system

Abstract

The system according to the embodiment aims to propose the most suitable mobile phone plan based on the frequency of use and family structure of the customer. [Solution] A system according to an embodiment includes a collection unit, a selection unit, and a proposal unit. The collection unit collects information on a customer's frequency of use or family structure. The selection unit analyzes the information collected by the collection unit and selects a plan. The proposal unit proposes the plan selected by the selection unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to select the most suitable mobile phone plan for a customer from the many available, resulting in a high workload.

[0005] The system according to the embodiment aims to propose the most suitable mobile phone plan based on the frequency of use and family structure of the customer. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a selection unit, and a proposal unit. The collection unit collects information on the frequency of use or family structure of customers. The selection unit analyzes the information collected by the collection unit and selects a plan. The proposal unit proposes the plan selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose the most suitable mobile phone plan based on the customer's frequency of use and family structure. [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 mobile phone plan proposal system according to an embodiment of the present invention listens to customers' needs, such as their usage frequency and family size, and then uses a generation AI to automatically propose an optimal plan based on that information. This system can reduce the workload of sales and back-office staff and improve customer satisfaction. Specifically, the system consists of the following steps: First, the system listens to customers' needs, such as their usage frequency and family size, and collects information such as call time, data usage, and number of family members. Next, the generation AI analyzes the input information and proposes the optimal plan from over 200,000 plans. This system reduces the workload of sales and back-office staff and improves customer satisfaction. For example, the system listens to customers' needs, such as their usage frequency and family size. For example, information such as call time, data usage, and number of family members is collected. This information is input into the generation AI. Next, the generation AI analyzes the input information and proposes the optimal plan. The generation AI selects the plan that best suits the customer's needs from over 200,000 plans. For example, an unlimited calling plan is proposed for customers with high call time, and a high-capacity data plan is proposed for customers with high data usage. This system reduces the workload of sales and back office staff. Previously, sales representatives manually selected plans, which required a lot of time and effort. However, using generative AI automates plan selection, improving operational efficiency and customer satisfaction. Generative AI suggests optimal plans, allowing customers to choose the one that best suits them. This reduces waste and improves customer satisfaction. For example, by suggesting a plan that allows the entire family to share data, everyone can use data efficiently. Furthermore, by suggesting an unlimited calling plan to customers who use a lot of calls, they can use the service without worrying about call charges. In this way, generative AI can automatically suggest the optimal plan from a complex selection of plans, reducing the workload of sales and back office staff and improving customer satisfaction. This allows the mobile phone plan suggestion system to automatically suggest the optimal plan based on the customer's needs.

[0029] A mobile phone plan proposal system according to an embodiment includes a collection unit, a selection unit, and a proposal unit. The collection unit collects information on a customer's usage frequency or family structure. The collection unit can collect information such as call time, data usage, and the number of family members. The collection unit can collect, for example, call time as monthly call time or number of calls. The collection unit can also collect data usage as monthly data usage or data type. The collection unit can also collect the number of family members as the number of family members living together and the number of dependents. The selection unit analyzes the information collected by the collection unit and selects a plan. The selection unit can select an optimal plan from, for example, over 200,000 plans. The selection unit can select a plan using, for example, statistical analysis or a machine learning algorithm. The selection unit can select, for example, an unlimited call plan for a customer with a high call time and a high-capacity data plan for a customer with a high data usage. The proposal unit proposes the plan selected by the selection unit. The proposal unit can propose specific plans such as unlimited call plans and high-capacity data plans. The proposal unit can, for example, propose a plan that allows the entire family to share data. The proposal unit can, for example, propose a plan that reduces unnecessary charges. As a result, the mobile phone plan proposal system according to the embodiment can automatically propose an optimal plan based on the customer's needs. Some or all of the above-described processing in the collection unit, selection unit, and proposal unit may be performed using, for example, AI. For example, the collection unit can collect information using an AI model for collecting information such as call time, data usage, and number of family members. The selection unit can select a plan using an AI model that takes the information collected by the collection unit as input and outputs an optimal plan. The proposal unit can propose a plan using an AI model that takes the plan selected by the selection unit as input and outputs a proposal.

[0030] The collection unit can collect information on call duration, data usage, and number of family members. For example, the collection unit can collect call duration as monthly call duration or number of calls. For example, the collection unit can collect data usage as monthly data usage or type of data. For example, the collection unit can collect family member count as the number of family members living together and the number of dependents. This allows the collection unit to grasp the customer's detailed needs. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information such as call duration, data usage, and number of family members using an AI model for collecting information.

[0031] The selection unit can select a plan from over 200,000 plans. The selection unit can select a plan using, for example, statistical analysis or a machine learning algorithm. The selection unit can select, for example, an unlimited calling plan for customers with long call times and a large-capacity data plan for customers with large data usage. This allows the selection unit to select the optimal plan from a variety of plans. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the selection unit can select a plan using a generation AI model that inputs information collected by the collection unit and outputs the optimal plan.

[0032] The proposal unit can propose an unlimited calling plan or a large-capacity data plan. The proposal unit can propose a specific plan, such as an unlimited calling plan or a large-capacity data plan. The proposal unit can propose a plan for sharing data with the entire family, for example. The proposal unit can propose a plan that reduces wasteful charges, for example. This allows the proposal unit to propose a specific plan to the customer. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a plan using an AI model that inputs the plan selected by the selection unit and outputs the proposal content.

[0033] The suggestion unit can suggest a plan for sharing data among all family members. The suggestion unit can, for example, suggest a plan for sharing data among all family members. The suggestion unit can, for example, suggest a plan that enables all family members to use data efficiently. In this way, the suggestion unit can enable all family members to use data efficiently. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest a plan using an AI model that inputs the plan selected by the selection unit and outputs the proposed content.

[0034] The proposal unit can propose a plan that reduces unnecessary fees. The proposal unit can, for example, propose a plan that reduces unnecessary fees. The proposal unit can, for example, propose a plan that eliminates unnecessary options. The proposal unit can, for example, reduce unnecessary fees by selecting an optimal plan. In this way, the proposal unit can reduce unnecessary fees for customers. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a plan using an AI model that inputs the plan selected by the selection unit and outputs the proposal content.

[0035] The collection unit can analyze the user's past plan change history and select the optimal information collection method. For example, the collection unit can analyze the characteristics of plans that the user has frequently changed in the past and prioritize related questions. For example, the collection unit can suggest appropriate questions based on trends in plans selected by the user in the past. For example, the collection unit can select questions that correspond to specific needs from the user's past plan change history. This allows the collection unit to select the optimal information collection method based on the past plan change history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select the optimal information collection method using an AI model for analyzing the user's past plan change history.

[0036] When collecting information, the collection unit may perform filtering based on the user's current living situation and areas of interest. For example, if the user has a new family, the collection unit may prioritize questions about family plans. For example, if the user has a specific hobby or interest, the collection unit may ask questions about plans related to the hobby or interest. For example, the collection unit may ask questions about appropriate plans based on the user's current living situation (e.g., moving or changing jobs). This allows the collection unit to collect appropriate information based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect information using an AI model for filtering the user's current living situation and areas of interest.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit can prioritize collecting information about plans related to that area. For example, the collection unit can collect information about appropriate plans based on places frequently visited by the user. For example, the collection unit can collect information about optimal plans based on the user's geographical location information. This allows the collection unit to collect highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for collecting highly relevant information by taking into account the user's geographical location information.

[0038] When collecting information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit can collect information about an appropriate plan based on information shared by the user on social media. For example, the collection unit can collect information about plans of interest from the user's social media activity. For example, the collection unit can analyze the user's social media activity history and collect information about the optimal plan. This allows the collection unit to collect related information based on the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect related information using an AI model for analyzing the user's social media activity.

[0039] When selecting a plan, the selection unit can select the optimal plan by referring to the user's past plan usage history. The selection unit can, for example, select the optimal plan based on the characteristics of plans used by the user in the past. The selection unit can, for example, select a frequently used plan from the user's past plan usage history. The selection unit can, for example, analyze the user's past plan usage history and select the most suitable plan. This allows the selection unit to select the optimal plan based on the past plan usage history. Some or all of the above-mentioned processing in the selection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the selection unit can select the optimal plan using a generation AI model for analyzing the user's past plan usage history.

[0040] When selecting a plan, the selection unit can customize plan selection criteria based on the user's current living situation. For example, if the user has a new family structure, the selection unit can preferentially select a family plan. For example, if the user has a specific hobby or interest, the selection unit can select a plan related to that hobby or interest. For example, the selection unit can select an appropriate plan based on the user's current living situation (e.g., moving or changing jobs). This allows the selection unit to select an optimal plan depending on the user's living situation. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the selection unit can select a plan using a generation AI model for customizing plan selection criteria taking into account the user's current living situation.

[0041] When selecting a plan, the selection unit can select an optimal plan by taking into account the user's geographical location information. For example, if the user lives in a specific area, the selection unit can preferentially select plans related to that area. For example, the selection unit can select an appropriate plan based on places frequently visited by the user. For example, the selection unit can select an optimal plan based on the user's geographical location information. This allows the selection unit to select an optimal plan based on the user's geographical location information. Some or all of the above-described processing in the selection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the selection unit can select a plan using a generation AI model for selecting an optimal plan by taking into account the user's geographical location information.

[0042] When selecting a plan, the selection unit can analyze the user's social media activity and select an optimal plan. The selection unit can select an appropriate plan based on, for example, information shared by the user on social media. The selection unit can select a plan of interest based on the user's social media activity, for example. The selection unit can analyze, for example, the user's social media activity history and select an optimal plan. This allows the selection unit to select an optimal plan based on the user's social media activity. Some or all of the above-described processing in the selection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the selection unit can select an optimal plan using a generation AI model for analyzing the user's social media activity.

[0043] The proposal unit can adjust the level of detail of the proposal based on the importance of the plan when making a proposal. For example, the proposal unit can provide a detailed explanation for a plan with a high level of importance. For example, the proposal unit can provide a concise explanation for a plan with a low level of importance. The proposal unit can adjust the level of detail of the proposal based on the importance, for example. This allows the proposal unit to make a proposal with an appropriate level of detail based on the importance of the plan. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can adjust the level of detail of the proposal based on the importance using an AI model for evaluating the importance of the plan.

[0044] When making a proposal, the proposal unit can apply different proposal algorithms depending on the plan category. For example, for a call plan, the proposal unit can apply a proposal algorithm based on call time. For example, for a data plan, the proposal unit can apply a proposal algorithm based on data usage. For example, for a family plan, the proposal unit can apply a proposal algorithm based on the number of family members. This allows the proposal unit to apply an optimal proposal algorithm depending on the plan category. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a plan using an AI model for applying different proposal algorithms depending on the plan category.

[0045] The proposal unit can determine the priority of proposals based on the submission time of the plans when making proposals. For example, the proposal unit can prioritize proposals that are due to be submitted soon. For example, the proposal unit can postpone proposals that are due to be submitted further away. For example, the proposal unit can adjust the priority of proposals according to the submission time. This allows the proposal unit to make proposals with appropriate priority according to the submission time of the plans. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can determine the priority of proposals based on the submission time using an AI model for evaluating the submission time of plans.

[0046] The proposal unit can adjust the order of proposals based on the relevance of the plans when making proposals. For example, the proposal unit can prioritize proposals for highly relevant plans. For example, the proposal unit can postpone proposals for less relevant plans. For example, the proposal unit can adjust the order of proposals based on the relevance. This allows the proposal unit to make proposals in an appropriate order based on the relevance of the plans. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can adjust the order of proposals based on the relevance using an AI model for evaluating the relevance of plans.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The collection unit can analyze the user's past purchase history and collect information to propose an optimal plan. For example, it can collect information about devices and accessories purchased by the user in the past and propose an appropriate plan based on that information. It can also collect usage information about services and applications used by the user in the past and propose an optimal plan based on that information. It can also collect a history of campaigns and promotions in which the user has participated in the past and propose benefits and discounts based on that information. This allows the collection unit to propose a more personalized plan based on the user's past purchase history.

[0049] The selection unit can select a plan taking into consideration the user's current health condition. For example, if the user uses a health management app, the data can be collected and a plan can be proposed that matches the user's health condition. Also, if the user uses a fitness tracker, the data can be used to select a plan that matches the amount of exercise and activity level. Furthermore, a plan suitable for the user's health condition can be proposed based on the results of a diagnosis the user received at a medical institution. This allows the selection unit to select an optimal plan that takes into consideration the user's health condition.

[0050] The collection unit can analyze the user's social media activity and collect related information. For example, information about suitable plans can be collected based on information shared by the user on social media. Information about plans of interest can also be collected from the user's social media activity. Furthermore, information about the optimal plan can be collected by analyzing the user's social media activity history. This allows the collection unit to collect related information based on the user's social media activity.

[0051] The suggestion unit can customize the content of the suggestion based on the user's current living situation. For example, if the user has a new family structure, it can suggest a family plan. Also, if the user has a specific hobby or interest, it can suggest a plan related to that. Furthermore, it can suggest an appropriate plan based on the user's current living situation (e.g., moving or changing jobs). This allows the suggestion unit to suggest the optimal plan according to the user's living situation.

[0052] The selection unit can select an optimal plan taking into consideration the user's geographical location information. For example, if the user lives in a specific area, plans related to that area can be preferentially selected. Also, an appropriate plan can be selected based on places frequently visited by the user. Furthermore, the optimal plan can be selected based on the user's geographical location information. This allows the selection unit to select an optimal plan based on the user's geographical location information.

[0053] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the plan. For example, a detailed explanation can be provided for a plan with a high level of importance. A concise explanation can be provided for a plan with a low level of importance. Furthermore, the proposal unit can adjust the level of detail of the proposal depending on the importance. This allows the proposal unit to make a proposal with an appropriate level of detail depending on the importance of the plan.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects information about the customer's frequency of use or family structure. For example, it can collect information such as call duration, data usage, and number of family members. The collection unit collects call duration as monthly call duration and number of calls, and data usage as monthly data usage and data type. It can also collect family member size as the number of family members living together and the number of dependents. Step 2: The selection unit analyzes the information collected by the collection unit and selects a plan. For example, it can select the optimal plan from over 200,000 plans. The selection unit uses statistical analysis and machine learning algorithms to select plans, and can choose unlimited calling plans for customers with long call times and large-capacity data plans for customers with large data usage. Step 3: The proposal unit proposes the plan selected by the selection unit. For example, it can propose specific plans such as unlimited calling plans or large-capacity data plans. The proposal unit can propose plans for sharing data among the whole family or plans that reduce wasteful charges.

[0056] (Example 2) A mobile phone plan proposal system according to an embodiment of the present invention listens to customers' needs, such as their usage frequency and family size, and then uses a generation AI to automatically propose an optimal plan based on that information. This system can reduce the workload of sales and back-office staff and improve customer satisfaction. Specifically, the system consists of the following steps: First, the system listens to customers' needs, such as their usage frequency and family size, and collects information such as call time, data usage, and number of family members. Next, the generation AI analyzes the input information and proposes the optimal plan from over 200,000 plans. This system reduces the workload of sales and back-office staff and improves customer satisfaction. For example, the system listens to customers' needs, such as their usage frequency and family size. For example, information such as call time, data usage, and number of family members is collected. This information is input into the generation AI. Next, the generation AI analyzes the input information and proposes the optimal plan. The generation AI selects the plan that best suits the customer's needs from over 200,000 plans. For example, an unlimited calling plan is proposed for customers with high call time, and a high-capacity data plan is proposed for customers with high data usage. This system reduces the workload of sales and back office staff. Previously, sales representatives manually selected plans, which required a lot of time and effort. However, using generative AI automates plan selection, improving operational efficiency and customer satisfaction. Generative AI suggests optimal plans, allowing customers to choose the one that best suits them. This reduces waste and improves customer satisfaction. For example, by suggesting a plan that allows the entire family to share data, everyone can use data efficiently. Furthermore, by suggesting an unlimited calling plan to customers who use a lot of calls, they can use the service without worrying about call charges. In this way, generative AI can automatically suggest the optimal plan from a complex selection of plans, reducing the workload of sales and back office staff and improving customer satisfaction. This allows the mobile phone plan suggestion system to automatically suggest the optimal plan based on the customer's needs.

[0057] A mobile phone plan proposal system according to an embodiment includes a collection unit, a selection unit, and a proposal unit. The collection unit collects information on a customer's usage frequency or family structure. The collection unit can collect information such as call time, data usage, and the number of family members. The collection unit can collect, for example, call time as monthly call time or number of calls. The collection unit can also collect data usage as monthly data usage or data type. The collection unit can also collect the number of family members as the number of family members living together and the number of dependents. The selection unit analyzes the information collected by the collection unit and selects a plan. The selection unit can select an optimal plan from, for example, over 200,000 plans. The selection unit can select a plan using, for example, statistical analysis or a machine learning algorithm. The selection unit can select, for example, an unlimited call plan for a customer with a high call time and a high-capacity data plan for a customer with a high data usage. The proposal unit proposes the plan selected by the selection unit. The proposal unit can propose specific plans such as unlimited call plans and high-capacity data plans. The proposal unit can, for example, propose a plan that allows the entire family to share data. The proposal unit can, for example, propose a plan that reduces unnecessary charges. As a result, the mobile phone plan proposal system according to the embodiment can automatically propose an optimal plan based on the customer's needs. Some or all of the above-described processing in the collection unit, selection unit, and proposal unit may be performed using, for example, AI. For example, the collection unit can collect information using an AI model for collecting information such as call time, data usage, and number of family members. The selection unit can select a plan using an AI model that takes the information collected by the collection unit as input and outputs an optimal plan. The proposal unit can propose a plan using an AI model that takes the plan selected by the selection unit as input and outputs a proposal.

[0058] The collection unit can collect information on call duration, data usage, and number of family members. For example, the collection unit can collect call duration as monthly call duration or number of calls. For example, the collection unit can collect data usage as monthly data usage or type of data. For example, the collection unit can collect family member count as the number of family members living together and the number of dependents. This allows the collection unit to grasp the customer's detailed needs. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information such as call duration, data usage, and number of family members using an AI model for collecting information.

[0059] The selection unit can select a plan from over 200,000 plans. The selection unit can select a plan using, for example, statistical analysis or a machine learning algorithm. The selection unit can select, for example, an unlimited calling plan for customers with long call times and a large-capacity data plan for customers with large data usage. This allows the selection unit to select the optimal plan from a variety of plans. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the selection unit can select a plan using a generation AI model that inputs information collected by the collection unit and outputs the optimal plan.

[0060] The proposal unit can propose an unlimited calling plan or a large-capacity data plan. The proposal unit can propose a specific plan, such as an unlimited calling plan or a large-capacity data plan. The proposal unit can propose a plan for sharing data with the entire family, for example. The proposal unit can propose a plan that reduces wasteful charges, for example. This allows the proposal unit to propose a specific plan to the customer. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a plan using an AI model that inputs the plan selected by the selection unit and outputs the proposal content.

[0061] The suggestion unit can suggest a plan for sharing data among all family members. The suggestion unit can, for example, suggest a plan for sharing data among all family members. The suggestion unit can, for example, suggest a plan that enables all family members to use data efficiently. In this way, the suggestion unit can enable all family members to use data efficiently. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest a plan using an AI model that inputs the plan selected by the selection unit and outputs the proposed content.

[0062] The proposal unit can propose a plan that reduces unnecessary fees. The proposal unit can, for example, propose a plan that reduces unnecessary fees. The proposal unit can, for example, propose a plan that eliminates unnecessary options. The proposal unit can, for example, reduce unnecessary fees by selecting an optimal plan. In this way, the proposal unit can reduce unnecessary fees for customers. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a plan using an AI model that inputs the plan selected by the selection unit and outputs the proposal content.

[0063] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit can ask detailed questions to collect more information. For example, if the user is feeling stressed, the collection unit can ask concise questions to reduce the burden of information collection. For example, if the user is in a hurry, the collection unit can quickly collect only the most important information. This allows the collection unit to collect more appropriate information by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can estimate the user's emotions using an AI model for estimating the user's emotions and adjust the timing of information collection.

[0064] The collection unit can analyze the user's past plan change history and select the optimal information collection method. For example, the collection unit can analyze the characteristics of plans that the user has frequently changed in the past and prioritize related questions. For example, the collection unit can suggest appropriate questions based on trends in plans selected by the user in the past. For example, the collection unit can select questions that correspond to specific needs from the user's past plan change history. This allows the collection unit to select the optimal information collection method based on the past plan change history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select the optimal information collection method using an AI model for analyzing the user's past plan change history.

[0065] When collecting information, the collection unit may perform filtering based on the user's current living situation and areas of interest. For example, if the user has a new family, the collection unit may prioritize questions about family plans. For example, if the user has a specific hobby or interest, the collection unit may ask questions about plans related to the hobby or interest. For example, the collection unit may ask questions about appropriate plans based on the user's current living situation (e.g., moving or changing jobs). This allows the collection unit to collect appropriate information based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect information using an AI model for filtering the user's current living situation and areas of interest.

[0066] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit can prioritize collecting detailed information. For example, when the user is stressed, the collection unit can prioritize collecting basic information. For example, when the user is in a hurry, the collection unit can prioritize collecting the most important information. This allows the collection unit to collect more appropriate information by determining the priority of information to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can estimate emotions and determine the priority of information using an AI model for estimating user emotions.

[0067] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit can prioritize collecting information about plans related to that area. For example, the collection unit can collect information about appropriate plans based on places frequently visited by the user. For example, the collection unit can collect information about optimal plans based on the user's geographical location information. This allows the collection unit to collect highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for collecting highly relevant information by taking into account the user's geographical location information.

[0068] When collecting information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit can collect information about an appropriate plan based on information shared by the user on social media. For example, the collection unit can collect information about plans of interest from the user's social media activity. For example, the collection unit can analyze the user's social media activity history and collect information about the optimal plan. This allows the collection unit to collect related information based on the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect related information using an AI model for analyzing the user's social media activity.

[0069] The selection unit can estimate the user's emotions and adjust the plan selection criteria based on the estimated user emotions. For example, if the user is relaxed, the selection unit can propose a detailed plan. For example, if the user is stressed, the selection unit can propose a simple plan. For example, if the user is in a hurry, the selection unit can propose a plan that can be selected quickly. This allows the selection unit to select a more appropriate plan by adjusting the plan selection criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the selection unit can estimate the user's emotions and adjust the plan selection criteria using a generative AI model for estimating the user's emotions.

[0070] When selecting a plan, the selection unit can select the optimal plan by referring to the user's past plan usage history. The selection unit can, for example, select the optimal plan based on the characteristics of plans used by the user in the past. The selection unit can, for example, select a frequently used plan from the user's past plan usage history. The selection unit can, for example, analyze the user's past plan usage history and select the most suitable plan. This allows the selection unit to select the optimal plan based on the past plan usage history. Some or all of the above-mentioned processing in the selection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the selection unit can select the optimal plan using a generation AI model for analyzing the user's past plan usage history.

[0071] When selecting a plan, the selection unit can customize plan selection criteria based on the user's current living situation. For example, if the user has a new family structure, the selection unit can preferentially select a family plan. For example, if the user has a specific hobby or interest, the selection unit can select a plan related to that hobby or interest. For example, the selection unit can select an appropriate plan based on the user's current living situation (e.g., moving or changing jobs). This allows the selection unit to select an optimal plan depending on the user's living situation. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the selection unit can select a plan using a generation AI model for customizing plan selection criteria taking into account the user's current living situation.

[0072] The selection unit can estimate the user's emotions and determine the priority of plans to be selected based on the estimated user emotions. For example, when the user is relaxed, the selection unit can prioritize detailed plans. For example, when the user is stressed, the selection unit can prioritize simple plans. For example, when the user is in a hurry, the selection unit can prioritize plans that can be selected quickly. This allows the selection unit to select a more appropriate plan by prioritizing plans according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the selection unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the selection unit can estimate emotions and determine the priority of plans using a generative AI model for estimating the user's emotions.

[0073] When selecting a plan, the selection unit can select an optimal plan by taking into account the user's geographical location information. For example, if the user lives in a specific area, the selection unit can preferentially select plans related to that area. For example, the selection unit can select an appropriate plan based on places frequently visited by the user. For example, the selection unit can select an optimal plan based on the user's geographical location information. This allows the selection unit to select an optimal plan based on the user's geographical location information. Some or all of the above-described processing in the selection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the selection unit can select a plan using a generation AI model for selecting an optimal plan by taking into account the user's geographical location information.

[0074] When selecting a plan, the selection unit can analyze the user's social media activity and select an optimal plan. The selection unit can select an appropriate plan based on, for example, information shared by the user on social media. The selection unit can select a plan of interest based on the user's social media activity, for example. The selection unit can analyze, for example, the user's social media activity history and select an optimal plan. This allows the selection unit to select an optimal plan based on the user's social media activity. Some or all of the above-described processing in the selection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the selection unit can select an optimal plan using a generation AI model for analyzing the user's social media activity.

[0075] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make suggestions including detailed explanations. For example, if the user is stressed, the suggestion unit can make concise and easy-to-understand suggestions. For example, if the user is in a hurry, the suggestion unit can make quick and understandable suggestions. This allows the suggestion unit to make more appropriate suggestions by adjusting the way the suggestions are expressed according to the user'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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed using an AI model for estimating the user's emotions.

[0076] The proposal unit can adjust the level of detail of the proposal based on the importance of the plan when making a proposal. For example, the proposal unit can provide a detailed explanation for a plan with a high level of importance. For example, the proposal unit can provide a concise explanation for a plan with a low level of importance. The proposal unit can adjust the level of detail of the proposal based on the importance, for example. This allows the proposal unit to make a proposal with an appropriate level of detail based on the importance of the plan. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can adjust the level of detail of the proposal based on the importance using an AI model for evaluating the importance of the plan.

[0077] When making a proposal, the proposal unit can apply different proposal algorithms depending on the plan category. For example, for a call plan, the proposal unit can apply a proposal algorithm based on call time. For example, for a data plan, the proposal unit can apply a proposal algorithm based on data usage. For example, for a family plan, the proposal unit can apply a proposal algorithm based on the number of family members. This allows the proposal unit to apply an optimal proposal algorithm depending on the plan category. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a plan using an AI model for applying different proposal algorithms depending on the plan category.

[0078] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is stressed, the suggestion unit can provide concise suggestions. For example, if the user is in a hurry, the suggestion unit can provide quick, understandable suggestions. This allows the suggestion unit to adjust the length of the suggestion according to the user's emotion, thereby making more appropriate suggestions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can estimate the user's emotion and adjust the length of the suggestion using an AI model for estimating the user's emotion.

[0079] The proposal unit can determine the priority of proposals based on the submission time of the plans when making proposals. For example, the proposal unit can prioritize proposals that are due to be submitted soon. For example, the proposal unit can postpone proposals that are due to be submitted further away. For example, the proposal unit can adjust the priority of proposals according to the submission time. This allows the proposal unit to make proposals with appropriate priority according to the submission time of the plans. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can determine the priority of proposals based on the submission time using an AI model for evaluating the submission time of plans.

[0080] The proposal unit can adjust the order of proposals based on the relevance of the plans when making proposals. For example, the proposal unit can prioritize proposals for highly relevant plans. For example, the proposal unit can postpone proposals for less relevant plans. For example, the proposal unit can adjust the order of proposals based on the relevance. This allows the proposal unit to make proposals in an appropriate order based on the relevance of the plans. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can adjust the order of proposals based on the relevance using an AI model for evaluating the relevance of plans. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, selection unit, and 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 collection unit is realized by the control unit 46A of the smart device 14 and collects information such as call time, data usage, and the number of family members. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to select an optimal plan. The proposal unit is realized, for example, by the control unit 46A of the smart device 14 and proposes the selected plan to the customer. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, selection unit, and proposal unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects information such as call time, data usage, and number of family members. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and selects an optimal plan. The proposal unit is realized, for example, by the control unit 46A of the smart glasses 214 and proposes the selected plan to the customer. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, selection unit, and 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 collection unit is realized by the control unit 46A of the headset type terminal 314 and collects information such as call time, data usage amount, and number of family members. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to select an optimal plan. The proposal unit is realized, for example, by the control unit 46A of the headset type terminal 314 and proposes the selected plan to the customer. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, selection unit, and 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 collection unit is realized by the control unit 46A of the robot 414 and collects information such as call time, data usage, and number of family members. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to select an optimal plan. The proposal unit is realized, for example, by the control unit 46A of the robot 414 and proposes the selected plan to the customer.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The collection unit can analyze the user's past purchase history and collect information to propose an optimal plan. For example, it can collect information about devices and accessories purchased by the user in the past and propose an appropriate plan based on that information. It can also collect usage information about services and applications used by the user in the past and propose an optimal plan based on that information. It can also collect a history of campaigns and promotions in which the user has participated in the past and propose benefits and discounts based on that information. This allows the collection unit to propose a more personalized plan based on the user's past purchase history.

[0083] The selection unit can select a plan taking into consideration the user's current health condition. For example, if the user uses a health management app, the data can be collected and a plan can be proposed that matches the user's health condition. Also, if the user uses a fitness tracker, the data can be used to select a plan that matches the amount of exercise and activity level. Furthermore, a plan suitable for the user's health condition can be proposed based on the results of a diagnosis the user received at a medical institution. This allows the selection unit to select an optimal plan that takes into consideration the user's health condition.

[0084] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can select the timing to make detailed suggestions. If the user is stressed, the suggestion unit can select the timing to make concise suggestions. If the user is in a hurry, the suggestion unit can select the timing to make suggestions that can be quickly understood. In this way, the suggestion unit can make more appropriate suggestions by adjusting the timing of suggestions according to the user's emotions.

[0085] The collection unit can analyze the user's social media activity and collect related information. For example, information about suitable plans can be collected based on information shared by the user on social media. Information about plans of interest can also be collected from the user's social media activity. Furthermore, information about the optimal plan can be collected by analyzing the user's social media activity history. This allows the collection unit to collect related information based on the user's social media activity.

[0086] The selection unit can estimate the user's emotions and adjust the criteria for plan selection based on the estimated user's emotions. For example, if the user is relaxed, a detailed plan can be proposed. If the user is stressed, a simple plan can be proposed. Furthermore, if the user is in a hurry, a plan that can be selected quickly can be proposed. In this way, the selection unit can select a more appropriate plan by adjusting the criteria for plan selection according to the user's emotions.

[0087] The suggestion unit can customize the content of the suggestion based on the user's current living situation. For example, if the user has a new family structure, it can suggest a family plan. Also, if the user has a specific hobby or interest, it can suggest a plan related to that. Furthermore, it can suggest an appropriate plan based on the user's current living situation (e.g., moving or changing jobs). This allows the suggestion unit to suggest the optimal plan according to the user's living situation.

[0088] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is relaxed, detailed information can be collected with priority. If the user is stressed, basic information can be collected with priority. If the user is in a hurry, the most important information can be collected with priority. In this way, the collection unit can collect more appropriate information by determining the priority of information to be collected according to the user's emotions.

[0089] The selection unit can select an optimal plan taking into consideration the user's geographical location information. For example, if the user lives in a specific area, plans related to that area can be preferentially selected. Also, an appropriate plan can be selected based on places frequently visited by the user. Furthermore, the optimal plan can be selected based on the user's geographical location information. This allows the selection unit to select an optimal plan based on the user's geographical location information.

[0090] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide a suggestion that includes a detailed explanation. If the user is stressed, the suggestion unit can provide a concise and easy-to-understand suggestion. If the user is in a hurry, the suggestion unit can provide a suggestion that can be quickly understood. This allows the suggestion unit to make more appropriate suggestions by adjusting the way in which suggestions are expressed depending on the user's emotions.

[0091] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the plan. For example, a detailed explanation can be provided for a plan with a high level of importance. A concise explanation can be provided for a plan with a low level of importance. Furthermore, the proposal unit can adjust the level of detail of the proposal depending on the importance. This allows the proposal unit to make a proposal with an appropriate level of detail depending on the importance of the plan.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection unit collects information about the customer's frequency of use or family structure. For example, it can collect information such as call duration, data usage, and number of family members. The collection unit collects call duration as monthly call duration and number of calls, and data usage as monthly data usage and data type. It can also collect family member size as the number of family members living together and the number of dependents. Step 2: The selection unit analyzes the information collected by the collection unit and selects a plan. For example, it can select the optimal plan from over 200,000 plans. The selection unit uses statistical analysis and machine learning algorithms to select plans, and can choose unlimited calling plans for customers with long call times and large-capacity data plans for customers with large data usage. Step 3: The proposal unit proposes the plan selected by the selection unit. For example, it can propose specific plans such as unlimited calling plans or large-capacity data plans. The proposal unit can propose plans for sharing data among the whole family or plans that reduce wasteful charges.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects information about your frequency of use or family structure; a selection unit that analyzes the information collected by the collection unit and selects a plan; a proposal unit that proposes the plan selected by the selection unit. A system characterized by:

2. The collecting unit Collect information about call time, data usage, and number of family members 2. The system of claim 1.

3. The selection unit Choose a plan from over 200,000 options 2. The system of claim 1.

4. The proposal unit Offer unlimited calling or high-capacity data plans 2. The system of claim 1.

5. The proposal unit Propose a data sharing plan for the whole family 2. The system of claim 1.

6. The proposal unit Propose a plan to reduce wasteful fees 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze users' past plan change history and select the optimal information collection method 2. The system of claim 1.

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