system

Abstract

The system according to the embodiment aims to respond to customer inquiries quickly and accurately and improve the quality of service. [Solution] A system according to an embodiment includes an input unit, an answer generation unit, and a service proposal unit. The input unit inputs all of the shop's services and information. The answer generation unit generates answers to customer questions based on the information input by the input unit. The service proposal unit proposes services that meet the customer's needs based on the answers generated by the answer 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] Conventional technology makes it difficult to respond to customer inquiries quickly and accurately, and there is room for improvement in the quality of service.

[0005] The system according to the embodiment aims to respond to customer inquiries quickly and accurately and improve the quality of service. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an answer generation unit, and a service proposal unit. The input unit inputs all of the shop's services and information. The answer generation unit generates answers to customer questions based on the information input by the input unit. The service proposal unit proposes services that meet the customer's needs based on the answers generated by the answer generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can respond to customer inquiries quickly and accurately, thereby improving the quality of service. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A robot system according to an embodiment of the present invention is equipped with a generation AI and replaces half of the shop staff by inputting all of the shop's services and information. This robot system first inputs all of the shop's services and information into the generation AI. The generation AI then generates appropriate answers to customer questions and suggests services based on the customer's requests. This system reduces the burden on shop staff and enables efficient operations. For example, when inputting all of the shop's services and information into the generation AI, details of the service content and information are entered into the generation AI. Specifically, product descriptions and service usage instructions are input. The generation AI then generates appropriate answers to customer questions. For example, when a customer asks about a product, the generation AI provides an appropriate answer to that question. This allows customers to obtain information quickly and accurately. Furthermore, the generation AI suggests services based on the customer's requests. For example, if a customer requests a specific service, the generation AI suggests that service. This allows the customer to select the service that best suits them. This system reduces the burden on shop staff and enables efficient operations. For example, the number of customers that shop staff need to deal with will decrease, allowing them to focus on more important tasks. Also, by having generative AI handle customer interactions, the quality of service will improve and customer satisfaction will increase. It is possible to start with a specific shop and then expand to many other shops. For example, by first introducing it in the shops of a specific telecommunications company and then expanding it to many other shops, it will be possible to provide services to a wide range of customers. In this way, the robot system can reduce the burden on shop staff and achieve efficient operations.

[0029] The robot system according to the embodiment includes an input unit, an answer generation unit, and a service proposal unit. The input unit inputs all of the shop's services and information. For example, the input unit inputs product descriptions and service usage instructions to the generation AI. The generation AI analyzes the input information and generates an appropriate answer using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The answer generation unit generates an answer to a customer's question based on the information input by the input unit. For example, when a customer asks a question about a product, the answer generation unit provides an appropriate answer to the question using the generation AI. The generation AI receives a prompt, for example, "Tell me about this product," and generates a detailed description of the product. The answer generation unit can also generate answers to customer questions in real time using the generation AI. For example, when a customer asks about how to use a service, the answer generation unit explains how to use the service using the generation AI. The service proposal unit proposes services that meet the customer's needs based on the answer generated by the answer generation unit. For example, if a customer requests a specific service, the service proposal unit uses a generation AI to propose that service. The generation AI can, for example, use an algorithm that analyzes the customer's requests and proposes the optimal service. This allows the robot system to propose services that meet the customer's requests. Furthermore, the service proposal unit can also use the generation AI to propose services that meet the customer's requests in real time. For example, if a customer requests a specific service, the service proposal unit uses the generation AI to propose that service. This allows the robot system to quickly and accurately propose services that meet the customer's requests. As a result, the robot system according to the embodiment can reduce the burden on shop staff and achieve efficient operations.

[0030] At the time of input, the input unit can select an appropriate input method by referring to past customer interaction history. For example, the input unit has the generation AI preferentially suggest text input to customers who have preferred text input in the past. For example, the generation AI analyzes past customer interaction history and suggests text input to customers who prefer text input. The input unit can also have the generation AI preferentially suggest voice input to customers who have preferred voice input in the past. For example, the generation AI analyzes past customer interaction history and suggests voice input to customers who prefer voice input. The input unit can also have the generation AI preferentially suggest visual input methods to customers who have preferred images or videos in the past. For example, the generation AI analyzes past customer interaction history and suggests visual input methods to customers who prefer visual input methods. This makes it possible to provide the optimal input method based on the customer's past interaction history. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input past customer interaction history data into the generation AI and have the generation AI select the optimal input method. This allows the input unit to provide the optimal input method based on the customer's past interaction history.

[0031] The input unit can automatically reflect seasonal service change information of the shop upon input. The input unit, for example, automatically inputs seasonal campaign information to the generation AI. For example, the generation AI automatically collects seasonal campaign information and provides it to the input unit. The input unit can also automatically input seasonal changes to the product lineup to the generation AI. For example, the generation AI automatically collects information on seasonal changes to the product lineup and provides it to the input unit. The input unit can also automatically input seasonal changes to business hours to the generation AI. For example, the generation AI automatically collects information on seasonal changes to business hours and provides it to the input unit. This allows the latest information to be provided by automatically reflecting seasonal service change information. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input seasonal service change information to the generation AI and cause the generation AI to automatically reflect the information. This allows the input unit to automatically reflect seasonal service change information.

[0032] The input unit can prioritize input of region-specific service information based on the customer's geographical location information at the time of input. For example, if the customer is in a specific region, the input unit causes the generation AI to prioritize input of service information specific to that region. For example, the generation AI analyzes the customer's geographical location information and provides service information specific to that region. Also, if the customer is traveling, the input unit can cause the generation AI to prioritize input of service information specific to the region of the customer's travel destination. For example, the generation AI analyzes the customer's geographical location information and provides service information specific to the region of the travel destination. Also, if the customer is considering moving, the input unit can cause the generation AI to prioritize input of service information for the new region. For example, the generation AI analyzes the customer's geographical location information and provides service information for the new region. This allows the customer to be provided with information that is appropriate for the region by prioritizing service information specific to the region. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input geographical location information data of a customer to the generation AI and cause the generation AI to execute preferential input of region-specific service information, thereby enabling the input unit to preferentially input region-specific service information based on the geographical location information of the customer.

[0033] The input unit can analyze the customer's social media activity and input related information at the time of input. For example, if a customer mentions a specific product on social media, the input unit inputs information about the product to the generation AI. For example, the generation AI analyzes the customer's social media activity and provides information about the product. Furthermore, if a customer indicates on social media that they plan to attend a specific event, the input unit can input information related to the event. For example, the generation AI analyzes the customer's social media activity and provides information related to the event. Furthermore, if a customer indicates a specific interest on social media, the input unit can input information related to the interest. For example, the generation AI analyzes the customer's social media activity and provides information related to the interest. This makes it possible to provide information based on the customer's social media activity. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input customer social media activity data to the generation AI and have the generation AI input related information. This allows the input unit to input relevant information based on the customer's social media activity.

[0034] When generating an answer, the answer generation unit can generate an optimal answer by referring to the customer's past question history. In the answer generation unit, for example, the generation AI generates a relevant answer based on the content of questions asked by the customer in the past. For example, the generation AI analyzes the customer's past question history and provides a relevant answer. The answer generation unit can also select and generate the most appropriate answer from the customer's past question history. For example, the generation AI analyzes the customer's past question history and selects and provides the most appropriate answer. The answer generation unit can also analyze the customer's past question history and generate the most efficient answer. For example, the generation AI analyzes the customer's past question history and provides the most efficient answer. This allows the optimal answer to be provided based on the customer's past question history. Some or all of the above-mentioned processing in the answer generation unit may be performed using, or without, AI. For example, the answer generation unit can input the customer's past question history data into the generation AI and cause the generation AI to generate the optimal answer. This allows the answer generation unit to generate the optimal answer based on the customer's past question history.

[0035] When generating an answer, the answer generation unit can include related product information by taking into account the customer's purchase history. The answer generation unit, for example, causes the generation AI to include information related to products previously purchased by the customer in the answer. For example, the generation AI analyzes the customer's purchase history and provides related product information. The answer generation unit can also generate an answer in which the generation AI suggests related products based on the customer's purchase history. For example, the generation AI analyzes the customer's purchase history and provides an answer that suggests related products. The answer generation unit can also analyze the customer's purchase history and cause the generation AI to include the most relevant product information in the answer. For example, the generation AI analyzes the customer's purchase history and provides the most relevant product information. This makes it possible to provide related product information based on the customer's purchase history. Some or all of the above-described processing in the answer generation unit may be performed using, or without, AI. For example, the answer generation unit can input customer purchase history data into the generation AI and cause the generation AI to generate related product information. This makes it possible for the answer generation unit to include related product information based on the customer's purchase history.

[0036] When generating an answer, the answer generation unit can include region-specific information by taking into account the customer's geographical location information. For example, if the customer is in a specific region, the answer generation unit causes the generation AI to include region-specific information in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information specific to that region. Furthermore, if the customer is traveling, the answer generation unit can also cause the generation AI to include region-specific information in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information specific to the region. Furthermore, if the customer is considering moving, the answer generation unit can also cause the generation AI to include information on the new region in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information on the new region. In this way, by including region-specific information, an answer suitable for the customer can be provided. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can input the customer's geographical location information data into the generation AI and cause the generation AI to generate region-specific information. In this way, the answer generation unit can include region-specific information based on the customer's geographical location information.

[0037] The answer generation unit can analyze the customer's social media activity and include relevant information when generating an answer. For example, if a customer mentions a specific product on social media, the answer generation unit causes the generation AI to include information about the product in the answer. For example, the generation AI can analyze the customer's social media activity and provide information about the product. Also, if a customer indicates on social media that they plan to attend a specific event, the answer generation unit can cause the generation AI to include information related to the event in the answer. For example, the generation AI can analyze the customer's social media activity and provide information related to the event. Also, if a customer indicates a specific interest on social media, the answer generation unit can cause the generation AI to include information related to the interest in the answer. For example, the generation AI can analyze the customer's social media activity and provide information related to the interest. This makes it possible to provide information based on the customer's social media activity. Some or all of the above-described processing in the answer generation unit may be performed using, or without, AI. For example, the answer generation unit can input the customer's social media activity data into the generation AI and cause the generation AI to generate relevant information. This allows the answer generation unit to include relevant information based on the customer's social media activity.

[0038] When proposing a service, the service proposal unit can suggest the optimal service by referring to the customer's past service usage history. In the service proposal unit, for example, the generation AI suggests a related service based on services the customer has used in the past. For example, the generation AI analyzes the customer's past service usage history and provides a related service. The service proposal unit can also select and suggest the most appropriate service based on the customer's past service usage history. For example, the generation AI analyzes the customer's past service usage history and selects and provides the most appropriate service. The service proposal unit can also analyze the customer's past service usage history and suggest the most efficient service. For example, the generation AI analyzes the customer's past service usage history and provides the most efficient service. This allows the optimal service to be provided based on the customer's past service usage history. Some or all of the above-described processing in the service proposal unit may be performed using, or without, AI. For example, the service proposal unit can input the customer's past service usage history data into the generation AI and have the generation AI suggest the optimal service. This allows the service proposal unit to suggest the optimal service based on the customer's past service usage history.

[0039] When proposing a service, the service proposal unit can customize the service based on the customer's current living situation. For example, if the customer is busy, the service proposal unit allows the generation AI to propose a service that can be used in a short time. For example, the generation AI analyzes the customer's current living situation and provides a service that can be used in a short time. Furthermore, if the customer is relaxing, the service proposal unit can also suggest a service that will help them relax. For example, the generation AI analyzes the customer's current living situation and provides a service that will help them relax. Furthermore, if the customer plans to attend a specific event, the service proposal unit can also suggest a service related to the event. For example, the generation AI analyzes the customer's current living situation and provides a service related to the event. This allows for the provision of a customized service based on the customer's current living situation. Some or all of the above-described processing in the service proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the service proposal unit can input data about the customer's current living situation into the generation AI and have the generation AI customize the service. This allows the service proposal unit to customize the service based on the customer's current living situation.

[0040] When proposing a service, the service proposal unit can propose region-specific services by taking into account the customer's geographical location information. For example, if the customer is in a specific region, the service proposal unit causes the generation AI to propose region-specific services. For example, the generation AI analyzes the customer's geographical location information and provides region-specific services. Furthermore, if the customer is traveling, the service proposal unit can also cause the generation AI to propose region-specific services for the customer's travel destination. For example, the generation AI analyzes the customer's geographical location information and provides region-specific services for the travel destination. Furthermore, if the customer is considering moving, the service proposal unit can also propose new region-specific services for the new region. For example, the generation AI analyzes the customer's geographical location information and provides new region-specific services. By proposing region-specific services, services suited to the customer can be provided. Some or all of the above-described processing in the service proposal unit may be performed using AI, for example, or without AI. For example, the service proposal unit can input the customer's geographical location information data into the generation AI and cause the generation AI to propose region-specific services. This allows the service proposal unit to propose region-specific services based on the customer's geographical location information.

[0041] The service suggestion unit can analyze a customer's social media activity and suggest related services when suggesting services. For example, if a customer mentions a specific product on social media, the generation AI of the service suggestion unit can suggest services related to the product. For example, the generation AI can analyze the customer's social media activity and provide services related to the product. Furthermore, if a customer indicates on social media that they plan to attend a specific event, the generation AI can suggest services related to the event. For example, the generation AI can analyze the customer's social media activity and provide services related to the event. Furthermore, if a customer indicates a specific interest on social media, the service suggestion unit can suggest services related to the interest. For example, the generation AI can analyze the customer's social media activity and provide services related to the interest. This enables service suggestions based on the customer's social media activity. Some or all of the above-described processing in the service suggestion unit may be performed using AI, for example, or without AI. For example, the service suggestion unit can input the customer's social media activity data into the generation AI and cause the generation AI to suggest related services. This enables the service suggestion unit to suggest related services based on the customer's social media activity.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The input unit can refer to the customer's purchase history and prioritize inputting information related to products purchased in the past. For example, if a customer has purchased products from a specific brand in the past, the generation AI can prioritize inputting information about new products from that brand. Also, if a customer has purchased products from a specific category in the past, the generation AI can prioritize inputting information related to that category. Furthermore, the generation AI can input usage and maintenance information for products purchased by the customer in the past. This makes it possible to provide information based on the customer's purchase history. Some or all of the above-mentioned processing in the input unit may be performed using, or without, AI. For example, the input unit can input customer purchase history data into the generation AI and cause the generation AI to input related information. This allows the input unit to input related information based on the customer's purchase history.

[0044] The answer generation unit can refer to the customer's purchase history and generate an answer that includes information related to products purchased in the past. For example, if a customer has purchased products from a specific brand in the past, the generation AI can provide an answer that includes information about new products from that brand. Also, if a customer has purchased products from a specific category in the past, the generation AI can provide an answer that includes information related to that category. Furthermore, the generation AI can provide an answer that includes usage and maintenance information for products purchased by the customer in the past. This allows the provision of an optimal answer based on the customer's purchase history. Some or all of the above-described processing in the answer generation unit may be performed using, or without, AI. For example, the answer generation unit can input customer purchase history data into the generation AI and cause the generation AI to generate an answer that includes related information. This allows the answer generation unit to generate an answer that includes related information based on the customer's purchase history.

[0045] The service suggestion unit can refer to a customer's purchase history and suggest services related to products purchased in the past. For example, if a customer has previously purchased products from a specific brand, the generation AI can suggest maintenance services for that brand. Also, if a customer has previously purchased products from a specific category, the generation AI can suggest services related to that category. Furthermore, the generation AI can suggest upgrade services or related accessories for products previously purchased by the customer. This makes it possible to suggest optimal services based on the customer's purchase history. Some or all of the above-described processing in the service suggestion unit may be performed using, or without, AI. For example, the service suggestion unit can input customer purchase history data into the generation AI and have the generation AI suggest related services. This allows the service suggestion unit to suggest related services based on the customer's purchase history.

[0046] The input unit can analyze a customer's social media activity and input related information. For example, if a customer mentions a specific product on social media, the generation AI inputs information about the product. For example, the generation AI analyzes the customer's social media activity and provides information about the product. Furthermore, if a customer indicates on social media that they plan to attend a specific event, the input unit can input information related to the event. For example, the generation AI analyzes the customer's social media activity and provides information related to the event. Furthermore, if a customer indicates a specific interest on social media, the input unit can input information related to the interest. For example, the generation AI analyzes the customer's social media activity and provides information related to the interest. This makes it possible to provide information based on the customer's social media activity. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input customer social media activity data into the generation AI and cause the generation AI to input related information. This allows the input unit to input related information based on the customer's social media activity.

[0047] When generating an answer, the answer generation unit can include region-specific information by taking into account the customer's geographical location information. For example, if the customer is in a specific region, the generation AI includes information specific to that region in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information specific to that region. Furthermore, if the customer is traveling, the answer generation unit can include region-specific information for the destination region in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information specific to the destination region. Furthermore, if the customer is considering moving, the answer generation unit can include information about the new region in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information about the new region. In this way, by including region-specific information, an answer suitable for the customer can be provided. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can input the customer's geographical location data into the generation AI and cause the generation AI to generate region-specific information. In this way, the answer generation unit can include region-specific information based on the customer's geographical location information.

[0048] When proposing a service, the service proposal unit can customize the service based on the customer's current living situation. For example, if the customer is busy, the generation AI can propose a service that can be used in a short time. For example, the generation AI can analyze the customer's current living situation and provide a service that can be used in a short time. Furthermore, if the customer is relaxing, the service proposal unit can propose a service that will help them relax. For example, the generation AI can analyze the customer's current living situation and provide a service that will help them relax. Furthermore, if the customer plans to attend a specific event, the service proposal unit can propose a service related to the event. For example, the generation AI can analyze the customer's current living situation and provide a service related to the event. This allows for the provision of a customized service based on the customer's current living situation. Some or all of the above-described processing in the service proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the service proposal unit can input data about the customer's current living situation into the generation AI and have the generation AI customize the service. This allows the service proposal unit to customize the service based on the customer's current living situation.

[0049] During input, the input unit can prioritize input of region-specific service information based on the customer's geographical location information. For example, if the customer is in a specific region, the generation AI prioritizes input of service information specific to that region. For example, the generation AI analyzes the customer's geographical location information and provides service information specific to that region. Furthermore, if the customer is traveling, the input unit can prioritize input of service information specific to the region of the customer's travel destination. For example, the generation AI analyzes the customer's geographical location information and provides service information specific to the region of the travel destination. Furthermore, if the customer is considering moving, the input unit can prioritize input of service information for the new region. For example, the generation AI analyzes the customer's geographical location information and provides service information for the new region. This prioritizes region-specific service information, thereby enabling the provision of information suited to the customer. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input geographical location information data of a customer to the generation AI and cause the generation AI to execute preferential input of region-specific service information, thereby enabling the input unit to preferentially input region-specific service information based on the geographical location information of the customer.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The input section inputs all of the shop's services and information, such as product descriptions and how to use the services, into the generation AI. Step 2: The answer generation unit generates an answer to the customer's question based on the information input by the input unit. For example, if a customer asks about a product, the generation AI is used to provide an appropriate answer to that question. For example, the generation AI receives a prompt such as "Tell me about this product" and generates a detailed description of the product. Also, if a customer asks about how to use a service, the generation AI is used to explain how to use it. Step 3: The service proposal unit proposes services that meet the customer's needs based on the answers generated by the answer generation unit. For example, if a customer requests a specific service, the generation AI is used to propose that service. The generation AI can use an algorithm to analyze the customer's needs and propose the most suitable service. This makes it possible to quickly and accurately propose services that meet the customer's needs.

[0052] (Example 2) A robot system according to an embodiment of the present invention is equipped with a generation AI and replaces half of the shop staff by inputting all of the shop's services and information. This robot system first inputs all of the shop's services and information into the generation AI. The generation AI then generates appropriate answers to customer questions and suggests services based on the customer's requests. This system reduces the burden on shop staff and enables efficient operations. For example, when inputting all of the shop's services and information into the generation AI, details of the service content and information are entered into the generation AI. Specifically, product descriptions and service usage instructions are input. The generation AI then generates appropriate answers to customer questions. For example, when a customer asks about a product, the generation AI provides an appropriate answer to that question. This allows customers to obtain information quickly and accurately. Furthermore, the generation AI suggests services based on the customer's requests. For example, if a customer requests a specific service, the generation AI suggests that service. This allows the customer to select the service that best suits them. This system reduces the burden on shop staff and enables efficient operations. For example, the number of customers that shop staff need to deal with will decrease, allowing them to focus on more important tasks. Also, by having generative AI handle customer interactions, the quality of service will improve and customer satisfaction will increase. It is possible to start with a specific shop and then expand to many other shops. For example, by first introducing it in the shops of a specific telecommunications company and then expanding it to many other shops, it will be possible to provide services to a wide range of customers. In this way, the robot system can reduce the burden on shop staff and achieve efficient operations.

[0053] The robot system according to the embodiment includes an input unit, an answer generation unit, and a service proposal unit. The input unit inputs all of the shop's services and information. For example, the input unit inputs product descriptions and service usage instructions to the generation AI. The generation AI analyzes the input information and generates an appropriate answer using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The answer generation unit generates an answer to a customer's question based on the information input by the input unit. For example, when a customer asks a question about a product, the answer generation unit provides an appropriate answer to the question using the generation AI. The generation AI receives a prompt, for example, "Tell me about this product," and generates a detailed description of the product. The answer generation unit can also generate answers to customer questions in real time using the generation AI. For example, when a customer asks about how to use a service, the answer generation unit explains how to use the service using the generation AI. The service proposal unit proposes services that meet the customer's needs based on the answer generated by the answer generation unit. For example, if a customer requests a specific service, the service proposal unit uses a generation AI to propose that service. The generation AI can, for example, use an algorithm that analyzes the customer's requests and proposes the optimal service. This allows the robot system to propose services that meet the customer's requests. Furthermore, the service proposal unit can also use the generation AI to propose services that meet the customer's requests in real time. For example, if a customer requests a specific service, the service proposal unit uses the generation AI to propose that service. This allows the robot system to quickly and accurately propose services that meet the customer's requests. As a result, the robot system according to the embodiment can reduce the burden on shop staff and achieve efficient operations.

[0054] The input unit can estimate the customer's emotions and prioritize the information to be input based on the estimated customer emotions. For example, if the customer is feeling anxious, the input unit allows the generation AI to first prioritize information that provides a sense of security. For example, if the generation AI analyzes the customer's facial expression and determines that the customer is feeling anxious, it prioritizes providing information that provides a sense of security. Furthermore, if the customer is excited, the input unit can also prioritize information that is interesting to the customer. For example, if the generation AI analyzes the tone of the customer's voice and determines that the customer is excited, it prioritizes providing information that is interesting to the customer. Furthermore, if the customer is relaxed, the input unit can also prioritize detailed information to the generation AI. For example, if the generation AI analyzes the customer's posture and determines that the customer is relaxed, it provides detailed information. This enables the provision of information according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may input customer facial expression data into the generation AI and cause the generation AI to estimate the customer's emotions. This allows the input unit to provide information according to the customer's emotions.

[0055] At the time of input, the input unit can select an appropriate input method by referring to past customer interaction history. For example, the generation AI of the input unit preferentially suggests text input to customers who have preferred text input in the past. For example, the generation AI analyzes past customer interaction history and suggests text input to customers who prefer text input. The input unit can also preferentially suggest voice input to customers who have preferred voice input in the past. For example, the generation AI analyzes past customer interaction history and suggests voice input to customers who prefer voice input. The input unit can also preferentially suggest visual input methods to customers who have preferred images or videos in the past. For example, the generation AI analyzes past customer interaction history and suggests visual input methods to customers who prefer visual input methods. This makes it possible to provide the optimal input method based on the customer's past interaction history. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input past customer interaction history data into the generation AI and have the generation AI select the optimal input method. This allows the input unit to provide the optimal input method based on the customer's past interaction history.

[0056] The input unit can automatically reflect seasonal service change information of the shop upon input. The input unit, for example, automatically inputs seasonal campaign information to the generation AI. For example, the generation AI automatically collects seasonal campaign information and provides it to the input unit. The input unit can also automatically input seasonal changes to the product lineup to the generation AI. For example, the generation AI automatically collects information on seasonal changes to the product lineup and provides it to the input unit. The input unit can also automatically input seasonal changes to business hours to the generation AI. For example, the generation AI automatically collects information on seasonal changes to business hours and provides it to the input unit. This allows the latest information to be provided by automatically reflecting seasonal service change information. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input seasonal service change information to the generation AI and cause the generation AI to automatically reflect the information. This allows the input unit to automatically reflect seasonal service change information.

[0057] The input unit can estimate the customer's emotions and adjust the level of detail of the information to be input based on the estimated customer emotions. For example, if the customer is feeling anxious, the generation AI in the input unit provides concise and easy-to-understand information. For example, if the generation AI analyzes the customer's facial expression and determines that the customer is feeling anxious, it provides concise and easy-to-understand information. Furthermore, if the customer is excited, the generation AI can provide detailed and interesting information. For example, if the generation AI analyzes the tone of the customer's voice and determines that the customer is excited, it provides detailed and interesting information. Furthermore, if the customer is relaxed, the generation AI can provide information including detailed explanations. For example, if the generation AI analyzes the customer's posture and determines that the customer is relaxed, it provides information including detailed explanations. This allows the provision of appropriate information by adjusting the level of detail of information according to 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, 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 input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may input customer facial expression data to the generation AI and cause the generation AI to adjust the level of detail of the information. This allows the input unit to adjust the level of detail of the information according to the customer's emotions.

[0058] The input unit can prioritize input of region-specific service information based on the customer's geographical location information at the time of input. For example, if the customer is in a specific region, the input unit causes the generation AI to prioritize input of service information specific to that region. For example, the generation AI analyzes the customer's geographical location information and provides service information specific to that region. Also, if the customer is traveling, the input unit can cause the generation AI to prioritize input of service information specific to the region of the customer's travel destination. For example, the generation AI analyzes the customer's geographical location information and provides service information specific to the region of the travel destination. Also, if the customer is considering moving, the input unit can cause the generation AI to prioritize input of service information for the new region. For example, the generation AI analyzes the customer's geographical location information and provides service information for the new region. This allows the customer to be provided with information that is appropriate for the region by prioritizing service information specific to the region. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input geographical location information data of a customer to the generation AI and cause the generation AI to execute preferential input of region-specific service information, thereby enabling the input unit to preferentially input region-specific service information based on the geographical location information of the customer.

[0059] The input unit can analyze the customer's social media activity and input related information at the time of input. For example, if a customer mentions a specific product on social media, the input unit inputs information about the product to the generation AI. For example, the generation AI analyzes the customer's social media activity and provides information about the product. Furthermore, if a customer indicates on social media that they plan to attend a specific event, the input unit can input information related to the event. For example, the generation AI analyzes the customer's social media activity and provides information related to the event. Furthermore, if a customer indicates a specific interest on social media, the input unit can input information related to the interest. For example, the generation AI analyzes the customer's social media activity and provides information related to the interest. This makes it possible to provide information based on the customer's social media activity. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input customer social media activity data to the generation AI and have the generation AI input related information. This allows the input unit to input relevant information based on the customer's social media activity.

[0060] The answer generation unit can estimate the customer's emotions and adjust the way the answer is expressed based on the estimated customer's emotions. For example, if the customer is feeling anxious, the answer generation unit generates an answer using an expression that gives a sense of security. For example, if the generation AI analyzes the customer's facial expression and determines that the customer is feeling anxious, it provides an answer using an expression that gives a sense of security. Furthermore, if the customer is excited, the answer generation unit can also generate an answer using an expression that attracts attention. For example, if the generation AI analyzes the customer's tone of voice and determines that the customer is excited, it provides an answer using an expression that attracts attention. Furthermore, if the customer is relaxed, the answer generation unit can also generate an answer using an expression that includes detailed explanations. For example, if the generation AI analyzes the customer's posture and determines that the customer is relaxed, it provides an answer using an expression that includes detailed explanations. This allows the answer to be provided using an expression that corresponds to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit may input customer facial expression data into the generation AI and cause the generation AI to adjust the way the answer is expressed. This allows the answer generation unit to provide an answer using an expression method that corresponds to the customer's emotions.

[0061] When generating an answer, the answer generation unit can generate an optimal answer by referring to the customer's past question history. In the answer generation unit, for example, the generation AI generates a relevant answer based on the content of questions asked by the customer in the past. For example, the generation AI analyzes the customer's past question history and provides a relevant answer. The answer generation unit can also select and generate the most appropriate answer from the customer's past question history. For example, the generation AI analyzes the customer's past question history and selects and provides the most appropriate answer. The answer generation unit can also analyze the customer's past question history and generate the most efficient answer. For example, the generation AI analyzes the customer's past question history and provides the most efficient answer. This allows the optimal answer to be provided based on the customer's past question history. Some or all of the above-mentioned processing in the answer generation unit may be performed using, or without, AI. For example, the answer generation unit can input the customer's past question history data into the generation AI and cause the generation AI to generate the optimal answer. This allows the answer generation unit to generate the optimal answer based on the customer's past question history.

[0062] When generating an answer, the answer generation unit can include related product information by taking into account the customer's purchase history. The answer generation unit, for example, causes the generation AI to include information related to products previously purchased by the customer in the answer. For example, the generation AI analyzes the customer's purchase history and provides related product information. The answer generation unit can also generate an answer in which the generation AI suggests related products based on the customer's purchase history. For example, the generation AI analyzes the customer's purchase history and provides an answer that suggests related products. The answer generation unit can also analyze the customer's purchase history and cause the generation AI to include the most relevant product information in the answer. For example, the generation AI analyzes the customer's purchase history and provides the most relevant product information. This makes it possible to provide related product information based on the customer's purchase history. Some or all of the above-described processing in the answer generation unit may be performed using, or without, AI. For example, the answer generation unit can input customer purchase history data into the generation AI and cause the generation AI to generate related product information. This makes it possible for the answer generation unit to include related product information based on the customer's purchase history.

[0063] The answer generation unit can estimate the customer's emotions and adjust the length of the answer based on the estimated customer emotions. For example, if the customer is feeling anxious, the generation AI generates a concise and to-the-point answer. For example, if the generation AI analyzes the customer's facial expression and determines that the customer is feeling anxious, it provides a concise and to-the-point answer. The answer generation unit can also generate a detailed and interesting answer if the customer is excited. For example, if the generation AI analyzes the customer's tone of voice and determines that the customer is excited, it provides a detailed and interesting answer. The answer generation unit can also generate a longer answer including detailed explanations if the customer is relaxed. For example, if the generation AI analyzes the customer's posture and determines that the customer is relaxed, it provides a longer answer including detailed explanations. This allows for the provision of appropriate information by adjusting the length of the answer according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit may input customer facial expression data into the generation AI and cause the generation AI to adjust the length of the answer. This allows the answer generation unit to adjust the length of the answer according to the customer's emotions.

[0064] When generating an answer, the answer generation unit can include region-specific information by taking into account the customer's geographical location information. For example, if the customer is in a specific region, the answer generation unit causes the generation AI to include region-specific information in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information specific to that region. Furthermore, if the customer is traveling, the answer generation unit can also cause the generation AI to include region-specific information in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information specific to the region. Furthermore, if the customer is considering moving, the answer generation unit can also cause the generation AI to include information on the new region in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information on the new region. In this way, by including region-specific information, an answer suitable for the customer can be provided. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can input the customer's geographical location information data into the generation AI and cause the generation AI to generate region-specific information. In this way, the answer generation unit can include region-specific information based on the customer's geographical location information.

[0065] The answer generation unit can analyze the customer's social media activity and include relevant information when generating an answer. For example, if a customer mentions a specific product on social media, the answer generation unit causes the generation AI to include information about the product in the answer. For example, the generation AI can analyze the customer's social media activity and provide information about the product. Also, if a customer indicates on social media that they plan to attend a specific event, the answer generation unit can cause the generation AI to include information related to the event in the answer. For example, the generation AI can analyze the customer's social media activity and provide information related to the event. Also, if a customer indicates a specific interest on social media, the answer generation unit can cause the generation AI to include information related to the interest in the answer. For example, the generation AI can analyze the customer's social media activity and provide information related to the interest. This makes it possible to provide information based on the customer's social media activity. Some or all of the above-described processing in the answer generation unit may be performed using, or without, AI. For example, the answer generation unit can input the customer's social media activity data into the generation AI and cause the generation AI to generate relevant information. This allows the answer generation unit to include relevant information based on the customer's social media activity.

[0066] The service proposal unit can estimate the customer's emotions and determine the priority of services to be proposed based on the estimated customer emotions. For example, if the customer is feeling anxious, the service proposal unit allows the generation AI to prioritize services that provide a sense of security. For example, if the generation AI analyzes the customer's facial expression and determines that the customer is feeling anxious, it proposes services that provide a sense of security. In addition, if the customer is excited, the service proposal unit can also prioritize services that attract the customer's attention. For example, if the generation AI analyzes the customer's tone of voice and determines that the customer is excited, it proposes services that attract the customer's attention. In addition, if the customer is relaxed, the generation AI can prioritize detailed services. For example, if the generation AI analyzes the customer's posture and determines that the customer is relaxed, it proposes detailed services. This makes it possible to propose services based on the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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 service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the service proposal unit may input customer facial expression data into a generation AI and have the generation AI determine the priority of services. This allows the service proposal unit to determine the priority of services according to the customer's emotions.

[0067] When proposing a service, the service proposal unit can suggest the optimal service by referring to the customer's past service usage history. In the service proposal unit, for example, the generation AI suggests a related service based on services the customer has used in the past. For example, the generation AI analyzes the customer's past service usage history and provides a related service. The service proposal unit can also select and suggest the most appropriate service based on the customer's past service usage history. For example, the generation AI analyzes the customer's past service usage history and selects and provides the most appropriate service. The service proposal unit can also analyze the customer's past service usage history and suggest the most efficient service. For example, the generation AI analyzes the customer's past service usage history and provides the most efficient service. This allows the optimal service to be provided based on the customer's past service usage history. Some or all of the above-described processing in the service proposal unit may be performed using, or without, AI. For example, the service proposal unit can input the customer's past service usage history data into the generation AI and have the generation AI suggest the optimal service. This allows the service proposal unit to suggest the optimal service based on the customer's past service usage history.

[0068] When proposing a service, the service proposal unit can customize the service based on the customer's current living situation. For example, if the customer is busy, the service proposal unit allows the generation AI to propose a service that can be used in a short time. For example, the generation AI analyzes the customer's current living situation and provides a service that can be used in a short time. Furthermore, if the customer is relaxing, the service proposal unit can also suggest a service that will help them relax. For example, the generation AI analyzes the customer's current living situation and provides a service that will help them relax. Furthermore, if the customer plans to attend a specific event, the service proposal unit can also suggest a service related to the event. For example, the generation AI analyzes the customer's current living situation and provides a service related to the event. This allows for the provision of a customized service based on the customer's current living situation. Some or all of the above-described processing in the service proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the service proposal unit can input data about the customer's current living situation into the generation AI and have the generation AI customize the service. This allows the service proposal unit to customize the service based on the customer's current living situation.

[0069] The service proposal unit can estimate the customer's emotions and adjust the level of detail of the proposed service based on the estimated customer emotions. For example, if the customer is feeling anxious, the generation AI of the service proposal unit can provide a concise and easy-to-understand service proposal. For example, if the generation AI analyzes the customer's facial expression and determines that the customer is feeling anxious, it can provide a concise and easy-to-understand service proposal. Furthermore, if the customer is excited, the generation AI can provide a detailed and interesting service proposal. For example, if the generation AI analyzes the customer's tone of voice and determines that the customer is excited, it can provide a detailed and interesting service proposal. Furthermore, if the customer is relaxed, the generation AI can provide a service proposal that includes detailed explanations. For example, if the generation AI analyzes the customer's posture and determines that the customer is relaxed, it can provide a service proposal that includes detailed explanations. This allows for the provision of appropriate information by adjusting the level of detail of the service proposal according to 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, 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 service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the service proposal unit may input customer facial expression data into the generation AI and cause the generation AI to adjust the level of detail of the service. This allows the service proposal unit to adjust the level of detail of the service according to the customer's emotions.

[0070] When proposing a service, the service proposal unit can propose region-specific services by taking into account the customer's geographical location information. For example, if the customer is in a specific region, the service proposal unit causes the generation AI to propose region-specific services. For example, the generation AI analyzes the customer's geographical location information and provides region-specific services. Furthermore, if the customer is traveling, the service proposal unit can also cause the generation AI to propose region-specific services for the customer's travel destination. For example, the generation AI analyzes the customer's geographical location information and provides region-specific services for the travel destination. Furthermore, if the customer is considering moving, the service proposal unit can also propose new region-specific services for the new region. For example, the generation AI analyzes the customer's geographical location information and provides new region-specific services. By proposing region-specific services, services suited to the customer can be provided. Some or all of the above-described processing in the service proposal unit may be performed using AI, for example, or without AI. For example, the service proposal unit can input the customer's geographical location information data into the generation AI and cause the generation AI to propose region-specific services. This allows the service proposal unit to propose region-specific services based on the customer's geographical location information.

[0071] The service suggestion unit can analyze a customer's social media activity and suggest related services when suggesting services. For example, if a customer mentions a specific product on social media, the generation AI of the service suggestion unit can suggest services related to the product. For example, the generation AI can analyze the customer's social media activity and provide services related to the product. Furthermore, if a customer indicates on social media that they plan to attend a specific event, the generation AI can suggest services related to the event. For example, the generation AI can analyze the customer's social media activity and provide services related to the event. Furthermore, if a customer indicates a specific interest on social media, the service suggestion unit can suggest services related to the interest. For example, the generation AI can analyze the customer's social media activity and provide services related to the interest. This enables service suggestions based on the customer's social media activity. Some or all of the above-described processing in the service suggestion unit may be performed using AI, for example, or without AI. For example, the service suggestion unit can input the customer's social media activity data into the generation AI and cause the generation AI to suggest related services. This enables the service suggestion unit to suggest related services based on the customer's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, answer generation unit, and service proposal unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart device 14, and inputs product descriptions, service usage methods, etc. to the generation AI. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates answers to customer questions. The service proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes services in accordance with customer requests. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, answer generation unit, and service proposal unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart glasses 214 and inputs product descriptions, service usage methods, etc. to the generation AI. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates answers to customer questions. The service proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes services in accordance with customer requests. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, answer generation unit, and service proposal unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the headset-type terminal 314, and inputs product descriptions, service usage methods, etc. to the generation AI. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates answers to customer questions. The service proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes services in accordance with customer requests. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, answer generation unit, and service proposal unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the robot 414, and inputs product descriptions, service usage methods, etc. to the generation AI. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates answers to customer questions. The service proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes services in accordance with customer requests.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The input unit can refer to the customer's purchase history and prioritize inputting information related to products purchased in the past. For example, if a customer has purchased products from a specific brand in the past, the generation AI can prioritize inputting information about new products from that brand. Also, if a customer has purchased products from a specific category in the past, the generation AI can prioritize inputting information related to that category. Furthermore, the generation AI can input usage and maintenance information for products purchased by the customer in the past. This makes it possible to provide information based on the customer's purchase history. Some or all of the above-mentioned processing in the input unit may be performed using, or without, AI. For example, the input unit can input customer purchase history data into the generation AI and cause the generation AI to input related information. This allows the input unit to input related information based on the customer's purchase history.

[0074] The answer generation unit can refer to the customer's purchase history and generate an answer that includes information related to products purchased in the past. For example, if a customer has purchased products from a specific brand in the past, the generation AI can provide an answer that includes information about new products from that brand. Also, if a customer has purchased products from a specific category in the past, the generation AI can provide an answer that includes information related to that category. Furthermore, the generation AI can provide an answer that includes usage and maintenance information for products purchased by the customer in the past. This allows the provision of an optimal answer based on the customer's purchase history. Some or all of the above-described processing in the answer generation unit may be performed using, or without, AI. For example, the answer generation unit can input customer purchase history data into the generation AI and cause the generation AI to generate an answer that includes related information. This allows the answer generation unit to generate an answer that includes related information based on the customer's purchase history.

[0075] The service suggestion unit can refer to a customer's purchase history and suggest services related to products purchased in the past. For example, if a customer has previously purchased products from a specific brand, the generation AI can suggest maintenance services for that brand. Also, if a customer has previously purchased products from a specific category, the generation AI can suggest services related to that category. Furthermore, the generation AI can suggest upgrade services or related accessories for products previously purchased by the customer. This makes it possible to suggest optimal services based on the customer's purchase history. Some or all of the above-described processing in the service suggestion unit may be performed using, or without, AI. For example, the service suggestion unit can input customer purchase history data into the generation AI and have the generation AI suggest related services. This allows the service suggestion unit to suggest related services based on the customer's purchase history.

[0076] The input unit can estimate the customer's emotions and adjust the format of the information to be input based on the estimated customer emotions. For example, if the customer is feeling anxious, the generation AI can provide concise information in text format. If the customer is excited, the generation AI can provide detailed information in video format. Furthermore, if the customer is relaxed, the generation AI can provide information in audio format. This allows the information format to be adjusted according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 input unit can be performed using AI, or without AI. For example, the input unit can input customer facial expression data into the generation AI and have the generation AI adjust the information format. This allows the input unit to adjust the information format according to the customer's emotions.

[0077] The answer generation unit can estimate the customer's emotions and adjust the tone of the answer based on the estimated customer's emotions. For example, if the customer is feeling anxious, the generation AI can generate an answer in a gentle tone. Furthermore, if the customer is excited, the generation AI can generate an answer in a lively tone. Furthermore, if the customer is relaxed, the generation AI can generate an answer in a calm tone. This enables the tone of the answer to be adjusted according to 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 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 answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can input customer facial expression data into the generation AI and have the generation AI adjust the tone of the answer. This allows the answer generation unit to provide an answer in a tone that corresponds to the customer's emotions.

[0078] The service suggestion unit can estimate the customer's emotions and adjust the type of service to be proposed based on the estimated customer emotions. For example, if the customer is feeling anxious, the generation AI can suggest a service that helps the customer relax. Furthermore, if the customer is excited, the generation AI can suggest an active service. Furthermore, if the customer is relaxed, the generation AI can suggest a service that helps the customer maintain relaxation. This enables the type of service proposal to be adjusted according to 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 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 service suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the service suggestion unit can input customer facial expression data into the generation AI and have the generation AI adjust the type of service. This allows the service suggestion unit to adjust the type of service according to the customer's emotions.

[0079] The input unit can analyze a customer's social media activity and input related information. For example, if a customer mentions a specific product on social media, the generation AI inputs information about the product. For example, the generation AI analyzes the customer's social media activity and provides information about the product. Furthermore, if a customer indicates on social media that they plan to attend a specific event, the input unit can input information related to the event. For example, the generation AI analyzes the customer's social media activity and provides information related to the event. Furthermore, if a customer indicates a specific interest on social media, the input unit can input information related to the interest. For example, the generation AI analyzes the customer's social media activity and provides information related to the interest. This makes it possible to provide information based on the customer's social media activity. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input customer social media activity data into the generation AI and cause the generation AI to input related information. This allows the input unit to input related information based on the customer's social media activity.

[0080] When generating an answer, the answer generation unit can include region-specific information by taking into account the customer's geographical location information. For example, if the customer is in a specific region, the generation AI includes information specific to that region in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information specific to that region. Furthermore, if the customer is traveling, the answer generation unit can include region-specific information for the destination region in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information specific to the destination region. Furthermore, if the customer is considering moving, the answer generation unit can include information about the new region in the answer. For example, the generation AI analyzes the customer's geographical location information and provides information about the new region. In this way, by including region-specific information, an answer suitable for the customer can be provided. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can input the customer's geographical location data into the generation AI and cause the generation AI to generate region-specific information. In this way, the answer generation unit can include region-specific information based on the customer's geographical location information.

[0081] When proposing a service, the service proposal unit can customize the service based on the customer's current living situation. For example, if the customer is busy, the generation AI can propose a service that can be used in a short time. For example, the generation AI can analyze the customer's current living situation and provide a service that can be used in a short time. Furthermore, if the customer is relaxing, the service proposal unit can propose a service that will help them relax. For example, the generation AI can analyze the customer's current living situation and provide a service that will help them relax. Furthermore, if the customer plans to attend a specific event, the service proposal unit can propose a service related to the event. For example, the generation AI can analyze the customer's current living situation and provide a service related to the event. This allows for the provision of a customized service based on the customer's current living situation. Some or all of the above-described processing in the service proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the service proposal unit can input data about the customer's current living situation into the generation AI and have the generation AI customize the service. This allows the service proposal unit to customize the service based on the customer's current living situation.

[0082] During input, the input unit can prioritize input of region-specific service information based on the customer's geographical location information. For example, if the customer is in a specific region, the generation AI prioritizes input of service information specific to that region. For example, the generation AI analyzes the customer's geographical location information and provides service information specific to that region. Furthermore, if the customer is traveling, the input unit can prioritize input of service information specific to the region of the customer's travel destination. For example, the generation AI analyzes the customer's geographical location information and provides service information specific to the region of the travel destination. Furthermore, if the customer is considering moving, the input unit can prioritize input of service information for the new region. For example, the generation AI analyzes the customer's geographical location information and provides service information for the new region. This prioritizes region-specific service information, thereby enabling the provision of information suited to the customer. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input geographical location information data of a customer to the generation AI and cause the generation AI to execute preferential input of region-specific service information, thereby enabling the input unit to preferentially input region-specific service information based on the geographical location information of the customer.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The input section inputs all of the shop's services and information, such as product descriptions and how to use the services, into the generation AI. Step 2: The answer generation unit generates an answer to the customer's question based on the information input by the input unit. For example, if a customer asks about a product, the generation AI is used to provide an appropriate answer to that question. For example, the generation AI receives a prompt such as "Tell me about this product" and generates a detailed description of the product. Also, if a customer asks about how to use a service, the generation AI is used to explain how to use it. Step 3: The service proposal unit proposes services that meet the customer's needs based on the answers generated by the answer generation unit. For example, if a customer requests a specific service, the generation AI is used to propose that service. The generation AI can use an algorithm to analyze the customer's needs and propose the most suitable service. This makes it possible to quickly and accurately propose services that meet the customer's needs.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0139] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0140] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0141] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0142] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0143] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0144] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0145] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0146] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0147] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0148] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0149] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0150] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0151] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0152] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0153] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0156] [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An input section that inputs all the shop's services and information; an answer generation unit that generates an answer to a customer's question based on the information input by the input unit; a service proposal unit that proposes a service according to a customer's request based on the answer generated by the answer generation unit; A system characterized by:

2. The input unit includes: Estimate customer sentiment and prioritize input information based on the estimated sentiment The system of claim 1 .

3. The input unit includes: When inputting data, refer to past customer interaction history to select the appropriate input method. The system of claim 1 .

4. The input unit includes: Automatically update seasonal service changes at the time of input The system of claim 1 .

5. The input unit includes: Estimate customer sentiment and adjust the level of detail of input information based on the estimated sentiment The system of claim 1 .

6. The input unit includes: At the time of input, prioritize local service information based on the customer's geographic location. The system of claim 1 .

7. The input unit includes: At the time of input, analyze the customer's social media activity and input relevant information. The system of claim 1 .

8. The answer generation unit Infer customer sentiment and adjust the wording of your answers based on that sentiment The system of claim 1 .

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