates an effective warning message based on this. The warning generation unit can also refer to past warning data to generate a warning message tailored to the user's preferences. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please refer to past warning data and generate a warning message tailored to the user's preferences," and the generation AI generates a warning message tailored to the user's preferences based on this. In this way, by referring to past warning data, a more effective warning message can be generated. Some or all of the above-mentioned processing in the warning generation unit is performed using the generation AI. For example, the warning generator inputs a prompt to the generation AI, and the generation AI generates a warning message based on this.

[0047] The warning generation unit can adjust the volume and tone of the warning message when generating a warning. For example, the warning generation unit generates a warning message at a high volume in an emergency. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message at a high volume in an emergency," and the generation AI generates a warning message at a high volume based on this. The warning generation unit can also generate a warning message at a low volume at night. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message at a low volume at night," and the generation AI generates a warning message at a low volume based on this. The warning generation unit can also generate a warning message at a low tone to create a relaxing atmosphere. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message at a low tone to create a relaxing atmosphere," and the generation AI generates a warning message at a low tone based on this. By adjusting the volume and tone, it is possible to provide an appropriate warning message according to the situation. Some or all of the above-mentioned processing in the warning generation unit is performed using the generation AI. For example, the warning generation unit inputs a prompt to the generation AI, and the generation AI generates a warning message based on this.

[0048] When generating a warning, the warning generation unit can generate a region-specific warning message by taking into account the user's geographical location information. The warning generation unit generates the warning message based on, for example, local crime information. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message based on local crime information," and the generation AI generates a region-specific warning message based on this. The warning generation unit can also generate a warning message based on local weather information. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message based on local weather information," and the generation AI generates a region-specific warning message based on this. The warning generation unit can also generate a warning message based on local event information. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message based on local event information," and the generation AI generates a region-specific warning message based on this. This allows for more effective warning messages to be provided by taking into account the geographical location information. Some or all of the above-described processing in the warning generation unit is performed using the generation AI. For example, the warning generation unit inputs a prompt to the generation AI, and the generation AI generates a warning message based on this.

[0049] When generating a warning, the warning generation unit can analyze the user's social media activity and generate a relevant warning message. The warning generation unit generates the warning message based on, for example, the content the user is discussing on social media. For example, the warning generation unit inputs a prompt to the generation AI, such as, "Please generate a warning message based on the content the user is discussing on social media," and the generation AI generates the relevant warning message based on this. The warning generation unit can also generate a relevant warning message by referring to the content posted on social media by the user. For example, the warning generation unit inputs a prompt to the generation AI, such as, "Please generate a relevant warning message by referring to the content posted on social media by the user," and the generation AI generates the relevant warning message based on this. The warning generation unit can also generate a relevant warning message by taking into account the user's social media friendships. For example, the warning generation unit inputs a prompt to the generation AI, such as, "Please generate a relevant warning message by taking into account the user's social media friendships," and the generation AI generates the relevant warning message based on this. This allows for more relevant warning messages to be provided by analyzing social media activity. Some or all of the above-described processing in the warning generation unit is performed using the generation AI. For example, the warning generator inputs a prompt to the generation AI, and the generation AI generates a warning message based on this.

[0050] When transmitting a warning message, the warning transmission unit can set multiple destinations for the warning message and transmit them simultaneously. For example, the warning transmission unit can transmit a warning message simultaneously to the external speaker and internal speaker of the home. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit a warning message to the external speaker and internal speaker of the home simultaneously," and the generation AI generates a warning message based on this and transmits it simultaneously to the external speaker and internal speaker. The warning transmission unit can also transmit a warning message simultaneously to speakers installed in each room of the home. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit a warning message to the speakers installed in each room of the home simultaneously," and the generation AI generates a warning message based on this and transmits it simultaneously to the speakers in each room. The warning transmission unit can also transmit a warning message simultaneously to the external speaker and smartphone of the home. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit a warning message to the external speaker and smartphone of the home simultaneously," and the generation AI generates a warning message based on this and transmits it simultaneously to the external speaker and smartphone. This increases the effectiveness of warning messages by simultaneously transmitting to multiple destinations. Some or all of the above-mentioned processing in the warning transmission unit is performed using the generation AI. For example, the warning transmission unit inputs a prompt to the generation AI, which then generates a warning message based on the prompt and transmits it to multiple destinations simultaneously.

[0051] When transmitting, the warning transmission unit can select a transmission method for the warning message and transmit it, for example, through a speaker or a smartphone. The warning transmission unit transmits the warning message, for example, through a speaker in the house. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit the warning message through the speaker in the house," which generates a warning message based on this and transmits it through the speaker. The warning transmission unit can also transmit the warning message through the user's smartphone. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit the warning message through the user's smartphone," which generates a warning message based on this and transmits it through the smartphone. The warning transmission unit can also transmit the warning message through the intercom in the house. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit the warning message through the intercom in the house," which generates a warning message based on this and transmits it through the intercom. This allows for appropriate transmission according to the situation by selecting the transmission method. Some or all of the above-mentioned processing in the warning transmission unit is performed using the generation AI. For example, the warning issuing unit inputs a prompt to the generation AI, and the generation AI generates a warning message based on this and selects a method for issuing the message.

[0052] When making a call, the alert sending unit can select the optimal call destination by taking into account the user's geographical location information. For example, if the user is near their home, the alert sending unit prioritizes calling an external speaker in the home. For example, the alert sending unit inputs a prompt to the generation AI saying, "If the user is near their home, please prioritize calling an external speaker in the home." The generation AI generates a voice message based on this and prioritizes calling an external speaker. The alert sending unit can also prioritize calling a smartphone if the user is far away. For example, the alert sending unit inputs a prompt to the generation AI saying, "If the user is far away, please prioritize calling a smartphone." The generation AI generates a voice message based on this and prioritizes calling a smartphone. The alert sending unit can also select the optimal call destination for a specific location if the user is in that location. For example, the alert sending unit inputs a prompt to the generation AI saying, "If the user is in a specific location, please select the optimal call destination for that location." The generation AI generates a voice message based on this and selects the optimal call destination. This enables more effective calls by taking geographical location information into account. Some or all of the above-mentioned processing in the warning transmission unit is performed using the generation AI. For example, the warning transmission unit inputs a prompt to the generation AI, which then generates a voice message based on the prompt and selects the optimal recipient.

[0053] When sending a warning, the warning unit can analyze the user's social media activity and select relevant destinations. For example, the warning unit selects destinations related to the content the user is discussing on social media. For example, the warning unit inputs a prompt to the generation AI, such as, "Please select destinations related to the content the user is discussing on social media," and the generation AI generates a voice message and selects relevant destinations based on this prompt. The warning unit can also select relevant destinations by taking into account the user's social media friendships. For example, the warning unit inputs a prompt to the generation AI, such as, "Please select relevant destinations by taking into account the user's social media friendships," and the generation AI generates a voice message and selects relevant destinations based on this prompt. The warning unit can also select relevant destinations by referring to the content of the user's social media posts. For example, the warning unit inputs a prompt to the generation AI, such as, "Please select relevant destinations by referring to the content of the user's social media posts," and the generation AI generates a voice message and selects relevant destinations based on this prompt. This enables more relevant messages to be sent by analyzing social media activity. Some or all of the above-described processing in the alert sending unit is performed using the generation AI. For example, the alert sending unit inputs a prompt to the generation AI, which then generates a voice message based on the prompt and selects the relevant recipients.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The smart home security system can further include a facial recognition unit. The facial recognition unit can use a camera to recognize people around the home and identify the faces of registered family and friends. For example, the facial recognition unit can analyze the video from the camera and determine whether the face matches a registered face. If the facial recognition unit detects a suspicious person's face, it can notify the warning generation unit and generate a warning message. This allows the use of facial recognition technology to provide a higher level of security.

[0056] The warning generation unit may further include a voice recognition function. The voice recognition function can recognize the user's voice and generate a warning message in response to a specific command. For example, if the user yells "help," the warning generation unit immediately generates a warning message and transmits it to the outside. The voice recognition function can also analyze the tone and urgency of the user's voice to generate an appropriate warning message. This allows for faster and more effective warnings to be provided using voice recognition technology.

[0057] The warning generation unit can further include a vibration notification function. The vibration notification function can notify the user's smartphone or smartwatch by vibrating when generating a warning message. For example, if a suspicious person intrudes, the warning generation unit generates a warning message and simultaneously notifies the user by vibrating their device. In addition, by adjusting the strength and pattern of the vibration, notifications can be made according to the level of urgency. As a result, using the vibration notification function can quickly and reliably convey warnings to the user.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The voice generation unit uses a voice generation AI to generate fake voice messages. For example, it generates voice messages that imitate family conversations or television sounds. The voice generation unit inputs prompts such as "Please imitate family conversations" or "Please imitate television sounds" to the generation AI, and the generation AI generates a voice message based on these prompts. Step 2: The transmitting unit transmits the voice message generated by the voice generating unit to the outside. For example, the voice message is transmitted to the outside through a speaker. The transmitting unit inputs the generated voice message to the speaker, and the speaker transmits the voice to the outside. Step 3: The lighting control unit automatically turns the room lights on and off using sensors or timers. For example, the living room lights can be automatically turned on in the evening, and the bedroom lights can be turned on at night. The lighting control unit controls the lights on and off based on the settings of the sensors and timers. Step 4: The warning generation unit generates a warning message when the sensor detects suspicious activity. For example, it generates a warning message such as "A suspicious person has entered. The police have been notified." The warning generation unit receives input from the sensor and inputs a prompt such as "A suspicious person has entered. The police have been notified." to the generation AI, which then generates a warning message based on this. Step 5: The warning transmission unit transmits the warning message generated by the warning generation unit to the outside. For example, the warning message is transmitted to the outside through a speaker. The warning transmission unit inputs the generated warning message into the speaker, and the speaker transmits the warning message to the outside.

[0060] (Example 2) A smart home security system according to an embodiment of the present invention utilizes a voice generation AI to enhance home security while the user is away, making it appear as if someone is present at home. This system has a function in which the voice generation AI generates fake voice messages and transmits them to the outside. For example, the system can mimic family conversations or television sounds, making them audible from outside. The system also has a function to automatically turn room lights on and off using sensors and timers, making it appear as if someone is moving around the room from the outside. Furthermore, if the sensor detects suspicious movement, the voice generation AI generates a warning message and transmits it to the outside. This allows the system to enhance home security even when the user is away, preventing intruders from entering the home. This smart home security system enhances home security even when the user is away, preventing intruders from entering the home.

[0061] A smart home security system according to an embodiment includes a voice generation unit, a transmission unit, a lighting control unit, a warning generation unit, and a warning transmission unit. The voice generation unit generates fake voice messages using a voice generation AI. For example, the voice generation unit generates voice messages that imitate family conversations or television sounds. The voice generation unit inputs prompts such as "Please imitate family conversations" or "Please imitate television sounds" to the generation AI, and the generation AI generates a voice message based on the prompts. The transmission unit transmits the voice message generated by the voice generation unit to the outside. For example, the voice message is transmitted to the outside via a speaker. The transmission unit inputs the generated voice message into a speaker, which then transmits the voice to the outside. The lighting control unit automatically turns on and off room lights using a sensor or a timer. For example, the living room light is automatically turned on in the evening and the bedroom light is turned on at night. The lighting control unit controls the on / off of lights based on the settings of the sensor or timer. The warning generation unit generates a warning message when the sensor detects suspicious activity. For example, the warning message is generated as follows: "A suspicious person has entered. The police have been notified." The warning generation unit receives input from the sensor and inputs a prompt to the generation AI, such as "A suspicious person has intruded. The police have been notified." Based on this, the generation AI generates a warning message. The warning transmission unit transmits the warning message generated by the warning generation unit to the outside. For example, the warning message is transmitted to the outside via a speaker. The warning transmission unit inputs the generated warning message into a speaker, which then transmits the warning message to the outside. In this way, the smart home security system according to the embodiment can strengthen home security and prevent intrusion of suspicious persons even when you are away from home.

[0062] The voice generation unit can generate fake voice messages that imitate family conversations or television sounds. The voice generation unit generates, for example, a voice message that imitates family conversations. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please imitate family conversations," and the generation AI generates a voice message that imitates family conversations based on this. The voice generation unit can also generate voice messages that imitate television sounds. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please imitate television sounds," and the generation AI generates a voice message that imitates television sounds based on this. This makes it possible to generate more natural voice messages by imitating family conversations or television sounds. Some or all of the above-described processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this.

[0063] The lighting control unit can use a sensor or a timer to automatically turn on the living room lights in the evening (e.g., 6 p.m.) and turn on the bedroom lights at night (e.g., 10 p.m.). The lighting control unit can, for example, use a sensor to automatically turn on the living room lights in the evening. For example, the lighting control unit turns on the living room lights when the sensor detects 6 p.m. The lighting control unit can also use a timer to turn on the bedroom lights at night. For example, the lighting control unit turns on the bedroom lights when the timer detects 10 p.m. In this way, by using a sensor or a timer, it can appear as if a person is moving around the room when viewed from outside. Some or all of the above-mentioned processing in the lighting control unit may be performed using AI, or may be performed without AI. For example, the lighting control unit can input data from a sensor or timer into AI, which can then control the on / off of the lights based on this data.

[0064] The warning generation unit can generate a warning message such as "A suspicious person has entered. The police have been notified." when the sensor detects suspicious movement. For example, the warning generation unit generates a warning message such as "A suspicious person has entered. The police have been notified." when the sensor detects suspicious movement. For example, the warning generation unit receives input from the sensor and inputs a prompt such as "A suspicious person has entered. The police have been notified." to the generation AI, and the generation AI generates a warning message based on this. In this way, by generating a warning message when suspicious movement is detected, it is possible to intimidate the suspicious person. Some or all of the above-mentioned processing in the warning generation unit is performed using the generation AI. For example, the warning generation unit inputs a prompt to the generation AI, and the generation AI generates a warning message based on this.

[0065] The warning transmission unit can transmit the warning message generated by the warning generation unit to the outside. The warning transmission unit transmits the warning message generated by the warning generation unit to the outside via a speaker, for example. For example, the warning transmission unit inputs the generated warning message into a speaker, which then transmits the warning message to the outside. In this way, transmitting the warning message to the outside can enhance the deterrent effect against suspicious individuals. Some or all of the above-mentioned processing in the warning transmission unit may be performed using AI, or may be performed without using AI. For example, the warning transmission unit inputs a prompt to the generation AI, which then generates a warning message based on the prompt and transmits the warning message to the outside via a speaker.

[0066] The voice generation unit can estimate the user's emotions and adjust the content of the generated voice message based on the estimated user's emotions. For example, if the user is relaxed, the voice generation unit generates a calm conversational voice. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a relaxed conversational voice," and the generation AI generates a calm conversational voice based on this. The voice generation unit can also generate a reassuring voice message if the user is nervous. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a reassuring voice message," and the generation AI generates a reassuring voice message based on this. The voice generation unit can also generate a voice message with a bright tone if the user is having fun. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a bright voice message," and the generation AI generates a bright voice message based on this. This allows for the generation of a voice message that corresponds to the user's emotions, thereby providing a more natural and effective voice message. Some or all of the above-described processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this.

[0067] The voice generation unit can generate more natural voice messages by referencing past voice data when generating voice. The voice generation unit, for example, references previously recorded family conversations to generate natural conversational voices. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate natural conversational voices by referring to past family conversations," and the generation AI generates natural conversational voices based on this. The voice generation unit can also generate realistic television voices by referencing the voice of past television programs. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate realistic television voices by referring to the voice of past television programs," and the generation AI generates realistic television voices based on this. The voice generation unit can also generate natural radio voices by referencing the voice of past radio broadcasts. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate natural radio voices by referring to the voice of past radio broadcasts," and the generation AI generates natural radio voices based on this. This allows for more natural voice messages to be generated by referencing past voice data. Some or all of the above-described processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this.

[0068] The voice generation unit can adjust the volume and tone of the voice message it generates when generating the voice. For example, the voice generation unit increases the volume during the day and decreases the volume at night. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate voice messages at a higher volume during the day and at a lower volume at night," and the generation AI generates a voice message with the volume adjusted based on this. The voice generation unit can also generate a warning voice with a higher tone in an emergency. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a warning voice with a higher tone in an emergency," and the generation AI generates a warning voice with a higher tone based on this. The voice generation unit can also generate a voice with a lower tone to create a relaxing atmosphere. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a voice with a lower tone to create a relaxing atmosphere," and the generation AI generates a voice with a lower tone based on this. In this way, by adjusting the volume and tone, it is possible to provide an appropriate voice message according to the situation. Some or all of the above-mentioned processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this.

[0069] The voice generation unit can estimate the user's emotions and adjust the frequency of voice messages based on the estimated user emotions. For example, if the user is feeling anxious, the voice generation unit may frequently transmit voice messages. For example, the voice generation unit may input a prompt to the generation AI, such as "If the user is feeling anxious, please transmit voice messages frequently," and the generation AI may then transmit voice messages frequently based on this prompt. The voice generation unit may also reduce the frequency of voice messages when the user is relaxed. For example, the voice generation unit may input a prompt to the generation AI, such as "If the user is relaxed, please transmit voice messages less frequently," and the generation AI may then adjust the transmission frequency based on this prompt. The voice generation unit may also transmit voice messages at an appropriate frequency when the user is having fun. For example, the voice generation unit may input a prompt to the generation AI, such as "If the user is having fun, please transmit voice messages at an appropriate frequency," and the generation AI may then transmit voice messages at an appropriate frequency based on this prompt. This allows for more effective voice messages to be provided by adjusting the transmission frequency according to the user's emotions. Some or all of the above-described processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs prompts to the generation AI, which then generates a voice message based on the prompts and adjusts the frequency of the messages.

[0070] The voice generation unit can generate a region-specific voice message by taking into account the user's geographical location information when generating the voice. The voice generation unit, for example, generates a voice message incorporating a regional dialect. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a voice message incorporating a regional dialect," and the generation AI generates a voice message incorporating a regional dialect based on this. The voice generation unit can also generate a voice message including regional event information. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a voice message including regional event information," and the generation AI generates a voice message including regional event information based on this. The voice generation unit can also generate a voice message including regional weather information. For example, the voice generation unit inputs a prompt to the generation AI, such as "Please generate a voice message including regional weather information," and the generation AI generates a voice message including regional weather information based on this. This generates a region-specific voice message, allowing for a more natural and effective voice message. Some or all of the above-described processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this.

[0071] The voice generation unit can analyze the user's social media activity and generate a relevant voice message when generating the voice. The voice generation unit generates the voice message based on, for example, the content the user is discussing on social media. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate a voice message based on the content the user is discussing on social media," and the generation AI generates the relevant voice message based on this. The voice generation unit can also generate a relevant voice message by referring to the content posted on social media by the user. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate a relevant voice message by referring to the content posted on social media by the user," and the generation AI generates the relevant voice message based on this. The voice generation unit can also generate a relevant voice message by taking into account the user's social media friendships. For example, the voice generation unit inputs a prompt to the generation AI, such as, "Please generate a relevant voice message by taking into account the user's social media friendships," and the generation AI generates the relevant voice message based on this. This allows for the provision of more relevant voice messages by analyzing social media activity. Some or all of the above-described processing in the voice generation unit is performed using the generation AI. For example, the voice generation unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this.

[0072] The transmitter can estimate the user's emotions and adjust the timing of voice message transmission based on the estimated user emotions. For example, if the user is feeling anxious, the transmitter can transmit voice messages frequently. For example, the transmitter can input a prompt to the generation AI, such as "If the user is feeling anxious, please transmit voice messages frequently," and the generation AI can transmit voice messages frequently based on this. The transmitter can also reduce the frequency of voice messages transmitted when the user is relaxed. For example, the transmitter can input a prompt to the generation AI, such as "If the user is relaxed, please transmit voice messages less frequently," and the generation AI can adjust the transmission frequency based on this. The transmitter can also transmit voice messages at an appropriate frequency when the user is having fun. For example, the transmitter can input a prompt to the generation AI, such as "If the user is having fun, please transmit voice messages at an appropriate frequency," and the generation AI can transmit voice messages at an appropriate frequency based on this. This allows for more effective voice messages to be provided by adjusting the transmission timing according to the user's emotions. Some or all of the above-described processing in the transmitter can be performed using the generation AI. For example, the sending unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this and adjusts the timing of the message.

[0073] When making a call, the sending unit can set multiple destinations for a voice message and send them simultaneously. For example, the sending unit can send a voice message to multiple speakers in a house simultaneously. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message to multiple speakers in the house simultaneously," which the generation AI generates based on the prompt and sends to the multiple speakers simultaneously. The sending unit can also send a voice message to both external and internal speakers in a house simultaneously. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message to external and internal speakers in the house simultaneously," which the generation AI generates based on the prompt and sends to the external and internal speakers simultaneously. The sending unit can also send a voice message to speakers installed in each room in a house simultaneously. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message to speakers installed in each room in a house simultaneously," which the generation AI generates based on the prompt and sends to the speakers in each room simultaneously. This increases the effectiveness of the voice message by simultaneously sending to multiple destinations. Some or all of the above-described processing in the sending unit is performed using the generation AI. For example, the sending unit inputs a prompt to the generation AI, and the generation AI generates a voice message based on this and sends it to multiple destinations simultaneously.

[0074] When making a call, the sending unit can select a method for sending a voice message and send it through, for example, a speaker or a smartphone. The sending unit, for example, sends a voice message through a home speaker. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message through the home speaker," which generates a voice message based on the prompt and sends it through the speaker. The sending unit can also send a voice message through the user's smartphone. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message through the user's smartphone," which generates a voice message based on the prompt and sends it through the smartphone. The sending unit can also send a voice message through the home intercom. For example, the sending unit inputs a prompt to the generation AI, such as "Please send a voice message through the home intercom," which generates a voice message based on the prompt and sends it through the intercom. This allows for appropriate calling according to the situation by selecting the calling method. Some or all of the above-mentioned processing in the sending unit is performed using the generation AI. For example, the sending unit inputs a prompt to the generation AI, which generates a voice message based on the prompt and selects the calling method.

[0075] The sending unit can estimate the user's emotions and adjust the order in which voice messages are sent based on the estimated user's emotions. For example, if the user is feeling anxious, the sending unit prioritizes sending voice messages that give a sense of security. For example, the sending unit inputs a prompt to the generation AI saying, "If the user is feeling anxious, please prioritize sending voice messages that give a sense of security," and the generation AI generates a voice message that gives a sense of security based on this and prioritizes sending it. The sending unit can also prioritize sending voice messages with relaxing content when the user is relaxed. For example, the sending unit inputs a prompt to the generation AI saying, "If the user is relaxing, please prioritize sending voice messages with relaxing content," and the generation AI generates a voice message with relaxing content based on this and prioritizes sending it. The sending unit can also prioritize sending voice messages that increase enjoyment when the user is having fun. For example, the sending unit inputs a prompt to the generation AI saying, "If the user is having fun, please prioritize sending voice messages that increase enjoyment," and the generation AI generates a voice message with enjoyment based on this and prioritizes sending it. This allows for more effective voice messages to be provided by adjusting the order of voice messages according to the user's emotions. Some or all of the above-described processing in the voice message sending unit is performed using a generation AI. For example, the voice message sending unit inputs a prompt to the generation AI, which then generates a voice message based on the prompt and adjusts the order of voice messages to be sent.

[0076] When making a call, the calling unit can select the optimal call destination by taking into account the user's geographical location information. For example, if the user is near their home, the calling unit prioritizes calling from an external speaker in the home. For example, the calling unit inputs a prompt to the generation AI saying, "If the user is near their home, please prioritize calling from an external speaker in the home." The generation AI generates a voice message based on this and prioritizes calling from the external speaker. The calling unit can also prioritize calling from a smartphone when the user is far away. For example, the calling unit inputs a prompt to the generation AI saying, "If the user is far away, please prioritize calling from a smartphone." The generation AI generates a voice message based on this and prioritizes calling from the smartphone. The calling unit can also select the optimal call destination for a specific location when the user is in that location. For example, the calling unit inputs a prompt to the generation AI saying, "If the user is in a specific location, please select the optimal call destination for that location." The generation AI generates a voice message based on this and selects the optimal call destination. This enables more effective calls by taking geographical location information into account. Some or all of the above-mentioned processing in the sending unit is performed using the generation AI. For example, the sending unit inputs a prompt to the generation AI, which then generates a voice message based on the prompt and selects the optimal destination.

[0077] When sending a message, the sending unit can analyze the user's social media activity and select relevant destinations. For example, the sending unit selects destinations related to the content the user is discussing on social media. For example, the sending unit inputs a prompt to the generation AI, such as, "Please select destinations related to the content the user is discussing on social media," and the generation AI generates a voice message and selects relevant destinations based on this. The sending unit can also select relevant destinations by taking into account the user's social media friendships. For example, the sending unit inputs a prompt to the generation AI, such as, "Please select relevant destinations by taking into account the user's social media friendships," and the generation AI generates a voice message and selects relevant destinations based on this. The sending unit can also select relevant destinations by referring to the content of the user's social media posts. For example, the sending unit inputs a prompt to the generation AI, such as, "Please select relevant destinations by referring to the content of the user's social media posts," and the generation AI generates a voice message and selects relevant destinations based on this. This enables more relevant messages to be sent by analyzing social media activity. Some or all of the above-mentioned processing in the sending unit is performed using the generation AI. For example, the sending unit inputs a prompt to the generation AI, which then generates a voice message based on the prompt and selects the relevant destination.

[0078] The lighting control unit can estimate the user's emotions and adjust the on / off timing of the lights based on the estimated user's emotions. For example, if the user is relaxed, the lighting control unit turns the lights on and off slowly. For example, the lighting control unit inputs a prompt to the generation AI, such as "If the user is relaxed, please turn the lights on and off slowly," and the generation AI adjusts the on / off timing of the lights based on this. The lighting control unit can also turn the lights on and off quickly if the user is nervous. For example, the lighting control unit inputs a prompt to the generation AI, such as "If the user is nervous, please turn the lights on and off quickly," and the generation AI adjusts the on / off timing of the lights based on this. The lighting control unit can also turn the lights on and off randomly if the user is having fun. For example, the lighting control unit inputs a prompt to the generation AI, such as "If the user is having fun, please turn the lights on and off randomly," and the generation AI adjusts the on / off timing of the lights based on this. This enables more effective lighting control by adjusting the on / off timing of the lights according to the user's emotions. Some or all of the above-mentioned processing in the lighting control unit is performed by the generation AI. For example, the lighting control unit inputs prompts to the generation AI, which then adjusts the timing of lighting on and off based on these.

[0079] The lighting control unit can set an optimal lighting pattern by referring to past lighting usage data when controlling the lighting. For example, the lighting control unit sets an optimal lighting pattern based on past lighting usage data. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please set an optimal lighting pattern based on past lighting usage data," and the generation AI sets the optimal lighting pattern based on this. The lighting control unit can also analyze past lighting usage data and set an energy-efficient lighting pattern. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please analyze past lighting usage data and set an energy-efficient lighting pattern," and the generation AI sets an energy-efficient lighting pattern based on this. The lighting control unit can also refer to past lighting usage data and set a lighting pattern tailored to the user's preferences. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please refer to past lighting usage data and set a lighting pattern tailored to the user's preferences," and the generation AI sets a lighting pattern tailored to the user's preferences based on this. This allows the optimal lighting pattern to be set by referring to past lighting usage data. Some or all of the above-described processing in the lighting control unit is performed using the generation AI. For example, the lighting control unit inputs a prompt to the generation AI, and the generation AI sets a lighting pattern based on this.

[0080] The lighting control unit can adjust the brightness and color of the lighting when controlling the lighting. For example, the lighting control unit uses bright lighting during the day and dim lighting at night. For example, the lighting control unit inputs a prompt to the generation AI, such as "Use bright lighting during the day and dim lighting at night," and the generation AI adjusts the brightness of the lighting based on this. The lighting control unit can also use warm-colored lighting to create a relaxing atmosphere. For example, the lighting control unit inputs a prompt to the generation AI, such as "Use warm-colored lighting to create a relaxing atmosphere," and the generation AI adjusts the color of the lighting based on this. The lighting control unit can also use bright white lighting in an emergency. For example, the lighting control unit inputs a prompt to the generation AI, such as "Use bright white lighting in an emergency," and the generation AI adjusts the brightness and color of the lighting based on this. In this way, by adjusting the brightness and color of the lighting, appropriate lighting can be provided according to the situation. Some or all of the above-mentioned processing in the lighting control unit is performed using the generation AI. For example, the lighting control unit inputs a prompt to the generation AI, and the generation AI adjusts the brightness and color of the lighting based on this.

[0081] The lighting control unit can estimate the user's emotions and adjust the frequency at which the lights are turned on and off based on the estimated user emotions. For example, if the user is feeling anxious, the lighting control unit turns the lights on and off more frequently. For example, the lighting control unit inputs a prompt to the generation AI, such as "If the user is feeling anxious, please turn the lights on and off more frequently," and the generation AI adjusts the frequency at which the lights are turned on and off based on this. The lighting control unit can also reduce the frequency at which the lights are turned on and off if the user is relaxed. For example, the lighting control unit inputs a prompt to the generation AI, such as "If the user is relaxing, please turn the lights on and off less frequently," and the generation AI adjusts the frequency at which the lights are turned on and off based on this. The lighting control unit can also turn the lights on and off at a moderate frequency if the user is enjoying themselves. For example, the lighting control unit inputs a prompt to the generation AI, such as "If the user is enjoying themselves, please turn the lights on and off at a moderate frequency," and the generation AI adjusts the frequency at which the lights are turned on and off based on this. This enables more effective lighting control by adjusting the frequency at which the lights are turned on and off according to the user's emotions. Some or all of the above-mentioned processes in the lighting control unit are performed using the generation AI. For example, the lighting control unit inputs prompts to the generation AI, which then adjusts the frequency at which the lights are turned on and off based on these prompts.

[0082] When controlling lighting, the lighting control unit can set an optimal lighting pattern taking into account the user's geographical location information. For example, if the user is near the house, the lighting control unit prioritizes turning on the exterior lights of the house. For example, the lighting control unit inputs a prompt to the generation AI such as, "When the user is near the house, please turn on the exterior lights of the house first," and the generation AI sets the lighting pattern based on this. The lighting control unit can also prioritize turning on the interior lights of the house if the user is far away. For example, the lighting control unit inputs a prompt to the generation AI such as, "When the user is far away, please turn on the interior lights of the house first," and the generation AI sets the lighting pattern based on this. The lighting control unit can also set an optimal lighting pattern for a specific location if the user is in that location. For example, the lighting control unit inputs a prompt to the generation AI such as, "When the user is in a specific location, please set the optimal lighting pattern for that location," and the generation AI sets the lighting pattern based on this. This enables more effective lighting control by taking into account the geographical location information. Some or all of the above-described processing in the lighting control unit is performed using the generation AI. For example, the lighting control unit inputs a prompt to the generation AI, and the generation AI sets a lighting pattern based on this.

[0083] When controlling the lighting, the lighting control unit can analyze the user's social media activity and set a related lighting pattern. For example, the lighting control unit sets a lighting pattern related to the content the user is talking about on social media. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please set a lighting pattern related to the content the user is talking about on social media," and the generation AI sets a lighting pattern based on this. The lighting control unit can also reference the user's social media posts and set a related lighting pattern. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please reference the user's social media posts and set a related lighting pattern," and the generation AI sets a lighting pattern based on this. The lighting control unit can also set a related lighting pattern taking into account the user's social media friendships. For example, the lighting control unit inputs a prompt to the generation AI, such as, "Please set a related lighting pattern taking into account the user's social media friendships," and the generation AI sets a lighting pattern based on this. This allows for more relevant lighting patterns to be provided by analyzing social media activity. Some or all of the above-described processing in the lighting control unit is performed using the generation AI. For example, the lighting control unit inputs a prompt to the generation AI, and the generation AI sets a lighting pattern based on this.

[0084] The warning generation unit can estimate the user's emotions and adjust the content of the warning message based on the estimated user emotions. For example, if the user is feeling anxious, the warning generation unit generates a warning message that provides a sense of security. For example, the warning generation unit inputs a prompt to the generation AI, such as, "If the user is feeling anxious, please generate a warning message that provides a sense of security," and the generation AI generates a warning message that provides a sense of security based on this prompt. The warning generation unit can also generate a warning message with a calm tone if the user is relaxed. For example, the warning generation unit inputs a prompt to the generation AI, such as, "If the user is relaxed, please generate a warning message with a calm tone," and the generation AI generates a warning message with a calm tone based on this prompt. The warning generation unit can also generate a quick and clear warning message if the user is nervous. For example, the warning generation unit inputs a prompt to the generation AI, such as, "If the user is nervous, please generate a quick and clear warning message," and the generation AI generates a quick and clear warning message based on this prompt. This enables more effective warnings by generating warning messages that correspond to the user's emotions. Some or all of the above-mentioned processing in the warning generation unit is performed using the generation AI. For example, the warning generator inputs a prompt to the generation AI, and the generation AI generates a warning message based on this.

[0085] When generating a warning, the warning generation unit can refer to past warning data to generate a more effective warning message. The warning generation unit, for example, generates an optimal warning message based on past warning data. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate an optimal warning message based on past warning data," and the generation AI generates an optimal warning message based on this. The warning generation unit can also analyze past warning data to generate an effective warning message. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please analyze past warning data and generate an effective warning message," and the generation AI generates an effective warning message based on this. The warning generation unit can also refer to past warning data to generate a warning message tailored to the user's preferences. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please refer to past warning data and generate a warning message tailored to the user's preferences," and the generation AI generates a warning message tailored to the user's preferences based on this. In this way, by referring to past warning data, a more effective warning message can be generated. Some or all of the above-mentioned processing in the warning generation unit is performed using the generation AI. For example, the warning generator inputs a prompt to the generation AI, and the generation AI generates a warning message based on this.

[0086] The warning generation unit can adjust the volume and tone of the warning message when generating a warning. For example, the warning generation unit generates a warning message at a high volume in an emergency. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message at a high volume in an emergency," and the generation AI generates a warning message at a high volume based on this. The warning generation unit can also generate a warning message at a low volume at night. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message at a low volume at night," and the generation AI generates a warning message at a low volume based on this. The warning generation unit can also generate a warning message at a low tone to create a relaxing atmosphere. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message at a low tone to create a relaxing atmosphere," and the generation AI generates a warning message at a low tone based on this. By adjusting the volume and tone, it is possible to provide an appropriate warning message according to the situation. Some or all of the above-mentioned processing in the warning generation unit is performed using the generation AI. For example, the warning generation unit inputs a prompt to the generation AI, and the generation AI generates a warning message based on this.

[0087] The warning generation unit can estimate the user's emotions and adjust the frequency of warning message transmission based on the estimated user emotions. For example, the warning generation unit frequently transmits warning messages when the user is feeling anxious. For example, the warning generation unit inputs a prompt to the generation AI, such as "If the user is feeling anxious, please transmit warning messages frequently," and the generation AI transmits warning messages frequently based on this prompt. The warning generation unit can also reduce the frequency of warning messages when the user is relaxed. For example, the warning generation unit inputs a prompt to the generation AI, such as "If the user is relaxed, please reduce the frequency of warning messages," and the generation AI adjusts the transmission frequency based on this prompt. The warning generation unit can also transmit warning messages at an appropriate frequency when the user is nervous. For example, the warning generation unit inputs a prompt to the generation AI, such as "If the user is nervous, please transmit warning messages at an appropriate frequency," and the generation AI transmits warning messages at an appropriate frequency based on this prompt. This allows for more effective warning messages to be provided by adjusting the transmission frequency according to the user's emotions. Some or all of the above-described processing in the warning generation unit is performed using the generation AI. For example, the warning generation unit inputs a prompt to the generation AI, and the generation AI generates a warning message based on this and adjusts the frequency of the message.

[0088] When generating a warning, the warning generation unit can generate a region-specific warning message by taking into account the user's geographical location information. The warning generation unit generates the warning message based on, for example, local crime information. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message based on local crime information," and the generation AI generates a region-specific warning message based on this. The warning generation unit can also generate a warning message based on local weather information. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message based on local weather information," and the generation AI generates a region-specific warning message based on this. The warning generation unit can also generate a warning message based on local event information. For example, the warning generation unit inputs a prompt to the generation AI, such as "Please generate a warning message based on local event information," and the generation AI generates a region-specific warning message based on this. This allows for more effective warning messages to be provided by taking into account the geographical location information. Some or all of the above-described processing in the warning generation unit is performed using the generation AI. For example, the warning generation unit inputs a prompt to the generation AI, and the generation AI generates a warning message based on this.

[0089] When generating a warning, the warning generation unit can analyze the user's social media activity and generate a relevant warning message. The warning generation unit generates the warning message based on, for example, the content the user is discussing on social media. For example, the warning generation unit inputs a prompt to the generation AI, such as, "Please generate a warning message based on the content the user is discussing on social media," and the generation AI generates the relevant warning message based on this. The warning generation unit can also generate a relevant warning message by referring to the content posted on social media by the user. For example, the warning generation unit inputs a prompt to the generation AI, such as, "Please generate a relevant warning message by referring to the content posted on social media by the user," and the generation AI generates the relevant warning message based on this. The warning generation unit can also generate a relevant warning message by taking into account the user's social media friendships. For example, the warning generation unit inputs a prompt to the generation AI, such as, "Please generate a relevant warning message by taking into account the user's social media friendships," and the generation AI generates the relevant warning message based on this. This allows for more relevant warning messages to be provided by analyzing social media activity. Some or all of the above-described processing in the warning generation unit is performed using the generation AI. For example, the warning generator inputs a prompt to the generation AI, and the generation AI generates a warning message based on this.

[0090] The warning unit can estimate the user's emotions and adjust the timing of issuing a warning message based on the estimated user emotions. For example, if the user is feeling anxious, the warning unit can promptly issue a warning message. For example, the warning unit can input a prompt to the generation AI, such as "If the user is feeling anxious, please issue a warning message promptly," and the generation AI can promptly issue a warning message based on this. The warning unit can also delay the timing of issuing a warning message if the user is relaxed. For example, the warning unit can input a prompt to the generation AI, such as "If the user is relaxed, please delay the timing of issuing a warning message," and the generation AI can adjust the timing of the message based on this. The warning unit can also issue a warning message at an appropriate time if the user is feeling nervous. For example, the warning unit can input a prompt to the generation AI, such as "If the user is nervous, please issue a warning message at an appropriate time," and the generation AI can adjust the timing of the message based on this. This allows for more effective warning messages to be provided by adjusting the timing of the message according to the user's emotions. Some or all of the above-described processing in the warning unit is performed using the generation AI. For example, the warning sending unit inputs a prompt to the generation AI, and the generation AI generates a warning message based on this and adjusts the timing of the message.

[0091] When transmitting a warning message, the warning transmission unit can set multiple destinations for the warning message and transmit them simultaneously. For example, the warning transmission unit can transmit a warning message simultaneously to the external speaker and internal speaker of the home. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit a warning message to the external speaker and internal speaker of the home simultaneously," and the generation AI generates a warning message based on this and transmits it simultaneously to the external speaker and internal speaker. The warning transmission unit can also transmit a warning message simultaneously to speakers installed in each room of the home. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit a warning message to the speakers installed in each room of the home simultaneously," and the generation AI generates a warning message based on this and transmits it simultaneously to the speakers in each room. The warning transmission unit can also transmit a warning message simultaneously to the external speaker and smartphone of the home. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit a warning message to the external speaker and smartphone of the home simultaneously," and the generation AI generates a warning message based on this and transmits it simultaneously to the external speaker and smartphone. This increases the effectiveness of warning messages by simultaneously transmitting to multiple destinations. Some or all of the above-mentioned processing in the warning transmission unit is performed using the generation AI. For example, the warning transmission unit inputs a prompt to the generation AI, which then generates a warning message based on the prompt and transmits it to multiple destinations simultaneously.

[0092] When transmitting, the warning transmission unit can select a transmission method for the warning message and transmit it, for example, through a speaker or a smartphone. The warning transmission unit transmits the warning message, for example, through a speaker in the house. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit the warning message through the speaker in the house," which generates a warning message based on this and transmits it through the speaker. The warning transmission unit can also transmit the warning message through the user's smartphone. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit the warning message through the user's smartphone," which generates a warning message based on this and transmits it through the smartphone. The warning transmission unit can also transmit the warning message through the intercom in the house. For example, the warning transmission unit inputs a prompt to the generation AI, such as "Please transmit the warning message through the intercom in the house," which generates a warning message based on this and transmits it through the intercom. This allows for appropriate transmission according to the situation by selecting the transmission method. Some or all of the above-mentioned processing in the warning transmission unit is performed using the generation AI. For example, the warning issuing unit inputs a prompt to the generation AI, and the generation AI generates a warning message based on this and selects a method for issuing the message.

[0093] The warning transmission unit can estimate the user's emotions and adjust the order of transmission of warning messages based on the estimated user's emotions. For example, if the user is feeling anxious, the warning transmission unit prioritizes transmission of warning messages that provide a sense of security. For example, the warning transmission unit inputs a prompt to the generation AI saying, "If the user is feeling anxious, please prioritize transmission of warning messages that provide a sense of security," and the generation AI generates and prioritizes transmission of warning messages that provide a sense of security based on this. The warning transmission unit can also prioritize transmission of warning messages with a calm tone if the user is relaxed. For example, the warning transmission unit inputs a prompt to the generation AI saying, "If the user is relaxed, please prioritize transmission of warning messages with a calm tone," and the generation AI generates and prioritizes transmission of warning messages with a calm tone based on this. The warning transmission unit can also prioritize transmission of quick and clear warning messages if the user is nervous. For example, the warning transmission unit inputs a prompt to the generation AI saying, "If the user is nervous, please prioritize transmission of quick and clear warning messages," and the generation AI generates and prioritizes transmission of quick and clear warning messages based on this. This allows for more effective warning messages to be provided by adjusting the order of warnings according to the user's emotions. Some or all of the above-described processing in the warning transmission unit is performed using a generation AI. For example, the warning transmission unit inputs a prompt to the generation AI, which then generates a warning message and adjusts the order of warnings based on the prompt.

[0094] When making a call, the alert sending unit can select the optimal call destination by taking into account the user's geographical location information. For example, if the user is near their home, the alert sending unit prioritizes calling an external speaker in the home. For example, the alert sending unit inputs a prompt to the generation AI saying, "If the user is near their home, please prioritize calling an external speaker in the home." The generation AI generates a voice message based on this and prioritizes calling an external speaker. The alert sending unit can also prioritize calling a smartphone if the user is far away. For example, the alert sending unit inputs a prompt to the generation AI saying, "If the user is far away, please prioritize calling a smartphone." The generation AI generates a voice message based on this and prioritizes calling a smartphone. The alert sending unit can also select the optimal call destination for a specific location if the user is in that location. For example, the alert sending unit inputs a prompt to the generation AI saying, "If the user is in a specific location, please select the optimal call destination for that location." The generation AI generates a voice message based on this and selects the optimal call destination. This enables more effective calls by taking geographical location information into account. Some or all of the above-mentioned processing in the warning transmission unit is performed using the generation AI. For example, the warning transmission unit inputs a prompt to the generation AI, which then generates a voice message based on the prompt and selects the optimal recipient.

[0095] When sending a warning, the warning unit can analyze the user's social media activity and select relevant destinations. For example, the warning unit selects destinations related to the content the user is discussing on social media. For example, the warning unit inputs a prompt to the generation AI, such as, "Please select destinations related to the content the user is discussing on social media," and the generation AI generates a voice message and selects relevant destinations based on this prompt. The warning unit can also select relevant destinations by taking into account the user's social media friendships. For example, the warning unit inputs a prompt to the generation AI, such as, "Please select relevant destinations by taking into account the user's social media friendships," and the generation AI generates a voice message and selects relevant destinations based on this prompt. The warning unit can also select relevant destinations by referring to the content of the user's social media posts. For example, the warning unit inputs a prompt to the generation AI, such as, "Please select relevant destinations by referring to the content of the user's social media posts," and the generation AI generates a voice message and selects relevant destinations based on this prompt. This enables more relevant messages to be sent by analyzing social media activity. Some or all of the above-described processing in the alert sending unit is performed using the generation AI. For example, the alert sending unit inputs a prompt to the generation AI, which then generates a voice message based on the prompt and selects the relevant recipients. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned voice generation unit, transmission unit, lighting control unit, warning generation unit, and warning transmission unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice generation unit is realized by the processor 46 of the smart device 14 and generates a fake voice message using a voice generation AI. The transmission unit transmits the voice message to the outside through the speaker 40B of the smart device 14. The lighting control unit is realized by the control unit 46A of the smart device 14 and automatically turns the lights on and off using a sensor and a timer. The warning generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a warning message when a sensor detects suspicious movement. The warning transmission unit transmits the warning message to the outside through the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned voice generation unit, transmitter unit, lighting control unit, warning generation unit, and warning transmitter unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice generation unit is realized by the processor 46 of the smart glasses 214 and generates a fake voice message using a voice generation AI. The transmitter transmits the voice message to the outside through the speaker 240 of the smart glasses 214. The lighting control unit is realized by the control unit 46A of the smart glasses 214 and automatically turns the lighting on and off using a sensor and a timer. The warning generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a warning message when a sensor detects suspicious movement. The warning transmitter transmits the warning message to the outside through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned voice generation unit, transmission unit, lighting control unit, warning generation unit, and warning transmission unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the voice generation unit is realized by the processor 46 of the headset-type terminal 314 and generates a fake voice message using a voice generation AI. The transmission unit transmits the voice message to the outside through the speaker 240 of the headset-type terminal 314. The lighting control unit is realized by the control unit 46A of the headset-type terminal 314 and automatically turns the lights on and off using a sensor and a timer. The warning generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a warning message when a sensor detects suspicious movement. The warning transmission unit transmits the warning message to the outside through the speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned voice generation unit, transmission unit, lighting control unit, warning generation unit, and warning transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice generation unit is realized by the processor 46 of the robot 414 and generates a fake voice message using a voice generation AI. The transmission unit transmits the voice message to the outside through the speaker 240 of the robot 414. The lighting control unit is realized by the control unit 46A of the robot 414 and automatically turns the lights on and off using a sensor and a timer. The warning generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a warning message when a sensor detects suspicious movement. The warning transmission unit transmits the warning message to the outside through the speaker 240 of the robot 414.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The smart home security system can further include a facial recognition unit. The facial recognition unit can use a camera to recognize people around the home and identify the faces of registered family and friends. For example, the facial recognition unit can analyze the video from the camera and determine whether the face matches a registered face. If the facial recognition unit detects a suspicious person's face, it can notify the warning generation unit and generate a warning message. This allows the use of facial recognition technology to provide a higher level of security.

[0098] The voice generation unit can estimate the user's emotions and adjust the content of the generated voice message based on the estimated user emotions. For example, if the user is relaxed, a calm conversational voice is generated. The voice generation unit inputs a prompt to the generation AI saying, "Please generate a relaxed conversational voice," and the generation AI generates a calm conversational voice based on this. In addition, if the user is nervous, a voice message that gives a sense of security can be generated. This makes it possible to provide a more natural and effective voice message by generating a voice message that corresponds to the user's emotions.

[0099] The lighting control unit can further include a color temperature adjustment function. The color temperature adjustment function can adjust the color temperature of the lighting depending on the time of day and the user's activity. For example, a pale blue light can be used in the morning to wake up the user, and a warm light can be used in the evening to encourage relaxation. The lighting control unit can also estimate the user's emotions and use warm light if the user is relaxed and white light if the user is concentrating. This allows the lighting color temperature to be adjusted to provide a more comfortable environment.

[0100] The warning generation unit may further include a voice recognition function. The voice recognition function can recognize the user's voice and generate a warning message in response to a specific command. For example, if the user yells "help," the warning generation unit immediately generates a warning message and transmits it to the outside. The voice recognition function can also analyze the tone and urgency of the user's voice to generate an appropriate warning message. This allows for faster and more effective warnings to be provided using voice recognition technology.

[0101] The warning transmission unit may further include a multilingual function. The multilingual function can generate and transmit warning messages in multiple languages. For example, the warning message may be generated in a language selected by the user, such as English, Spanish, or Chinese. The warning transmission unit may also estimate the user's emotions and transmit warning messages in multiple languages ​​simultaneously in an emergency. This allows the multilingual function to accommodate users who speak different languages.

[0102] The voice generation unit may further include a background sound generation function. The background sound generation function can add natural background sounds to a voice message. For example, the function can add the ambient sounds of a living room to the voice of a family conversation, or the sound of a remote control being operated to the voice of a television. The voice generation unit can also estimate the user's emotions and add calm background sounds if the user is relaxed, or quiet background sounds if the user is tense. By adding background sounds, the voice message can be made to sound more realistic.

[0103] The lighting control unit can further include an energy efficiency optimization function. The energy efficiency optimization function can analyze lighting usage and adjust lighting to minimize energy consumption. For example, it can maximize natural light use during the day and use the minimum amount of lighting necessary at night. The lighting control unit can also estimate the user's emotions and use an energy-efficient lighting pattern if the user is relaxed and a bright lighting pattern if the user is concentrating. This enables environmentally friendly lighting control by optimizing energy efficiency.

[0104] The warning generation unit can further include a vibration notification function. The vibration notification function can notify the user's smartphone or smartwatch by vibrating when generating a warning message. For example, if a suspicious person intrudes, the warning generation unit generates a warning message and simultaneously notifies the user by vibrating their device. In addition, by adjusting the strength and pattern of the vibration, notifications can be made according to the level of urgency. As a result, using the vibration notification function can quickly and reliably convey warnings to the user.

[0105] The voice generation unit can further include a voice filtering function. The voice filtering function can remove unnecessary noise from the generated voice message to provide clearer voice. For example, it can remove background noise and echoes to make family conversations or television audio clearer. The voice generation unit can also estimate the user's emotions and apply a softer voice filter if the user is relaxed and a sharper voice filter if the user is nervous. This allows the voice filtering function to provide a higher quality voice message.

[0106] The lighting control unit can also be equipped with a motion detection function. The motion detection function can detect movement in the room and automatically control the on / off of the lights. For example, the lights can be automatically turned on when someone enters the room and automatically turned off when the person leaves the room. The lighting control unit can also estimate the user's emotions and turn on the lights slowly if the user is relaxed and quickly if the user is tense. This makes it possible to use the motion detection function to enable more efficient lighting control.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The voice generation unit uses a voice generation AI to generate fake voice messages. For example, it generates voice messages that imitate family conversations or television sounds. The voice generation unit inputs prompts such as "Please imitate family conversations" or "Please imitate television sounds" to the generation AI, and the generation AI generates a voice message based on these prompts. Step 2: The transmitting unit transmits the voice message generated by the voice generating unit to the outside. For example, the voice message is transmitted to the outside through a speaker. The transmitting unit inputs the generated voice message to the speaker, and the speaker transmits the voice to the outside. Step 3: The lighting control unit automatically turns the room lights on and off using sensors or timers. For example, the living room lights can be automatically turned on in the evening, and the bedroom lights can be turned on at night. The lighting control unit controls the lights on and off based on the settings of the sensors and timers. Step 4: The warning generation unit generates a warning message when the sensor detects suspicious activity. For example, it generates a warning message such as "A suspicious person has entered. The police have been notified." The warning generation unit receives input from the sensor and inputs a prompt such as "A suspicious person has entered. The police have been notified." to the generation AI, which then generates a warning message based on this. Step 5: The warning transmission unit transmits the warning message generated by the warning generation unit to the outside. For example, the warning message is transmitted to the outside through a speaker. The warning transmission unit inputs the generated warning message into the speaker, and the speaker transmits the warning message to the outside.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 voice generation unit that generates a fake voice message using a voice generation AI; a sending unit that sends the voice message generated by the voice generating unit to an external device; a lighting control unit that automatically turns on and off room lighting using a sensor or a timer; a warning generation unit that generates a warning message when the sensor detects a suspicious movement; a warning transmission unit that transmits the warning message generated by the warning generation unit to the outside. A system characterized by:

2. The voice generation unit Generate fake voice messages that mimic family conversations or TV sounds 2. The system of claim 1.

3. The lighting control unit Use sensors or timers to automatically turn on the living room lights in the evening and the bedroom lights at night.

2. The system of claim 1.

4. The warning issuing unit The warning message generated by the warning generating unit is transmitted to the outside.

2. The system of claim 1.

5. The voice generation unit The user's emotions are estimated, and the content of the generated voice message is adjusted based on the estimated user's emotions.

2. The system of claim 1.

6. The voice generation unit When generating speech, refer to past speech data to generate more natural voice messages.

2. The system of claim 1.

7. The voice generation unit Adjust the volume and tone of the generated voice message when generating voice.

2. The system of claim 1.

Images