system

Abstract

The system according to the embodiment aims to automatically generate and send responses to questions and requests from users. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a transmission unit. The reception unit receives a question or request from a user. The generation unit analyzes the content received by the reception unit and generates a response. The transmission unit transmits the response generated by the generation unit to the user via SMS.

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 techniques do not sufficiently automate the process of efficiently generating and sending responses to user questions and requests, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate and send responses to questions and requests from users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a transmission unit. The reception unit receives a question or request from a user. The generation unit analyzes the content received by the reception unit and generates a response. The transmission unit transmits the response generated by the generation unit to the user via SMS. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate and send responses to questions and requests from users. [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) The generation AI service provision system according to an embodiment of the present invention accepts questions and requests from users via two-way SMS, generates responses from the generation AI, and then sends them back to the user via SMS. This system monetizes by generating SMS transmission fees. For example, when a user sends an SMS asking, "What's the weather going to be like tomorrow?", the generation AI checks the weather forecast and replies, "It's going to be sunny tomorrow." This system allows users to easily obtain information, and the service provider can earn revenue from the SMS transmission fees. Specifically, the system consists of the following steps: First, the user sends a question or request to the generation AI via SMS. Next, the generation AI analyzes the content and generates a response. The generated response is then sent back to the user via SMS. This service monetizes by generating SMS transmission fees. For example, when a user sends an SMS asking, "What's the weather going to be like tomorrow?", the generation AI checks the weather forecast and replies, "It's going to be sunny tomorrow." This system allows users to easily obtain information, and the service provider can earn revenue from the SMS transmission fees. This enables the generative AI service provision system to efficiently accept questions and requests from users, analyze them, generate responses, and send them via SMS.

[0029] A generation AI service providing system according to an embodiment includes a reception unit, a generation unit, and a transmission unit. The reception unit receives questions and requests from users. Examples of user questions and requests include, but are not limited to, technical questions and customer support requests. The reception unit receives questions and requests from users via, for example, SMS. The generation unit analyzes the content received by the reception unit and generates a response. The analysis may be performed using, for example, natural language processing technology or keyword extraction, but is not limited to, examples. The generation unit generates an appropriate response to the user's question or request using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an optimal response to the user's question or request. The transmission unit transmits the response generated by the generation unit to the user via SMS. Transmission may be performed using, for example, an SMS gateway, but is not limited to, examples. The transmission unit provides a means for quickly delivering the generated response to the user. As a result, the generation AI service providing system according to the embodiment can efficiently accept questions and requests from users, analyze them, generate responses, and send them via SMS.

[0030] The reception unit can accept questions and requests from users via SMS. The reception unit accepts questions and requests from users using, for example, an SMS gateway. The SMS gateway receives SMS messages and converts them into a format that can be processed within the system. For example, if a user sends an SMS asking, "What's the weather going to be like tomorrow?", the SMS gateway receives the message and forwards it to the reception unit. The reception unit analyzes the received message and performs appropriate processing. This allows questions and requests from users to be accepted via SMS.

[0031] The generation unit can analyze the content received by the reception unit and generate a response. The generation unit analyzes the content received by the reception unit, for example, using natural language processing technology. Natural language processing technology is a technology for analyzing text data and understanding its meaning. For example, the generation unit analyzes a user's question or request and generates an appropriate response based on the content. The generation unit uses a generation AI to generate a response to the user's question or request. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an optimal response to the user's question or request. This makes it possible to analyze the content received by the reception unit and generate a response.

[0032] The sending unit can send the response generated by the generating unit to the user by SMS. The sending unit sends the response generated by the generating unit to the user, for example, using an SMS gateway. The SMS gateway converts a message generated in the system into SMS format and sends it to the user. For example, if the generating unit generates a response such as "It will be sunny tomorrow," the sending unit sends the response to the user through the SMS gateway. This allows the response generated by the generating unit to be sent to the user by SMS.

[0033] The sending unit can charge the user for the SMS sending fee and obtain the fee as revenue. The sending unit has, for example, a function for charging the user for the SMS sending fee. The sending fee is calculated, for example, based on the length of the message and the region of the destination. The sending unit can charge the user for the sending fee and obtain the fee as revenue. For example, if a user asks, "What's the weather like tomorrow?" and the generating unit generates a response such as "It will be sunny tomorrow," and the sending unit sends the response by SMS, the sending unit will charge the user for the sending fee. In this way, the sending unit can charge the user for the SMS sending fee and obtain the fee as revenue.

[0034] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit stores the user's past question history in a database and analyzes that data. The question history includes the contents of questions and requests sent by the user in the past. The reception unit analyzes the past question history and selects the optimal reception method for the user. For example, it can prioritize reception of questions that the user has frequently asked in the past. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. This makes it possible to analyze the user's past question history and select the optimal reception method.

[0035] The reception unit can filter questions and requests based on the user's current areas of interest when receiving the questions and requests. The reception unit identifies the user's current areas of interest, for example, based on the user's past behavioral data or survey results. The areas of interest include topics and themes in which the user is currently interested. The reception unit filters questions and requests based on the user's current areas of interest when receiving the questions and requests. For example, questions related to topics in which the user is currently interested can be preferentially received. In addition, questions with low relevance can also be filtered based on the user's areas of interest. This makes it possible to filter questions and requests based on the user's current areas of interest.

[0036] When accepting questions or requests, the acceptance unit can prioritize accepting highly relevant content by taking into account the user's geographical location information. The acceptance unit acquires the user's geographical location information, for example, by using location estimation from GPS data or an IP address. The geographical location information includes the user's current location or region. When accepting questions or requests, the acceptance unit prioritizes accepting highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific region, questions related to that region are prioritized. In addition, it is also possible to filter less relevant questions based on the user's current location. This makes it possible to prioritize accepting highly relevant questions or requests based on the user's geographical location information.

[0037] The reception unit can analyze the user's social media activity and receive related content when receiving a question or request. The reception unit, for example, stores the user's social media activity in a database and analyzes the data. The social media activity includes topics and themes in which the user is interested on social media. The reception unit analyzes the user's social media activity and receives related content when receiving a question or request. For example, the reception unit can preferentially receive questions related to topics in which the user is interested on social media. It can also filter out questions that are less relevant based on the user's social media activity. This makes it possible to receive questions and requests that are relevant based on the user's social media activity.

[0038] When generating a response, the generator can adjust the level of detail of the response based on the importance of the question or request. For example, the generator evaluates the importance of the question or request and adjusts the level of detail of the response based on the importance. The importance is evaluated based on, for example, the user's priority or the urgency of the question. The generator generates a detailed response for a question with a high level of importance. For example, if a user asks, "I have an urgent problem," the generator determines that the question has a high level of importance and generates a detailed response. On the other hand, for a question with a low level of importance, the generator generates a concise response. This allows the level of detail of the response to be adjusted based on the importance of the question or request.

[0039] When generating a response, the generation unit can apply different generation algorithms depending on the category of the question or request. For example, the generation unit identifies the category of the question or request and applies the optimal generation algorithm depending on the category. Categories include, for example, weather forecasts, news, and entertainment. For questions about weather forecasts, the generation unit applies a generation algorithm based on meteorological data. For questions about news, the generation unit applies a generation algorithm based on the latest news data. This makes it possible to apply the optimal generation algorithm depending on the category of the question or request.

[0040] When generating a response, the generation unit can determine the priority of the response based on the time of submission of the question or request. For example, the generation unit evaluates the time of submission of the question or request and determines the priority of the response based on the time of submission. The time of submission includes, for example, the date and time of submission and the time elapsed since submission. The generation unit determines the priority of the response based on the time of day when the question was submitted. For example, a question submitted at night is given priority for response the next morning. The priority of the response can also be determined based on the day of the week or season when the question was submitted. This makes it possible to determine the priority of the response based on the time of submission of the question or request.

[0041] When generating responses, the generator can adjust the order of responses based on the relevance of the questions or requests. The generator, for example, evaluates the relevance of the questions or requests and adjusts the order of responses based on the relevance. Relevance includes, for example, the similarity of the question content and related topics. The generator determines the order of responses based on the relevance of the questions. For example, highly relevant questions are given priority in response, and less relevant questions are postponed. The generator can also determine the order of responses based on the importance or category of the questions. This makes it possible to adjust the order of responses based on the relevance of the questions or requests.

[0042] When sending a response, the transmission unit can select the optimal transmission method by referring to the user's past response history. The transmission unit, for example, stores the user's past response history in a database and analyzes the data. The response history includes the content and transmission method of responses the user has received in the past. The transmission unit selects the optimal transmission method for the user by referring to the past response history. For example, the transmission unit preferentially selects a transmission method that the user has preferred in the past. The transmission unit can also predict the optimal transmission timing from the user's past response history. This allows the transmission unit to select the optimal transmission method by referring to the user's past response history.

[0043] The sending unit can customize the sending means based on the user's current situation when sending a response. The sending unit customizes the sending means based on, for example, the user's current activity status and environmental information. The current situation includes, for example, whether the user is on the move, at home, or in a meeting. If the user is on the move, the sending unit sends the response by a short SMS. If the user is at home, the sending unit sends the response by a detailed SMS. Furthermore, if the user is in a meeting, the sending unit can also send the response by a quiet notification method. This makes it possible to customize the sending means based on the user's current situation.

[0044] When transmitting a response, the transmission unit can select the optimal transmission method taking into account the user's geographical location information. The transmission unit acquires the user's geographical location information, for example, by using location estimation from GPS data or an IP address. The geographical location information includes the user's current location or region. When transmitting a response, the transmission unit selects the optimal transmission method taking into account the user's geographical location information. For example, if the user is in a specific region, information related to that region is preferentially transmitted. It is also possible to filter out less relevant information based on the user's current location. This makes it possible to select the optimal transmission method based on the user's geographical location information.

[0045] The sending unit can analyze the user's social media activity and suggest a sending means when sending a response. The sending unit, for example, stores the user's social media activity in a database and analyzes the data. The social media activity includes topics and themes in which the user is interested on social media. The sending unit analyzes the user's social media activity and suggests the optimal sending means when sending a response. For example, the sending unit prioritizes sending information related to topics in which the user is interested on social media. It can also filter out less relevant information based on the user's social media activity. This makes it possible to suggest the optimal sending means based on the user's social media activity.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can learn the user's preferences and patterns based on the user's past behavioral data and provide personalized responses when receiving questions or requests. For example, if a user has frequently asked questions about a specific topic in the past, questions related to that topic can be preferentially received. Also, if a user tends to send questions during a specific time period, the reception unit's response speed can be adjusted to match that time period. Furthermore, it is possible to suggest new topics that the user may be interested in based on the user's past behavioral data. This allows the reception unit to utilize the user's past behavioral data to provide more personalized responses.

[0048] The reception unit can acquire information about the user's current device and accept questions and requests in the format optimal for that device. For example, if the user is using a smartphone, the reception unit can accept questions in the form of short text messages. If the user is using a personal computer, the reception unit can accept long questions containing detailed information. Furthermore, if the user is using a voice assistant, the reception unit can also accept questions in response to voice input. This allows the reception unit to accept questions and requests in the format optimal for the user's device information.

[0049] The sending unit can select the optimal sending method taking into account the user's current network status. For example, if the user is in a slow network environment, the response can be sent in a lightweight text message format. If the user is in a fast network environment, the response can be sent in a rich media format including images and videos. Furthermore, if the user is offline, the response can be sent the next time the user is online. This allows the sending unit to select the optimal sending method based on the user's network status.

[0050] The reception unit can adjust the method of receiving questions and requests taking into account the user's current activity status. For example, if the user is driving, questions can be received in response to voice input. If the user is in a meeting, questions can be received in a quiet notification method. Furthermore, if the user is relaxed, questions containing detailed information can be received. This allows the reception unit to select the optimal reception method based on the user's current activity status.

[0051] The generator can analyze the user's past response history and generate an optimal response. For example, if the user has preferred detailed responses on a particular topic in the past, the generator can generate a detailed response on that topic. Also, if the user has preferred concise responses in the past, the generator can generate a concise response. Furthermore, it is also possible to provide new information that the user is likely to be interested in based on the user's past response history. This allows the generator to generate an optimal response by utilizing the user's past response history.

[0052] The transmitting unit can select the optimal transmission means by taking into account the user's current environmental information. For example, if the user is in a quiet environment, the response can be sent in the form of a voice message. If the user is in a noisy environment, the response can be sent in the form of a text message. Furthermore, if the user is on the move, the response can also be sent in the form of a short text message. This allows the transmitting unit to select the optimal transmission means based on the user's current environmental information.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives questions and requests from users. Questions and requests from users include, but are not limited to, technical questions and customer support requests. The reception unit receives questions and requests from users via, for example, SMS. Step 2: The generation unit analyzes the content received by the reception unit and generates a response. The analysis is performed using, for example, natural language processing technology, keyword extraction, or other methods, but is not limited to these examples. The generation unit generates an appropriate response to the user's question or request using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an optimal response to the user's question or request. Step 3: The sending unit sends the response generated by the generating unit to the user by SMS. The sending is performed, for example, using an SMS gateway, but is not limited to such an example. The sending unit provides a means for quickly delivering the generated response to the user.

[0055] (Example 2) The generation AI service provision system according to an embodiment of the present invention accepts questions and requests from users via two-way SMS, generates responses from the generation AI, and then sends them back to the user via SMS. This system monetizes by generating SMS transmission fees. For example, when a user sends an SMS asking, "What's the weather going to be like tomorrow?", the generation AI checks the weather forecast and replies, "It's going to be sunny tomorrow." This system allows users to easily obtain information, and the service provider can earn revenue from the SMS transmission fees. Specifically, the system consists of the following steps: First, the user sends a question or request to the generation AI via SMS. Next, the generation AI analyzes the content and generates a response. The generated response is then sent back to the user via SMS. This service monetizes by generating SMS transmission fees. For example, when a user sends an SMS asking, "What's the weather going to be like tomorrow?", the generation AI checks the weather forecast and replies, "It's going to be sunny tomorrow." This system allows users to easily obtain information, and the service provider can earn revenue from the SMS transmission fees. This enables the generative AI service provision system to efficiently accept questions and requests from users, analyze them, generate responses, and send them via SMS.

[0056] A generation AI service providing system according to an embodiment includes a reception unit, a generation unit, and a transmission unit. The reception unit receives questions and requests from users. Examples of user questions and requests include, but are not limited to, technical questions and customer support requests. The reception unit receives questions and requests from users via, for example, SMS. The generation unit analyzes the content received by the reception unit and generates a response. The analysis may be performed using, for example, natural language processing technology or keyword extraction, but is not limited to, examples. The generation unit generates an appropriate response to the user's question or request using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an optimal response to the user's question or request. The transmission unit transmits the response generated by the generation unit to the user via SMS. Transmission may be performed using, for example, an SMS gateway, but is not limited to, examples. The transmission unit provides a means for quickly delivering the generated response to the user. As a result, the generation AI service providing system according to the embodiment can efficiently accept questions and requests from users, analyze them, generate responses, and send them via SMS.

[0057] The reception unit can accept questions and requests from users via SMS. The reception unit accepts questions and requests from users using, for example, an SMS gateway. The SMS gateway receives SMS messages and converts them into a format that can be processed within the system. For example, if a user sends an SMS asking, "What's the weather going to be like tomorrow?", the SMS gateway receives the message and forwards it to the reception unit. The reception unit analyzes the received message and performs appropriate processing. This allows questions and requests from users to be accepted via SMS.

[0058] The generation unit can analyze the content received by the reception unit and generate a response. The generation unit analyzes the content received by the reception unit, for example, using natural language processing technology. Natural language processing technology is a technology for analyzing text data and understanding its meaning. For example, the generation unit analyzes a user's question or request and generates an appropriate response based on the content. The generation unit uses a generation AI to generate a response to the user's question or request. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an optimal response to the user's question or request. This makes it possible to analyze the content received by the reception unit and generate a response.

[0059] The sending unit can send the response generated by the generating unit to the user by SMS. The sending unit sends the response generated by the generating unit to the user, for example, using an SMS gateway. The SMS gateway converts a message generated in the system into SMS format and sends it to the user. For example, if the generating unit generates a response such as "It will be sunny tomorrow," the sending unit sends the response to the user through the SMS gateway. This allows the response generated by the generating unit to be sent to the user by SMS.

[0060] The sending unit can charge the user for the SMS sending fee and obtain the fee as revenue. The sending unit has, for example, a function for charging the user for the SMS sending fee. The sending fee is calculated, for example, based on the length of the message and the region of the destination. The sending unit can charge the user for the sending fee and obtain the fee as revenue. For example, if a user asks, "What's the weather like tomorrow?" and the generating unit generates a response such as "It will be sunny tomorrow," and the sending unit sends the response by SMS, the sending unit will charge the user for the sending fee. In this way, the sending unit can charge the user for the SMS sending fee and obtain the fee as revenue.

[0061] The reception unit can estimate the user's emotions and adjust the timing of accepting questions and requests based on the estimated user emotions. The reception unit estimates the user's emotions using, for example, an emotion analysis algorithm. The emotion analysis algorithm is a technology for analyzing text data and estimating the user's emotions. For example, if a user sends a message saying "I'm in a hurry," the reception unit analyzes the message and estimates that the user is in a hurry. The reception unit adjusts the timing of accepting questions and requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit delays the timing of accepting questions and requests until the user is relaxed. This makes it possible to adjust the timing of accepting questions and requests based on the user's emotions.

[0062] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit stores the user's past question history in a database and analyzes that data. The question history includes the contents of questions and requests sent by the user in the past. The reception unit analyzes the past question history and selects the optimal reception method for the user. For example, it can prioritize reception of questions that the user has frequently asked in the past. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. This makes it possible to analyze the user's past question history and select the optimal reception method.

[0063] The reception unit can filter questions and requests based on the user's current areas of interest when receiving the questions and requests. The reception unit identifies the user's current areas of interest, for example, based on the user's past behavioral data or survey results. The areas of interest include topics and themes in which the user is currently interested. The reception unit filters questions and requests based on the user's current areas of interest when receiving the questions and requests. For example, questions related to topics in which the user is currently interested can be preferentially received. In addition, questions with low relevance can also be filtered based on the user's areas of interest. This makes it possible to filter questions and requests based on the user's current areas of interest.

[0064] The reception unit can estimate the user's emotions and determine the priority of questions and requests to be received based on the estimated user's emotions. The reception unit estimates the user's emotions using, for example, an emotion analysis algorithm. The emotion analysis algorithm is a technology for analyzing text data and estimating the user's emotions. For example, if a user sends a message saying "I'm in a hurry," the reception unit analyzes the message and estimates that the user is in a hurry. The reception unit determines the priority of questions and requests based on the estimated user's emotions. For example, if the user is feeling stressed, questions with a high priority are preferentially received. This makes it possible to determine the priority of questions and requests based on the user's emotions.

[0065] When accepting questions or requests, the acceptance unit can prioritize accepting highly relevant content by taking into account the user's geographical location information. The acceptance unit acquires the user's geographical location information, for example, by using location estimation from GPS data or an IP address. The geographical location information includes the user's current location or region. When accepting questions or requests, the acceptance unit prioritizes accepting highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific region, questions related to that region are prioritized. In addition, it is also possible to filter less relevant questions based on the user's current location. This makes it possible to prioritize accepting highly relevant questions or requests based on the user's geographical location information.

[0066] The reception unit can analyze the user's social media activity and receive related content when receiving a question or request. The reception unit, for example, stores the user's social media activity in a database and analyzes the data. The social media activity includes topics and themes in which the user is interested on social media. The reception unit analyzes the user's social media activity and receives related content when receiving a question or request. For example, the reception unit can preferentially receive questions related to topics in which the user is interested on social media. It can also filter out questions that are less relevant based on the user's social media activity. This makes it possible to receive questions and requests that are relevant based on the user's social media activity.

[0067] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, a sentiment analysis algorithm. The sentiment analysis algorithm is a technology for analyzing text data and estimating the user's emotions. For example, if a user sends a message saying "I'm in a hurry," the generation unit analyzes the message and estimates that the user is in a hurry. The generation unit adjusts the way the response is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a polite and detailed response. On the other hand, if the user is in a hurry, the generation unit generates a concise and to-the-point response. This makes it possible to adjust the way the response is expressed based on the user's emotions.

[0068] When generating a response, the generator can adjust the level of detail of the response based on the importance of the question or request. For example, the generator evaluates the importance of the question or request and adjusts the level of detail of the response based on the importance. The importance is evaluated based on, for example, the user's priority or the urgency of the question. The generator generates a detailed response for a question with a high level of importance. For example, if a user asks, "I have an urgent problem," the generator determines that the question has a high level of importance and generates a detailed response. On the other hand, for a question with a low level of importance, the generator generates a concise response. This allows the level of detail of the response to be adjusted based on the importance of the question or request.

[0069] When generating a response, the generation unit can apply different generation algorithms depending on the category of the question or request. For example, the generation unit identifies the category of the question or request and applies the optimal generation algorithm depending on the category. Categories include, for example, weather forecasts, news, and entertainment. For questions about weather forecasts, the generation unit applies a generation algorithm based on meteorological data. For questions about news, the generation unit applies a generation algorithm based on the latest news data. This makes it possible to apply the optimal generation algorithm depending on the category of the question or request.

[0070] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, a sentiment analysis algorithm. The sentiment analysis algorithm is a technology for analyzing text data and estimating the user's emotions. For example, if a user sends a message saying "I'm in a hurry," the generation unit analyzes the message and estimates that the user is in a hurry. The generation unit adjusts the length of the response based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point response. On the other hand, if the user is relaxed, the generation unit generates a longer response that includes detailed explanations. This makes it possible to adjust the length of the response based on the user's emotions.

[0071] When generating a response, the generation unit can determine the priority of the response based on the time of submission of the question or request. For example, the generation unit evaluates the time of submission of the question or request and determines the priority of the response based on the time of submission. The time of submission includes, for example, the date and time of submission and the time elapsed since submission. The generation unit determines the priority of the response based on the time of day when the question was submitted. For example, a question submitted at night is given priority for response the next morning. The priority of the response can also be determined based on the day of the week or season when the question was submitted. This makes it possible to determine the priority of the response based on the time of submission of the question or request.

[0072] When generating responses, the generator can adjust the order of responses based on the relevance of the questions or requests. The generator, for example, evaluates the relevance of the questions or requests and adjusts the order of responses based on the relevance. Relevance includes, for example, the similarity of the question content and related topics. The generator determines the order of responses based on the relevance of the questions. For example, highly relevant questions are given priority in response, and less relevant questions are postponed. The generator can also determine the order of responses based on the importance or category of the questions. This makes it possible to adjust the order of responses based on the relevance of the questions or requests.

[0073] The sending unit can estimate the user's emotions and adjust the response sending method based on the estimated user's emotions. The sending unit estimates the user's emotions using, for example, an emotion analysis algorithm. The emotion analysis algorithm is a technology for analyzing text data and estimating the user's emotions. For example, if a user sends a message saying "I'm in a hurry," the sending unit analyzes the message and estimates that the user is in a hurry. The sending unit adjusts the response sending method based on the estimated user's emotions. For example, if the user is relaxed, the sending unit sends the response by regular SMS. On the other hand, if the user is in a hurry, the sending unit sends the response by SMS immediately. This makes it possible to adjust the response sending method based on the user's emotions.

[0074] When sending a response, the transmission unit can select the optimal transmission method by referring to the user's past response history. The transmission unit, for example, stores the user's past response history in a database and analyzes the data. The response history includes the content and transmission method of responses the user has received in the past. The transmission unit selects the optimal transmission method for the user by referring to the past response history. For example, the transmission unit preferentially selects a transmission method that the user has preferred in the past. The transmission unit can also predict the optimal transmission timing from the user's past response history. This allows the transmission unit to select the optimal transmission method by referring to the user's past response history.

[0075] The sending unit can customize the sending means based on the user's current situation when sending a response. The sending unit customizes the sending means based on, for example, the user's current activity status and environmental information. The current situation includes, for example, whether the user is on the move, at home, or in a meeting. If the user is on the move, the sending unit sends the response by a short SMS. If the user is at home, the sending unit sends the response by a detailed SMS. Furthermore, if the user is in a meeting, the sending unit can also send the response by a quiet notification method. This makes it possible to customize the sending means based on the user's current situation.

[0076] The sending unit can estimate the user's emotions and determine the transmission priority of responses based on the estimated user's emotions. The sending unit estimates the user's emotions using, for example, an emotion analysis algorithm. The emotion analysis algorithm is a technology for analyzing text data and estimating the user's emotions. For example, if a user sends a message saying "I'm in a hurry," the sending unit analyzes the message and estimates that the user is in a hurry. The sending unit determines the transmission priority of responses based on the estimated user's emotions. For example, if the user is feeling stressed, responses with higher importance are sent with higher priority. This makes it possible to determine the transmission priority of responses based on the user's emotions.

[0077] When transmitting a response, the transmission unit can select the optimal transmission method taking into account the user's geographical location information. The transmission unit acquires the user's geographical location information, for example, by using location estimation from GPS data or an IP address. The geographical location information includes the user's current location or region. When transmitting a response, the transmission unit selects the optimal transmission method taking into account the user's geographical location information. For example, if the user is in a specific region, information related to that region is preferentially transmitted. It is also possible to filter out less relevant information based on the user's current location. This makes it possible to select the optimal transmission method based on the user's geographical location information.

[0078] The sending unit can analyze the user's social media activity and suggest a sending means when sending a response. The sending unit, for example, stores the user's social media activity in a database and analyzes the data. The social media activity includes topics and themes in which the user is interested on social media. The sending unit analyzes the user's social media activity and suggests the optimal sending means when sending a response. For example, the sending unit prioritizes sending information related to topics in which the user is interested on social media. It can also filter out less relevant information based on the user's social media activity. This makes it possible to suggest the optimal sending means based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, generation unit, and transmission unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives questions or requests from the user via SMS. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's questions or requests using natural language processing technology and generates an appropriate response. The transmission unit is realized, for example, by the control unit 46A of the smart device 14 and transmits the generated response to the user via SMS. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, and transmission unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives questions or requests from the user via SMS. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's questions or requests using natural language processing technology and generates an appropriate response. The transmission unit is realized, for example, by the control unit 46A of the smart glasses 214 and transmits the generated response to the user via SMS. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and 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 reception unit is realized by the control unit 46A of the headset type terminal 314 and receives questions and requests from the user via SMS. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's questions and requests using natural language processing technology and generates an appropriate response. The transmission unit is realized, for example, by the control unit 46A of the headset type terminal 314 and transmits the generated response to the user via SMS. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives questions and requests from the user via SMS. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's questions and requests using natural language processing technology and generates an appropriate response. The transmission unit is realized, for example, by the control unit 46A of the robot 414 and transmits the generated response to the user via SMS.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The reception unit can learn the user's preferences and patterns based on the user's past behavioral data and provide personalized responses when receiving questions or requests. For example, if a user has frequently asked questions about a specific topic in the past, questions related to that topic can be preferentially received. Also, if a user tends to send questions during a specific time period, the reception unit's response speed can be adjusted to match that time period. Furthermore, it is possible to suggest new topics that the user may be interested in based on the user's past behavioral data. This allows the reception unit to utilize the user's past behavioral data to provide more personalized responses.

[0081] The reception unit can acquire information about the user's current device and accept questions and requests in the format optimal for that device. For example, if the user is using a smartphone, the reception unit can accept questions in the form of short text messages. If the user is using a personal computer, the reception unit can accept long questions containing detailed information. Furthermore, if the user is using a voice assistant, the reception unit can also accept questions in response to voice input. This allows the reception unit to accept questions and requests in the format optimal for the user's device information.

[0082] The generation unit can estimate the user's emotions and adjust the tone and style of the response based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can generate a response in a gentle tone. If the user is excited, the generation unit can generate a response in an energetic tone. Furthermore, if the user is relaxed, the generation unit can also generate a response in a casual style. This allows the generation unit to adjust the tone and style of the response based on the user's emotions.

[0083] The sending unit can select the optimal sending method taking into account the user's current network status. For example, if the user is in a slow network environment, the response can be sent in a lightweight text message format. If the user is in a fast network environment, the response can be sent in a rich media format including images and videos. Furthermore, if the user is offline, the response can be sent the next time the user is online. This allows the sending unit to select the optimal sending method based on the user's network status.

[0084] The transmission unit can estimate the user's emotions and adjust the timing of transmitting a response based on the estimated user's emotions. For example, if the user is feeling stressed, the transmission unit can immediately transmit the response. Alternatively, if the user is relaxed, the transmission unit can slightly delay the timing of transmission. Furthermore, if the user is busy, the transmission unit can postpone the response and transmit it when the user is calm. In this way, the transmission unit can adjust the timing of transmitting a response based on the user's emotions.

[0085] The reception unit can adjust the method of receiving questions and requests taking into account the user's current activity status. For example, if the user is driving, questions can be received in response to voice input. If the user is in a meeting, questions can be received in a quiet notification method. Furthermore, if the user is relaxed, questions containing detailed information can be received. This allows the reception unit to select the optimal reception method based on the user's current activity status.

[0086] The reception unit can estimate the user's emotions and customize the content of questions and requests based on the estimated user's emotions. For example, if the user is feeling anxious, the reception unit can accept questions in a way that gives the user a sense of security. If the user is excited, the reception unit can respond in an energetic manner. Furthermore, if the user is relaxed, the reception unit can also respond in a casual manner. This allows the reception unit to customize the content of questions and requests based on the user's emotions.

[0087] The generator can analyze the user's past response history and generate an optimal response. For example, if the user has preferred detailed responses on a particular topic in the past, the generator can generate a detailed response on that topic. Also, if the user has preferred concise responses in the past, the generator can generate a concise response. Furthermore, it is also possible to provide new information that the user is likely to be interested in based on the user's past response history. This allows the generator to generate an optimal response by utilizing the user's past response history.

[0088] The sending unit can estimate the user's emotion and adjust the sending format of the response based on the estimated user's emotion. For example, if the user is stressed, the sending unit can send the response in a simple text message format. If the user is relaxed, the sending unit can send the response in a rich media format including detailed information. Furthermore, if the user is excited, the sending unit can send the response in an energetic tone. This allows the sending unit to adjust the sending format of the response based on the user's emotion.

[0089] The transmitting unit can select the optimal transmission means by taking into account the user's current environmental information. For example, if the user is in a quiet environment, the response can be sent in the form of a voice message. If the user is in a noisy environment, the response can be sent in the form of a text message. Furthermore, if the user is on the move, the response can also be sent in the form of a short text message. This allows the transmitting unit to select the optimal transmission means based on the user's current environmental information.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The reception unit receives questions and requests from users. Questions and requests from users include, but are not limited to, technical questions and customer support requests. The reception unit receives questions and requests from users via, for example, SMS. Step 2: The generation unit analyzes the content received by the reception unit and generates a response. The analysis is performed using, for example, natural language processing technology, keyword extraction, or other methods, but is not limited to these examples. The generation unit generates an appropriate response to the user's question or request using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an optimal response to the user's question or request. Step 3: The sending unit sends the response generated by the generating unit to the user by SMS. The sending is performed, for example, using an SMS gateway, but is not limited to such an example. The sending unit provides a means for quickly delivering the generated response to the user.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[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 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.

[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 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.

[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 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.

[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] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 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 reception unit that receives questions or requests from users; a generation unit that analyzes the content received by the reception unit and generates a response; a sending unit that sends the response generated by the generating unit to a user by SMS; Equipped with A system characterized by:

2. The reception unit Accept user questions and requests via SMS The system of claim 1 .

3. The generation unit Analyzing the content received by the reception unit and generating a response The system of claim 1 .

4. The transmission unit The response generated by the generating unit is sent to the user via SMS. The system of claim 1 .

5. The transmission unit Charge users for sending SMS and earn revenue from that fee The system of claim 1 .

6. The reception unit Estimate the user's emotions and adjust the timing of accepting questions and requests based on the estimated user emotions. The system of claim 1 .

7. The reception unit Analyze the user's past question history and select the appropriate reception method The system of claim 1 .

8. The reception unit Filtering questions and requests based on the user's current interests The system of claim 1 .

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