System

Abstract

An object of the system according to the embodiment is to extract important information from a plurality of data sources of a company and generate a presentation or a settlement report material in a form optimized for each recipient.SOLUTION: A system includes a data collection unit, an analysis unit, an optimization unit, and a generation unit. The data collector accesses a plurality of data sources of the company and extracts important values and policies. The analysis unit analyzes the data collected by the data collection unit. Based on the data analyzed by the analysis unit, the optimization unit selects data points based on the job title and interest of each recipient and edits the layout. The generation unit generates a presentation or a financial report material based on the data point selected by the optimization 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] Conventional technologies have faced the challenge of extracting important information from a company's multiple data sources and generating presentations and financial report documents in a format optimized for each recipient.

[0005] The system according to the embodiment aims to extract important information from multiple data sources of a company and generate presentations and financial report materials in a format optimized for each recipient. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, an optimization unit, and a generation unit. The data collection unit accesses multiple data sources of a company and extracts important figures and policies. The analysis unit analyzes the data collected by the data collection unit. The optimization unit selects data points based on the job title and interests of each recipient based on the data analyzed by the analysis unit and edits the layout. The generation unit generates presentations and financial report materials based on the data points selected by the optimization unit. [Effects of the Invention]

[0007] The system according to the embodiment can extract important information from multiple data sources of a company and generate presentations and financial reports in a format optimized for each recipient. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A data presentation platform according to an embodiment of the present invention is a system for automatically converting corporate data into presentations and financial report materials. This system utilizes generative AI to collect, analyze, optimize, and generate data. This allows the data presentation platform to efficiently and effectively utilize corporate data and automatically create presentations and financial report materials.

[0029] A data presentation platform according to an embodiment includes a data collection unit, an analysis unit, an optimization unit, and a generation unit. The data collection unit accesses multiple data sources of a company and extracts important figures and policies. For example, the data collection unit collects data from an internal database, an external API, cloud storage, etc. The data collection unit also collects sales data, customer data, inventory data, etc., and analyzes them to understand the company's current situation. For example, the data collection unit collects sales data and analyzes monthly sales, sales by product, sales by region, etc. Customer data is collected and analyzed for customer attributes, purchase history, customer satisfaction, etc. Inventory data is collected and analyzed for inventory quantity, inventory turnover, inventory value, etc. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using techniques such as statistical analysis, machine learning algorithms, and data mining. The optimization unit selects data points based on the position and interests of each recipient and edits the layout based on the data analyzed by the analysis unit. For example, the optimization unit optimizes the presentation to emphasize strategic figures for management and sales data for the sales team. The generation unit generates presentations and financial report materials based on the data points selected by the optimization unit. For example, the generation unit may graph sales trends and create slides that emphasize important points. It may also automatically create tables and graphs showing financial status to provide visually easy-to-understand materials. This enables the data presentation platform to automatically convert a company's data into presentations and financial report materials.

[0030] The data collection unit collects sales data, customer data, and inventory data, and analyzes them to understand the current situation of the company. For example, the data collection unit collects sales data and analyzes monthly sales, sales by product, sales by region, etc. The data collection unit also collects customer data and analyzes customer attributes, purchase history, customer satisfaction, etc. Furthermore, the data collection unit collects inventory data and analyzes inventory quantity, inventory turnover, inventory value, etc. This makes it possible to collect and analyze data to understand the current situation of the company.

[0031] The optimization department can optimize presentations to emphasize strategic figures for management and sales data for the sales team. For example, the optimization department creates presentations that emphasize strategic figures for management. For example, they might emphasize strategic figures such as KPIs, ROI, and growth rates. The optimization department can also create presentations that emphasize sales data for the sales team. For example, they might graph the sales data and adjust the color and font size to emphasize it. This allows the presentation to be optimized based on the role and interests of each recipient.

[0032] The generation unit can graph sales trends and create slides that emphasize important points. For example, the generation unit graphs sales trends and creates slides that emphasize important points. For example, it emphasizes sales peaks and trend changes. The generation unit also detects outliers based on sales data and creates slides that analyze the causes. Furthermore, the generation unit creates slides that compare sales data with overall market trends. This makes it possible to visually display sales trends in an easy-to-understand manner.

[0033] The generation unit can automatically create tables or graphs showing financial status and provide visually easy-to-understand materials. The generation unit, for example, automatically creates tables and graphs showing financial status. For example, it creates balance sheets, income statements, cash flow statements, etc. The generation unit also detects outliers based on financial data and creates materials that analyze the causes. Furthermore, the generation unit creates materials that compare the financial data with overall market trends. This makes it possible to display financial status in a visually easy-to-understand manner.

[0034] The generation unit can automatically update the presentation materials every time the sales data or expense data is updated. The generation unit automatically updates the presentation materials, for example, every time the sales data or expense data is updated. For example, when the sales data is updated, a graph showing sales trends is automatically updated. Also, when the expense data is updated, a table showing the breakdown of expenses is automatically updated. Furthermore, the generation unit updates the data in real time, always providing materials based on the latest information. This makes it possible to always provide presentation materials based on the latest information.

[0035] The generation unit can automatically apply a design that reflects a company's brand color or logo. For example, the generation unit automatically applies a design that reflects a company's brand color or logo. For example, the generation unit sets the background color of a slide using the company's color code. The generation unit also places the company's logo in the header or footer of the slide. Furthermore, the generation unit adjusts the font and layout according to the company's design guidelines. This makes it possible to provide presentation materials that reflect the company's brand image.

[0036] When collecting data, the data collection unit compares it with a company's past data and can automatically detect outliers and trend changes. In the data collection unit, for example, the generation AI collects sales data and compares it with past sales data to detect outliers. For example, if there is a sudden increase in sales in a particular month, the cause is analyzed. The generation AI also collects customer data and compares it with past customer data to detect trend changes. For example, if there is a change in the purchasing behavior of a particular customer group, the factors are analyzed. Furthermore, the generation AI collects inventory data and compares it with past inventory data to detect outliers. For example, if a particular product suddenly goes out of stock, the cause is analyzed. This makes it possible to automatically detect outliers and trend changes.

[0037] When collecting data, the data collection unit simultaneously collects external market data and competitor data, allowing for comparative analysis with the company's data. For example, the data collection unit's generation AI simultaneously collects a company's sales data and external market data and performs comparative analysis. For example, it analyzes whether the company's sales are in line with overall market trends. The generation AI also simultaneously collects a company's customer data and competitor data and performs comparative analysis. For example, it compares the company's strengths and weaknesses with those of competitors. The generation AI also simultaneously collects a company's financial data and external economic data and performs comparative analysis. For example, it analyzes whether the company's financial condition is in line with overall economic trends. This allows for comparative analysis with external market data and competitor data.

[0038] The data collection unit can simultaneously collect voice data and image data when collecting data, and perform multimodal data analysis. For example, the data collection unit's generation AI collects customer voice feedback along with sales data and performs multimodal data analysis. For example, it analyzes the tone and content of customer voices to evaluate customer satisfaction. The generation AI also collects product image data along with inventory data and performs multimodal data analysis. For example, it analyzes the appearance and packaging condition of products to evaluate quality. Furthermore, the generation AI collects meeting voice data along with financial data and performs multimodal data analysis. For example, it analyzes the content and tone of statements made in meetings to evaluate financial strategies. This makes it possible to perform multimodal data analysis that includes voice data and image data.

[0039] The analysis unit can evaluate the extracted figures and policies against the company's long-term strategy and automatically rank their strategic importance. For example, the analysis unit evaluates sales data extracted by the generation AI against the company's long-term strategy and ranks its strategic importance. For example, it gives a high rating to data on products that are expected to grow in the long term. It also evaluates customer data extracted by the generation AI against the company's long-term strategy and ranks its strategic importance. For example, it gives a high rating to data on customer segments that have many long-term repeat customers. It also evaluates financial data extracted by the generation AI against the company's long-term strategy and ranks its strategic importance. For example, it gives a high rating to data on departments that have high long-term profit margins. This makes it possible to automatically rank data on strategic importance.

[0040] The analysis unit can simulate the extracted figures and policies based on different scenarios and propose the optimal policy. For example, the analysis unit simulates changes in economic conditions based on sales data extracted by the generation AI and proposes the optimal policy. For example, it makes sales forecasts during an economic downturn and proposes countermeasures. It also simulates changes in economic conditions based on customer data extracted by the generation AI and proposes the optimal policy. For example, it proposes customer retention measures when consumer confidence declines. It also simulates changes in economic conditions based on financial data extracted by the generation AI and proposes the optimal policy. For example, it proposes a fundraising strategy when interest rates rise. This makes it possible to propose optimal policies based on different scenarios.

[0041] The analysis unit can automatically translate the extracted figures and policies into different languages ​​and evaluate them from an international perspective. For example, the analysis unit automatically translates sales data extracted by the generation AI into different languages ​​and evaluates it from an international perspective. For example, it translates it into English, French, Chinese, etc. and evaluates it. It also automatically translates customer data extracted by the generation AI into different languages ​​and evaluates it from an international perspective. For example, it evaluates customer satisfaction data in multiple languages. It also automatically translates financial data extracted by the generation AI into different languages ​​and evaluates it from an international perspective. For example, it evaluates financial soundness data in multiple languages. This allows it to automatically translate into different languages ​​and evaluate it from an international perspective.

[0042] The analysis unit can convert the extracted figures and policies into visual notes or mind maps to make them easier to understand visually. For example, the analysis unit converts sales data extracted by the generation AI into visual notes and displays them visually. For example, it displays sales trends in a graph. It also converts customer data extracted by the generation AI into mind maps and displays them visually. For example, it displays satisfaction levels for each customer segment in a mind map. It also converts financial data extracted by the generation AI into visual notes and displays them visually. For example, it creates tables and graphs showing financial status. This makes it possible to display figures and policies in a way that is easier to understand visually.

[0043] The optimization unit can compare the selected data points with the recipient's past presentation history and automatically suggest the optimal layout. For example, the optimization unit compares the sales data selected by the generation AI with the recipient's past presentation history and suggests the optimal layout. For example, by reusing a graph format that was well received in the past. Also, the optimization unit compares the customer data selected by the generation AI with the recipient's past presentation history and suggests the optimal layout. For example, by reusing a slide design that was effective in the past. Furthermore, the optimization unit compares the financial data selected by the generation AI with the recipient's past presentation history and suggests the optimal layout. For example, by reusing a table format that was successful in the past. This makes it possible to suggest the optimal layout by comparing with the past presentation history.

[0044] The optimization unit can convert the selected data points into different presentation formats to attract the recipient's attention. For example, the optimization unit converts sales data selected by the generation AI into a video format to attract the recipient's attention. For example, it can display sales trends using animation. Also, the optimization unit can convert customer data selected by the generation AI into an interactive dashboard to attract the recipient's attention. For example, it can create a dashboard that displays customer satisfaction in real time. Furthermore, the optimization unit can convert financial data selected by the generation AI into a video format to attract the recipient's attention. For example, it can display profit margin trends using animation. This allows it to be converted into a different presentation format to attract the recipient's attention.

[0045] The optimization unit can introduce an agile methodology in which selected data points are implemented as prototypes and improved based on feedback. For example, the optimization unit implements sales data selected by the generation AI as a prototype and improves it based on user feedback. For example, the design of a sales graph is improved by reflecting user opinions. Furthermore, the optimization unit implements customer data selected by the generation AI as a prototype and improves it based on user feedback. For example, the layout of a customer satisfaction slide is improved by reflecting user opinions. Furthermore, the optimization unit implements financial data selected by the generation AI as a prototype and improves it based on user feedback. For example, the design of a financial table is improved by reflecting user opinions. This makes it possible to introduce an agile methodology in which prototypes are implemented and improved based on feedback.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] When collecting company data, the data collection department simultaneously collects environmental data, enabling the evaluation of the company's environmental impact. For example, the data collection department collects energy consumption data and exhaust gas data and analyzes the company's environmental impact. The data collection department also collects environmental data from the entire supply chain and evaluates the environmental impact of each supplier. Furthermore, the data collection department collects environmental data from the entire product life cycle of the company and evaluates the environmental impact of the product. This allows for a comprehensive evaluation of the company's environmental impact.

[0048] The data collection department can use the collected data to evaluate corporate social responsibility (CSR) activities. For example, the data collection department collects data on a company's environmental protection activities and social contribution activities, analyzes the data, and evaluates its CSR activities. The data collection department also collects data on a company's working environment and human rights protection, analyzes the data, and evaluates its CSR activities. Furthermore, the data collection department collects data on a company's governance, analyzes the data, and evaluates its CSR activities. This allows for a comprehensive evaluation of a company's CSR activities.

[0049] The analysis department can evaluate the company's risk management based on the extracted figures and policies. For example, the analysis department evaluates the risk of sales fluctuations based on sales data. The analysis department also evaluates the risk of customer churn based on customer data. Furthermore, the analysis department evaluates financial risk based on financial data. In this way, the company's risk management can be evaluated.

[0050] The generator can add interactive elements to presentation materials. For example, the generator can create interactive graphs based on sales data that users can click to view more detailed information. The generator can also create interactive dashboards based on customer data that users can filter to view data for specific customer segments. The generator can also create interactive slides based on financial data that users can use sliders to simulate different scenarios. This allows interactive elements to be added to presentation materials.

[0051] The generation unit can add a voice assistant function to the presentation materials. For example, the generation unit adds a function in which the voice assistant explains sales trends based on sales data. The generation unit also adds a function in which the voice assistant explains customer satisfaction data based on customer data. The generation unit also adds a function in which the voice assistant explains financial status based on financial data. This allows the voice assistant function to be added to the presentation materials.

[0052] The generation unit can add a function to update data in real time to presentation materials. For example, the generation unit updates a graph showing sales trends in real time every time sales data is updated. The generation unit also updates a slide showing customer satisfaction in real time every time customer data is updated. Furthermore, the generation unit updates a table showing financial status in real time every time financial data is updated. This allows the addition of a function to update data in real time to presentation materials.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The data collection department accesses the company's multiple data sources and extracts important figures and policies. For example, the data collection department collects data from internal databases, external APIs, cloud storage, etc. It also collects sales data, customer data, inventory data, etc. and analyzes them to understand the company's current situation. Specifically, sales data is collected and analyzed for monthly sales, sales by product, sales by region, etc. Customer data is collected and analyzed for customer attributes, purchase history, customer satisfaction, etc. Inventory data is collected and analyzed for inventory quantity, inventory turnover, inventory value, etc. Step 2: The analysis unit analyzes the data collected by the data collection unit, for example, using techniques such as statistical analysis, machine learning algorithms, and data mining. Step 3: The optimization department uses the data analyzed by the analytics department to select data points based on the recipient's role and interests, and then edits the layout. For example, the presentation is optimized to highlight strategic figures for executives and sales data for the sales team. Step 4: The generator generates presentations and financial report materials based on the data points selected by the optimizer. For example, it creates slides that graph sales trends and emphasize key points. It automatically creates tables and graphs showing financial status, providing visually easy-to-understand materials.

[0055] (Example 2) A data presentation platform according to an embodiment of the present invention is a system for automatically converting corporate data into presentations and financial report materials. This system utilizes generative AI to collect, analyze, optimize, and generate data. This allows the data presentation platform to efficiently and effectively utilize corporate data and automatically create presentations and financial report materials.

[0056] A data presentation platform according to an embodiment includes a data collection unit, an analysis unit, an optimization unit, and a generation unit. The data collection unit accesses multiple data sources of a company and extracts important figures and policies. For example, the data collection unit collects data from an internal database, an external API, cloud storage, etc. The data collection unit also collects sales data, customer data, inventory data, etc., and analyzes them to understand the company's current situation. For example, the data collection unit collects sales data and analyzes monthly sales, sales by product, sales by region, etc. Customer data is collected and analyzed for customer attributes, purchase history, customer satisfaction, etc. Inventory data is collected and analyzed for inventory quantity, inventory turnover, inventory value, etc. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using techniques such as statistical analysis, machine learning algorithms, and data mining. The optimization unit selects data points based on the position and interests of each recipient and edits the layout based on the data analyzed by the analysis unit. For example, the optimization unit optimizes the presentation to emphasize strategic figures for management and sales data for the sales team. The generation unit generates presentations and financial report materials based on the data points selected by the optimization unit. For example, the generation unit may graph sales trends and create slides that emphasize important points. It may also automatically create tables and graphs showing financial status to provide visually easy-to-understand materials. This enables the data presentation platform to automatically convert a company's data into presentations and financial report materials.

[0057] The data collection unit collects sales data, customer data, and inventory data, and analyzes them to understand the current situation of the company. For example, the data collection unit collects sales data and analyzes monthly sales, sales by product, sales by region, etc. The data collection unit also collects customer data and analyzes customer attributes, purchase history, customer satisfaction, etc. Furthermore, the data collection unit collects inventory data and analyzes inventory quantity, inventory turnover, inventory value, etc. This makes it possible to collect and analyze data to understand the current situation of the company.

[0058] The optimization department can optimize presentations to emphasize strategic figures for management and sales data for the sales team. For example, the optimization department creates presentations that emphasize strategic figures for management. For example, they might emphasize strategic figures such as KPIs, ROI, and growth rates. The optimization department can also create presentations that emphasize sales data for the sales team. For example, they might graph the sales data and adjust the color and font size to emphasize it. This allows the presentation to be optimized based on the role and interests of each recipient.

[0059] The generation unit can graph sales trends and create slides that emphasize important points. For example, the generation unit graphs sales trends and creates slides that emphasize important points. For example, it emphasizes sales peaks and trend changes. The generation unit also detects outliers based on sales data and creates slides that analyze the causes. Furthermore, the generation unit creates slides that compare sales data with overall market trends. This makes it possible to visually display sales trends in an easy-to-understand manner.

[0060] The generation unit can automatically create tables or graphs showing financial status and provide visually easy-to-understand materials. The generation unit, for example, automatically creates tables and graphs showing financial status. For example, it creates balance sheets, income statements, cash flow statements, etc. The generation unit also detects outliers based on financial data and creates materials that analyze the causes. Furthermore, the generation unit creates materials that compare the financial data with overall market trends. This makes it possible to display financial status in a visually easy-to-understand manner.

[0061] The generation unit can automatically update the presentation materials every time the sales data or expense data is updated. The generation unit automatically updates the presentation materials, for example, every time the sales data or expense data is updated. For example, when the sales data is updated, a graph showing sales trends is automatically updated. Also, when the expense data is updated, a table showing the breakdown of expenses is automatically updated. Furthermore, the generation unit updates the data in real time, always providing materials based on the latest information. This makes it possible to always provide presentation materials based on the latest information.

[0062] The generation unit can automatically apply a design that reflects a company's brand color or logo. For example, the generation unit automatically applies a design that reflects a company's brand color or logo. For example, the generation unit sets the background color of a slide using the company's color code. The generation unit also places the company's logo in the header or footer of the slide. Furthermore, the generation unit adjusts the font and layout according to the company's design guidelines. This makes it possible to provide presentation materials that reflect the company's brand image.

[0063] The data collection unit can analyze user emotions on the collected data using an emotion estimation function and prioritize extract data points that elicit positive emotions. For example, the data collection unit performs emotion analysis on sales data and customer data collected by the generation AI and prioritize extracts data points that elicit positive emotions. For example, it emphasizes sales data for products with high customer satisfaction. It also performs emotion analysis on financial data collected by the generation AI and prioritize extracts data points that elicit positive emotions. For example, it emphasizes data from departments with high profit margins. It also performs emotion analysis on market data collected by the generation AI and prioritize extracts data points that elicit positive emotions. For example, it emphasizes data from markets with high growth rates. This makes it possible to prioritize extract data points that elicit positive emotions.

[0064] When collecting data, the data collection unit compares it with a company's past data and can automatically detect outliers and trend changes. In the data collection unit, for example, the generation AI collects sales data and compares it with past sales data to detect outliers. For example, if there is a sudden increase in sales in a particular month, the cause is analyzed. The generation AI also collects customer data and compares it with past customer data to detect trend changes. For example, if there is a change in the purchasing behavior of a particular customer group, the factors are analyzed. Furthermore, the generation AI collects inventory data and compares it with past inventory data to detect outliers. For example, if a particular product suddenly goes out of stock, the cause is analyzed. This makes it possible to automatically detect outliers and trend changes.

[0065] When collecting data, the data collection unit simultaneously collects external market data and competitor data, allowing for comparative analysis with the company's data. For example, the data collection unit's generation AI simultaneously collects a company's sales data and external market data and performs comparative analysis. For example, it analyzes whether the company's sales are in line with overall market trends. The generation AI also simultaneously collects a company's customer data and competitor data and performs comparative analysis. For example, it compares the company's strengths and weaknesses with those of competitors. The generation AI also simultaneously collects a company's financial data and external economic data and performs comparative analysis. For example, it analyzes whether the company's financial condition is in line with overall economic trends. This allows for comparative analysis with external market data and competitor data.

[0066] The data collection unit can simultaneously collect voice data and image data when collecting data, and perform multimodal data analysis. For example, the data collection unit's generation AI collects customer voice feedback along with sales data and performs multimodal data analysis. For example, it analyzes the tone and content of customer voices to evaluate customer satisfaction. The generation AI also collects product image data along with inventory data and performs multimodal data analysis. For example, it analyzes the appearance and packaging condition of products to evaluate quality. Furthermore, the generation AI collects meeting voice data along with financial data and performs multimodal data analysis. For example, it analyzes the content and tone of statements made in meetings to evaluate financial strategies. This makes it possible to perform multimodal data analysis that includes voice data and image data.

[0067] The data collection unit uses an emotion estimation function during data collection to monitor the user's emotions in real time during data collection, thereby improving the quality of the collected data. For example, the data collection unit has the generation AI analyze the user's facial expressions during data collection and monitor emotions in real time. For example, it prioritizes collecting data in which the user shows positive emotions. The generation AI also analyzes the user's tone of voice during data collection and monitors emotions in real time. For example, it prioritizes collecting data in which the user is excited. Furthermore, the generation AI analyzes the user's biometric data (heart rate and galvanic skin response) during data collection and monitors emotions in real time. For example, it prioritizes collecting data in which the user is relaxed. This allows the user's emotions during data collection to be monitored in real time, improving the quality of the collected data.

[0068] The analysis unit uses an emotion estimation function to analyze the user's emotions on the extracted numerical values ​​and policies, and can prioritize presenting policies that elicit positive emotions. For example, the analysis unit performs emotion analysis on sales data extracted by the generation AI, and prioritizes presenting policies that elicit positive emotions. For example, it highlights data on products with strong sales. It also performs emotion analysis on customer data extracted by the generation AI, and prioritizes presenting policies that elicit positive emotions. For example, it highlights data on services with high customer satisfaction. It also performs emotion analysis on financial data extracted by the generation AI, and prioritizes presenting policies that elicit positive emotions. For example, it highlights data on departments with high profit margins. This makes it possible to prioritize presenting policies that elicit positive emotions.

[0069] The analysis unit can evaluate the extracted figures and policies against the company's long-term strategy and automatically rank their strategic importance. For example, the analysis unit evaluates sales data extracted by the generation AI against the company's long-term strategy and ranks its strategic importance. For example, it gives a high rating to data on products that are expected to grow in the long term. It also evaluates customer data extracted by the generation AI against the company's long-term strategy and ranks its strategic importance. For example, it gives a high rating to data on customer segments that have many long-term repeat customers. It also evaluates financial data extracted by the generation AI against the company's long-term strategy and ranks its strategic importance. For example, it gives a high rating to data on departments that have high long-term profit margins. This makes it possible to automatically rank data on strategic importance.

[0070] The analysis unit can simulate the extracted figures and policies based on different scenarios and propose the optimal policy. For example, the analysis unit simulates changes in economic conditions based on sales data extracted by the generation AI and proposes the optimal policy. For example, it makes sales forecasts during an economic downturn and proposes countermeasures. It also simulates changes in economic conditions based on customer data extracted by the generation AI and proposes the optimal policy. For example, it proposes customer retention measures when consumer confidence declines. It also simulates changes in economic conditions based on financial data extracted by the generation AI and proposes the optimal policy. For example, it proposes a fundraising strategy when interest rates rise. This makes it possible to propose optimal policies based on different scenarios.

[0071] The analysis unit can automatically translate the extracted figures and policies into different languages ​​and evaluate them from an international perspective. For example, the analysis unit automatically translates sales data extracted by the generation AI into different languages ​​and evaluates it from an international perspective. For example, it translates it into English, French, Chinese, etc. and evaluates it. It also automatically translates customer data extracted by the generation AI into different languages ​​and evaluates it from an international perspective. For example, it evaluates customer satisfaction data in multiple languages. It also automatically translates financial data extracted by the generation AI into different languages ​​and evaluates it from an international perspective. For example, it evaluates financial soundness data in multiple languages. This allows it to automatically translate into different languages ​​and evaluate it from an international perspective.

[0072] The analysis unit can convert the extracted figures and policies into visual notes or mind maps to make them easier to understand visually. For example, the analysis unit converts sales data extracted by the generation AI into visual notes and displays them visually. For example, it displays sales trends in a graph. It also converts customer data extracted by the generation AI into mind maps and displays them visually. For example, it displays satisfaction levels for each customer segment in a mind map. It also converts financial data extracted by the generation AI into visual notes and displays them visually. For example, it creates tables and graphs showing financial status. This makes it possible to display figures and policies in a way that is easier to understand visually.

[0073] The analysis unit collects the user's emotional reactions to the extracted figures and policies, and can improve the accuracy of extraction based on that. The analysis unit, for example, collects the user's emotional reactions to sales data extracted by the generation AI, and improves the accuracy of extraction based on that data. For example, it prioritizes the extraction of data with a high number of positive reactions. The analysis unit also collects the user's emotional reactions to customer data extracted by the generation AI, and improves the accuracy of extraction based on that data. For example, it prioritizes the extraction of data with high customer satisfaction. Furthermore, it collects the user's emotional reactions to financial data extracted by the generation AI, and improves the accuracy of extraction based on that data. For example, it prioritizes the extraction of data with high profit margins. This makes it possible to improve the accuracy of extraction based on the user's emotional reactions.

[0074] The optimization unit can analyze user emotions for selected data points using an emotion estimation function and preferentially apply a layout that elicits positive emotions. For example, the optimization unit performs emotion analysis on sales data selected by the generation AI and applies a layout that elicits positive emotions. For example, it creates a graph that emphasizes sales growth. It also performs emotion analysis on customer data selected by the generation AI and applies a layout that elicits positive emotions. For example, it creates a slide that emphasizes data with high customer satisfaction. It also performs emotion analysis on financial data selected by the generation AI and applies a layout that elicits positive emotions. For example, it creates a table that emphasizes data from departments with high profit margins. This makes it possible to preferentially apply a layout that elicits positive emotions.

[0075] The optimization unit can compare the selected data points with the recipient's past presentation history and automatically suggest the optimal layout. For example, the optimization unit compares the sales data selected by the generation AI with the recipient's past presentation history and suggests the optimal layout. For example, by reusing a graph format that was well received in the past. Also, the optimization unit compares the customer data selected by the generation AI with the recipient's past presentation history and suggests the optimal layout. For example, by reusing a slide design that was effective in the past. Furthermore, the optimization unit compares the financial data selected by the generation AI with the recipient's past presentation history and suggests the optimal layout. For example, by reusing a table format that was successful in the past. This makes it possible to suggest the optimal layout by comparing with the past presentation history.

[0076] The optimization unit can convert the selected data points into different presentation formats to attract the recipient's attention. For example, the optimization unit converts sales data selected by the generation AI into a video format to attract the recipient's attention. For example, it can display sales trends using animation. Also, the optimization unit can convert customer data selected by the generation AI into an interactive dashboard to attract the recipient's attention. For example, it can create a dashboard that displays customer satisfaction in real time. Furthermore, the optimization unit can convert financial data selected by the generation AI into a video format to attract the recipient's attention. For example, it can display profit margin trends using animation. This allows it to be converted into a different presentation format to attract the recipient's attention.

[0077] The optimization unit can introduce an agile methodology in which selected data points are implemented as prototypes and improved based on feedback. For example, the optimization unit implements sales data selected by the generation AI as a prototype and improves it based on user feedback. For example, the design of a sales graph is improved by reflecting user opinions. Furthermore, the optimization unit implements customer data selected by the generation AI as a prototype and improves it based on user feedback. For example, the layout of a customer satisfaction slide is improved by reflecting user opinions. Furthermore, the optimization unit implements financial data selected by the generation AI as a prototype and improves it based on user feedback. For example, the design of a financial table is improved by reflecting user opinions. This makes it possible to introduce an agile methodology in which prototypes are implemented and improved based on feedback.

[0078] The optimization unit can monitor users' emotional reactions to selected data points in real time and continuously search for the optimal layout. For example, the optimization unit can monitor users' emotional reactions to sales data selected by the generation AI in real time and search for the optimal layout. For example, it can prioritize the adoption of layouts with a high number of positive reactions. It can also monitor users' emotional reactions to customer data selected by the generation AI in real time and search for the optimal layout. For example, it can adopt a layout that emphasizes data with high customer satisfaction. It can also monitor users' emotional reactions to financial data selected by the generation AI in real time and search for the optimal layout. For example, it can adopt a layout that emphasizes data with high profit margins. This makes it possible to monitor users' emotional reactions in real time and continuously search for the optimal layout.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] When collecting company data, the data collection department simultaneously collects environmental data, enabling the evaluation of the company's environmental impact. For example, the data collection department collects energy consumption data and exhaust gas data and analyzes the company's environmental impact. The data collection department also collects environmental data from the entire supply chain and evaluates the environmental impact of each supplier. Furthermore, the data collection department collects environmental data from the entire product life cycle of the company and evaluates the environmental impact of the product. This allows for a comprehensive evaluation of the company's environmental impact.

[0081] The data collection unit can analyze user emotions on the collected data using an emotion estimation function, and prioritize extract data points that elicit negative emotions. For example, the data collection unit highlights data on products that have a high level of customer dissatisfaction. The data collection unit also performs emotion analysis on financial data, and prioritize extracts data points that elicit negative emotions. For example, it highlights data on departments with low profit margins. Furthermore, the data collection unit performs emotion analysis on market data, and prioritize extracts data points that elicit negative emotions. For example, it highlights data on markets with low growth rates. This allows data points that elicit negative emotions to be preferentially extracted.

[0082] The optimization unit can analyze user emotions for selected data points using an emotion estimation function and preferentially apply a layout that elicits negative emotions. For example, the optimization unit performs emotion analysis on sales data and applies a layout that elicits negative emotions. For example, a graph that emphasizes declining sales is created. The optimization unit also performs emotion analysis on customer data and applies a layout that elicits negative emotions. For example, a slide that emphasizes data on low customer satisfaction is created. The optimization unit also performs emotion analysis on financial data and applies a layout that elicits negative emotions. For example, a table that emphasizes data on departments with low profit margins is created. This allows layouts that elicit negative emotions to be applied preferentially.

[0083] The generation unit can use an emotion estimation function to analyze user emotions in presentation materials and apply a design that elicits positive emotions. For example, the generation unit performs emotion analysis on sales data and applies a design that elicits positive emotions. For example, it selects colors and fonts that emphasize increased sales. The generation unit also performs emotion analysis on customer data and applies a design that elicits positive emotions. For example, it selects a layout that emphasizes data that shows high customer satisfaction. The generation unit also performs emotion analysis on financial data and applies a design that elicits positive emotions. For example, it creates a graph that emphasizes data from departments with high profit margins. This makes it possible to apply a design that elicits positive emotions.

[0084] The generation unit can use an emotion estimation function to analyze user emotions in presentation materials and apply a design that elicits negative emotions. For example, the generation unit performs emotion analysis on sales data and applies a design that elicits negative emotions. For example, it selects colors and fonts that emphasize declining sales. The generation unit also performs emotion analysis on customer data and applies a design that elicits negative emotions. For example, it selects a layout that emphasizes data on low customer satisfaction. Furthermore, the generation unit performs emotion analysis on financial data and applies a design that elicits negative emotions. For example, it creates a graph that emphasizes data on departments with low profit margins. This makes it possible to apply a design that elicits negative emotions.

[0085] The data collection department can use the collected data to evaluate corporate social responsibility (CSR) activities. For example, the data collection department collects data on a company's environmental protection activities and social contribution activities, analyzes the data, and evaluates its CSR activities. The data collection department also collects data on a company's working environment and human rights protection, analyzes the data, and evaluates its CSR activities. Furthermore, the data collection department collects data on a company's governance, analyzes the data, and evaluates its CSR activities. This allows for a comprehensive evaluation of a company's CSR activities.

[0086] The analysis department can evaluate the company's risk management based on the extracted figures and policies. For example, the analysis department evaluates the risk of sales fluctuations based on sales data. The analysis department also evaluates the risk of customer churn based on customer data. Furthermore, the analysis department evaluates financial risk based on financial data. In this way, the company's risk management can be evaluated.

[0087] The generator can add interactive elements to presentation materials. For example, the generator can create interactive graphs based on sales data that users can click to view more detailed information. The generator can also create interactive dashboards based on customer data that users can filter to view data for specific customer segments. The generator can also create interactive slides based on financial data that users can use sliders to simulate different scenarios. This allows interactive elements to be added to presentation materials.

[0088] The generation unit can add a voice assistant function to the presentation materials. For example, the generation unit adds a function in which the voice assistant explains sales trends based on sales data. The generation unit also adds a function in which the voice assistant explains customer satisfaction data based on customer data. The generation unit also adds a function in which the voice assistant explains financial status based on financial data. This allows the voice assistant function to be added to the presentation materials.

[0089] The generation unit can add a function to update data in real time to presentation materials. For example, the generation unit updates a graph showing sales trends in real time every time sales data is updated. The generation unit also updates a slide showing customer satisfaction in real time every time customer data is updated. Furthermore, the generation unit updates a table showing financial status in real time every time financial data is updated. This allows the addition of a function to update data in real time to presentation materials.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The data collection department accesses the company's multiple data sources and extracts important figures and policies. For example, the data collection department collects data from internal databases, external APIs, cloud storage, etc. It also collects sales data, customer data, inventory data, etc. and analyzes them to understand the company's current situation. Specifically, sales data is collected and analyzed for monthly sales, sales by product, sales by region, etc. Customer data is collected and analyzed for customer attributes, purchase history, customer satisfaction, etc. Inventory data is collected and analyzed for inventory quantity, inventory turnover, inventory value, etc. Step 2: The analysis unit analyzes the data collected by the data collection unit, for example, using techniques such as statistical analysis, machine learning algorithms, and data mining. Step 3: The optimization department uses the data analyzed by the analytics department to select data points based on the recipient's role and interests, and then edits the layout. For example, the presentation is optimized to highlight strategic figures for executives and sales data for the sales team. Step 4: The generator generates presentations and financial report materials based on the data points selected by the optimizer. For example, it creates slides that graph sales trends and emphasize key points. It automatically creates tables and graphs showing financial status, providing visually easy-to-understand materials.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 data collection department that accesses multiple data sources within the company and extracts important figures and policies. an analysis unit that analyzes the data collected by the data collection unit; an optimization unit that selects data points based on the job title and interests of each recipient and edits the layout based on the data analyzed by the analysis unit; a generation unit that generates a presentation or financial report based on the data points selected by the optimization unit. A system characterized by:

2. The data collection unit Collect sales data, customer data, and inventory data, and analyze them to understand the current situation of the company.

2. The system of claim 1.

3. The optimization unit Optimize presentations to highlight strategic numbers for executives and sales data for sales teams 2. The system of claim 1.

4. The generation unit Create a slide that graphs sales trends and highlights the key points.

2. The system of claim 1.

5. The generation unit The table or graph showing the financial status is automatically created to provide visually easy-to-understand materials.

2. The system of claim 1.

Images