system

Abstract

The system according to the embodiment aims to effectively approach and attract the interest of customers passing by. [Solution] The system according to the embodiment comprises a detection unit, a dialogue unit, a generation unit, and a dialogue control unit. The detection unit detects customers passing by. The dialogue unit speaks to the customers detected by the detection unit. The generation unit generates the content of the dialogue conducted by the dialogue unit. The dialogue control unit engages in dialogue with the customer based on the dialogue content generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to effectively approach and capture the interest of passing customers.

[0005] The system according to the embodiment aims to effectively approach and attract the interest of customers passing by. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, a dialogue unit, a generation unit, and a dialogue control unit. The detection unit detects customers passing by. The dialogue unit speaks to the customers detected by the detection unit. The generation unit generates dialogue content to be carried out by the dialogue unit. The dialogue control unit carries out a dialogue with the customer based on the dialogue content generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can effectively approach and attract the interest of passing customers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An interactive robot system according to an embodiment of the present invention is a system for attracting customers to smartphone acquisition events. This interactive robot system detects passing customers and automatically engages them in conversation, attracting their interest and encouraging them to stop and enjoy the conversation. Specifically, the robot uses camera and audio sensors to detect passing customers and automatically engages them. The robot then uses natural language processing technology to engage with the customers, engaging in interactive games and discussing current events. This allows customers to enjoy interacting with the robot and stopping to talk. The robot's appearance is designed to be "the robot!" for children, yet its fully interactive design allows adults to stop and talk. Its tone of voice, speaking style, and dialect can be freely changed to accommodate interactive games and current events. This allows it to accommodate a wide range of customers, from children to adults. It is also designed for use in fields such as education and medicine. For example, the robot uses camera and audio sensors to detect passing customers and automatically engages them. For example, it greets them with a greeting such as, "Hello! Welcome to the smartphone acquisition event!" The robot can also adapt its tone of voice and speaking style depending on the customer's age and gender. For example, the robot speaks to children in a bright and cheerful voice and to adults in a calm voice. Next, the robot uses natural language processing technology to converse with the customer. For example, it makes suggestions such as, "Would you like to play a word game?" or "Would you like to try a riddle?" If the customer responds, the robot begins an interactive game. This allows customers to enjoy interacting with the robot and encourages them to stop and talk. The robot also responds to current events. For example, it asks questions such as, "What do you think about the recent news?", deepening the dialogue with customers. This also piques the interest of adult customers, encouraging them to stop and talk. In this way, the robot can talk to passing customers and attract more customers through dialogue. Its child-friendly appearance and interactive design allow it to accommodate a wide range of customers, from children to adults.Furthermore, the robot can freely change its tone of voice, speaking style, and dialect, making it suitable for a variety of situations. It also offers a wide range of interactive content, including interactive games and current events, ensuring that customers will never get bored. This allows the interactive robot system to attract customers to smartphone acquisition events and entertain them through dialogue.

[0029] An interactive robot system according to an embodiment includes a detection unit, a dialogue unit, a generation unit, and a dialogue control unit. The detection unit detects customers passing by. The detection unit detects customers using, for example, a camera sensor and an audio sensor. The camera sensor acquires high-resolution images and detects the customer's movements using a motion detection algorithm. The audio sensor collects ambient sounds and detects the customer's voice using voice recognition technology. For example, the detection unit can track the customer's movements in real time using the camera sensor and detect the customer's voice using the audio sensor. The detection unit can also detect the presence of customers with high accuracy by combining multiple sensors. The dialogue unit speaks to the customer detected by the detection unit. The dialogue unit can change the tone of voice and speaking style depending on, for example, the customer's age and gender. The dialogue unit can generate various voice tones, such as a bright and cheerful voice or a calm voice, using voice synthesis technology. For example, the dialogue unit can speak to children in a bright and cheerful voice and to adults in a calm voice. The dialogue unit can also adjust the speaking speed and intonation. For example, the dialogue unit can speak slowly to children and at a faster pace to adults. The generation unit uses a generation AI to analyze the customer's utterance and generate an appropriate response. The generation unit uses natural language processing technology to analyze the meaning of the customer's utterance and generate a response based on the context. For example, the generation unit analyzes the customer's utterance and inputs prompts to the generation AI to generate an appropriate response. The generation AI generates a response based on the prompt and outputs it to the generation unit. The generation unit receives the response from the generation AI and provides an appropriate response to the customer. The dialogue control unit engages in a dialogue with the customer based on the dialogue content generated by the generation unit. The dialogue control unit controls the progress of the dialogue and ensures smooth progress of the dialogue with the customer. For example, the dialogue control unit engages in a dialogue with the customer about interactive games or current events. The dialogue control unit manages the flow of the dialogue and can advance the dialogue to keep the customer interested. The dialogue control unit is also designed for use in fields such as education and medicine. For example, the dialogue control unit can provide learning support and knowledge in the education field, and communication with patients and rehabilitation support in the medical field.As a result, the interactive robot system according to the embodiment can detect passing customers and attract them through dialogue. Some or all of the above-described processing in the dialogue control unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue control unit may input the dialogue content generated by the generation unit and use an AI model that controls the progress of the dialogue to carry out the dialogue.

[0030] The detection unit can detect customers using a camera sensor and an audio sensor. The camera sensor, for example, acquires high-resolution images and detects customer movements using a motion detection algorithm. For example, the camera sensor can be installed at the entrance to an event venue and track the movements of passing customers in real time. The camera sensor can also cover a wide area using a wide-angle lens, allowing it to detect many customers simultaneously. The audio sensor, for example, collects ambient sounds and detects customer voices using voice recognition technology. For example, the audio sensor can be installed on the ceiling of an event venue and can collect the voices of passing customers with high sensitivity. The audio sensor can also use noise canceling technology to remove ambient noise and clearly detect customer voices. This allows accurate detection of customers using the camera sensor and audio sensor. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input data acquired from the camera sensor and audio sensor into AI and have the AI ​​perform customer detection.

[0031] The dialogue unit can change the tone of voice or speaking style depending on the age or gender of the customer. The dialogue unit can generate various tone of voice, such as a bright and cheerful voice or a calm voice, using, for example, speech synthesis technology. For example, the dialogue unit can speak in a bright and cheerful voice to children and in a calm voice to adults. The dialogue unit can also adjust the speaking speed and intonation. For example, the dialogue unit can speak slowly to children and at a faster pace to adults. Furthermore, the dialogue unit can change the dialect or accent. For example, the dialogue unit can speak to the customer using a regional dialect. This allows for a more friendly dialogue by changing the tone of voice and speaking style depending on the customer's age and gender. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit can input the customer's age and gender into AI and have the AI ​​adjust the tone of voice and speaking style.

[0032] The generation unit can use the generation AI to analyze the customer's utterance and generate an appropriate response. The generation unit uses natural language processing technology to analyze the meaning of the customer's utterance and generate a response based on the context. For example, the generation unit analyzes the customer's utterance and inputs a prompt to the generation AI to generate an appropriate response. The generation AI generates a response based on the prompt and outputs it to the generation unit. The generation unit receives the response from the generation AI and provides the customer with an appropriate response. For example, if a customer utters, "Shall we play Shiritori?", the generation unit inputs a prompt to the generation AI saying, "Generate the next word in Shiritori," and provides the word generated by the generation AI to the customer. Also, if a customer utters, "What do you think about the recent news?", the generation unit inputs a prompt to the generation AI saying, "Generate your opinion about the recent news," and provides the customer with the opinion generated by the generation AI. In this way, an appropriate response can be generated by using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input customer statements into the AI ​​and have the AI ​​generate responses.

[0033] The dialogue control unit can engage in dialogue with the customer about interactive games or current events. The dialogue control unit controls the progress of the dialogue and smoothly advances the dialogue with the customer. For example, the dialogue control unit engages in dialogue with the customer about interactive games or current events. The dialogue control unit can manage the flow of the dialogue and advance the dialogue so as to maintain the customer's interest. For example, the dialogue control unit can suggest, "Would you like to play a word chain game?" and, if the customer responds, start a word chain dialogue. The dialogue control unit can also suggest, "Would you like to try a riddle?" and, if the customer responds, start a riddle dialogue. The dialogue control unit can further deepen the dialogue with the customer by asking, "What do you think about the recent news?" In this way, by engaging in dialogue about interactive games or current events, the customer can be attracted and stop to talk. Some or all of the above-described processing in the dialogue control unit may be performed, for example, using AI, or may be performed without AI. For example, the dialogue control unit can engage in dialogue using an AI model that uses the dialogue content generated by the generation unit as input and controls the progress of the dialogue.

[0034] The dialogue control unit is designed for use in the fields of education or medicine. In the field of education, the dialogue control unit can provide learning support and knowledge, and in the field of medicine, it can communicate with patients and support rehabilitation. For example, in the field of education, the dialogue control unit can explain learning content to children and support their learning through quizzes and games. In the field of medicine, the dialogue control unit can also support the progress of rehabilitation through dialogue with patients and monitor their condition. As a result, since it is designed for use in fields such as education and medicine, it can be used for a wide range of applications. Some or all of the above-mentioned processing in the dialogue control unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue control unit can input dialogue content in the fields of education or medicine into AI and conduct a dialogue using an AI model that controls the progress of the dialogue.

[0035] The detection unit can analyze a customer's past visit history and select the most appropriate detection method. For example, if a customer has visited many times in the past, the detection unit will offer a friendly greeting. The detection unit can also provide a thorough explanation to first-time visitors. Furthermore, the detection unit can select topics that will interest the customer based on their past visit history. This allows for the selection of a more appropriate detection method by analyzing the customer's past visit history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the customer's past visit history data into AI and have the AI ​​select the most appropriate detection method.

[0036] The detection unit can filter based on the customer's current behavior or interests when detection occurs. For example, if the customer is looking at their smartphone, the detection unit can provide topics related to smartphones. It can also provide topics suitable for children if the customer is with children. Furthermore, if the customer is talking with friends, the detection unit can provide topics that can be enjoyed as a group. This allows for more relevant conversations by filtering based on the customer's current behavior and interests. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the customer's current behavior and interest data into an AI and have the AI ​​perform the filtering.

[0037] The detection unit can prioritize detecting highly relevant customers by taking into account the geographical location information of the customer during detection. For example, if the customer is near an event venue, the detection unit will prioritize speaking to the customer. Also, if the customer is far away, the detection unit can speak to the customer next. Furthermore, if the customer is in a specific area, the detection unit can provide topics related to that area. In this way, by taking into account the geographical location information of the customer, more relevant customers can be preferentially detected. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the geographical location information of the customer into AI and have the AI ​​detect highly relevant customers.

[0038] The detection unit can analyze the customer's social media activity at the time of detection and detect related customers. For example, if the customer posts about an event, the detection unit will prioritize speaking to the customer. In addition, if the customer has a specific interest, the detection unit can also provide topics related to that interest. Furthermore, if the customer is with friends, the detection unit can also provide topics that can be enjoyed by the group. In this way, by analyzing the customer's social media activity, more related customers can be detected. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the customer's social media activity data into AI and have the AI ​​detect related customers.

[0039] The dialogue unit can select an appropriate dialogue method by referring to the customer's past dialogue history during dialogue. For example, the dialogue unit may re-present topics that the customer enjoyed in the past. The dialogue unit may also prioritize topics in which the customer has shown interest in the past. Furthermore, the dialogue unit may avoid topics that the customer has avoided in the past. In this way, by referring to the customer's past dialogue history, a more appropriate dialogue method can be selected. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit may input the customer's past dialogue history data into AI and have the AI ​​select the optimal dialogue method.

[0040] The dialogue unit can customize the dialogue content based on the customer's current interests at the time of dialogue. For example, if the customer is looking at a smartphone, the dialogue unit can provide smartphone-related topics. Furthermore, if the customer is with children, the dialogue unit can provide topics for children. Furthermore, if the customer is talking with friends, the dialogue unit can provide topics that the group can enjoy. This allows for more relevant dialogue by customizing the dialogue content based on the customer's current interests. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI, for example. For example, the dialogue unit can input the customer's current interest data into AI and have the AI ​​customize the dialogue content.

[0041] The dialogue unit can provide appropriate dialogue content by taking into account the geographical location information of the customer during dialogue. For example, if the customer is near an event venue, the dialogue unit can provide topics related to the event. Also, if the customer is far away, the dialogue unit can hold the next dialogue. Furthermore, if the customer is in a specific area, the dialogue unit can provide topics related to that area. In this way, by taking the customer's geographical location information into account, more relevant dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input the customer's geographical location information into AI and have the AI ​​provide the dialogue content.

[0042] The dialogue unit can analyze the customer's social media activity during the dialogue and provide relevant dialogue content. For example, if the customer posts about an event, the dialogue unit can provide topics related to the event. Also, if the customer has a specific interest, the dialogue unit can provide topics related to that interest. Furthermore, if the customer is with friends, the dialogue unit can provide topics that the group can enjoy. In this way, by analyzing the customer's social media activity, more relevant dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue unit may be performed using, or without, AI, for example. For example, the dialogue unit can input the customer's social media activity data into AI and have the AI ​​provide relevant dialogue content.

[0043] The generation unit can adjust the level of detail of the response based on the importance of the customer's statement when generating a response. For example, if the customer's statement is important, the generation unit generates a detailed response. The generation unit can also generate a concise response if the customer's statement is general. Furthermore, the generation unit can also generate a brief response if the customer's statement is on a light topic. This allows for a more appropriate response by adjusting the level of detail of the response based on the importance of the customer's statement. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input customer statement data into AI and have the AI ​​adjust the level of detail of the response.

[0044] The generation unit can apply different generation algorithms depending on the customer category when generating a response. For example, the generation unit can apply a generation algorithm that uses simple and easy-to-understand expressions to responses aimed at children. The generation unit can also apply a generation algorithm that uses detailed and specialized expressions to responses aimed at adults. Furthermore, the generation unit can apply a generation algorithm that explains at a slower pace to responses aimed at the elderly. This allows for a more appropriate response by applying the optimal generation algorithm depending on the customer category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input customer category data into AI and have the AI ​​apply the generation algorithm.

[0045] When generating a response, the generation unit can determine the priority of the response based on the time when the customer's comment was submitted. For example, if the customer's comment is recent, the generation unit will prioritize generating a response. The generation unit can also generate a response next if the customer's comment is from the past. Furthermore, the generation unit can postpone an older customer comment. This enables a more appropriate response by determining the priority of responses based on the time when the customer's comment was submitted. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time when the customer's comment was submitted into AI and have the AI ​​determine the priority of responses.

[0046] The generation unit can adjust the order of responses based on the relevance of the customer's utterances when generating responses. For example, if the customer's utterance is highly relevant, the generation unit will generate a response as a priority. The generation unit can also generate a response next if the customer's utterance is general. Furthermore, the generation unit can postpone a customer's utterance that is less relevant. This allows for a more appropriate response by adjusting the order of responses based on the relevance of the customer's utterances. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input relevance data of the customer's utterances into AI and have the AI ​​adjust the order of responses.

[0047] The dialogue control unit can select the optimal dialogue progression method by referring to the customer's past dialogue history during dialogue control. For example, the dialogue control unit may reuse dialogue progression methods that the customer has enjoyed in the past. It can also prioritize dialogue progression methods that the customer has shown interest in in the past. Furthermore, the dialogue control unit can avoid dialogue progression methods that the customer has avoided in the past. In this way, a more appropriate dialogue progression method can be selected by referring to the customer's past dialogue history. Some or all of the above processing in the dialogue control unit may be performed using AI, for example, or without AI. For example, the dialogue control unit can input the customer's past dialogue history data into AI and have the AI ​​perform the selection of the optimal dialogue progression method.

[0048] The dialogue control unit can customize the dialogue content based on the customer's current interests during dialogue control. For example, if the customer is looking at a smartphone, the dialogue control unit will provide topics related to smartphones. It can also provide topics suitable for children if the customer is with children. Furthermore, if the customer is talking with friends, it can provide topics that can be enjoyed as a group. This allows for more relevant dialogue by customizing the dialogue content based on the customer's current interests. Some or all of the above processing in the dialogue control unit may be performed using AI, for example, or without AI. For example, the dialogue control unit can input the customer's current interest data into the AI ​​and have the AI ​​customize the dialogue content.

[0049] The dialogue control unit can provide optimal dialogue content by taking into account the geographical location information of the customer when controlling the dialogue. For example, if the customer is near an event venue, the dialogue control unit can provide topics related to the event. Also, if the customer is far away, the dialogue control unit can hold the next dialogue. Furthermore, if the customer is in a specific area, the dialogue control unit can provide topics related to that area. In this way, by taking the customer's geographical location information into consideration, more relevant dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue control unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue control unit can input the customer's geographical location information into AI and have the AI ​​provide the dialogue content.

[0050] The dialogue control unit can analyze a customer's social media activity during dialogue control and provide relevant dialogue content. For example, if a customer posts about an event, the dialogue control unit can provide topics related to the event. In addition, if a customer has a specific interest, the dialogue control unit can also provide topics related to that interest. Furthermore, if a customer is with friends, the dialogue control unit can also provide topics that can be enjoyed by the group. In this way, by analyzing a customer's social media activity, more relevant dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue control unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue control unit can input the customer's social media activity data into AI and have the AI ​​provide relevant dialogue content.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The interactive robot system can further analyze a customer's purchasing history and provide individually customized dialogue content. For example, the detection unit can identify products that a customer has purchased in the past and provide information about new products and campaigns related to those products. The dialogue unit can also provide related topics based on product categories in which the customer has shown interest in the past. Furthermore, the generation unit can generate and provide individually customized coupons and special offers based on the customer's purchasing history. This makes it possible to utilize a customer's purchasing history to provide more personalized dialogue.

[0053] The interactive robot system can also monitor the customer's health condition and provide health advice. For example, the detection unit can analyze the customer's walking speed and posture to estimate their health condition. The dialogue unit can also provide appropriate exercise and diet advice based on the customer's health condition. Furthermore, the generation unit can generate and provide an individually customized health plan based on the customer's health data. This enables dialogue that takes the customer's health condition into consideration.

[0054] The interactive robot system can further analyze the customer's hobbies and interests and suggest related events and activities. For example, the detection unit analyzes the customer's past interaction history and social media activity to identify the customer's hobbies and interests. The interaction unit can also suggest related events and activities based on the customer's hobbies and interests. Furthermore, the generation unit can generate and provide individually customized event information and activity plans based on the customer's hobbies and interests. This enables interaction that takes the customer's hobbies and interests into consideration.

[0055] The interactive robot system can further analyze the customer's travel history and provide travel advice. For example, the detection unit identifies the customer's past travel history and analyzes the places visited and tourist spots that the customer is interested in. The dialogue unit can also suggest the customer's next travel destination or recommended tourist spots based on the customer's travel history. Furthermore, the generation unit can generate and provide individually customized travel plans and tourist guides based on the customer's travel history. This makes it possible to have a dialogue that utilizes the customer's travel history.

[0056] The interactive robot system can further analyze the customer's learning history and provide learning advice. For example, the detection unit identifies the customer's past learning history and analyzes the content they have studied and the areas they are interested in. The dialogue unit can also suggest the next content to study and recommended learning resources based on the customer's learning history. Furthermore, the generation unit can generate and provide individually customized learning plans and learning materials based on the customer's learning history. This makes it possible to have a dialogue that utilizes the customer's learning history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The detection unit detects customers passing by. The detection unit detects customers using a camera sensor and an audio sensor. The camera sensor captures high-resolution images and detects customer movement using a motion detection algorithm. The audio sensor collects surrounding sounds and detects the customer's voice using voice recognition technology. This allows the detection unit to combine multiple sensors to detect the presence of customers with high accuracy. Step 2: The dialogue unit speaks to the customer detected by the detection unit. The dialogue unit can change its tone of voice and speaking style depending on the customer's age and gender. Using voice synthesis technology, it can generate a variety of voices, such as a bright, lively voice or a calm voice. For example, it can speak in a bright, lively voice to children and a calm voice to adults. It can also adjust the speaking speed and intonation. For example, it can speak slowly to children and at a faster pace to adults. Step 3: The generation unit uses the generation AI to analyze the customer's utterance and generate an appropriate response. The generation unit uses natural language processing technology to analyze the meaning of the customer's utterance and generate a response based on the context. For example, the generation unit analyzes the customer's utterance and inputs a prompt to the generation AI to generate an appropriate response. The generation AI generates a response based on the prompt and outputs it to the generation unit. The generation unit receives the response from the generation AI and provides an appropriate response to the customer. Step 4: The dialogue control unit engages in a dialogue with the customer based on the dialogue content generated by the generation unit. The dialogue control unit controls the progress of the dialogue and ensures smooth dialogue with the customer. For example, the dialogue control unit engages in a dialogue with the customer about interactive games or current events. The dialogue control unit manages the flow of the dialogue and can advance the dialogue in a way that keeps the customer interested. The dialogue control unit is also designed to be used in fields such as education and medicine. For example, in the education field, it can provide learning support and knowledge, and in the medical field, it can communicate with patients and support rehabilitation.

[0059] (Example 2) An interactive robot system according to an embodiment of the present invention is a system for attracting customers to smartphone acquisition events. This interactive robot system detects passing customers and automatically engages them in conversation, attracting their interest and encouraging them to stop and enjoy the conversation. Specifically, the robot uses camera and audio sensors to detect passing customers and automatically engages them. The robot then uses natural language processing technology to engage with the customers, engaging in interactive games and discussing current events. This allows customers to enjoy interacting with the robot and stopping to talk. The robot's appearance is designed to be "the robot!" for children, yet its fully interactive design allows adults to stop and talk. Its tone of voice, speaking style, and dialect can be freely changed to accommodate interactive games and current events. This allows it to accommodate a wide range of customers, from children to adults. It is also designed for use in fields such as education and medicine. For example, the robot uses camera and audio sensors to detect passing customers and automatically engages them. For example, it greets them with a greeting such as, "Hello! Welcome to the smartphone acquisition event!" The robot can also adapt its tone of voice and speaking style depending on the customer's age and gender. For example, the robot speaks to children in a bright and cheerful voice and to adults in a calm voice. Next, the robot uses natural language processing technology to converse with the customer. For example, it makes suggestions such as, "Would you like to play a word game?" or "Would you like to try a riddle?" If the customer responds, the robot begins an interactive game. This allows customers to enjoy interacting with the robot and encourages them to stop and talk. The robot also responds to current events. For example, it asks questions such as, "What do you think about the recent news?", deepening the dialogue with customers. This also piques the interest of adult customers, encouraging them to stop and talk. In this way, the robot can talk to passing customers and attract more customers through dialogue. Its child-friendly appearance and interactive design allow it to accommodate a wide range of customers, from children to adults.Furthermore, the robot can freely change its tone of voice, speaking style, and dialect, making it suitable for a variety of situations. It also offers a wide range of interactive content, including interactive games and current events, ensuring that customers will never get bored. This allows the interactive robot system to attract customers to smartphone acquisition events and entertain them through dialogue.

[0060] An interactive robot system according to an embodiment includes a detection unit, a dialogue unit, a generation unit, and a dialogue control unit. The detection unit detects customers passing by. The detection unit detects customers using, for example, a camera sensor and an audio sensor. The camera sensor acquires high-resolution images and detects the customer's movements using a motion detection algorithm. The audio sensor collects ambient sounds and detects the customer's voice using voice recognition technology. For example, the detection unit can track the customer's movements in real time using the camera sensor and detect the customer's voice using the audio sensor. The detection unit can also detect the presence of customers with high accuracy by combining multiple sensors. The dialogue unit speaks to the customer detected by the detection unit. The dialogue unit can change the tone of voice and speaking style depending on, for example, the customer's age and gender. The dialogue unit can generate various voice tones, such as a bright and cheerful voice or a calm voice, using voice synthesis technology. For example, the dialogue unit can speak to children in a bright and cheerful voice and to adults in a calm voice. The dialogue unit can also adjust the speaking speed and intonation. For example, the dialogue unit can speak slowly to children and at a faster pace to adults. The generation unit uses a generation AI to analyze the customer's utterance and generate an appropriate response. The generation unit uses natural language processing technology to analyze the meaning of the customer's utterance and generate a response based on the context. For example, the generation unit analyzes the customer's utterance and inputs prompts to the generation AI to generate an appropriate response. The generation AI generates a response based on the prompt and outputs it to the generation unit. The generation unit receives the response from the generation AI and provides an appropriate response to the customer. The dialogue control unit engages in a dialogue with the customer based on the dialogue content generated by the generation unit. The dialogue control unit controls the progress of the dialogue and ensures smooth progress of the dialogue with the customer. For example, the dialogue control unit engages in a dialogue with the customer about interactive games or current events. The dialogue control unit manages the flow of the dialogue and can advance the dialogue to keep the customer interested. The dialogue control unit is also designed for use in fields such as education and medicine. For example, the dialogue control unit can provide learning support and knowledge in the education field, and communication with patients and rehabilitation support in the medical field.As a result, the interactive robot system according to the embodiment can detect passing customers and attract them through dialogue. Some or all of the above-described processing in the dialogue control unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue control unit may input the dialogue content generated by the generation unit and use an AI model that controls the progress of the dialogue to carry out the dialogue.

[0061] The detection unit can detect customers using a camera sensor and an audio sensor. The camera sensor, for example, acquires high-resolution images and detects customer movements using a motion detection algorithm. For example, the camera sensor can be installed at the entrance to an event venue and track the movements of passing customers in real time. The camera sensor can also cover a wide area using a wide-angle lens, allowing it to detect many customers simultaneously. The audio sensor, for example, collects ambient sounds and detects customer voices using voice recognition technology. For example, the audio sensor can be installed on the ceiling of an event venue and can collect the voices of passing customers with high sensitivity. The audio sensor can also use noise canceling technology to remove ambient noise and clearly detect customer voices. This allows accurate detection of customers using the camera sensor and audio sensor. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input data acquired from the camera sensor and audio sensor into AI and have the AI ​​perform customer detection.

[0062] The dialogue unit can change the tone of voice or speaking style depending on the age or gender of the customer. The dialogue unit can generate various tone of voice, such as a bright and cheerful voice or a calm voice, using, for example, speech synthesis technology. For example, the dialogue unit can speak in a bright and cheerful voice to children and in a calm voice to adults. The dialogue unit can also adjust the speaking speed and intonation. For example, the dialogue unit can speak slowly to children and at a faster pace to adults. Furthermore, the dialogue unit can change the dialect or accent. For example, the dialogue unit can speak to the customer using a regional dialect. This allows for a more friendly dialogue by changing the tone of voice and speaking style depending on the customer's age and gender. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit can input the customer's age and gender into AI and have the AI ​​adjust the tone of voice and speaking style.

[0063] The generation unit can use the generation AI to analyze the customer's utterance and generate an appropriate response. The generation unit uses natural language processing technology to analyze the meaning of the customer's utterance and generate a response based on the context. For example, the generation unit analyzes the customer's utterance and inputs a prompt to the generation AI to generate an appropriate response. The generation AI generates a response based on the prompt and outputs it to the generation unit. The generation unit receives the response from the generation AI and provides the customer with an appropriate response. For example, if a customer utters, "Shall we play Shiritori?", the generation unit inputs a prompt to the generation AI saying, "Generate the next word in Shiritori," and provides the word generated by the generation AI to the customer. Also, if a customer utters, "What do you think about the recent news?", the generation unit inputs a prompt to the generation AI saying, "Generate your opinion about the recent news," and provides the customer with the opinion generated by the generation AI. In this way, an appropriate response can be generated by using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input customer statements into the AI ​​and have the AI ​​generate responses.

[0064] The dialogue control unit can engage in dialogue with the customer about interactive games or current events. The dialogue control unit controls the progress of the dialogue and smoothly advances the dialogue with the customer. For example, the dialogue control unit engages in dialogue with the customer about interactive games or current events. The dialogue control unit can manage the flow of the dialogue and advance the dialogue so as to maintain the customer's interest. For example, the dialogue control unit can suggest, "Would you like to play a word chain game?" and, if the customer responds, start a word chain dialogue. The dialogue control unit can also suggest, "Would you like to try a riddle?" and, if the customer responds, start a riddle dialogue. The dialogue control unit can further deepen the dialogue with the customer by asking, "What do you think about the recent news?" In this way, by engaging in dialogue about interactive games or current events, the customer can be attracted and stop to talk. Some or all of the above-described processing in the dialogue control unit may be performed, for example, using AI, or may be performed without AI. For example, the dialogue control unit can engage in dialogue using an AI model that uses the dialogue content generated by the generation unit as input and controls the progress of the dialogue.

[0065] The dialogue control unit is designed for use in the fields of education or medicine. In the field of education, the dialogue control unit can provide learning support and knowledge, and in the field of medicine, it can communicate with patients and support rehabilitation. For example, in the field of education, the dialogue control unit can explain learning content to children and support their learning through quizzes and games. In the field of medicine, the dialogue control unit can also support the progress of rehabilitation through dialogue with patients and monitor their condition. As a result, since it is designed for use in fields such as education and medicine, it can be used for a wide range of applications. Some or all of the above-mentioned processing in the dialogue control unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue control unit can input dialogue content in the fields of education or medicine into AI and conduct a dialogue using an AI model that controls the progress of the dialogue.

[0066] The detection unit can estimate the customer's emotions and adjust the timing of speaking to them based on the estimated emotions. For example, if the customer is excited, the detection unit can immediately speak to them to attract their interest. Alternatively, if the customer is relaxed, the detection unit can wait a short time before speaking to them. Furthermore, if the customer is in a hurry, the detection unit can only give a short greeting and immediately move on to the next action. This allows for more effective dialogue by adjusting the timing of speaking to them based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the detection unit may be performed using an AI, or may be performed without an AI. For example, the detection unit can input customer emotion data into an AI and have the AI ​​adjust the timing of speaking to them.

[0067] The detection unit can analyze a customer's past visit history and select the most appropriate detection method. For example, if a customer has visited many times in the past, the detection unit will offer a friendly greeting. The detection unit can also provide a thorough explanation to first-time visitors. Furthermore, the detection unit can select topics that will interest the customer based on their past visit history. This allows for the selection of a more appropriate detection method by analyzing the customer's past visit history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the customer's past visit history data into AI and have the AI ​​select the most appropriate detection method.

[0068] The detection unit can filter based on the customer's current behavior or interests when detection occurs. For example, if the customer is looking at their smartphone, the detection unit can provide topics related to smartphones. It can also provide topics suitable for children if the customer is with children. Furthermore, if the customer is talking with friends, the detection unit can provide topics that can be enjoyed as a group. This allows for more relevant conversations by filtering based on the customer's current behavior and interests. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the customer's current behavior and interest data into an AI and have the AI ​​perform the filtering.

[0069] The detection unit can estimate the customer's emotions and determine the priority of customers to be detected based on the estimated customer emotions. For example, if the customer is excited, the detection unit can prioritize speaking to the customer. The detection unit can also speak to the customer next if the customer is relaxed. Furthermore, the detection unit can postpone speaking to the customer if the customer is in a hurry. This enables more effective dialogue by determining priorities based on the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input customer emotion data into an AI and have the AI ​​determine the priority.

[0070] The detection unit can prioritize detecting highly relevant customers by taking into account the geographical location information of the customer during detection. For example, if the customer is near an event venue, the detection unit will prioritize speaking to the customer. Also, if the customer is far away, the detection unit can speak to the customer next. Furthermore, if the customer is in a specific area, the detection unit can provide topics related to that area. In this way, by taking into account the geographical location information of the customer, more relevant customers can be preferentially detected. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the geographical location information of the customer into AI and have the AI ​​detect highly relevant customers.

[0071] The detection unit can analyze the customer's social media activity at the time of detection and detect related customers. For example, if the customer posts about an event, the detection unit will prioritize speaking to the customer. In addition, if the customer has a specific interest, the detection unit can also provide topics related to that interest. Furthermore, if the customer is with friends, the detection unit can also provide topics that can be enjoyed by the group. In this way, by analyzing the customer's social media activity, more related customers can be detected. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the customer's social media activity data into AI and have the AI ​​detect related customers.

[0072] The dialogue unit can estimate the customer's emotions and adjust the tone of voice and speaking style based on the estimated customer emotions. For example, if the customer is excited, the dialogue unit can speak in a bright and cheerful voice. If the customer is relaxed, the dialogue unit can also speak in a calm voice. Furthermore, if the customer is in a hurry, the dialogue unit can speak concisely and quickly. This allows for a more friendly dialogue by adjusting the tone of voice and speaking style based on the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input customer emotion data into an AI and have the AI ​​adjust the tone of voice and speaking style.

[0073] The dialogue unit can select an appropriate dialogue method by referring to the customer's past dialogue history during dialogue. For example, the dialogue unit may re-present topics that the customer enjoyed in the past. The dialogue unit may also prioritize topics in which the customer has shown interest in the past. Furthermore, the dialogue unit may avoid topics that the customer has avoided in the past. In this way, by referring to the customer's past dialogue history, a more appropriate dialogue method can be selected. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit may input the customer's past dialogue history data into AI and have the AI ​​select the optimal dialogue method.

[0074] The dialogue unit can customize the dialogue content based on the customer's current interests at the time of dialogue. For example, if the customer is looking at a smartphone, the dialogue unit can provide smartphone-related topics. Furthermore, if the customer is with children, the dialogue unit can provide topics for children. Furthermore, if the customer is talking with friends, the dialogue unit can provide topics that the group can enjoy. This allows for more relevant dialogue by customizing the dialogue content based on the customer's current interests. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI, for example. For example, the dialogue unit can input the customer's current interest data into AI and have the AI ​​customize the dialogue content.

[0075] The dialogue unit can estimate the customer's emotions and determine the priority of dialogues based on the estimated customer emotions. For example, if the customer is excited, the dialogue unit can prioritize the dialogue. Furthermore, if the customer is relaxed, the dialogue unit can also conduct the dialogue next. Furthermore, if the customer is in a hurry, the dialogue unit can postpone the dialogue. This enables more effective dialogues by prioritizing dialogues based on the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input customer emotion data into an AI and have the AI ​​determine the priority of dialogues.

[0076] The dialogue unit can provide appropriate dialogue content by taking into account the geographical location information of the customer during dialogue. For example, if the customer is near an event venue, the dialogue unit can provide topics related to the event. Also, if the customer is far away, the dialogue unit can hold the next dialogue. Furthermore, if the customer is in a specific area, the dialogue unit can provide topics related to that area. In this way, by taking the customer's geographical location information into account, more relevant dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input the customer's geographical location information into AI and have the AI ​​provide the dialogue content.

[0077] The dialogue unit can analyze the customer's social media activity during the dialogue and provide relevant dialogue content. For example, if the customer posts about an event, the dialogue unit can provide topics related to the event. Also, if the customer has a specific interest, the dialogue unit can provide topics related to that interest. Furthermore, if the customer is with friends, the dialogue unit can provide topics that the group can enjoy. In this way, by analyzing the customer's social media activity, more relevant dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue unit may be performed using, or without, AI, for example. For example, the dialogue unit can input the customer's social media activity data into AI and have the AI ​​provide relevant dialogue content.

[0078] The generation unit can estimate the customer's emotions and adjust the way it expresses its response based on those emotions. For example, if the customer is excited, the generation unit can use bright and cheerful language. If the customer is relaxed, it can use calm language. Furthermore, if the customer is in a hurry, it can use concise and quick language. This allows for more approachable responses by adjusting the way the response is expressed based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input customer emotion data into an AI and have the AI ​​adjust the way the response is expressed.

[0079] The generation unit can adjust the level of detail of the response based on the importance of the customer's statement when generating a response. For example, if the customer's statement is important, the generation unit generates a detailed response. The generation unit can also generate a concise response if the customer's statement is general. Furthermore, the generation unit can also generate a brief response if the customer's statement is on a light topic. This allows for a more appropriate response by adjusting the level of detail of the response based on the importance of the customer's statement. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input customer statement data into AI and have the AI ​​adjust the level of detail of the response.

[0080] The generation unit can apply different generation algorithms depending on the customer category when generating a response. For example, the generation unit can apply a generation algorithm that uses simple and easy-to-understand expressions to responses aimed at children. The generation unit can also apply a generation algorithm that uses detailed and specialized expressions to responses aimed at adults. Furthermore, the generation unit can apply a generation algorithm that explains at a slower pace to responses aimed at the elderly. This allows for a more appropriate response by applying the optimal generation algorithm depending on the customer category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input customer category data into AI and have the AI ​​apply the generation algorithm.

[0081] The generation unit can estimate the customer's emotions and adjust the length of the response based on the estimated customer emotions. For example, if the customer is excited, the generation unit can generate a short, to-the-point response. Alternatively, if the customer is relaxed, the generation unit can generate a longer response with detailed explanations. Furthermore, if the customer is in a hurry, the generation unit can generate a concise, quick response. This allows for a more appropriate response by adjusting the length of the response based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input customer emotion data into an AI and have the AI ​​adjust the length of the response.

[0082] When generating a response, the generation unit can determine the priority of the response based on the time when the customer's comment was submitted. For example, if the customer's comment is recent, the generation unit will prioritize generating a response. The generation unit can also generate a response next if the customer's comment is from the past. Furthermore, the generation unit can postpone an older customer comment. This enables a more appropriate response by determining the priority of responses based on the time when the customer's comment was submitted. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time when the customer's comment was submitted into AI and have the AI ​​determine the priority of responses.

[0083] The generation unit can adjust the order of responses based on the relevance of the customer's utterances when generating responses. For example, if the customer's utterance is highly relevant, the generation unit will generate a response as a priority. The generation unit can also generate a response next if the customer's utterance is general. Furthermore, the generation unit can postpone a customer's utterance that is less relevant. This allows for a more appropriate response by adjusting the order of responses based on the relevance of the customer's utterances. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input relevance data of the customer's utterances into AI and have the AI ​​adjust the order of responses.

[0084] The dialogue control unit can estimate the customer's emotions and adjust the dialogue progression based on the estimated customer emotions. For example, if the customer is excited, the dialogue control unit can conduct a fast-paced dialogue. If the customer is relaxed, the dialogue control unit can also conduct a relaxed dialogue. Furthermore, if the customer is in a hurry, the dialogue control unit can also conduct a concise and quick dialogue. This allows for more effective dialogue by adjusting the dialogue progression based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the dialogue control unit may be performed using an AI, or may be performed without an AI. For example, the dialogue control unit can input customer emotion data into an AI and have the AI ​​adjust the dialogue progression.

[0085] The dialogue control unit can select the optimal dialogue progression method by referring to the customer's past dialogue history during dialogue control. For example, the dialogue control unit may reuse dialogue progression methods that the customer has enjoyed in the past. It can also prioritize dialogue progression methods that the customer has shown interest in in the past. Furthermore, the dialogue control unit can avoid dialogue progression methods that the customer has avoided in the past. In this way, a more appropriate dialogue progression method can be selected by referring to the customer's past dialogue history. Some or all of the above processing in the dialogue control unit may be performed using AI, for example, or without AI. For example, the dialogue control unit can input the customer's past dialogue history data into AI and have the AI ​​perform the selection of the optimal dialogue progression method.

[0086] The dialogue control unit can customize the dialogue content based on the customer's current interests during dialogue control. For example, if the customer is looking at a smartphone, the dialogue control unit will provide topics related to smartphones. It can also provide topics suitable for children if the customer is with children. Furthermore, if the customer is talking with friends, it can provide topics that can be enjoyed as a group. This allows for more relevant dialogue by customizing the dialogue content based on the customer's current interests. Some or all of the above processing in the dialogue control unit may be performed using AI, for example, or without AI. For example, the dialogue control unit can input the customer's current interest data into the AI ​​and have the AI ​​customize the dialogue content.

[0087] The dialogue control unit can estimate the customer's emotions and determine the priority of dialogues based on the estimated customer emotions. For example, if the customer is excited, the dialogue control unit can prioritize dialogues. Furthermore, if the customer is relaxed, the dialogue control unit can also conduct dialogues next. Furthermore, if the customer is in a hurry, the dialogue control unit can postpone dialogues. This enables more effective dialogues by determining dialogue priorities based on the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the dialogue control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue control unit can input customer emotion data into an AI and have the AI ​​determine the priority of dialogues.

[0088] The dialogue control unit can provide optimal dialogue content by taking into account the geographical location information of the customer when controlling the dialogue. For example, if the customer is near an event venue, the dialogue control unit can provide topics related to the event. Also, if the customer is far away, the dialogue control unit can hold the next dialogue. Furthermore, if the customer is in a specific area, the dialogue control unit can provide topics related to that area. In this way, by taking the customer's geographical location information into consideration, more relevant dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue control unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue control unit can input the customer's geographical location information into AI and have the AI ​​provide the dialogue content.

[0089] The dialogue control unit can analyze a customer's social media activity during dialogue control and provide relevant dialogue content. For example, if a customer posts about an event, the dialogue control unit can provide topics related to the event. In addition, if a customer has a specific interest, the dialogue control unit can also provide topics related to that interest. Furthermore, if a customer is with friends, the dialogue control unit can also provide topics that can be enjoyed by the group. In this way, by analyzing a customer's social media activity, more relevant dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue control unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue control unit can input the customer's social media activity data into AI and have the AI ​​provide relevant dialogue content. === Hard Collateral 1-1 === Each of the multiple elements described above, including the detection unit, dialogue unit, generation unit, and dialogue control unit, is implemented in, for example, at least one of the smart device 14 and the data processing unit 12. For example, the detection unit detects the customer using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A performs motion detection and speech recognition. The dialogue unit speaks to the customer using speech synthesis technology, for example, the control unit 46A of the smart device 14. The generation unit analyzes the customer's statements using natural language processing technology, for example, the specific processing unit 290 of the data processing unit 12, and generates an appropriate response. The dialogue control unit proceeds with the dialogue based on the dialogue content generated by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the detection unit, dialogue unit, generation unit, and dialogue control unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit detects the customer using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A performs motion detection and speech recognition. The dialogue unit speaks to the customer using speech synthesis technology, for example, the control unit 46A of the smart glasses 214. The generation unit analyzes the customer's statements using natural language processing technology, for example, the specific processing unit 290 of the data processing unit 12, and generates an appropriate response. The dialogue control unit proceeds with the dialogue based on the dialogue content generated by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned detection unit, dialogue unit, generation unit, and dialogue control unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the detection unit detects a customer using the camera 42 and microphone 238 of the headset-type terminal 314, and the control unit 46A performs motion detection and voice recognition. The dialogue unit speaks to the customer using, for example, voice synthesis technology by the control unit 46A of the headset-type terminal 314. The generation unit analyzes the customer's utterance using, for example, natural language processing technology by the specific processing unit 290 of the data processing device 12, and generates an appropriate response. The dialogue control unit progresses the dialogue based on the dialogue content generated by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned detection unit, dialogue unit, generation unit, and dialogue control unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit detects a customer using the camera 42 and microphone 238 of the robot 414, and the control unit 46A performs motion detection and voice recognition. The dialogue unit speaks to the customer using, for example, voice synthesis technology by the control unit 46A of the robot 414. The generation unit analyzes the customer's utterance using natural language processing technology by, for example, the specific processing unit 290 of the data processing device 12, and generates an appropriate response. The dialogue control unit progresses the dialogue based on the dialogue content generated by, for example, the specific processing unit 290 of the data processing device 12.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The interactive robot system can further analyze a customer's purchasing history and provide individually customized dialogue content. For example, the detection unit can identify products that a customer has purchased in the past and provide information about new products and campaigns related to those products. The dialogue unit can also provide related topics based on product categories in which the customer has shown interest in the past. Furthermore, the generation unit can generate and provide individually customized coupons and special offers based on the customer's purchasing history. This makes it possible to utilize a customer's purchasing history to provide more personalized dialogue.

[0092] The interactive robot system can also monitor the customer's health condition and provide health advice. For example, the detection unit can analyze the customer's walking speed and posture to estimate their health condition. The dialogue unit can also provide appropriate exercise and diet advice based on the customer's health condition. Furthermore, the generation unit can generate and provide an individually customized health plan based on the customer's health data. This enables dialogue that takes the customer's health condition into consideration.

[0093] The interactive robot system can further analyze the customer's hobbies and interests and suggest related events and activities. For example, the detection unit analyzes the customer's past interaction history and social media activity to identify the customer's hobbies and interests. The interaction unit can also suggest related events and activities based on the customer's hobbies and interests. Furthermore, the generation unit can generate and provide individually customized event information and activity plans based on the customer's hobbies and interests. This enables interaction that takes the customer's hobbies and interests into consideration.

[0094] The interactive robot system can further analyze the customer's travel history and provide travel advice. For example, the detection unit identifies the customer's past travel history and analyzes the places visited and tourist spots that the customer is interested in. The dialogue unit can also suggest the customer's next travel destination or recommended tourist spots based on the customer's travel history. Furthermore, the generation unit can generate and provide individually customized travel plans and tourist guides based on the customer's travel history. This makes it possible to have a dialogue that utilizes the customer's travel history.

[0095] The interactive robot system can further analyze the customer's learning history and provide learning advice. For example, the detection unit identifies the customer's past learning history and analyzes the content they have studied and the areas they are interested in. The dialogue unit can also suggest the next content to study and recommended learning resources based on the customer's learning history. Furthermore, the generation unit can generate and provide individually customized learning plans and learning materials based on the customer's learning history. This makes it possible to have a dialogue that utilizes the customer's learning history.

[0096] The interactive robot system can further estimate the customer's emotions and adjust the tone of the dialogue based on the estimated customer's emotions. For example, the dialogue unit can speak to the customer in a gentle tone if the customer is sad. Alternatively, the dialogue unit can speak to the customer in a bright and cheerful tone if the customer is happy. Furthermore, the generation unit can generate a response in an appropriate tone based on the customer's emotions. This makes it possible to have a dialogue that is appropriate for the customer's emotions.

[0097] The interactive robot system can further estimate the customer's emotions and adjust the content of the dialogue based on the estimated customer's emotions. For example, if the dialogue unit is feeling stressed, it can provide a topic that will help the customer relax. If the dialogue unit is excited, it can also provide a topic that will pique the customer's interest. Furthermore, the generation unit can generate a response with appropriate content based on the customer's emotions. This makes it possible to have a dialogue that is appropriate for the customer's emotions.

[0098] The interactive robot system can further estimate the customer's emotions and adjust the speed of the dialogue based on the estimated customer's emotions. For example, the dialogue unit can proceed with the dialogue at a slow pace if the customer is relaxed. Alternatively, the dialogue unit can proceed with the dialogue quickly if the customer is in a hurry. Furthermore, the generation unit can generate a response at an appropriate speed based on the customer's emotions. This enables a dialogue that is appropriate for the customer's emotions.

[0099] The interactive robot system can further estimate the customer's emotions and customize the content of the dialogue based on the estimated customer's emotions. For example, if the dialogue unit is feeling anxious, it can provide a topic that will reassure the customer. If the customer is enjoying themselves, it can also provide a topic that will make the customer more enjoyable. Furthermore, the generation unit can generate a response with appropriate content based on the customer's emotions. This makes it possible to have a dialogue that is appropriate for the customer's emotions.

[0100] The interactive robot system can further estimate the customer's emotions and adjust the timing of ending the dialogue based on the estimated customer's emotions. For example, the dialogue unit can end the dialogue early if the customer is tired. The dialogue unit can also extend the dialogue if the customer is enjoying themselves. Furthermore, the generation unit can generate a response at an appropriate time based on the customer's emotions. This makes it possible to have a dialogue that is appropriate for the customer's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The detection unit detects customers passing by. The detection unit detects customers using a camera sensor and an audio sensor. The camera sensor captures high-resolution images and detects customer movement using a motion detection algorithm. The audio sensor collects surrounding sounds and detects the customer's voice using voice recognition technology. This allows the detection unit to combine multiple sensors to detect the presence of customers with high accuracy. Step 2: The dialogue unit speaks to the customer detected by the detection unit. The dialogue unit can change its tone of voice and speaking style depending on the customer's age and gender. Using voice synthesis technology, it can generate a variety of voices, such as a bright, lively voice or a calm voice. For example, it can speak in a bright, lively voice to children and a calm voice to adults. It can also adjust the speaking speed and intonation. For example, it can speak slowly to children and at a faster pace to adults. Step 3: The generation unit uses the generation AI to analyze the customer's utterance and generate an appropriate response. The generation unit uses natural language processing technology to analyze the meaning of the customer's utterance and generate a response based on the context. For example, the generation unit analyzes the customer's utterance and inputs a prompt to the generation AI to generate an appropriate response. The generation AI generates a response based on the prompt and outputs it to the generation unit. The generation unit receives the response from the generation AI and provides an appropriate response to the customer. Step 4: The dialogue control unit engages in a dialogue with the customer based on the dialogue content generated by the generation unit. The dialogue control unit controls the progress of the dialogue and ensures smooth dialogue with the customer. For example, the dialogue control unit engages in a dialogue with the customer about interactive games or current events. The dialogue control unit manages the flow of the dialogue and can advance the dialogue in a way that keeps the customer interested. The dialogue control unit is also designed to be used in fields such as education and medicine. For example, in the education field, it can provide learning support and knowledge, and in the medical field, it can communicate with patients and support rehabilitation.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

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

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A detection unit that detects customers passing by, a dialogue unit that speaks to the customer detected by the detection unit; a generation unit that generates a dialogue content to be performed by the dialogue unit; a dialogue control unit that dialogues with the customer based on the dialogue content generated by the generation unit; A system characterized by:

2. The detection unit Detect customers using camera and audio sensors 2. The system of claim 1.

3. The dialogue unit Vary your tone of voice or speaking style depending on the customer's age or gender 2. The system of claim 1.

4. The generation unit Use generative AI to analyze customer comments and generate appropriate responses 2. The system of claim 1.

5. The dialogue control unit Interactive play or current events to engage with customers 2. The system of claim 1.

6. The dialogue control unit Designed for use in educational or medical settings 2. The system of claim 1.

7. The detection unit Infer customer emotions and adjust timing of conversations based on the inferred emotions 2. The system of claim 1.

8. The detection unit Analyze your past visit history and select the most appropriate detection method 2. The system of claim 1.

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