system

Abstract

The system according to the embodiment aims to identify appropriate services based on customer information and quickly display them. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a display unit, and a linking unit. The collection unit collects customer information. The analysis unit analyzes the information collected by the collection unit and identifies services appropriate for the customer. The display unit displays the services identified by the analysis unit. The linking unit links the collection unit and the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that the process of identifying and displaying appropriate services based on customer information is inefficient and time-consuming.

[0005] The system according to the embodiment aims to identify appropriate services based on customer information and quickly display them. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a display unit, and a linking unit. The collection unit collects customer information. The analysis unit analyzes the information collected by the collection unit and identifies services appropriate for the customer. The display unit displays the services identified by the analysis unit. The linking unit links the collection unit and the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify appropriate services based on customer information and quickly display them. [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 service proposal system according to an embodiment of the present invention links Genie with customer information to recommend optimal services to each customer. This service proposal system analyzes customer information collected by Genie, identifies optimal services for each customer, and displays the identified services in a clear, easily understandable manner. This system enables rapid and accurate service proposals, thereby reducing time. For example, customer information collected by Genie is analyzed. In this process, data such as purchase history, browsing history, and survey results is analyzed to identify optimal services for each customer. For example, for a customer who previously purchased a specific product, services related to that product can be proposed. The identified services are then displayed in a clear, easily understandable manner. For example, the proposed services are displayed using visually easy-to-understand icons and graphs, allowing customers to intuitively understand the proposed services. Furthermore, data linking between Genie and customer information is achieved by using APIs and cloud services. This clarifies technical details and increases feasibility. This system enables rapid and accurate service proposals. By proposing optimal services to each customer, customer satisfaction is improved and business efficiency is enhanced. Furthermore, the proposed services are displayed in a clear, easily understandable manner, thereby reducing time. For example, while it used to take a long time to compare and consider multiple services, the present invention allows users to quickly understand and select the proposed services. This allows the service proposal system to efficiently collect, analyze, and display customer information, and quickly propose the most suitable service.

[0029] The service proposal system according to the embodiment includes a collection unit, an analysis unit, a display unit, and a linking unit. The collection unit collects customer information. The customer information includes, but is not limited to, purchase history, browsing history, and survey results, for example. The collection unit collects, for example, purchase history. The purchase history includes purchase date and time, purchased items, and purchase amounts. The collection unit collects, for example, browsing history. The browsing history includes viewed pages, browsing time, and browsing frequency. The collection unit collects, for example, survey results. The survey results include survey question content, answer format, and respondent attributes. The analysis unit analyzes the information collected by the collection unit to identify services optimal for the customer. For example, the analysis unit analyzes purchase history, and proposes services related to a specific product to a customer who has purchased the product in the past. For example, the analysis unit analyzes browsing history, and proposes services related to a specific page to a customer who frequently views the specific page. For example, the analysis unit analyzes survey results, and proposes services related to a specific answer to a specific question to a customer. The display unit displays the services identified by the analysis unit. The display unit displays the identified services, for example, using visually easy-to-understand icons or graphs. The display unit displays the identified services, for example, using icons. The display unit displays the identified services, for example, using graphs. The linking unit links the collection unit and the analysis unit. The linking unit links data, for example, using an API or a cloud service. The linking unit links data, for example, using an API. The linking unit links data, for example, using a cloud service. As a result, the service proposal system according to the embodiment can efficiently collect, analyze, and display customer information, and quickly propose optimal services.

[0030] The collection unit can collect data on purchase history, browsing history, and survey results. The collection unit, for example, collects purchase history. The purchase history includes purchase date and time, purchased items, purchase amounts, etc. The collection unit, for example, collects browsing history. The browsing history includes pages viewed, browsing time, browsing frequency, etc. The collection unit, for example, collects survey results. The survey results include the content of the survey questions, response format, respondent attributes, etc. By collecting a variety of data, it becomes possible to propose services with greater accuracy.

[0031] The analysis unit analyzes the data collected by the collection unit and can identify the most suitable service for a customer. For example, the analysis unit analyzes purchase history, and for customers who have purchased a specific product in the past, suggests services related to that product. For example, the analysis unit analyzes browsing history, and for customers who frequently visit a specific page, suggests services related to that page. For example, the analysis unit analyzes survey results, and for customers who provide a specific answer to a specific question, suggests services related to that answer. In this way, by analyzing the collected data, the most suitable service can be identified.

[0032] The display unit can display the identified services using visually easy-to-understand icons or graphs. The display unit displays the identified services using, for example, icons. The display unit displays the identified services using, for example, graphs. The display unit displays the identified services in a visually easy-to-understand manner, for example, by using colors or a design that is ingenious. This allows the user to intuitively understand the proposed services through a visually easy-to-understand display.

[0033] The linking unit can link specific systems with customer information. The linking unit, for example, uses an API to link data. The linking unit, for example, uses a cloud service to link data. The linking unit, for example, provides protocols and interfaces for linking specific systems with customer information. This clarifies the technical details of linking Genie with customer information, increasing feasibility.

[0034] The collection unit can analyze the user's past behavioral history and select the optimal data collection method. The collection unit, for example, prioritizes collection of related data based on services that the user has frequently used in the past. The collection unit, for example, analyzes the user's past behavioral patterns and determines the optimal timing for data collection. The collection unit, for example, adjusts the data collection method by referring to feedback provided by the user in the past. In this way, the optimal data collection method can be selected by analyzing the past behavioral history.

[0035] The collection unit can filter data based on the user's current interests and concerns when collecting data. For example, the collection unit preferentially collects data related to topics in which the user is currently interested. For example, the collection unit filters related data based on keywords recently searched by the user. For example, the collection unit collects related data based on information about communities and forums in which the user participates. In this way, highly relevant data can be collected by filtering data based on the user's interests and concerns.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting event information related to the user's current location. For example, the collection unit collects information on nearby stores and services based on the user's location information. For example, if the user is traveling, the collection unit prioritizes collecting tourist information for the travel destination. In this way, highly relevant data can be collected by taking into account the geographical location information.

[0037] When collecting data, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects related data based on content shared by the user on social media. For example, the collection unit collects related data by analyzing posts from accounts the user follows. For example, the collection unit collects related data based on groups and events the user participates in. This makes it possible to collect highly relevant data by analyzing social media activity.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis of data with high importance. For example, the analysis unit performs a concise analysis of data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a purchasing pattern analysis algorithm to purchase history data. For example, the analysis unit applies an interest analysis algorithm to browsing history data. For example, the analysis unit applies a text mining algorithm to survey result data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the data category.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes the analysis of the most recent data. For example, the analysis unit determines the analysis priority for past data according to its importance. The analysis unit, for example, adjusts the analysis schedule based on the time when the data was collected. This enables efficient analysis by determining the analysis priority based on the time when the data was collected.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit postpones analysis of less relevant data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0042] The display unit can adjust the level of detail of the display based on the importance of the service when displaying. For example, the display unit displays detailed information for a service with a high level of importance. For example, the display unit displays concise information for a service with a low level of importance. For example, the display unit determines the display priority according to the importance of the service. This allows for efficient display by adjusting the level of detail of the display based on the importance of the service.

[0043] The display unit can apply different display algorithms depending on the service category when displaying. For example, the display unit applies a visually appealing display algorithm to entertainment services. For example, the display unit applies a display algorithm that summarizes information concisely to business services. For example, the display unit applies a display algorithm according to the user's health condition to health-related services. This improves the accuracy of the display by applying an appropriate display algorithm depending on the service category.

[0044] The display unit can determine the display priority based on the time when the service was proposed when displaying. The display unit, for example, prioritizes display of the latest service proposal. For example, the display unit determines the display priority for past service proposals according to their importance. The display unit, for example, adjusts the display schedule based on the time when the service was proposed. This enables efficient display by determining the display priority based on the time when the service was proposed.

[0045] The display unit can adjust the display order based on the relevance of services when displaying them. For example, the display unit prioritizes displaying highly relevant services. For example, the display unit postpones displaying less relevant services. For example, the display unit adjusts the display order based on the relevance of services. This allows for efficient display by adjusting the display order based on the relevance of services.

[0046] When data is linked, the linking unit can select the optimal linking method by referring to past linking history. The linking unit selects the optimal linking method, for example, based on linking methods that have been successful in the past. The linking unit, for example, avoids linking methods that have caused problems based on past linking history. The linking unit, for example, analyzes past linking history and selects the most efficient linking method. In this way, the optimal linking method can be selected by referring to past linking history.

[0047] When linking data, the linking unit can apply different linking methods depending on the data category. For example, the linking unit applies a purchase pattern analysis algorithm to purchase history data. For example, the linking unit applies an interest analysis algorithm to browsing history data. For example, the linking unit applies a text mining algorithm to survey result data. This improves the accuracy of linking by applying an appropriate linking method depending on the data category.

[0048] When linking data, the linking unit can adjust the order of linking based on the time of data collection. For example, the linking unit prioritizes linking the most recent data. For example, for past data, the linking unit determines the order of linking based on the importance. For example, the linking unit adjusts the linking schedule based on the time of data collection. This allows for efficient linking by adjusting the order of linking based on the time of data collection.

[0049] When linking data, the linking unit can adjust the linking method based on the relevance of the data. For example, the linking unit prioritizes linking of highly related data. For example, the linking unit postpones linking of less related data. For example, the linking unit adjusts the linking method based on the relevance of the data. This enables efficient linking by adjusting the linking method based on the relevance of the data.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The service proposal system may further include a prediction unit. The prediction unit can predict future user behavior based on the data collected by the collection unit. For example, the prediction unit analyzes past purchase history and browsing history to predict products that the user is likely to purchase next. The prediction unit predicts services that the user may be interested in, for example, based on seasons and trends. The prediction unit predicts related services based on the user's life events (marriage, moving, childbirth, etc.). This makes it possible to anticipate and propose future needs to the user, which is expected to improve customer satisfaction.

[0052] The collection unit can collect device information about the user and use it to propose optimal services. For example, it can collect model information about the smartphone or tablet used by the user and propose services optimized for that device. For example, it can collect information about the usage status of the user's device (remaining battery level, storage capacity, etc.) and propose services at an appropriate time. For example, it can collect location information about the user's device and propose services related to the current location. This makes it possible to utilize the user's device information to propose more personalized services.

[0053] The service proposal system may further include a feedback unit. The feedback unit may collect feedback from users and utilize it to improve the accuracy of service proposals. For example, the feedback unit may provide a function for users to evaluate proposed services. For example, the feedback unit may collect users' impressions and opinions after using the proposed services. For example, if a user is dissatisfied with a proposed service, the feedback unit may collect the reason for the dissatisfaction. This makes it possible to utilize feedback from users to improve the accuracy of service proposals.

[0054] The collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects related data based on content shared by the user on social media. For example, the collection unit collects related data by analyzing the content posted by accounts the user follows. For example, the collection unit collects related data based on groups and events the user participates in. In this way, highly relevant data can be collected by analyzing social media activity.

[0055] The service proposal system may further include a learning unit. The learning unit may continuously improve the service proposal algorithm based on collected data and user feedback. For example, the learning unit may analyze past proposal results and user responses to improve the accuracy of the proposal algorithm. The learning unit may, for example, add new data sources to increase the diversity of proposals. The learning unit may, for example, apply different proposal strategies to different user groups to find the optimal proposal method. In this way, the learning unit may be utilized to improve the accuracy and diversity of service proposals.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects customer information. This customer information includes purchase history, browsing history, survey results, etc. For example, purchase history includes the date and time of purchase, purchased items, and purchase amount, browsing history includes pages viewed, browsing time, and browsing frequency, and survey results include the content of questions, response format, and respondent attributes. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies the most suitable services for the customer. For example, it may analyze purchase history to suggest related services, analyze browsing history to suggest services related to frequently visited pages, or analyze survey results to suggest services related to specific answers. Step 3: The display unit displays the services identified by the analysis unit, for example, by using visually easy-to-understand icons or graphs. Step 4: The linking unit links the collection unit and analysis unit. For example, data is linked using APIs or cloud services.

[0058] (Example 2) A service proposal system according to an embodiment of the present invention links Genie with customer information to recommend optimal services to each customer. This service proposal system analyzes customer information collected by Genie, identifies optimal services for each customer, and displays the identified services in a clear, easily understandable manner. This system enables rapid and accurate service proposals, thereby reducing time. For example, customer information collected by Genie is analyzed. In this process, data such as purchase history, browsing history, and survey results is analyzed to identify optimal services for each customer. For example, for a customer who previously purchased a specific product, services related to that product can be proposed. The identified services are then displayed in a clear, easily understandable manner. For example, the proposed services are displayed using visually easy-to-understand icons and graphs, allowing customers to intuitively understand the proposed services. Furthermore, data linking between Genie and customer information is achieved by using APIs and cloud services. This clarifies technical details and increases feasibility. This system enables rapid and accurate service proposals. By proposing optimal services to each customer, customer satisfaction is improved and business efficiency is enhanced. Furthermore, the proposed services are displayed in a clear, easily understandable manner, thereby reducing time. For example, while it used to take a long time to compare and consider multiple services, the present invention allows users to quickly understand and select the proposed services. This allows the service proposal system to efficiently collect, analyze, and display customer information, and quickly propose the most suitable service.

[0059] The service proposal system according to the embodiment includes a collection unit, an analysis unit, a display unit, and a linking unit. The collection unit collects customer information. The customer information includes, but is not limited to, purchase history, browsing history, and survey results, for example. The collection unit collects, for example, purchase history. The purchase history includes purchase date and time, purchased items, and purchase amounts. The collection unit collects, for example, browsing history. The browsing history includes viewed pages, browsing time, and browsing frequency. The collection unit collects, for example, survey results. The survey results include survey question content, answer format, and respondent attributes. The analysis unit analyzes the information collected by the collection unit to identify services optimal for the customer. For example, the analysis unit analyzes purchase history, and proposes services related to a specific product to a customer who has purchased the product in the past. For example, the analysis unit analyzes browsing history, and proposes services related to a specific page to a customer who frequently views the specific page. For example, the analysis unit analyzes survey results, and proposes services related to a specific answer to a specific question to a customer. The display unit displays the services identified by the analysis unit. The display unit displays the identified services, for example, using visually easy-to-understand icons or graphs. The display unit displays the identified services, for example, using icons. The display unit displays the identified services, for example, using graphs. The linking unit links the collection unit and the analysis unit. The linking unit links data, for example, using an API or a cloud service. The linking unit links data, for example, using an API. The linking unit links data, for example, using a cloud service. As a result, the service proposal system according to the embodiment can efficiently collect, analyze, and display customer information, and quickly propose optimal services.

[0060] The collection unit can collect data on purchase history, browsing history, and survey results. The collection unit, for example, collects purchase history. The purchase history includes purchase date and time, purchased items, purchase amounts, etc. The collection unit, for example, collects browsing history. The browsing history includes pages viewed, browsing time, browsing frequency, etc. The collection unit, for example, collects survey results. The survey results include the content of the survey questions, response format, respondent attributes, etc. By collecting a variety of data, it becomes possible to propose services with greater accuracy.

[0061] The analysis unit analyzes the data collected by the collection unit and can identify the most suitable service for a customer. For example, the analysis unit analyzes purchase history, and for customers who have purchased a specific product in the past, suggests services related to that product. For example, the analysis unit analyzes browsing history, and for customers who frequently visit a specific page, suggests services related to that page. For example, the analysis unit analyzes survey results, and for customers who provide a specific answer to a specific question, suggests services related to that answer. In this way, by analyzing the collected data, the most suitable service can be identified.

[0062] The display unit can display the identified services using visually easy-to-understand icons or graphs. The display unit displays the identified services using, for example, icons. The display unit displays the identified services using, for example, graphs. The display unit displays the identified services in a visually easy-to-understand manner, for example, by using colors or a design that is ingenious. This allows the user to intuitively understand the proposed services through a visually easy-to-understand display.

[0063] The linking unit can link specific systems with customer information. The linking unit, for example, uses an API to link data. The linking unit, for example, uses a cloud service to link data. The linking unit, for example, provides protocols and interfaces for linking specific systems with customer information. This clarifies the technical details of linking Genie with customer information, increasing feasibility.

[0064] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. For example, if the user is relaxed, the collection unit increases the frequency of data collection to collect more detailed information. For example, if the user is in a hurry, the collection unit temporarily stops data collection and resumes it later. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The collection unit can analyze the user's past behavioral history and select the optimal data collection method. The collection unit, for example, prioritizes collection of related data based on services that the user has frequently used in the past. The collection unit, for example, analyzes the user's past behavioral patterns and determines the optimal timing for data collection. The collection unit, for example, adjusts the data collection method by referring to feedback provided by the user in the past. In this way, the optimal data collection method can be selected by analyzing the past behavioral history.

[0066] The collection unit can filter data based on the user's current interests and concerns when collecting data. For example, the collection unit preferentially collects data related to topics in which the user is currently interested. For example, the collection unit filters related data based on keywords recently searched by the user. For example, the collection unit collects related data based on information about communities and forums in which the user participates. In this way, highly relevant data can be collected by filtering data based on the user's interests and concerns.

[0067] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting relaxing content. For example, if the user is excited, the collection unit prioritizes collecting entertainment-related data. For example, if the user is tired, the collection unit prioritizes collecting refreshing information. This allows more appropriate data to be collected by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting event information related to the user's current location. For example, the collection unit collects information on nearby stores and services based on the user's location information. For example, if the user is traveling, the collection unit prioritizes collecting tourist information for the travel destination. In this way, highly relevant data can be collected by taking into account the geographical location information.

[0069] When collecting data, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects related data based on content shared by the user on social media. For example, the collection unit collects related data by analyzing posts from accounts the user follows. For example, the collection unit collects related data based on groups and events the user participates in. This makes it possible to collect highly relevant data by analyzing social media activity.

[0070] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that focus on the main points. For example, if the user is excited, the analysis unit provides analysis results that include visually stimulating effects. This allows for adjusting the way the analysis is presented according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis of data with high importance. For example, the analysis unit performs a concise analysis of data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a purchasing pattern analysis algorithm to purchase history data. For example, the analysis unit applies an interest analysis algorithm to browsing history data. For example, the analysis unit applies a text mining algorithm to survey result data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the data category.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides an analysis result with a visually stimulating effect. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes the analysis of the most recent data. For example, the analysis unit determines the analysis priority for past data according to its importance. The analysis unit, for example, adjusts the analysis schedule based on the time when the data was collected. This enables efficient analysis by determining the analysis priority based on the time when the data was collected.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit postpones analysis of less relevant data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0076] The display unit can estimate the user's emotions and adjust the display expression method based on the estimated user emotions. For example, when the user is nervous, the display unit provides a simple, highly visible display method. For example, when the user is relaxed, the display unit provides a display method including detailed information. For example, when the user is in a hurry, the display unit provides a display method that focuses on the main points. This allows for a more appropriate display by adjusting the display expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The display unit can adjust the level of detail of the display based on the importance of the service when displaying. For example, the display unit displays detailed information for a service with a high level of importance. For example, the display unit displays concise information for a service with a low level of importance. For example, the display unit determines the display priority according to the importance of the service. This allows for efficient display by adjusting the level of detail of the display based on the importance of the service.

[0078] The display unit can apply different display algorithms depending on the service category when displaying. For example, the display unit applies a visually appealing display algorithm to entertainment services. For example, the display unit applies a display algorithm that summarizes information concisely to business services. For example, the display unit applies a display algorithm according to the user's health condition to health-related services. This improves the accuracy of the display by applying an appropriate display algorithm depending on the service category.

[0079] The display unit can estimate the user's emotions and adjust the length of the display based on the estimated user emotions. For example, if the user is in a hurry, the display unit provides a short, to-the-point display. For example, if the user is relaxed, the display unit provides a longer display including detailed information. For example, if the user is excited, the display unit provides a display with a visually stimulating effect. This allows for a more appropriate display by adjusting the length of the display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The display unit can determine the display priority based on the time when the service was proposed when displaying. The display unit, for example, prioritizes display of the latest service proposal. For example, the display unit determines the display priority for past service proposals according to their importance. The display unit, for example, adjusts the display schedule based on the time when the service was proposed. This enables efficient display by determining the display priority based on the time when the service was proposed.

[0081] The display unit can adjust the display order based on the relevance of services when displaying them. For example, the display unit prioritizes displaying highly relevant services. For example, the display unit postpones displaying less relevant services. For example, the display unit adjusts the display order based on the relevance of services. This allows for efficient display by adjusting the display order based on the relevance of services.

[0082] The linking unit can estimate the user's emotions and adjust the data linking method based on the estimated user emotions. For example, if the user is feeling stressed, the linking unit reduces the frequency of data linking to reduce the user's burden. For example, if the user is relaxed, the linking unit increases the frequency of data linking and links more detailed information. For example, if the user is in a hurry, the linking unit temporarily stops data linking and resumes it later. In this way, the data linking method is adjusted according to the user's emotions, thereby reducing the user's burden. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] When data is linked, the linking unit can select the optimal linking method by referring to past linking history. The linking unit selects the optimal linking method, for example, based on linking methods that have been successful in the past. The linking unit, for example, avoids linking methods that have caused problems based on past linking history. The linking unit, for example, analyzes past linking history and selects the most efficient linking method. In this way, the optimal linking method can be selected by referring to past linking history.

[0084] When linking data, the linking unit can apply different linking methods depending on the data category. For example, the linking unit applies a purchase pattern analysis algorithm to purchase history data. For example, the linking unit applies an interest analysis algorithm to browsing history data. For example, the linking unit applies a text mining algorithm to survey result data. This improves the accuracy of linking by applying an appropriate linking method depending on the data category.

[0085] The linking unit can estimate the user's emotions and determine the priority of linking based on the estimated user's emotions. For example, if the user is feeling stressed, the linking unit prioritizes linking of data with high importance. For example, if the user is relaxed, the linking unit prioritizes linking of detailed data. For example, if the user is in a hurry, the linking unit prioritizes linking of the minimum necessary data. This enables efficient linking by determining the priority of linking according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] When linking data, the linking unit can adjust the order of linking based on the time of data collection. For example, the linking unit prioritizes linking the most recent data. For example, for past data, the linking unit determines the order of linking based on the importance. For example, the linking unit adjusts the linking schedule based on the time of data collection. This allows for efficient linking by adjusting the order of linking based on the time of data collection.

[0087] When linking data, the linking unit can adjust the linking method based on the relevance of the data. For example, the linking unit prioritizes linking of highly related data. For example, the linking unit postpones linking of less related data. For example, the linking unit adjusts the linking method based on the relevance of the data. This enables efficient linking by adjusting the linking method based on the relevance of the data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, display unit, and linking 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 collects customer information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected customer information to identify the optimal service. The display unit visually displays the identified service using, for example, the display 40A and speaker 40B of the smart device 14. The linking unit links data, for example, via the communication I / F 26 of the data processing device 12 and the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, display unit, and linking 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 collects customer information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected customer information to identify the optimal service. The display unit visually displays the identified service using, for example, the display or speaker 240 of the smart glasses 214. The linking unit links data, for example, via the communication I / F 26 of the data processing device 12 and the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, display unit, and linking unit described above 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 collects customer information using the camera 42 and microphone 238 of the headset type terminal 314, and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected customer information to identify the optimal service. The display unit visually displays the identified service using, for example, the display 343 or speaker 240 of the headset type terminal 314. The linking unit links data, for example, via the communication I / F 26 of the data processing device 12 and the communication I / F 44 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, display unit, and linking 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 collects customer information using the camera 42 and microphone 238 of the robot 414, and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected customer information to identify the optimal service. The display unit visually displays the identified service using, for example, the display or speaker 240 of the robot 414. The linking unit links data, for example, via the communication I / F 26 of the data processing device 12 and the communication I / F 44 of the robot 414.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The service proposal system may further include a prediction unit. The prediction unit can predict future user behavior based on the data collected by the collection unit. For example, the prediction unit analyzes past purchase history and browsing history to predict products that the user is likely to purchase next. The prediction unit predicts services that the user may be interested in, for example, based on seasons and trends. The prediction unit predicts related services based on the user's life events (marriage, moving, childbirth, etc.). This makes it possible to anticipate and propose future needs to the user, which is expected to improve customer satisfaction.

[0090] The collection unit can collect device information about the user and use it to propose optimal services. For example, it can collect model information about the smartphone or tablet used by the user and propose services optimized for that device. For example, it can collect information about the usage status of the user's device (remaining battery level, storage capacity, etc.) and propose services at an appropriate time. For example, it can collect location information about the user's device and propose services related to the current location. This makes it possible to utilize the user's device information to propose more personalized services.

[0091] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit provides a concise and to-the-point analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides an analysis result with a visually stimulating effect. In this way, by adjusting the presentation method of the analysis results according to the user's emotions, more appropriate analysis results can be provided.

[0092] The display unit can estimate the user's emotions and adjust the display design based on the estimated user's emotions. For example, if the user is nervous, the display unit provides a simple, highly visible design. For example, if the user is relaxed, the display unit provides a design including detailed information. For example, if the user is in a hurry, the display unit provides a design that focuses on the main points. This allows for a more appropriate display by adjusting the display design according to the user's emotions.

[0093] The linking unit can estimate the user's emotions and adjust the frequency of data linking based on the estimated user emotions. For example, if the user is feeling stressed, the linking unit reduces the frequency of data linking to reduce the burden on the user. For example, if the user is relaxed, the linking unit increases the frequency of data linking and links more detailed information. For example, if the user is in a hurry, the linking unit temporarily stops data linking and resumes it later. In this way, the frequency of data linking is adjusted according to the user's emotions, thereby reducing the burden on the user.

[0094] The service proposal system may further include a feedback unit. The feedback unit may collect feedback from users and utilize it to improve the accuracy of service proposals. For example, the feedback unit may provide a function for users to evaluate proposed services. For example, the feedback unit may collect users' impressions and opinions after using the proposed services. For example, if a user is dissatisfied with a proposed service, the feedback unit may collect the reason for the dissatisfaction. This makes it possible to utilize feedback from users to improve the accuracy of service proposals.

[0095] The collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects related data based on content shared by the user on social media. For example, the collection unit collects related data by analyzing the content posted by accounts the user follows. For example, the collection unit collects related data based on groups and events the user participates in. In this way, highly relevant data can be collected by analyzing social media activity.

[0096] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit prioritizes analyzing data of high importance. For example, if the user is relaxed, the analysis unit prioritizes analyzing detailed data. For example, if the user is in a hurry, the analysis unit prioritizes analyzing the minimum necessary data. In this way, efficient analysis is possible by determining the priority of analysis according to the user's emotions.

[0097] The display unit can estimate the user's emotions and adjust the length of the display based on the estimated user emotions. For example, if the user is in a hurry, the display unit provides a short, to-the-point display. For example, if the user is relaxed, the display unit provides a longer display including detailed information. For example, if the user is excited, the display unit provides a display with a visually stimulating effect. This allows for a more appropriate display by adjusting the length of the display according to the user's emotions.

[0098] The service proposal system may further include a learning unit. The learning unit may continuously improve the service proposal algorithm based on collected data and user feedback. For example, the learning unit may analyze past proposal results and user responses to improve the accuracy of the proposal algorithm. The learning unit may, for example, add new data sources to increase the diversity of proposals. The learning unit may, for example, apply different proposal strategies to different user groups to find the optimal proposal method. In this way, the learning unit may be utilized to improve the accuracy and diversity of service proposals.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects customer information. This customer information includes purchase history, browsing history, survey results, etc. For example, purchase history includes the date and time of purchase, purchased items, and purchase amount, browsing history includes pages viewed, browsing time, and browsing frequency, and survey results include the content of questions, response format, and respondent attributes. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies the most suitable services for the customer. For example, it may analyze purchase history to suggest related services, analyze browsing history to suggest services related to frequently visited pages, or analyze survey results to suggest services related to specific answers. Step 3: The display unit displays the services identified by the analysis unit, for example, by using visually easy-to-understand icons or graphs. Step 4: The linking unit links the collection unit and analysis unit. For example, data is linked using APIs or cloud services.

[0101] 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.

[0102] 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.

[0103] 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.

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

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The 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.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 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.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the 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.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 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.

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

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 customer information; an analysis unit that analyzes the information collected by the collection unit and identifies services appropriate for the customer; a display unit that displays the service identified by the analysis unit; a linking unit that links the collecting unit and the analyzing unit A system characterized by:

2. The collecting unit Collect data on purchase history, browsing history, and survey results 2. The system of claim 1.

3. The analysis unit Analyzing the data collected by the collection unit and identifying the most suitable service for the customer 2. The system of claim 1.

4. The display unit Display identified services using visually appealing icons and graphs 2. The system of claim 1.

5. The linking unit is Linking customer information with specific systems 2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze users' past behavioral history and select the optimal data collection method 2. The system of claim 1.

8. The collecting unit At the time of data collection, filtering based on the user's current interests 2. The system of claim 1.

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