System

Abstract

An object of the system according to the embodiment is to find an appropriate solution by effectively connecting and analyzing a problem of each division and data of a core system.SOLUTION: A system includes a prompt generation part, a data acquisition part, a data analysis part, and a determination part. The prompt generation unit generates a prompt in advance with respect to a sense of challenge and a core system configuration in each division. The data acquisition unit accesses a database held in each system and acquires an outline or a feature of data. The data analysis unit analyzes the outline and features of the data acquired by the data acquisition unit. The determination unit determines whether or not to meet the task prompted in advance based on the data analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to effectively link and analyze the issues of each business division with data from core systems and find appropriate solutions.

[0005] The system according to the embodiment aims to effectively link and analyze the issues of each business division with data from the core system, and find appropriate solutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a prompt generation unit, a data acquisition unit, a data analysis unit, and a judgment unit. The prompt generation unit generates prompts in advance regarding the issues each business division faces and the configuration of its core system. The data acquisition unit accesses the databases held by each system and acquires the outline and characteristics of the data. The data analysis unit analyzes the outline and characteristics of the data acquired by the data acquisition unit. The judgment unit judges whether the issues prompted in advance are met based on the data analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively link and analyze the issues of each business division with data from the core system, and find appropriate solutions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​service according to the embodiment of the present invention is a system that prompts each business division regarding the issues they face and the configuration of their core systems in advance, accesses the databases held by each system, acquires the outline and characteristics of the data, and determines whether the data meets the issues prompted in advance. This enables the AI ​​service to analyze data based on the issues each business division faces and provide appropriate solutions.

[0029] An AI service according to an embodiment includes a prompt generation unit, a data acquisition unit, a data analysis unit, and a judgment unit. The prompt generation unit generates prompts in advance regarding the perceived challenges faced by each business division and the configuration of its core system. For example, the prompt generates a prompt that proposes measures to increase sales using sales data and customer data from the sales department. The data acquisition unit accesses databases held by each system and acquires the outline and characteristics of the data. For example, it acquires information such as sales amounts and product categories from the sales database and customer ages and purchase histories from the customer database. The data analysis unit analyzes the outline and characteristics of the data acquired by the data acquisition unit. For example, it combines the sales data and customer data to analyze whether sales improvement measures for a specific customer segment are effective. The judgment unit determines whether the pre-prompted issues are met based on the data analyzed by the data analysis unit. For example, the generation AI determines that sales improvement measures for a specific customer segment are effective. This allows the AI ​​service according to an embodiment to analyze data based on the perceived challenges faced by each business division and provide appropriate solutions.

[0030] The prompt generation unit can analyze the sense of issues in comparison with past success cases and failure cases, and generate specific prompts. For example, the prompt generation unit analyzes the sense of issues of each business division in comparison with past success cases, and generates specific prompts. For example, a prompt is created based on past success cases of the sales department. The prompt generation unit also analyzes the sense of issues of each business division in comparison with past failure cases, and generates specific prompts. For example, a prompt is created based on past failure cases of the marketing department. The prompt generation unit also analyzes the sense of issues of each business division in comparison with past success cases and failure cases, and generates specific prompts. For example, a prompt is created based on past cases of the manufacturing department. In this way, by referring to past cases, more specific solutions can be provided.

[0031] The prompt generation unit can analyze the sense of issues by comparing them with industry-wide trends and the trends of competitors, and generate strategic prompts. For example, the prompt generation unit compares the sense of issues of each business unit with industry-wide trends to generate strategic prompts. For example, the prompt generation unit creates a prompt by comparing the sense of issues of the sales department with industry trends. The prompt generation unit also compares the sense of issues of each business unit with the trends of competitors to generate strategic prompts. For example, the prompt generation unit creates a prompt by comparing the sense of issues of the marketing department with the trends of competitors. The prompt generation unit also compares the sense of issues of each business unit with industry-wide trends and the trends of competitors to generate strategic prompts. For example, the prompt generation unit creates a prompt by comparing the sense of issues of the manufacturing department with industry trends. This makes it possible to provide strategic solutions by taking into account industry-wide trends and the trends of competitors.

[0032] The data acquisition unit is capable of evaluating the reliability and quality of data and acquiring only highly reliable data. For example, when accessing a database, the data acquisition unit evaluates the reliability of data and acquires only highly reliable data. For example, the reliability is evaluated based on the source of the data and the update frequency. Furthermore, when accessing a database, the data acquisition unit evaluates the quality of data and acquires only high-quality data. For example, the quality is evaluated based on the consistency and completeness of the data. Furthermore, when accessing a database, the data acquisition unit evaluates the reliability and quality of data and acquires only highly reliable data. For example, the reliability is evaluated based on the accuracy and recency of the data. In this way, by acquiring only highly reliable data, the accuracy of the analysis results can be improved.

[0033] The data acquisition unit can grasp trends by considering time-series changes in the data and comparing past data with current data. For example, when acquiring data, the data acquisition unit considers time-series changes in the data and compares past data with current data to grasp trends. For example, it analyzes time-series changes in sales data. Also, when acquiring data, the data acquisition unit considers time-series changes in the data and compares past data with current data to grasp trends. For example, it analyzes time-series changes in customer data. Also, when acquiring data, the data acquisition unit considers time-series changes in the data and compares past data with current data to grasp trends. For example, it analyzes time-series changes in inventory data. In this way, by considering time-series changes, it becomes easier to grasp trends in the data.

[0034] The data acquisition unit acquires data from different data sources and can perform more multifaceted analysis. For example, the data acquisition unit acquires data from different data sources and performs more multifaceted analysis. For example, social media data is acquired to analyze customer sentiment. The data acquisition unit also acquires data from different data sources and performs more multifaceted analysis. For example, sensor data is acquired to analyze product usage. The data acquisition unit also acquires data from different data sources and performs more multifaceted analysis. For example, website access data is acquired to analyze user behavior. In this way, by acquiring data from different data sources, more multifaceted analysis is possible.

[0035] The data acquisition unit can simultaneously acquire data of different formats and perform multimodal analysis. For example, the data acquisition unit simultaneously acquires data of different formats when acquiring data and performs multimodal analysis. For example, text data and image data are simultaneously acquired and analyzed. The data acquisition unit also simultaneously acquires data of different formats when acquiring data and performs multimodal analysis. For example, audio data and text data are simultaneously acquired and analyzed. The data acquisition unit also simultaneously acquires data of different formats when acquiring data and performs multimodal analysis. For example, video data and text data are simultaneously acquired and analyzed. This makes it possible to simultaneously acquire data of different formats and perform multimodal analysis.

[0036] The data analysis unit can compare the data summary and features on different time axes to identify long-term trends and patterns. For example, the data analysis unit compares the data summary and features on different time axes to identify long-term trends and patterns. For example, sales data is compared with data from the past five years to analyze trends. The data analysis unit can also compare the data summary and features on different time axes to identify long-term trends and patterns. For example, customer data is compared with data from the past 10 years to analyze patterns. The data analysis unit can also compare the data summary and features on different time axes to identify long-term trends and patterns. For example, inventory data is compared with data from the past three years to analyze trends. In this way, by comparing data on different time axes, long-term trends and patterns can be identified.

[0037] The data analysis unit compares the summary and characteristics of the data with data from different industries and fields to gain new insights. For example, the data analysis unit compares the summary and characteristics of the data with data from different industries to gain new insights. For example, new insights are gained by comparing data from the IT industry with data from the manufacturing industry. The data analysis unit also compares the summary and characteristics of the data with data from different fields to gain new insights. For example, new insights are gained by comparing data from the medical field with marketing data. The data analysis unit also compares the summary and characteristics of the data with data from different industries and fields to gain new insights. For example, new insights are gained by comparing data from the financial industry with sales data. In this way, new insights can be gained by comparing data from different industries and fields.

[0038] The data analysis unit can automatically detect data correlations between different systems and link related data. The data analysis unit, for example, automatically detects data correlations between different systems and links related data. For example, it detects and links the correlations between customer data and sales data. The data analysis unit also automatically detects data correlations between different systems and links related data. For example, it detects and links the correlations between inventory data and sales data. The data analysis unit also automatically detects data correlations between different systems and links related data. For example, it detects and links the correlations between marketing data and customer data. In this way, it is possible to link related data by automatically detecting data correlations between different systems.

[0039] The data analysis unit can visualize the outline and characteristics of the data, allowing the user to intuitively understand it. The data analysis unit, for example, visualizes the outline and characteristics of the data, allowing the user to intuitively understand it. For example, sales data may be displayed in a graph or chart. The data analysis unit may also visualize the outline and characteristics of the data, allowing the user to intuitively understand it. For example, customer data may be displayed in a heat map. The data analysis unit may also visualize the outline and characteristics of the data, allowing the user to intuitively understand it. For example, inventory data may be displayed in a dashboard. In this way, by visualizing the data, the user can intuitively understand the data.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The prompt generation unit can also generate individually customized prompts based on the user's past behavioral history. For example, the prompt generation unit analyzes what measures a user in the sales department has implemented in the past and generates prompts that suggest new measures based on that analysis. The prompt generation unit can also find specific patterns based on the user's past behavioral history and generate prompts based on those patterns. For example, the prompt generation unit can find patterns of campaigns that a user in the marketing department has successfully implemented in the past and generate prompts that suggest new campaigns based on those patterns. The prompt generation unit can also generate prompts to avoid unsuccessful measures based on the user's past behavioral history. For example, the prompt generation unit can generate prompts to avoid unsuccessful measures for a user in the manufacturing department. This makes it possible to provide more appropriate solutions by taking the user's past behavioral history into consideration.

[0042] The data acquisition unit can also automatically generate metadata for evaluating the reliability of data when acquiring data. For example, it generates metadata such as the source of the data, update frequency, consistency and completeness of the data, and evaluates the reliability of the data based on the generated metadata. The data acquisition unit can also automatically generate metadata for evaluating the quality of the data when acquiring data. For example, it generates metadata such as the accuracy and recency of the data, and evaluates the quality of the data based on the generated metadata. The data acquisition unit can also automatically generate metadata for evaluating the reliability and quality of data when acquiring data. For example, it generates metadata such as the source of the data, update frequency, consistency and completeness of the data, accuracy and recency, and evaluates the reliability and quality of the data based on the generated metadata. This allows only highly reliable data to be acquired, thereby improving the accuracy of the analysis results.

[0043] The data analysis unit can also combine different algorithms when analyzing the summary and characteristics of the data. For example, it can analyze data by combining a machine learning algorithm and a statistical analysis algorithm. The data analysis unit can also combine different algorithms when analyzing the summary and characteristics of the data. For example, it can analyze data by combining a natural language processing algorithm and an image analysis algorithm. The data analysis unit can also combine different algorithms when analyzing the summary and characteristics of the data. For example, it can analyze data by combining a deep learning algorithm and a time series analysis algorithm. In this way, by combining different algorithms, more multifaceted data analysis is possible.

[0044] The data acquisition unit may take data privacy protection into consideration when acquiring data and acquire only anonymized data. For example, when acquiring customer data, the data acquisition unit may acquire data that has been anonymized so that individuals cannot be identified. The data acquisition unit may also take data privacy protection into consideration when acquiring data and acquire only anonymized data. For example, when acquiring sales data, the data acquisition unit may acquire data that has been anonymized so that individuals cannot be identified. The data acquisition unit may also take data privacy protection into consideration when acquiring data and acquire only anonymized data. For example, when acquiring inventory data, the data has been anonymized so that individuals cannot be identified. This allows for data privacy protection and prevents the leakage of personal information.

[0045] The data acquisition unit can evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of the data. The data acquisition unit can also evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of sensor data. The data acquisition unit can also evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of social media data. In this way, by acquiring only data with a high degree of real-timeness, the accuracy of the analysis results can be improved.

[0046] The data acquisition unit can evaluate the security of the data when acquiring the data, and acquire only data with high security. For example, the security is evaluated based on the encryption state of the data and access control. The data acquisition unit can also evaluate the security of the data when acquiring the data, and acquire only data with high security. For example, the security is evaluated based on the data storage location and access log. The data acquisition unit can also evaluate the security of the data when acquiring the data, and acquire only data with high security. For example, the security is evaluated based on the encryption state of the data, access control, storage location and access log. In this way, the safety of the data can be ensured by acquiring only data with high security.

[0047] The data acquisition unit can evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of the data. The data acquisition unit can also evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of sensor data. The data acquisition unit can also evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of social media data. In this way, by acquiring only data with a high degree of real-timeness, the accuracy of the analysis results can be improved.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The prompt generation unit generates prompts in advance regarding the issues each business division faces and the configuration of its core systems. For example, it generates prompts that suggest measures to increase sales using sales data and customer data from the sales division. Step 2: The data acquisition unit accesses the databases held by each system and acquires an overview and characteristics of the data. For example, it acquires information such as sales amount and product category from the sales database, and customer age and purchase history from the customer database. Step 3: The data analysis unit analyzes the overview and characteristics of the data acquired by the data acquisition unit. For example, by combining sales data and customer data, it analyzes whether sales improvement measures for a specific customer segment are effective. Step 4: The decision unit determines whether the proposed solution satisfies the pre-prompted task based on the data analyzed by the data analysis unit. For example, the generation AI determines that a sales improvement measure for a specific customer segment is effective.

[0050] (Example 2) The AI ​​service according to the embodiment of the present invention is a system that prompts each business division regarding the issues they face and the configuration of their core systems in advance, accesses the databases held by each system, acquires the outline and characteristics of the data, and determines whether the data meets the issues prompted in advance. This enables the AI ​​service to analyze data based on the issues each business division faces and provide appropriate solutions.

[0051] An AI service according to an embodiment includes a prompt generation unit, a data acquisition unit, a data analysis unit, and a judgment unit. The prompt generation unit generates prompts in advance regarding the perceived challenges faced by each business division and the configuration of its core system. For example, the prompt generates a prompt that proposes measures to increase sales using sales data and customer data from the sales department. The data acquisition unit accesses databases held by each system and acquires the outline and characteristics of the data. For example, it acquires information such as sales amounts and product categories from the sales database and customer ages and purchase histories from the customer database. The data analysis unit analyzes the outline and characteristics of the data acquired by the data acquisition unit. For example, it combines the sales data and customer data to analyze whether sales improvement measures for a specific customer segment are effective. The judgment unit determines whether the pre-prompted issues are met based on the data analyzed by the data analysis unit. For example, the generation AI determines that sales improvement measures for a specific customer segment are effective. This allows the AI ​​service according to an embodiment to analyze data based on the perceived challenges faced by each business division and provide appropriate solutions.

[0052] The prompt generation unit can use the emotion estimation function to analyze the sense of problem and generate prompts that take emotional factors into consideration. For example, the prompt generation unit analyzes the sense of problem of each business division using the emotion estimation function and quantifies the intensity and type of emotion. For example, it analyzes the sense of problem of the sales department and generates a specific prompt based on the emotion score. The prompt generation unit also uses the emotion estimation function to analyze the sense of problem of each business division in real time and generate prompts that reflect emotional factors. For example, it analyzes the sense of problem of the marketing department and creates a prompt based on the emotion score. The prompt generation unit also analyzes the sense of problem of each business division using the emotion estimation function and generates prompts that take emotional factors into consideration. For example, it analyzes the sense of problem of the manufacturing department and creates a specific prompt based on the emotion score. In this way, by generating prompts that take emotional factors into consideration, more appropriate solutions can be provided.

[0053] The prompt generation unit can analyze the sense of issues in comparison with past success cases and failure cases, and generate specific prompts. For example, the prompt generation unit analyzes the sense of issues of each business division in comparison with past success cases, and generates specific prompts. For example, a prompt is created based on past success cases of the sales department. The prompt generation unit also analyzes the sense of issues of each business division in comparison with past failure cases, and generates specific prompts. For example, a prompt is created based on past failure cases of the marketing department. The prompt generation unit also analyzes the sense of issues of each business division in comparison with past success cases and failure cases, and generates specific prompts. For example, a prompt is created based on past cases of the manufacturing department. In this way, by referring to past cases, more specific solutions can be provided.

[0054] The prompt generation unit can analyze the sense of issues by comparing them with industry-wide trends and the trends of competitors, and generate strategic prompts. For example, the prompt generation unit compares the sense of issues of each business unit with industry-wide trends to generate strategic prompts. For example, the prompt generation unit creates a prompt by comparing the sense of issues of the sales department with industry trends. The prompt generation unit also compares the sense of issues of each business unit with the trends of competitors to generate strategic prompts. For example, the prompt generation unit creates a prompt by comparing the sense of issues of the marketing department with the trends of competitors. The prompt generation unit also compares the sense of issues of each business unit with industry-wide trends and the trends of competitors to generate strategic prompts. For example, the prompt generation unit creates a prompt by comparing the sense of issues of the manufacturing department with industry trends. This makes it possible to provide strategic solutions by taking into account industry-wide trends and the trends of competitors.

[0055] The data acquisition unit is capable of evaluating the reliability and quality of data and acquiring only highly reliable data. For example, when accessing a database, the data acquisition unit evaluates the reliability of data and acquires only highly reliable data. For example, the reliability is evaluated based on the source of the data and the update frequency. Furthermore, when accessing a database, the data acquisition unit evaluates the quality of data and acquires only high-quality data. For example, the quality is evaluated based on the consistency and completeness of the data. Furthermore, when accessing a database, the data acquisition unit evaluates the reliability and quality of data and acquires only highly reliable data. For example, the reliability is evaluated based on the accuracy and recency of the data. In this way, by acquiring only highly reliable data, the accuracy of the analysis results can be improved.

[0056] The data acquisition unit can grasp trends by considering time-series changes in the data and comparing past data with current data. For example, when acquiring data, the data acquisition unit considers time-series changes in the data and compares past data with current data to grasp trends. For example, it analyzes time-series changes in sales data. Also, when acquiring data, the data acquisition unit considers time-series changes in the data and compares past data with current data to grasp trends. For example, it analyzes time-series changes in customer data. Also, when acquiring data, the data acquisition unit considers time-series changes in the data and compares past data with current data to grasp trends. For example, it analyzes time-series changes in inventory data. In this way, by considering time-series changes, it becomes easier to grasp trends in the data.

[0057] The data acquisition unit can use the emotion estimation function to analyze user emotions related to the data and take emotional factors into consideration. The data acquisition unit, for example, uses the emotion estimation function to analyze user emotions related to the data when acquiring data. For example, it analyzes an emotion score for customer data. The data acquisition unit also uses the emotion estimation function to analyze user emotions related to the data and take emotional factors into consideration when acquiring data. For example, it analyzes an emotion score for sales data. The data acquisition unit also uses the emotion estimation function to analyze user emotions related to the data and take emotional factors into consideration when acquiring data. For example, it analyzes an emotion score for inventory data. This enables more appropriate data analysis by taking user emotions into consideration.

[0058] The data acquisition unit acquires data from different data sources and can perform more multifaceted analysis. For example, the data acquisition unit acquires data from different data sources and performs more multifaceted analysis. For example, social media data is acquired to analyze customer sentiment. The data acquisition unit also acquires data from different data sources and performs more multifaceted analysis. For example, sensor data is acquired to analyze product usage. The data acquisition unit also acquires data from different data sources and performs more multifaceted analysis. For example, website access data is acquired to analyze user behavior. In this way, by acquiring data from different data sources, more multifaceted analysis is possible.

[0059] The data acquisition unit can simultaneously acquire data of different formats and perform multimodal analysis. For example, the data acquisition unit simultaneously acquires data of different formats when acquiring data and performs multimodal analysis. For example, text data and image data are simultaneously acquired and analyzed. The data acquisition unit also simultaneously acquires data of different formats when acquiring data and performs multimodal analysis. For example, audio data and text data are simultaneously acquired and analyzed. The data acquisition unit also simultaneously acquires data of different formats when acquiring data and performs multimodal analysis. For example, video data and text data are simultaneously acquired and analyzed. This makes it possible to simultaneously acquire data of different formats and perform multimodal analysis.

[0060] The data acquisition unit can use the emotion estimation function to monitor the user's emotion in real time when acquiring data, and prioritize acquisition of emotionally important data. The data acquisition unit, for example, uses the emotion estimation function to monitor the user's emotion in real time when acquiring data, and prioritize acquisition of emotionally important data. For example, data with a strong positive emotion is prioritized. The data acquisition unit can also use the emotion estimation function to monitor the user's emotion in real time when acquiring data, and prioritize acquisition of emotionally important data. For example, data with a strong negative emotion is prioritized. The data acquisition unit can also use the emotion estimation function to monitor the user's emotion in real time when acquiring data, and prioritize acquisition of emotionally important data. For example, data with a high emotion score is prioritized. In this way, by monitoring the user's emotion in real time, it is possible to prioritize acquisition of emotionally important data.

[0061] The data analysis unit can use the emotion estimation function to prioritize emotionally positive combinations when combining data summaries and features. For example, the data analysis unit uses the emotion estimation function to prioritize combinations that elicit positive emotions when combining data summaries and features. For example, customer data and sales data are combined to select combinations with high positive emotion scores. The data analysis unit also uses the emotion estimation function to prioritize combinations that elicit positive emotions when combining data summaries and features. For example, product reviews and sales data are combined to select combinations with high positive emotion scores. The data analysis unit also uses the emotion estimation function to prioritize combinations that elicit positive emotions when combining data summaries and features. For example, customer feedback and marketing data are combined to select combinations with high positive emotion scores. This enables more effective data analysis by prioritizing emotionally positive combinations.

[0062] The data analysis unit can compare the data summary and features on different time axes to identify long-term trends and patterns. For example, the data analysis unit compares the data summary and features on different time axes to identify long-term trends and patterns. For example, sales data is compared with data from the past five years to analyze trends. The data analysis unit can also compare the data summary and features on different time axes to identify long-term trends and patterns. For example, customer data is compared with data from the past 10 years to analyze patterns. The data analysis unit can also compare the data summary and features on different time axes to identify long-term trends and patterns. For example, inventory data is compared with data from the past three years to analyze trends. In this way, by comparing data on different time axes, long-term trends and patterns can be identified.

[0063] The data analysis unit compares the summary and characteristics of the data with data from different industries and fields to gain new insights. For example, the data analysis unit compares the summary and characteristics of the data with data from different industries to gain new insights. For example, new insights are gained by comparing data from the IT industry with data from the manufacturing industry. The data analysis unit also compares the summary and characteristics of the data with data from different fields to gain new insights. For example, new insights are gained by comparing data from the medical field with marketing data. The data analysis unit also compares the summary and characteristics of the data with data from different industries and fields to gain new insights. For example, new insights are gained by comparing data from the financial industry with sales data. In this way, new insights can be gained by comparing data from different industries and fields.

[0064] The data analysis unit can automatically detect data correlations between different systems and link related data. The data analysis unit, for example, automatically detects data correlations between different systems and links related data. For example, it detects and links the correlations between customer data and sales data. The data analysis unit also automatically detects data correlations between different systems and links related data. For example, it detects and links the correlations between inventory data and sales data. The data analysis unit also automatically detects data correlations between different systems and links related data. For example, it detects and links the correlations between marketing data and customer data. In this way, it is possible to link related data by automatically detecting data correlations between different systems.

[0065] The data analysis unit can visualize the outline and characteristics of the data, allowing the user to intuitively understand it. The data analysis unit, for example, visualizes the outline and characteristics of the data, allowing the user to intuitively understand it. For example, sales data may be displayed in a graph or chart. The data analysis unit may also visualize the outline and characteristics of the data, allowing the user to intuitively understand it. For example, customer data may be displayed in a heat map. The data analysis unit may also visualize the outline and characteristics of the data, allowing the user to intuitively understand it. For example, inventory data may be displayed in a dashboard. In this way, by visualizing the data, the user can intuitively understand the data.

[0066] The data analysis unit can use the emotion estimation function to analyze the user's emotional response to the summary and features of the data, and identify data that is likely to be emotionally relatable. For example, the data analysis unit can use the emotion estimation function to analyze the user's emotional response to the summary and features of the data, and identify data that is likely to be emotionally relatable. For example, data with a large number of positive emotional responses can be identified. The data analysis unit can also use the emotion estimation function to analyze the user's emotional response to the summary and features of the data, and identify data that is likely to be emotionally relatable. For example, data with a small number of negative emotional responses can be identified. The data analysis unit can also use the emotion estimation function to analyze the user's emotional response to the summary and features of the data, and identify data that is likely to be emotionally relatable. For example, data with a high emotion score can be identified. In this way, by analyzing the user's emotional response, data that is likely to be emotionally relatable can be identified.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The prompt generation unit can also generate individually customized prompts based on the user's past behavioral history. For example, the prompt generation unit analyzes what measures a user in the sales department has implemented in the past and generates prompts that suggest new measures based on that analysis. The prompt generation unit can also find specific patterns based on the user's past behavioral history and generate prompts based on those patterns. For example, the prompt generation unit can find patterns of campaigns that a user in the marketing department has successfully implemented in the past and generate prompts that suggest new campaigns based on those patterns. The prompt generation unit can also generate prompts to avoid unsuccessful measures based on the user's past behavioral history. For example, the prompt generation unit can generate prompts to avoid unsuccessful measures for a user in the manufacturing department. This makes it possible to provide more appropriate solutions by taking the user's past behavioral history into consideration.

[0069] The data acquisition unit can also automatically generate metadata for evaluating the reliability of data when acquiring data. For example, it generates metadata such as the source of the data, update frequency, consistency and completeness of the data, and evaluates the reliability of the data based on the generated metadata. The data acquisition unit can also automatically generate metadata for evaluating the quality of the data when acquiring data. For example, it generates metadata such as the accuracy and recency of the data, and evaluates the quality of the data based on the generated metadata. The data acquisition unit can also automatically generate metadata for evaluating the reliability and quality of data when acquiring data. For example, it generates metadata such as the source of the data, update frequency, consistency and completeness of the data, accuracy and recency, and evaluates the reliability and quality of the data based on the generated metadata. This allows only highly reliable data to be acquired, thereby improving the accuracy of the analysis results.

[0070] The data analysis unit can also combine different algorithms when analyzing the summary and characteristics of the data. For example, it can analyze data by combining a machine learning algorithm and a statistical analysis algorithm. The data analysis unit can also combine different algorithms when analyzing the summary and characteristics of the data. For example, it can analyze data by combining a natural language processing algorithm and an image analysis algorithm. The data analysis unit can also combine different algorithms when analyzing the summary and characteristics of the data. For example, it can analyze data by combining a deep learning algorithm and a time series analysis algorithm. In this way, by combining different algorithms, more multifaceted data analysis is possible.

[0071] The data acquisition unit may take data privacy protection into consideration when acquiring data and acquire only anonymized data. For example, when acquiring customer data, the data acquisition unit may acquire data that has been anonymized so that individuals cannot be identified. The data acquisition unit may also take data privacy protection into consideration when acquiring data and acquire only anonymized data. For example, when acquiring sales data, the data acquisition unit may acquire data that has been anonymized so that individuals cannot be identified. The data acquisition unit may also take data privacy protection into consideration when acquiring data and acquire only anonymized data. For example, when acquiring inventory data, the data has been anonymized so that individuals cannot be identified. This allows for data privacy protection and prevents the leakage of personal information.

[0072] The data analysis unit can also use the emotion estimation function to avoid emotionally negative combinations when analyzing the summary and features of the data. For example, by combining customer data and sales data, combinations with high negative emotion scores are avoided. The data analysis unit can also use the emotion estimation function to avoid combinations that cause negative emotions when analyzing the summary and features of the data. For example, by combining product reviews and sales data, combinations with high negative emotion scores are avoided. The data analysis unit can also use the emotion estimation function to avoid combinations that cause negative emotions when analyzing the summary and features of the data. For example, by combining customer feedback and marketing data, combinations with high negative emotion scores are avoided. This allows for more effective data analysis by avoiding emotionally negative combinations.

[0073] The data acquisition unit can evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of the data. The data acquisition unit can also evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of sensor data. The data acquisition unit can also evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of social media data. In this way, by acquiring only data with a high degree of real-timeness, the accuracy of the analysis results can be improved.

[0074] The data analysis unit can also use the emotion estimation function to prioritize emotionally positive combinations when analyzing the summary and features of the data. For example, it can combine customer data and sales data and select combinations with high positive emotion scores. The data analysis unit can also use the emotion estimation function to prioritize combinations that elicit positive emotions when analyzing the summary and features of the data. For example, it can combine product reviews and sales data and select combinations with high positive emotion scores. The data analysis unit can also use the emotion estimation function to prioritize combinations that elicit positive emotions when analyzing the summary and features of the data. For example, it can combine customer feedback and marketing data and select combinations with high positive emotion scores. This allows for more effective data analysis by prioritizing emotionally positive combinations.

[0075] The data acquisition unit can evaluate the security of the data when acquiring the data, and acquire only data with high security. For example, the security is evaluated based on the encryption state of the data and access control. The data acquisition unit can also evaluate the security of the data when acquiring the data, and acquire only data with high security. For example, the security is evaluated based on the data storage location and access log. The data acquisition unit can also evaluate the security of the data when acquiring the data, and acquire only data with high security. For example, the security is evaluated based on the encryption state of the data, access control, storage location and access log. In this way, the safety of the data can be ensured by acquiring only data with high security.

[0076] The data analysis unit can also use the emotion estimation function to avoid emotionally negative combinations when analyzing the summary and features of the data. For example, by combining customer data and sales data, combinations with high negative emotion scores are avoided. The data analysis unit can also use the emotion estimation function to avoid combinations that cause negative emotions when analyzing the summary and features of the data. For example, by combining product reviews and sales data, combinations with high negative emotion scores are avoided. The data analysis unit can also use the emotion estimation function to avoid combinations that cause negative emotions when analyzing the summary and features of the data. For example, by combining customer feedback and marketing data, combinations with high negative emotion scores are avoided. This allows for more effective data analysis by avoiding emotionally negative combinations.

[0077] The data acquisition unit can evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of the data. The data acquisition unit can also evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of sensor data. The data acquisition unit can also evaluate the real-time nature of the data when acquiring the data, and acquire only data with a high degree of real-timeness. For example, the real-time nature is evaluated based on the update frequency and acquisition timing of social media data. In this way, by acquiring only data with a high degree of real-timeness, the accuracy of the analysis results can be improved.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The prompt generation unit generates prompts in advance regarding the issues each business division faces and the configuration of its core systems. For example, it generates prompts that suggest measures to increase sales using sales data and customer data from the sales division. Step 2: The data acquisition unit accesses the databases held by each system and acquires an overview and characteristics of the data. For example, it acquires information such as sales amount and product category from the sales database, and customer age and purchase history from the customer database. Step 3: The data analysis unit analyzes the overview and characteristics of the data acquired by the data acquisition unit. For example, by combining sales data and customer data, it analyzes whether sales improvement measures for a specific customer segment are effective. Step 4: The decision unit determines whether the proposed solution satisfies the pre-prompted task based on the data analyzed by the data analysis unit. For example, the generation AI determines that a sales improvement measure for a specific customer segment is effective.

[0080] 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.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The 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.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 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.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 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 prompt generation department generates prompts in advance regarding the issues each business division faces and the configuration of its core systems. A data acquisition section that accesses the databases held by each system and acquires the data summary and characteristics; a data analysis unit that analyzes the outline and characteristics of the data acquired by the data acquisition unit; and a determination unit that determines whether or not the task prompted in advance is met based on the data analyzed by the data analysis unit. A system characterized by:

2. The prompt generation unit Analyze the sense of challenge and generate prompts that take emotional factors into account 2. The system of claim 1.

3. The prompt generation unit Analyze the above-mentioned issues by comparing them with past successes and failures, and generate specific prompts.

2. The system of claim 1.

4. The prompt generation unit Analyze the above issues in comparison with industry trends and competitors' activities to generate strategic prompts 2. The system of claim 1.

5. The data acquisition unit Evaluate the reliability and quality of data and acquire only reliable data 2. The system of claim 1.

6. The data acquisition unit Consider changes in data over time and compare past data with current data to understand trends 2. The system of claim 1.

7. The data acquisition unit Analyzing user sentiment related to said data and taking emotional factors into account 2. The system of claim 1.

8. The data acquisition unit Obtain the data from different sources to perform more comprehensive analysis 2. The system of claim 1.

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