System

Abstract

An object of the system according to the embodiment is to realize two way communication at a store or an event site using a robot equipped with a generation AI.SOLUTION: A system according to an embodiment includes a AI robot, a data-loading unit, and an installation unit. The AI robot carries a generation AI. The data loading unit loads data of an introduction company. The installation unit installs a AI robot equipped with a generation AI in a store or an event site.SELECTED DRAWING: Figure 1

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] With conventional technology, there were issues with the limited scope of robots' use and the inability to fully realize two-way communication.

[0005] The system of the embodiment aims to realize two-way communication in stores and event venues using a robot equipped with generative AI. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI robot, a data loading unit, and an installation unit. The AI ​​robot is equipped with a generation AI. The data loading unit loads data from the adopting company. The installation unit installs the AI ​​robot equipped with the generation AI in a store or event venue. [Effects of the Invention]

[0007] The system of the embodiment uses a robot equipped with generative AI to enable two-way communication in stores and at event venues. [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 AI ​​robot system according to the embodiment of the present invention is equipped with a generative AI and is a system that performs two-way communication at stores, event venues, etc. This enables the AI ​​robot system to achieve two-way communication with users and to respond in an advanced manner based on data from the company that has adopted the system.

[0029] An AI robot system according to an embodiment includes an AI robot equipped with a generation AI, a data loading unit, and an installation unit. The AI ​​robot equipped with the generation AI engages in two-way communication. For example, the generation AI analyzes questions from users and generates appropriate responses. The generation AI can also provide detailed product information and event schedule information based on user input. The data loading unit loads data from the adopting company. For example, the data loading unit loads data related to company-specific information and services, and the generation AI generates responses based on that data. The installation unit installs the AI ​​robot equipped with the generation AI in a storefront or event venue. For example, the installation unit installs the AI ​​robot near the entrance to a storefront or at an information desk at an event venue to promote communication with visitors. This enables the AI ​​robot system according to an embodiment to engage in two-way communication with users and provide advanced support based on the adopting company's data.

[0030] Generative AI can analyze user questions and provide detailed product information. For example, generative AI can analyze user questions using natural language processing technology and provide detailed product information. For example, if a user asks, "What are the features of this product?", generative AI can analyze the question and provide detailed information such as, "This product is made of high-quality materials and is highly durable." Generative AI can also provide product price information and stock status in response to user questions. For example, in response to the question, "How much does this product cost?", generative AI might respond, "The price of this product is 5,000 yen." Generative AI can also provide detailed information about how to use and maintain a product. For example, in response to the question, "Please tell me how to maintain this product," it might respond, "Please clean this product regularly, dry it, and store it." This improves user satisfaction by providing detailed information in response to user questions.

[0031] Generative AI can provide event schedule information based on corporate data. For example, if a user asks, "What is the schedule for this event?", the generative AI analyzes the question and provides schedule information such as, "This event starts at 10:00 and ends at 12:00" based on corporate data. Generative AI can also provide detailed event information and program content. For example, in response to the question, "What is the program content for this event?", the generative AI might respond, "This event will feature a keynote speech starting at 10:00 and a panel discussion starting at 11:00." Generative AI can also provide real-time information about changes to events and the latest schedule information. For example, in response to the question, "Are there any changes to the schedule for this event?", the generative AI might respond, "There are currently no changes to the schedule." This allows accurate information to be provided to users by providing event schedule information based on corporate data.

[0032] Generative AI can learn a user's past interaction history and generate personalized responses for individual users. For example, generative AI can learn a user's past interaction history and generate personalized responses for individual users. For example, generative AI can memorize questions the user has previously asked and respond based on that information. Generative AI can also understand the user's preferences and interests based on the user's past interaction history and make corresponding suggestions. For example, it can re-suggest products or services in which the user previously showed interest. Generative AI can also analyze a user's past interaction history and predict the user's needs and problems and respond accordingly. For example, it can make new suggestions to solve problems the user previously had. In this way, user satisfaction can be improved by generating personalized responses based on the user's past interaction history.

[0033] Generative AI can use natural language processing technology to analyze user intent with high accuracy and respond to complex questions. Generative AI can use natural language processing technology to analyze user intent with high accuracy. For example, even if a user asks an ambiguous question, generative AI can accurately understand the user's intent and generate an appropriate response. Generative AI can also use natural language processing technology to respond to complex questions. For example, if a user asks a question that includes multiple conditions, generative AI can analyze those conditions and provide the optimal answer. Generative AI can also take into account context and background information to analyze user intent with high accuracy. For example, if a user wants to know more about a specific product, it can provide related information about that product. This allows generative AI to analyze user intent with high accuracy and respond to complex questions.

[0034] Robots equipped with generative AI can also be deployed in different fields, such as medical settings or educational institutions, to provide specialized information and support. Robots equipped with generative AI can be deployed in medical settings, for example, to answer patients' questions and provide medical information. For example, if a patient asks about the symptoms of an illness, the generative AI can provide information about those symptoms. Robots equipped with generative AI can also be introduced into educational institutions to answer students' questions and provide learning support. For example, if a student asks about a particular academic field, the generative AI can provide information about that field. Robots equipped with generative AI can also be deployed in corporate customer support to answer customers' questions and provide support. For example, if a customer asks about how to use a product, the generative AI can explain how to use it. This makes it possible to provide specialized information and support in different fields, such as medical settings and educational institutions.

[0035] Robots equipped with generative AI can be combined with speech recognition technology to enable dialogue via voice input. Robots equipped with generative AI can, for example, combine speech recognition technology to allow users to ask questions or give instructions via voice. For example, if a user asks, "What is the price of this item?", the generative AI will respond to the question. Generative AI can also use speech recognition technology to analyze the user's voice input and generate an appropriate response. For example, if a user asks for an event schedule via voice, the generative AI will provide that schedule information. Combining generative AI with speech recognition technology can also enable users to interact hands-free. For example, a user can give instructions to a robot via voice without using their hands and receive a response based on that instruction. This enables dialogue via voice input, improving user convenience.

[0036] The data loading unit updates the data of the adopting company in real time and can generate responses based on the latest information. For example, the data loading unit updates the data of the adopting company in real time, and the generation AI generates responses based on the latest information. For example, the data loading unit updates product inventory and price information in real time and responds based on that information. The data loading unit also connects to the company's database, allowing the generation AI to obtain the latest company information and reflect it in responses. For example, it provides new product release information and campaign information in real time. The data loading unit can also build a system that updates the data of the adopting company in real time, allowing the generation AI to always respond based on the latest information. For example, it provides event schedules and change information in real time. This allows the adopting company's data to be updated in real time and responses to be generated based on the latest information.

[0037] The data loading unit can analyze a user's purchasing history and behavioral patterns based on the data of the introducing company and make optimal suggestions to individual users. For example, the data loading unit can analyze a user's purchasing history and behavioral patterns based on the data of the introducing company and make optimal suggestions to individual users. For example, the data loading unit can suggest related products based on past purchase history. The data loading unit can also use company data to analyze a user's behavioral patterns and make personalized suggestions based on the results. For example, it can suggest products in categories that the user frequently visits. The data loading unit can also analyze the data of the introducing company and make customized suggestions based on the user's needs and preferences. For example, it can suggest new products or services that the user might be interested in. In this way, the data loading unit can analyze a user's purchasing history and behavioral patterns and make optimal suggestions to individual users, thereby improving user satisfaction.

[0038] The data loading unit can use the data of the introducing company to provide not only responses to user questions but also related additional information and advice. For example, the data loading unit can use the data of the introducing company to provide not only responses to user questions but also related additional information and advice. For example, the data loading unit can provide detailed product information as well as advice on usage and maintenance. The data loading unit can also provide related information along with responses to user questions based on the company's data. For example, it can provide information on nearby tourist attractions and access methods in addition to event schedules. The data loading unit can also utilize the data of the introducing company to provide related advice along with responses to user questions. For example, it can suggest points to note when purchasing a specific product or recommended ways to use it. This improves user satisfaction by providing not only responses to user questions but also related additional information and advice.

[0039] The data loading unit can integrate the data of the adopting company with data from other companies and industries to provide a wider range of information. For example, the data loading unit can integrate the data of the adopting company with data from other companies and industries, allowing the generation AI to provide a wider range of information. For example, the data loading unit can integrate product information from multiple companies and provide comparative information. Furthermore, by integrating data from other industries, the generation AI can provide multifaceted information. For example, in addition to information on a specific product, it can provide trend information on the industry to which that product is related. Furthermore, the data loading unit can integrate the data of the adopting company with data from other companies to provide comprehensive information to users. For example, it can integrate information on multiple companies that handle products in the same category and suggest the best option to the user. In this way, the data of the adopting company can be integrated with data from other companies and industries to provide a wider range of information, thereby improving user satisfaction.

[0040] The data loading unit uses the data of the adopting company to enable the generation AI to collect user feedback, which can be used to improve the company's services. For example, the data loading unit uses the data of the adopting company to enable the generation AI to collect user feedback and use that data to improve the company's services. For example, the data loading unit analyzes user opinions and requests and identifies areas for service improvement. The data loading unit also allows the generation AI to collect user feedback based on the company's data and provide the results to the company. For example, the data loading unit analyzes user satisfaction and dissatisfaction and reflects this in service improvements. The data loading unit also utilizes the data of the adopting company to enable the generation AI to collect user feedback in real time and use it to improve the company's services. For example, the data loading unit instantly adjusts services based on real-time user opinions. In this way, collecting user feedback and using it to improve the company's services can be expected to improve the company's services.

[0041] The installation unit can optimize the installation location in a storefront or event venue and place the robot in a location that is easily accessible to users. The installation unit can optimize the installation location in a storefront or event venue and place the robot in a location that is easily accessible to users. For example, the installation unit can be installed near an entrance or in a place with a lot of foot traffic so that users can naturally approach the robot. In addition, to optimize the installation location, the installation unit can analyze users' movement paths and behavior patterns and place the robot in the most effective location. For example, the installation unit can be installed in an area where users often stop by or in a place where waiting times occur. In addition, the installation unit can place the robot in a location where it is noticeable, taking into account the layout of the storefront or event venue. For example, the installation unit can be installed in conjunction with a display or sign to attract users' attention. In this way, the installation location can be optimized and placed in a location that is easily accessible to users, thereby improving user convenience.

[0042] When the installation unit is installed in a store or event venue, the generation AI can collect surrounding environmental information in real time and generate a response appropriate to the environment. When the installation unit is installed in a store or event venue, for example, the generation AI can collect surrounding environmental information in real time and generate a response appropriate to the environment. For example, the installation unit can detect changes in surrounding sound and light and respond accordingly. The installation unit is also equipped with sensors to collect environmental information in real time, and the generation AI can analyze the data and generate a response. For example, the installation unit can suggest a comfortable environment in response to changes in temperature and humidity. The installation unit can also provide appropriate guidance and information to the user based on the surrounding environmental information. For example, the installation unit can detect congestion levels and guide the user to the optimal route. In this way, by collecting surrounding environmental information in real time and generating responses appropriate to the environment, user satisfaction is improved.

[0043] When the installation unit is installed in a store or event venue, the generation AI can monitor the user's behavior and provide information and guidance at the appropriate time. When the installation unit is installed in a store or event venue, for example, the generation AI can monitor the user's behavior and provide information and guidance at the appropriate time. For example, if a user shows interest in a particular product, the installation unit can provide detailed information about that product. The installation unit can also monitor the user's behavior in real time, and the generation AI can respond according to that behavior. For example, the installation unit can detect when the user appears lost and provide directions. The installation unit can also monitor the user's behavior and provide promotion and campaign information at the appropriate time. For example, the installation unit can display related campaign information when the user approaches a specific area. In this way, user satisfaction can be improved by monitoring the user's behavior and providing information and guidance at the appropriate time.

[0044] The installation unit can install robots equipped with generative AI not only in storefronts or event venues, but also in other public places such as public transportation and tourist attractions. For example, the installation unit can install robots equipped with generative AI not only in storefronts or event venues, but also in public transportation to provide passengers with guidance and information. For example, the installation unit can provide transfer information and operation information at stations and bus stops. The installation unit can also install robots equipped with generative AI in tourist attractions to provide guidance and information to tourists. For example, the installation unit can introduce tourist spots and provide route guidance. The installation unit can also install robots equipped with generative AI in public places to provide support to visitors. For example, the installation unit can provide guidance and explanations of exhibits in libraries and museums. As a result, by installing robots equipped with generative AI in other public places such as public transportation and tourist attractions, it becomes possible to provide information and support in a wide range of situations.

[0045] When the installation unit is installed in a storefront or event venue, the generation AI can acquire the user's location information and propose the optimal guidance route. When the installation unit is installed in a storefront or event venue, for example, the generation AI can acquire the user's location information and propose the optimal guidance route. For example, the installation unit guides the user to the route that will allow them to reach their destination in the shortest time. In addition, the installation unit can have the generation AI provide real-time guidance to the user based on the location information. For example, the installation unit displays the route from the user's current location to the destination and guides them to points along the way. In addition, the installation unit can have the generation AI analyze the user's location information and propose the optimal route that avoids congestion and obstacles. For example, the installation unit guides the user to a route that allows them to move smoothly while avoiding congestion. In this way, by acquiring the user's location information and proposing the optimal guidance route, user convenience is improved.

[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 AI ​​robot system can also be equipped with a health management unit that monitors the user's health condition. For example, the health management unit can measure the user's heart rate and blood pressure and provide appropriate advice if an abnormality is detected. The health management unit can also record the user's exercise volume and dietary habits and make suggestions to support healthy lifestyle habits. Furthermore, the health management unit can create an individual health plan based on the user's health data and perform regular follow-ups. This can support the user's health management and promote a healthy lifestyle.

[0048] Generative AI can further analyze a user's purchasing history and make personalized product suggestions to individual users. For example, Generative AI can suggest related products based on products the user has previously purchased. Generative AI can also analyze a user's purchasing history and suggest new products that match the user's preferences and interests. Furthermore, Generative AI can provide specific campaign and discount information based on the user's purchasing history. This can improve the user's purchasing experience and increase satisfaction.

[0049] Generative AI can further analyze a user's learning history and provide personalized learning content for each individual user. For example, generative AI can suggest relevant learning content based on what the user has previously studied. Generative AI can also analyze a user's learning history and create a learning plan based on the user's level of understanding and progress. Furthermore, generative AI can provide additional resources and reference materials in specific fields based on the user's learning history. This can improve the user's learning experience and support effective learning.

[0050] Generative AI can further analyze user behavior patterns and make personalized lifestyle suggestions to individual users. For example, generative AI can suggest healthy lifestyle habits based on the user's past behavioral data. Generative AI can also analyze the user's behavior patterns and suggest leisure activities and travel plans based on the user's preferences and interests. Furthermore, generative AI can provide advice on time management and efficient task management based on the user's behavioral patterns. This can improve the user's lifestyle and support a fulfilling life.

[0051] Generative AI can further analyze users' hobbies and interests and suggest personalized hobby activities for each individual user. For example, generative AI can suggest new hobby activities based on the hobbies the user has shown interest in in the past. Generative AI can also analyze users' hobbies and interests and provide information on related events and workshops. Furthermore, generative AI can encourage participation in online communities and forums based on the user's hobbies. This can enrich users' hobby activities and support a richer life.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The AI ​​robot equipped with the generative AI engages in two-way communication. For example, the generative AI analyzes questions from users and generates appropriate responses. Based on user input, the generative AI can also provide detailed product information and event schedule information. Step 2: The data loading unit loads the data of the adopting company. For example, the data loading unit loads data related to company-specific information and services, and the generation AI generates a response based on that data. Step 3: The installation department installs the AI ​​robot equipped with the generation AI in a storefront or event venue. For example, the installation department may install the AI ​​robot near the entrance of a storefront or at the information desk of an event venue to promote communication with visitors.

[0054] (Example 2) The AI ​​robot system according to the embodiment of the present invention is equipped with a generative AI and is a system that performs two-way communication at stores, event venues, etc. This enables the AI ​​robot system to achieve two-way communication with users and to respond in an advanced manner based on data from the company that has adopted the system.

[0055] An AI robot system according to an embodiment includes an AI robot equipped with a generation AI, a data loading unit, and an installation unit. The AI ​​robot equipped with the generation AI engages in two-way communication. For example, the generation AI analyzes questions from users and generates appropriate responses. The generation AI can also provide detailed product information and event schedule information based on user input. The data loading unit loads data from the adopting company. For example, the data loading unit loads data related to company-specific information and services, and the generation AI generates responses based on that data. The installation unit installs the AI ​​robot equipped with the generation AI in a storefront or event venue. For example, the installation unit installs the AI ​​robot near the entrance to a storefront or at an information desk at an event venue to promote communication with visitors. This enables the AI ​​robot system according to an embodiment to engage in two-way communication with users and provide advanced support based on the adopting company's data.

[0056] Generative AI can analyze user questions and provide detailed product information. For example, generative AI can analyze user questions using natural language processing technology and provide detailed product information. For example, if a user asks, "What are the features of this product?", generative AI can analyze the question and provide detailed information such as, "This product is made of high-quality materials and is highly durable." Generative AI can also provide product price information and stock status in response to user questions. For example, in response to the question, "How much does this product cost?", generative AI might respond, "The price of this product is 5,000 yen." Generative AI can also provide detailed information about how to use and maintain a product. For example, in response to the question, "Please tell me how to maintain this product," it might respond, "Please clean this product regularly, dry it, and store it." This improves user satisfaction by providing detailed information in response to user questions.

[0057] Generative AI can provide event schedule information based on corporate data. For example, if a user asks, "What is the schedule for this event?", the generative AI analyzes the question and provides schedule information such as, "This event starts at 10:00 and ends at 12:00" based on corporate data. Generative AI can also provide detailed event information and program content. For example, in response to the question, "What is the program content for this event?", the generative AI might respond, "This event will feature a keynote speech starting at 10:00 and a panel discussion starting at 11:00." Generative AI can also provide real-time information about changes to events and the latest schedule information. For example, in response to the question, "Are there any changes to the schedule for this event?", the generative AI might respond, "There are currently no changes to the schedule." This allows accurate information to be provided to users by providing event schedule information based on corporate data.

[0058] Generative AI can estimate a user's emotions and generate responses that correspond to those emotions. For example, generative AI can estimate emotions by analyzing a user's facial expressions and voice tone. For example, if a user expresses dissatisfaction, generative AI can detect that emotion and suggest appropriate comfort or solutions. Furthermore, if a user is excited, generative AI can suggest products or services that will further increase that excitement. For example, when a user is excited, generative AI can provide information about activities or events. Generative AI can also adjust the tone and content of responses based on the user's emotions. For example, if a user is sad, generative AI can offer encouraging words in a gentle tone. This increases user satisfaction by generating responses that correspond to the user's emotions.

[0059] Generative AI can learn a user's past interaction history and generate personalized responses for individual users. For example, generative AI can learn a user's past interaction history and generate personalized responses for individual users. For example, generative AI can memorize questions the user has previously asked and respond based on that information. Generative AI can also understand the user's preferences and interests based on the user's past interaction history and make corresponding suggestions. For example, it can re-suggest products or services in which the user previously showed interest. Generative AI can also analyze a user's past interaction history and predict the user's needs and problems and respond accordingly. For example, it can make new suggestions to solve problems the user previously had. In this way, user satisfaction can be improved by generating personalized responses based on the user's past interaction history.

[0060] Generative AI can use natural language processing technology to analyze user intent with high accuracy and respond to complex questions. Generative AI can use natural language processing technology to analyze user intent with high accuracy. For example, even if a user asks an ambiguous question, generative AI can accurately understand the user's intent and generate an appropriate response. Generative AI can also use natural language processing technology to respond to complex questions. For example, if a user asks a question that includes multiple conditions, generative AI can analyze those conditions and provide the optimal answer. Generative AI can also take into account context and background information to analyze user intent with high accuracy. For example, if a user wants to know more about a specific product, it can provide related information about that product. This allows generative AI to analyze user intent with high accuracy and respond to complex questions.

[0061] Robots equipped with generative AI can also be deployed in different fields, such as medical settings or educational institutions, to provide specialized information and support. Robots equipped with generative AI can be deployed in medical settings, for example, to answer patients' questions and provide medical information. For example, if a patient asks about the symptoms of an illness, the generative AI can provide information about those symptoms. Robots equipped with generative AI can also be introduced into educational institutions to answer students' questions and provide learning support. For example, if a student asks about a particular academic field, the generative AI can provide information about that field. Robots equipped with generative AI can also be deployed in corporate customer support to answer customers' questions and provide support. For example, if a customer asks about how to use a product, the generative AI can explain how to use it. This makes it possible to provide specialized information and support in different fields, such as medical settings and educational institutions.

[0062] Robots equipped with generative AI can be combined with speech recognition technology to enable dialogue via voice input. Robots equipped with generative AI can, for example, combine speech recognition technology to allow users to ask questions or give instructions via voice. For example, if a user asks, "What is the price of this item?", the generative AI will respond to the question. Generative AI can also use speech recognition technology to analyze the user's voice input and generate an appropriate response. For example, if a user asks for an event schedule via voice, the generative AI will provide that schedule information. Combining generative AI with speech recognition technology can also enable users to interact hands-free. For example, a user can give instructions to a robot via voice without using their hands and receive a response based on that instruction. This enables dialogue via voice input, improving user convenience.

[0063] The generative AI can use the emotion estimation function to suggest products and services based on the user's emotions. The generative AI, for example, uses the emotion estimation function to suggest products and services based on the user's emotions. For example, if the user is excited, the generative AI will suggest products that will further increase that excitement. The generative AI can also analyze the user's emotions and provide services that correspond to those emotions. For example, if the user feels like relaxing, the generative AI will suggest services that will help the user relax. The generative AI can also use the emotion estimation function to make customized suggestions based on the user's emotions. For example, if the user is feeling stressed, the generative AI will suggest products and services that will help relieve stress. In this way, by suggesting products and services based on the user's emotions, user satisfaction is improved.

[0064] The data loading unit updates the data of the adopting company in real time and can generate responses based on the latest information. For example, the data loading unit updates the data of the adopting company in real time, and the generation AI generates responses based on the latest information. For example, the data loading unit updates product inventory and price information in real time and responds based on that information. The data loading unit also connects to the company's database, allowing the generation AI to obtain the latest company information and reflect it in responses. For example, it provides new product release information and campaign information in real time. The data loading unit can also build a system that updates the data of the adopting company in real time, allowing the generation AI to always respond based on the latest information. For example, it provides event schedules and change information in real time. This allows the adopting company's data to be updated in real time and responses to be generated based on the latest information.

[0065] The data loading unit can analyze a user's purchasing history and behavioral patterns based on the data of the introducing company and make optimal suggestions to individual users. For example, the data loading unit can analyze a user's purchasing history and behavioral patterns based on the data of the introducing company and make optimal suggestions to individual users. For example, the data loading unit can suggest related products based on past purchase history. The data loading unit can also use company data to analyze a user's behavioral patterns and make personalized suggestions based on the results. For example, it can suggest products in categories that the user frequently visits. The data loading unit can also analyze the data of the introducing company and make customized suggestions based on the user's needs and preferences. For example, it can suggest new products or services that the user might be interested in. In this way, the data loading unit can analyze a user's purchasing history and behavioral patterns and make optimal suggestions to individual users, thereby improving user satisfaction.

[0066] The data loading unit can use the data of the introducing company to provide not only responses to user questions but also related additional information and advice. For example, the data loading unit can use the data of the introducing company to provide not only responses to user questions but also related additional information and advice. For example, the data loading unit can provide detailed product information as well as advice on usage and maintenance. The data loading unit can also provide related information along with responses to user questions based on the company's data. For example, it can provide information on nearby tourist attractions and access methods in addition to event schedules. The data loading unit can also utilize the data of the introducing company to provide related advice along with responses to user questions. For example, it can suggest points to note when purchasing a specific product or recommended ways to use it. This improves user satisfaction by providing not only responses to user questions but also related additional information and advice.

[0067] The data loading unit can integrate the data of the adopting company with data from other companies and industries to provide a wider range of information. For example, the data loading unit can integrate the data of the adopting company with data from other companies and industries, allowing the generation AI to provide a wider range of information. For example, the data loading unit can integrate product information from multiple companies and provide comparative information. Furthermore, by integrating data from other industries, the generation AI can provide multifaceted information. For example, in addition to information on a specific product, it can provide trend information on the industry to which that product is related. Furthermore, the data loading unit can integrate the data of the adopting company with data from other companies to provide comprehensive information to users. For example, it can integrate information on multiple companies that handle products in the same category and suggest the best option to the user. In this way, the data of the adopting company can be integrated with data from other companies and industries to provide a wider range of information, thereby improving user satisfaction.

[0068] The data loading unit uses the data of the adopting company to enable the generation AI to collect user feedback, which can be used to improve the company's services. For example, the data loading unit uses the data of the adopting company to enable the generation AI to collect user feedback and use that data to improve the company's services. For example, the data loading unit analyzes user opinions and requests and identifies areas for service improvement. The data loading unit also allows the generation AI to collect user feedback based on the company's data and provide the results to the company. For example, the data loading unit analyzes user satisfaction and dissatisfaction and reflects this in service improvements. The data loading unit also utilizes the data of the adopting company to enable the generation AI to collect user feedback in real time and use it to improve the company's services. For example, the data loading unit instantly adjusts services based on real-time user opinions. In this way, collecting user feedback and using it to improve the company's services can be expected to improve the company's services.

[0069] The data loading unit can use the emotion estimation function to analyze the emotional impact of responses based on the adopting company's data on the user and generate an optimal response. For example, the data loading unit can use the emotion estimation function to analyze the emotional impact of responses based on the adopting company's data on the user. For example, the data loading unit prioritizes generating responses that evoke positive emotions from the user. Furthermore, the data loading unit can use the generation AI to analyze the user's emotional reactions based on the adopting company's data and generate an optimal response. For example, if the user expresses dissatisfaction, the data loading unit can respond to resolve that dissatisfaction. Furthermore, the data loading unit can use the emotion estimation function to analyze the emotional impact of responses based on the adopting company's data on the user in real time and adjust the response. For example, the data loading unit dynamically changes the content of the response in response to changes in the user's emotions. This allows the data loading unit to analyze the emotional impact of responses based on the adopting company's data on the user and generate an optimal response, thereby improving user satisfaction.

[0070] The installation unit can optimize the installation location in a storefront or event venue and place the robot in a location that is easily accessible to users. The installation unit can optimize the installation location in a storefront or event venue and place the robot in a location that is easily accessible to users. For example, the installation unit can be installed near an entrance or in a place with a lot of foot traffic so that users can naturally approach the robot. In addition, to optimize the installation location, the installation unit can analyze users' movement paths and behavior patterns and place the robot in the most effective location. For example, the installation unit can be installed in an area where users often stop by or in a place where waiting times occur. In addition, the installation unit can place the robot in a location where it is noticeable, taking into account the layout of the storefront or event venue. For example, the installation unit can be installed in conjunction with a display or sign to attract users' attention. In this way, the installation location can be optimized and placed in a location that is easily accessible to users, thereby improving user convenience.

[0071] When the installation unit is installed in a store or event venue, the generation AI can collect surrounding environmental information in real time and generate a response appropriate to the environment. When the installation unit is installed in a store or event venue, for example, the generation AI can collect surrounding environmental information in real time and generate a response appropriate to the environment. For example, the installation unit can detect changes in surrounding sound and light and respond accordingly. The installation unit is also equipped with sensors to collect environmental information in real time, and the generation AI can analyze the data and generate a response. For example, the installation unit can suggest a comfortable environment in response to changes in temperature and humidity. The installation unit can also provide appropriate guidance and information to the user based on the surrounding environmental information. For example, the installation unit can detect congestion levels and guide the user to the optimal route. In this way, by collecting surrounding environmental information in real time and generating responses appropriate to the environment, user satisfaction is improved.

[0072] When the installation unit is installed in a store or event venue, the generation AI can monitor the user's behavior and provide information and guidance at the appropriate time. When the installation unit is installed in a store or event venue, for example, the generation AI can monitor the user's behavior and provide information and guidance at the appropriate time. For example, if a user shows interest in a particular product, the installation unit can provide detailed information about that product. The installation unit can also monitor the user's behavior in real time, and the generation AI can respond according to that behavior. For example, the installation unit can detect when the user appears lost and provide directions. The installation unit can also monitor the user's behavior and provide promotion and campaign information at the appropriate time. For example, the installation unit can display related campaign information when the user approaches a specific area. In this way, user satisfaction can be improved by monitoring the user's behavior and providing information and guidance at the appropriate time.

[0073] The installation unit can install robots equipped with generative AI not only in storefronts or event venues, but also in other public places such as public transportation and tourist attractions. For example, the installation unit can install robots equipped with generative AI not only in storefronts or event venues, but also in public transportation to provide passengers with guidance and information. For example, the installation unit can provide transfer information and operation information at stations and bus stops. The installation unit can also install robots equipped with generative AI in tourist attractions to provide guidance and information to tourists. For example, the installation unit can introduce tourist spots and provide route guidance. The installation unit can also install robots equipped with generative AI in public places to provide support to visitors. For example, the installation unit can provide guidance and explanations of exhibits in libraries and museums. As a result, by installing robots equipped with generative AI in other public places such as public transportation and tourist attractions, it becomes possible to provide information and support in a wide range of situations.

[0074] When the installation unit is installed in a storefront or event venue, the generation AI can acquire the user's location information and propose the optimal guidance route. When the installation unit is installed in a storefront or event venue, for example, the generation AI can acquire the user's location information and propose the optimal guidance route. For example, the installation unit guides the user to the route that will allow them to reach their destination in the shortest time. In addition, the installation unit can have the generation AI provide real-time guidance to the user based on the location information. For example, the installation unit displays the route from the user's current location to the destination and guides them to points along the way. In addition, the installation unit can have the generation AI analyze the user's location information and propose the optimal route that avoids congestion and obstacles. For example, the installation unit guides the user to a route that allows them to move smoothly while avoiding congestion. In this way, by acquiring the user's location information and proposing the optimal guidance route, user convenience is improved.

[0075] The installation unit can use the emotion estimation function to provide guidance and information according to the user's emotions when installed in a storefront or event venue. The installation unit, for example, uses the emotion estimation function to provide guidance and information according to the user's emotions when installed in a storefront or event venue. For example, if the user is lost, the installation unit provides guidance that gives a sense of security. The installation unit can also use the generation AI to analyze the user's emotions and provide information according to those emotions. For example, if the user is excited, the installation unit provides event information that will further increase the user's excitement. The installation unit can also use the emotion estimation function to provide customized guidance based on the user's emotions. For example, if the user is tired, the installation unit guides the user to rest spots or places where they can refresh themselves. This improves user satisfaction by providing guidance and information according to the user's emotions.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The AI ​​robot system can also be equipped with a health management unit that monitors the user's health condition. For example, the health management unit can measure the user's heart rate and blood pressure and provide appropriate advice if an abnormality is detected. The health management unit can also record the user's exercise volume and dietary habits and make suggestions to support healthy lifestyle habits. Furthermore, the health management unit can create an individual health plan based on the user's health data and perform regular follow-ups. This can support the user's health management and promote a healthy lifestyle.

[0078] The generative AI can further estimate the user's emotions and evaluate the user's stress level based on the estimated emotions. For example, the generative AI can analyze the user's facial expressions and voice tone to evaluate the user's stress level. The generative AI can also provide relaxing music or meditation guides according to the user's stress level. Furthermore, the generative AI can suggest activities and exercises to reduce the user's stress level. This helps the user manage their stress and maintain their physical and mental health.

[0079] Generative AI can further analyze a user's purchasing history and make personalized product suggestions to individual users. For example, Generative AI can suggest related products based on products the user has previously purchased. Generative AI can also analyze a user's purchasing history and suggest new products that match the user's preferences and interests. Furthermore, Generative AI can provide specific campaign and discount information based on the user's purchasing history. This can improve the user's purchasing experience and increase satisfaction.

[0080] The generative AI can further estimate the user's emotions and provide entertainment content to improve the user's mood based on the estimated emotions. For example, if the user is feeling down, the generative AI can provide fun videos or jokes. If the user is tired, the generative AI can provide relaxing music or nature sounds. Furthermore, the generative AI can suggest interactive games or activities according to the user's emotions, thereby improving the user's mood and refreshing them.

[0081] Generative AI can further analyze a user's learning history and provide personalized learning content for each individual user. For example, generative AI can suggest relevant learning content based on what the user has previously studied. Generative AI can also analyze a user's learning history and create a learning plan based on the user's level of understanding and progress. Furthermore, generative AI can provide additional resources and reference materials in specific fields based on the user's learning history. This can improve the user's learning experience and support effective learning.

[0082] The generative AI can also estimate the user's emotions and provide feedback to improve the user's motivation based on the estimated emotions. For example, if the user is feeling unmotivated, the generative AI can provide words of encouragement or success stories. Also, if the user feels a sense of accomplishment, the generative AI can provide praise or rewards that will further enhance that emotion. Furthermore, the generative AI can support goal setting and progress management according to the user's emotions. This can improve the user's motivation and support goal achievement.

[0083] Generative AI can further analyze user behavior patterns and make personalized lifestyle suggestions to individual users. For example, generative AI can suggest healthy lifestyle habits based on the user's past behavioral data. Generative AI can also analyze the user's behavior patterns and suggest leisure activities and travel plans based on the user's preferences and interests. Furthermore, generative AI can provide advice on time management and efficient task management based on the user's behavioral patterns. This can improve the user's lifestyle and support a fulfilling life.

[0084] The generative AI can also estimate the user's emotions and, based on the estimated emotions, provide support to improve the user's sociability. For example, if the user feels lonely, the generative AI can make suggestions to promote communication with friends and family. If the user feels nervous, the generative AI can provide ways to relax and tips for conversation. Furthermore, the generative AI can provide information about social events and group activities according to the user's emotions. This can improve the user's sociability and help them build richer relationships.

[0085] Generative AI can further analyze users' hobbies and interests and suggest personalized hobby activities for each individual user. For example, generative AI can suggest new hobby activities based on the hobbies the user has shown interest in in the past. Generative AI can also analyze users' hobbies and interests and provide information on related events and workshops. Furthermore, generative AI can encourage participation in online communities and forums based on the user's hobbies. This can enrich users' hobby activities and support a richer life.

[0086] Generative AI can further estimate the user's emotions and provide advice to support the user's mental health based on the estimated emotions. For example, if the user is feeling stressed, generative AI can suggest relaxation techniques and stress management methods. If the user is feeling anxious, generative AI can provide counseling and support to give them a sense of security. Furthermore, generative AI can provide information on mental health resources and experts according to the user's emotions. This can support the user's mental health and help them maintain their mental well-being.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The AI ​​robot equipped with the generative AI engages in two-way communication. For example, the generative AI analyzes questions from users and generates appropriate responses. Based on user input, the generative AI can also provide detailed product information and event schedule information. Step 2: The data loading unit loads the data of the adopting company. For example, the data loading unit loads data related to company-specific information and services, and the generation AI generates a response based on that data. Step 3: The installation department installs the AI ​​robot equipped with the generation AI in a storefront or event venue. For example, the installation department may install the AI ​​robot near the entrance of a storefront or at the information desk of an event venue to promote communication with visitors.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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. [Explanation of symbols]

[0156] 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. An AI robot equipped with generative AI, A data loading section that loads data from the adopting company, and an installation unit that installs the AI ​​robot equipped with the generation AI in a storefront or event venue. A system characterized by:

2. The generated AI is Analyze user questions and provide detailed product information 2. The system of claim 1.

3. The generated AI is Providing event schedule information based on company data 2. The system of claim 1.

4. The generated AI is Estimating a user's emotion and generating a response according to the emotion 2. The system of claim 1.

5. The generated AI is Learns from users' past interaction history and generates personalized responses for each individual user 2. The system of claim 1.

6. The generated AI is Using natural language processing technology, we can accurately analyze user intent and respond to complex questions.

2. The system of claim 1.

7. The robot equipped with the generation AI is Expanding to different fields in medical settings and educational institutions to provide specialized information and support 2. The system of claim 1.

8. The robot equipped with the generation AI is Combined with voice recognition technology, it enables dialogue via voice input.

2. The system of claim 1.

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