System

Abstract

To provide a system capable of precisely predicting future sales and profits based on past sales and profit data.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, a prediction unit, and a providing unit. The collection unit collects past sales and profit data. The analysis unit analyzes the data collected by the collection unit and extracts trends. The prediction unit predicts future sales and profits based on current and future trends extracted by the analysis unit. The providing unit provides the prediction result obtained by the prediction 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to accurately predict future sales and profits based on past sales and profit data.

[0005] The system according to the embodiment aims to accurately predict future sales and profits based on past sales and profit data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects past sales and profit data. The analysis unit analyzes the data collected by the collection unit and extracts trends. The prediction unit predicts future sales and profits based on the trends extracted by the analysis unit and current and future trends. The provision unit provides the prediction results obtained by the prediction unit. Effect of the Invention

[0007] The system according to the embodiment can accurately predict future sales and profits based on past sales and profit data. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The sales and profit forecasting system according to the embodiment of the present invention is a system for forecasting the sales and profits of a company. This system is intended for use in business strategy, finance departments, sales field departments, and the like. First, the AI ​​is loaded with information on actual past sales and profits. At this time, the company's past sales and profit data is collected in detail and input into the AI. For example, sales and profit data from the past five years is collected and the AI ​​is made to learn from it. This data also includes information on customers by industry, size, and product. Next, the AI ​​analyzes the past sales and profit data and extracts trends. For example, it analyzes the increase and decrease trends in sales to customers of a specific industry and size. It also analyzes sales trends by product to understand which products are easy to sell at what time. Furthermore, the AI ​​predicts future sales and profits by taking into account current and future trends in the world, the economic situation by industry, the investment situation, and the trends of rival companies. For example, it predicts future sales and profits by taking into account the current economic situation and the economic trends of the industry. It also takes into account the trends of new products and market entry by rival companies to make predictions that reflect the competitive environment. In this way, AI combines past data with current and future trends to predict a company's future sales and profits. The prediction results are used to formulate business strategies, financial plans, and sales strategies at the sales site. For example, based on the prediction results, it is possible to consider strengthening sales of specific products and entering new markets. This system allows companies to make more accurate sales and profit predictions, improving the accuracy of business strategies and financial plans. In addition, at the sales site, effective sales strategies can be formulated based on the prediction results to maximize sales. As a result, the sales and profit prediction system can accurately predict a company's sales and profits, and can be used to formulate business strategies, financial plans, and sales strategies.

[0029] The sales and profit forecasting system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects past sales and profit data of a company. The past sales and profit data of a company includes, for example, monthly data, quarterly data, and annual data, but is not limited to such examples. The collection unit collects, for example, sales and profit data for the past five years, and makes an AI learn from the data. The collection unit can also collect information on customers by industry, size, and product. For example, information on manufacturing, retail, large companies, small and medium-sized enterprises, and specific product categories is collected. The analysis unit analyzes the data collected by the collection unit and extracts trends. Trends are extracted by, for example, moving averages, seasonal adjustments, regression analysis, and other methods, but are not limited to such examples. For example, the analysis unit analyzes the increase and decrease trends in sales to customers of a specific industry and size. The analysis unit can also analyze sales trends by product, and grasp which products are likely to sell at what time. The prediction unit predicts future sales and profits based on the trends extracted by the analysis unit and current and future trends. The current and future trends are predicted based on, for example, economic indicators, industry reports, expert predictions, etc., but are not limited to such examples. For example, the prediction unit predicts future sales and profits taking into account the current economic situation and the economic trends of the industry. The prediction unit can also make predictions that reflect the competitive environment by taking into account new products and market entry trends of rival companies. The provision unit provides the prediction results obtained by the prediction unit. The provision unit provides the prediction results in a form that can be used for management strategies, financial plans, and sales strategies, for example. For example, the provision unit can provide the prediction results in a report format, dashboard, alert function, etc. As a result, the sales and profit prediction system according to the embodiment can accurately predict the sales and profits of a company and be useful in planning management strategies, financial plans, and sales strategies. Some or all of the above-mentioned processes in the collection unit, analysis unit, prediction unit, and provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect past sales and profit data and input it into AI, the analysis unit can use AI to extract trends, the prediction unit can use AI to predict future sales and profits, and the provision unit can use AI to provide the prediction results.

[0030] The collection unit collects the company's past sales and profit data. The company's past sales and profit data includes, but is not limited to, monthly data, quarterly data, and annual data. The collection unit collects, for example, sales and profit data for the past five years and makes the AI ​​learn from it. Specifically, the collection unit automatically extracts data from the company's financial system and ERP system and stores it in a database. This prevents errors caused by manual data entry and allows efficient data collection. The collection unit can also collect information on customers by industry, size, and product. For example, it collects information on manufacturing, retail, large companies, small and medium-sized enterprises, and specific product categories. This information is important as a factor that affects a company's sales and profits, and is necessary for detailed analysis. The collection unit can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the analysis unit and forecast unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system. Additionally, the collection unit can also integrate with external data sources to incorporate external data, such as economic indicators and market data, allowing for analysis based on a broader data set and improving the accuracy of forecasts.

[0031] The analysis unit analyzes the data collected by the collection unit and extracts trends. Trends are extracted by, for example, moving averages, seasonal adjustments, regression analysis, and other methods, but are not limited to these examples. Specifically, the analysis unit uses AI to process data in real time and grasp the increase / decrease trends in sales and profits. For example, moving averages are used to smooth short-term fluctuations and clarify long-term trends. In addition, seasonal adjustments can be used to remove seasonal fluctuations and extract pure trends. Furthermore, regression analysis can be used to identify factors that affect sales and profits and quantitatively evaluate the extent to which these factors affect sales and profits. The analysis unit analyzes the increase / decrease trends in sales to customers of a specific industry and size. For example, the analysis unit analyzes the increase / decrease trends in sales at large manufacturing companies to grasp which periods are likely to increase sales. The analysis unit can also analyze sales trends by product and grasp which products are likely to sell at which periods. For example, the analysis unit analyzes sales trends in a specific product category to clarify seasonal sales patterns. This allows the analysis unit to quickly and accurately analyze collected data and grasp sales and profit trends. Furthermore, the analysis unit can use past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past sales data, it can predict risk fluctuations in specific regions and time periods and plan future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The prediction unit predicts future sales and profits based on the trends extracted by the analysis unit and current and future trends. The current and future trends are predicted based on, for example, economic indicators, industry reports, expert predictions, etc., but are not limited to such examples. Specifically, the prediction unit uses AI to analyze these data and simulate multiple scenarios to identify the most likely risks. For example, future sales and profits are predicted taking into account the current economic situation and the economic trends of the industry. The prediction unit can also make predictions that reflect the competitive environment by taking into account new products and market entry trends of rival companies. For example, if a rival company plans to release a new product, the risk of a decrease in sales is predicted taking into account the impact of that. Furthermore, the prediction unit can continuously revise the prediction results based on data updated in real time to respond to the latest situation. For example, when economic indicators or industry reports are updated, the prediction unit immediately incorporates new data and updates the prediction results. The prediction unit can also perform more accurate risk assessments by taking into account the characteristics of each region and past disaster history. As a result, the prediction unit can always provide highly accurate risk predictions based on the latest information and support rapid and appropriate responses. Furthermore, the prediction unit can improve prediction accuracy by continuously training the prediction model using AI. For example, the model is retrained every time new data is added to improve prediction accuracy. This allows the prediction unit to always provide highly accurate predictions based on the latest information, which can be useful in formulating a company's management strategy, financial plan, and sales strategy.

[0033] The provision unit provides the prediction results obtained by the prediction unit. For example, the provision unit provides the prediction results in a form that can be used for management strategies, financial plans, and sales strategies. Specifically, the provision unit can provide the prediction results in report format, dashboard, alert function, and the like. For example, for managers, the sales and profit prediction results can be visually displayed in graphs and charts to help with planning management strategies. For the financial department, a simulation of a financial plan based on the prediction results can be performed to help with cash flow and investment planning. For the sales department, the provision unit can support the planning of a sales strategy based on the prediction results and set sales targets for specific products and regions. Furthermore, the provision unit can collect user feedback and continuously improve the accuracy of the prediction results and the provision method. For example, the provision unit collects user evaluations and opinions on the prediction results and reflects them in improving the prediction model and the provision method. Furthermore, the provision unit can reliably transmit information using multiple communication means. For example, important information can be provided quickly through email, SMS, and dedicated applications. This allows the providing unit to provide the user with prediction results quickly and reliably, and to be useful in formulating a company's management strategy, financial plan, and sales strategy. Furthermore, the providing unit can provide an alert function based on the prediction results, and automatically issue a notification when a specific condition is met. For example, an alert can be issued when sales fall significantly below the forecast or when sales of a specific product increase sharply, thereby supporting a quick response. This not only allows the providing unit to be useful in formulating a company's management strategy, financial plan, and sales strategy, but also supports quick decision-making and improves the company's competitiveness.

[0034] The collection unit can collect information on the customer by industry, size, and product. The collection unit collects information on, for example, manufacturing, retail, large companies, small and medium-sized enterprises, and specific product categories. For example, the collection unit can collect sales data for manufacturing customers and sales data for retail customers. The collection unit can also collect sales data for large company customers and sales data for small and medium-sized enterprise customers. Furthermore, the collection unit can collect sales data for specific product categories. For example, the collection unit collects sales data for product categories such as electronic devices, food, and clothing. This allows for more detailed data analysis by collecting information on the customer by industry, size, and product. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the customer by industry, size, and product into AI and collect information using AI.

[0035] The analysis unit can analyze the collected data and extract the increase / decrease trend of sales to customers of a specific industry and size. The analysis unit analyzes the collected data using, for example, a method such as time series analysis, trend analysis, and regression analysis. For example, the analysis unit uses time series analysis to extract the increase / decrease trend of sales to customers of a specific industry and size. The analysis unit can also use trend analysis to extract the increase / decrease trend of sales to customers of a specific industry and size. Furthermore, the analysis unit can also use regression analysis to extract the increase / decrease trend of sales to customers of a specific industry and size. For example, the analysis unit can analyze the increase / decrease trend of sales to customers in the manufacturing industry and analyze the increase / decrease trend of sales to customers in the retail industry. The analysis unit can also analyze the increase / decrease trend of sales to customers of large companies and analyze the increase / decrease trend of sales to customers of small and medium-sized enterprises. As a result, by grasping the increase / decrease trend of sales to customers of a specific industry and size, more accurate sales forecasting is possible. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and use the AI ​​to extract trends in sales increases or decreases.

[0036] The analysis unit can analyze sales trends by product and grasp which product is likely to sell at which time. The analysis unit can analyze sales trends by product, for example, using sales data by category, seasonal analysis, etc. For example, the analysis unit can grasp which product is likely to sell at which time using sales data by category. The analysis unit can also grasp which product is likely to sell at which time using seasonal analysis. For example, the analysis unit can analyze sales trends of electronic devices and analyze sales trends of food. The analysis unit can also analyze sales trends of clothing and grasp which product is likely to sell at a specific time. As a result, grasping sales trends by product is useful for planning sales strategies. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input sales data by product into AI and analyze sales trends using AI.

[0037] The prediction unit can predict future sales and profits based on current and future trends, the economic situation by industry, the investment situation, and the trends of rival companies. The prediction unit predicts current and future trends based on, for example, economic indicators, industry reports, expert predictions, etc. For example, the prediction unit predicts current and future trends based on economic indicators. The prediction unit can also predict current and future trends based on industry reports. The prediction unit can also predict current and future trends based on expert predictions. For example, the prediction unit predicts future sales and profits taking into account the current economic situation and the economic trends of the industry. The prediction unit can also make predictions that reflect the competitive environment by taking into account new products and market entry trends of rival companies. This makes it possible to make more accurate sales and profit predictions by taking into account current and future trends, the economic situation by industry, and the trends of rival companies. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input economic indicators and industry reports into AI and predict future sales and profits using AI.

[0038] The providing unit can provide the prediction results in a form that can be utilized for business strategies, financial plans, and sales strategies. The providing unit provides the prediction results in, for example, a report format, a dashboard, an alert function, and the like. For example, the providing unit provides the prediction results in a report format, and provides them in a form that can be utilized for business strategies, financial plans, and sales strategies. The providing unit can also provide the prediction results in a dashboard so that the prediction results can be confirmed in real time. Furthermore, the providing unit can provide the prediction results in an alert function, and can immediately notify important prediction results. This supports corporate decision-making by utilizing the prediction results in business strategies, financial plans, and sales strategies. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the prediction results to AI and provide the prediction results using AI.

[0039] The collection unit can analyze the past data collection history and select an appropriate collection method. The collection unit, for example, selects the most efficient collection method from the past data collection history. The collection unit can also optimize the collection frequency based on the past data collection history. Furthermore, the collection unit can analyze the past data collection history and propose a method for improving the quality of data. For example, the collection unit selects an optimal collection method based on the past data collection history and improves the efficiency of data collection. As a result, the optimal collection method can be selected by analyzing the past data collection history, and the efficiency of data collection can be improved. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into AI and select the optimal collection method using AI.

[0040] The collection unit can perform filtering based on the company's current business situation and market trends when collecting data. For example, the collection unit preferentially collects important data based on the company's current business situation. The collection unit can also collect highly relevant data in consideration of market trends. Furthermore, the collection unit can combine the company's business situation and market trends to select the optimal data collection method. For example, the collection unit preferentially collects important data based on the company's financial report and industry report. As a result, highly relevant data can be collected by filtering data based on the company's business situation and market trends. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the company's business situation and market trends into AI and filter the data using AI.

[0041] The collection unit can collect highly relevant data preferentially in consideration of the geographical location information of the company when collecting data. The collection unit, for example, collects region-specific data based on the location of the company. The collection unit can also collect highly relevant market data in consideration of the geographical location information. Furthermore, the collection unit can select an optimal data collection method based on the geographical location information of the company. For example, the collection unit collects region-specific data based on the location of the company and grasps the market trends of the region. In this way, region-specific data can be collected by considering the geographical location information of the company. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the company into AI and collect highly relevant data using AI.

[0042] The collection unit can analyze the social media activity of the company and collect relevant data when collecting data. The collection unit, for example, collects relevant data based on the social media activity of the company. The collection unit can also analyze social media trends and collect important data. Furthermore, the collection unit can select an optimal data collection method in consideration of the social media activity of the company. For example, the collection unit collects relevant data based on the social media activity of the company and grasps social media trends. In this way, highly relevant data can be collected by analyzing the social media activity of the company. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media activity of the company into AI and collect related data using AI.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimize analysis resources according to the importance of the data. For example, the analysis unit evaluates the importance of the data based on the business impact and the reliability of the data, and adjusts the level of detail of the analysis. As a result, efficient data analysis is possible by adjusting the level of detail of the analysis according to the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and adjust the level of detail of the analysis using AI.

[0044] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an industry-specific analysis algorithm to industry-specific data. The analysis unit can also apply a product-specific analysis algorithm to product-specific data. The analysis unit can also apply a scale-specific analysis algorithm to scale-specific data. For example, the analysis unit applies a manufacturing-specific analysis algorithm to manufacturing data, and applies a retail-specific analysis algorithm to retail data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into AI and apply an appropriate analysis algorithm using AI.

[0045] The analysis unit can determine the analysis priority based on the collection time of the data during the analysis. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also prioritize the analysis of important data based on past data. Furthermore, the analysis unit can optimize the analysis resources according to the collection time of the data. For example, the analysis unit prioritizes the analysis of the latest data, and prioritizes the analysis of important data based on past data. In this way, the analysis priority can be determined based on the collection time of the data, so that the latest data can be analyzed with priority. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to AI and determine the analysis priority using AI.

[0046] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can optimize analysis resources according to the relevance of data. For example, the analysis unit evaluates the relevance of data using correlation analysis or causal analysis and adjusts the order of analysis. This enables efficient data analysis by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI and adjust the order of analysis using AI.

[0047] The prediction unit can adjust the prediction algorithm by referring to past prediction data at the time of prediction. The prediction unit optimizes the prediction algorithm based on, for example, past prediction data. The prediction unit can also analyze past prediction data and improve prediction accuracy. Furthermore, the prediction unit can select an optimal prediction method by referring to past prediction data. For example, the prediction unit optimizes the prediction algorithm based on past sales prediction data and improves prediction accuracy. As a result, the prediction algorithm can be optimized and prediction accuracy can be improved by referring to past prediction data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input past prediction data into AI and adjust the prediction algorithm using AI.

[0048] The prediction unit can customize the means of prediction based on the current business situation of the company at the time of prediction. The prediction unit selects an optimal prediction means based on, for example, the current business situation of the company. The prediction unit can also adjust the level of detail of the prediction according to the business situation. Furthermore, the prediction unit can optimize the resources of the prediction in consideration of the business situation of the company. For example, the prediction unit selects an optimal prediction means based on the financial report and performance indicators of the company and adjusts the level of detail of the prediction. As a result, by customizing the means of prediction based on the current business situation of the company, more appropriate prediction results can be provided. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the business situation of the company into AI and customize the means of prediction using AI.

[0049] The prediction unit can select an appropriate prediction method in consideration of the geographical location information of the company at the time of prediction. The prediction unit selects a region-specific prediction method based on, for example, the location of the company. The prediction unit can also provide an optimal prediction method in consideration of the geographical location information. Furthermore, the prediction unit can optimize prediction resources based on the geographical location information of the company. For example, the prediction unit selects a region-specific prediction method based on the location of the company and grasps the market trends of the region. In this way, a region-specific prediction method can be provided by considering the geographical location information of the company. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the geographical location information of the company into AI and select the optimal prediction method using AI.

[0050] At the time of prediction, the prediction unit can analyze the social media activity of the company and propose a means of prediction. The prediction unit can, for example, propose an optimal prediction means based on the social media activity of the company. The prediction unit can also analyze social media trends and improve the accuracy of prediction. Furthermore, the prediction unit can optimize prediction resources in consideration of the social media activity of the company. For example, the prediction unit proposes an optimal prediction means based on the social media activity of the company and grasps social media trends. In this way, the optimal prediction means can be proposed by analyzing the social media activity of the company. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the social media activity of the company into AI and propose a means of prediction using AI.

[0051] The providing unit can select an appropriate providing method by referring to the user's past usage history at the time of providing. The providing unit selects an optimal providing method based on the user's past usage history, for example. The providing unit can also analyze the past usage history and provide optimal information to the user. Furthermore, the providing unit can optimize the providing resources by referring to the user's past usage history. For example, the providing unit selects an optimal providing method based on past access logs and purchase history, and provides information useful to the user. As a result, by referring to the user's past usage history, the optimal information providing method can be selected and information useful to the user can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history into AI and select the optimal providing method using AI.

[0052] The providing unit can customize information based on the current needs of the user at the time of providing. The providing unit provides optimal information based on the current needs of the user, for example. The providing unit can also adjust the level of detail of the information according to the needs of the user. Furthermore, the providing unit can optimize information resources in consideration of the current needs of the user. For example, the providing unit grasps the current needs of the user based on survey results and behavioral data, and provides optimal information. As a result, the information can be customized based on the current needs of the user, thereby providing optimal information for the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the current needs of the user into AI and customize the information using AI.

[0053] The providing unit can select the optimal providing method in consideration of the geographical location information of the user at the time of providing. The providing unit provides area-specific information based on, for example, the user's location. The providing unit can also provide highly relevant information in consideration of the geographical location information. Furthermore, the providing unit can select the optimal providing method based on the user's geographical location information. For example, the providing unit provides area-specific information based on the user's location and grasps the market trend of the area. Thereby, it is possible to provide area-specific information by considering the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI and select the optimal providing method using AI.

[0054] The providing unit can analyze the user's social media activity and propose a means of providing information at the time of providing. The providing unit can propose an optimal means of providing information based on, for example, the user's social media activity. The providing unit can also analyze social media trends and provide important information. Furthermore, the providing unit can select an optimal information providing method in consideration of the user's social media activity. For example, the providing unit proposes an optimal means of providing information based on the user's social media activity and grasps social media trends. In this way, the optimal means of providing information can be proposed by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI and propose a means of providing information using AI.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The collection department can collect employee satisfaction data of companies in addition to the company's past sales and profit data. For example, by collecting employee survey results, turnover rates, employee performance data, etc. and inputting this data into AI, it is possible to analyze the impact of employee satisfaction on sales and profits. Furthermore, the collection department can propose measures to improve employee motivation based on the employee satisfaction data. This allows companies to increase sales and profits by improving employee satisfaction.

[0057] The collection department can collect customer satisfaction data of companies in addition to their past sales and profit data. For example, by collecting customer survey results, customer feedback, and customer repeat rates, and inputting this data into AI, it is possible to analyze the impact of customer satisfaction on sales and profits. Furthermore, the collection department can propose measures to improve customer satisfaction based on the customer satisfaction data. This allows companies to increase sales and profits by improving customer satisfaction.

[0058] The analysis unit can analyze the brand value data of the company in addition to the collected data. For example, it can analyze data such as brand awareness, brand loyalty, and brand image, and understand the impact of these data on sales and profits. Furthermore, the analysis unit can propose improvements to the brand strategy based on the brand value data. This allows companies to increase their sales and profits by improving their brand value.

[0059] The analysis unit can analyze the company's supply chain data in addition to the collected data. For example, it can analyze data such as supply chain efficiency, costs, and lead times, and understand the impact of these data on sales and profits. Furthermore, the analysis unit can propose supply chain optimization measures based on the supply chain data. This allows companies to increase sales and profits by improving the efficiency of their supply chains.

[0060] The forecasting department can forecast future sales and profits based on the company's innovation activity data, in addition to current and future trends. For example, the forecasting department can forecast the impact of innovation activities on sales and profits based on data such as the company's research and development investment, the development status of new products, and the status of patent acquisition. Furthermore, the forecasting department can make predictions that take into account future market trends and the competitive environment based on the innovation activity data. This allows companies to improve their future sales and profits by strengthening their innovation activities.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects the company's past sales and profit data. The company's past sales and profit data includes, but is not limited to, monthly data, quarterly data, and annual data. The collection unit collects, for example, sales and profit data for the past five years and allows the AI ​​to learn from it. The collection unit can also collect information on customers by industry, size, and product type. For example, information on manufacturing, retail, large companies, small and medium-sized enterprises, and specific product categories is collected. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts trends. Trends are extracted by methods such as, but not limited to, moving averages, seasonal adjustments, and regression analysis. For example, the analysis unit analyzes the increase / decrease trends in sales to customers in a particular industry or size. The analysis unit can also analyze sales trends by product type to determine which products are likely to sell at what times. Step 3: The forecasting unit forecasts future sales and profits based on the trends extracted by the analysis unit and current and future trends. The current and future trends are forecast based on, for example, economic indicators, industry reports, expert forecasts, etc., but are not limited to such examples. For example, the forecasting unit forecasts future sales and profits taking into account the current economic situation and the business trends of the industry. The forecasting unit can also make forecasts that reflect the competitive environment by taking into account new products and market entry trends of rival companies. Step 4: The provision unit provides the prediction results obtained by the prediction unit. For example, the provision unit provides the prediction results in a form that can be used for management strategies, financial plans, and sales strategies. For example, the provision unit can provide the prediction results in a report format, a dashboard, an alert function, or the like.

[0063] (Example 2) The sales and profit forecasting system according to the embodiment of the present invention is a system for forecasting the sales and profits of a company. This system is intended for use in business strategy, finance departments, sales field departments, and the like. First, the AI ​​is loaded with information on actual past sales and profits. At this time, the company's past sales and profit data is collected in detail and input into the AI. For example, sales and profit data from the past five years is collected and the AI ​​is made to learn from it. This data also includes information on customers by industry, size, and product. Next, the AI ​​analyzes the past sales and profit data and extracts trends. For example, it analyzes the increase and decrease trends in sales to customers of a specific industry and size. It also analyzes sales trends by product to understand which products are easy to sell at what time. Furthermore, the AI ​​predicts future sales and profits by taking into account current and future trends in the world, the economic situation by industry, the investment situation, and the trends of rival companies. For example, it predicts future sales and profits by taking into account the current economic situation and the economic trends of the industry. It also takes into account the trends of new products and market entry by rival companies to make predictions that reflect the competitive environment. In this way, AI combines past data with current and future trends to predict a company's future sales and profits. The prediction results are used to formulate business strategies, financial plans, and sales strategies at the sales site. For example, based on the prediction results, it is possible to consider strengthening sales of specific products and entering new markets. This system allows companies to make more accurate sales and profit predictions, improving the accuracy of business strategies and financial plans. In addition, at the sales site, effective sales strategies can be formulated based on the prediction results to maximize sales. As a result, the sales and profit prediction system can accurately predict a company's sales and profits, and can be used to formulate business strategies, financial plans, and sales strategies.

[0064] The sales and profit forecasting system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects past sales and profit data of a company. The past sales and profit data of a company includes, for example, monthly data, quarterly data, and annual data, but is not limited to such examples. The collection unit collects, for example, sales and profit data for the past five years, and makes an AI learn from the data. The collection unit can also collect information on customers by industry, size, and product. For example, information on manufacturing, retail, large companies, small and medium-sized enterprises, and specific product categories is collected. The analysis unit analyzes the data collected by the collection unit and extracts trends. Trends are extracted by, for example, moving averages, seasonal adjustments, regression analysis, and other methods, but are not limited to such examples. For example, the analysis unit analyzes the increase and decrease trends in sales to customers of a specific industry and size. The analysis unit can also analyze sales trends by product, and grasp which products are likely to sell at what time. The prediction unit predicts future sales and profits based on the trends extracted by the analysis unit and current and future trends. The current and future trends are predicted based on, for example, economic indicators, industry reports, expert predictions, etc., but are not limited to such examples. For example, the prediction unit predicts future sales and profits taking into account the current economic situation and the economic trends of the industry. The prediction unit can also make predictions that reflect the competitive environment by taking into account new products and market entry trends of rival companies. The provision unit provides the prediction results obtained by the prediction unit. The provision unit provides the prediction results in a form that can be used for management strategies, financial plans, and sales strategies, for example. For example, the provision unit can provide the prediction results in a report format, dashboard, alert function, etc. As a result, the sales and profit prediction system according to the embodiment can accurately predict the sales and profits of a company and be useful in planning management strategies, financial plans, and sales strategies. Some or all of the above-mentioned processes in the collection unit, analysis unit, prediction unit, and provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect past sales and profit data and input it into AI, the analysis unit can use AI to extract trends, the prediction unit can use AI to predict future sales and profits, and the provision unit can use AI to provide the prediction results.

[0065] The collection unit collects the company's past sales and profit data. The company's past sales and profit data includes, but is not limited to, monthly data, quarterly data, and annual data. The collection unit collects, for example, sales and profit data for the past five years and makes the AI ​​learn from it. Specifically, the collection unit automatically extracts data from the company's financial system and ERP system and stores it in a database. This prevents errors caused by manual data entry and allows efficient data collection. The collection unit can also collect information on customers by industry, size, and product. For example, it collects information on manufacturing, retail, large companies, small and medium-sized enterprises, and specific product categories. This information is important as a factor that affects a company's sales and profits, and is necessary for detailed analysis. The collection unit can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the analysis unit and forecast unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system. Additionally, the collection unit can also integrate with external data sources to incorporate external data, such as economic indicators and market data, allowing for analysis based on a broader data set and improving the accuracy of forecasts.

[0066] The analysis unit analyzes the data collected by the collection unit and extracts trends. Trends are extracted by, for example, moving averages, seasonal adjustments, regression analysis, and other methods, but are not limited to these examples. Specifically, the analysis unit uses AI to process data in real time and grasp the increase / decrease trends in sales and profits. For example, moving averages are used to smooth short-term fluctuations and clarify long-term trends. In addition, seasonal adjustments can be used to remove seasonal fluctuations and extract pure trends. Furthermore, regression analysis can be used to identify factors that affect sales and profits and quantitatively evaluate the extent to which these factors affect sales and profits. The analysis unit analyzes the increase / decrease trends in sales to customers of a specific industry and size. For example, the analysis unit analyzes the increase / decrease trends in sales at large manufacturing companies to grasp which periods are likely to increase sales. The analysis unit can also analyze sales trends by product and grasp which products are likely to sell at which periods. For example, the analysis unit analyzes sales trends in a specific product category to clarify seasonal sales patterns. This allows the analysis unit to quickly and accurately analyze collected data and grasp sales and profit trends. Furthermore, the analysis unit can use past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past sales data, it can predict risk fluctuations in specific regions and time periods and plan future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0067] The prediction unit predicts future sales and profits based on the trends extracted by the analysis unit and current and future trends. The current and future trends are predicted based on, for example, economic indicators, industry reports, expert predictions, etc., but are not limited to such examples. Specifically, the prediction unit uses AI to analyze these data and simulate multiple scenarios to identify the most likely risks. For example, future sales and profits are predicted taking into account the current economic situation and the economic trends of the industry. The prediction unit can also make predictions that reflect the competitive environment by taking into account new products and market entry trends of rival companies. For example, if a rival company plans to release a new product, the risk of a decrease in sales is predicted taking into account the impact of that. Furthermore, the prediction unit can continuously revise the prediction results based on data updated in real time to respond to the latest situation. For example, when economic indicators or industry reports are updated, the prediction unit immediately incorporates new data and updates the prediction results. The prediction unit can also perform more accurate risk assessments by taking into account the characteristics of each region and past disaster history. As a result, the prediction unit can always provide highly accurate risk predictions based on the latest information and support rapid and appropriate responses. Furthermore, the prediction unit can improve prediction accuracy by continuously training the prediction model using AI. For example, the model is retrained every time new data is added to improve prediction accuracy. This allows the prediction unit to always provide highly accurate predictions based on the latest information, which can be useful in formulating a company's management strategy, financial plan, and sales strategy.

[0068] The provision unit provides the prediction results obtained by the prediction unit. For example, the provision unit provides the prediction results in a form that can be used for management strategies, financial plans, and sales strategies. Specifically, the provision unit can provide the prediction results in report format, dashboard, alert function, and the like. For example, for managers, the sales and profit prediction results can be visually displayed in graphs and charts to help with planning management strategies. For the financial department, a simulation of a financial plan based on the prediction results can be performed to help with cash flow and investment planning. For the sales department, the provision unit can support the planning of a sales strategy based on the prediction results and set sales targets for specific products and regions. Furthermore, the provision unit can collect user feedback and continuously improve the accuracy of the prediction results and the provision method. For example, the provision unit collects user evaluations and opinions on the prediction results and reflects them in improving the prediction model and the provision method. Furthermore, the provision unit can reliably transmit information using multiple communication means. For example, important information can be provided quickly through email, SMS, and dedicated applications. This allows the providing unit to provide the user with prediction results quickly and reliably, and to be useful in formulating a company's management strategy, financial plan, and sales strategy. Furthermore, the providing unit can provide an alert function based on the prediction results, and automatically issue a notification when a specific condition is met. For example, an alert can be issued when sales fall significantly below the forecast or when sales of a specific product increase sharply, thereby supporting a quick response. This not only allows the providing unit to be useful in formulating a company's management strategy, financial plan, and sales strategy, but also supports quick decision-making and improves the company's competitiveness.

[0069] The collection unit can collect information on the customer by industry, size, and product. The collection unit collects information on, for example, manufacturing, retail, large companies, small and medium-sized enterprises, and specific product categories. For example, the collection unit can collect sales data for manufacturing customers and sales data for retail customers. The collection unit can also collect sales data for large company customers and sales data for small and medium-sized enterprise customers. Furthermore, the collection unit can collect sales data for specific product categories. For example, the collection unit collects sales data for product categories such as electronic devices, food, and clothing. This allows for more detailed data analysis by collecting information on the customer by industry, size, and product. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the customer by industry, size, and product into AI and collect information using AI.

[0070] The analysis unit can analyze the collected data and extract the increase / decrease trend of sales to customers of a specific industry and size. The analysis unit analyzes the collected data using, for example, a method such as time series analysis, trend analysis, and regression analysis. For example, the analysis unit uses time series analysis to extract the increase / decrease trend of sales to customers of a specific industry and size. The analysis unit can also use trend analysis to extract the increase / decrease trend of sales to customers of a specific industry and size. Furthermore, the analysis unit can also use regression analysis to extract the increase / decrease trend of sales to customers of a specific industry and size. For example, the analysis unit can analyze the increase / decrease trend of sales to customers in the manufacturing industry and analyze the increase / decrease trend of sales to customers in the retail industry. The analysis unit can also analyze the increase / decrease trend of sales to customers of large companies and analyze the increase / decrease trend of sales to customers of small and medium-sized enterprises. As a result, by grasping the increase / decrease trend of sales to customers of a specific industry and size, more accurate sales forecasting is possible. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and use the AI ​​to extract trends in sales increases or decreases.

[0071] The analysis unit can analyze sales trends by product and grasp which product is likely to sell at which time. The analysis unit can analyze sales trends by product, for example, using sales data by category, seasonal analysis, etc. For example, the analysis unit can grasp which product is likely to sell at which time using sales data by category. The analysis unit can also grasp which product is likely to sell at which time using seasonal analysis. For example, the analysis unit can analyze sales trends of electronic devices and analyze sales trends of food. The analysis unit can also analyze sales trends of clothing and grasp which product is likely to sell at a specific time. As a result, grasping sales trends by product is useful for planning sales strategies. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input sales data by product into AI and analyze sales trends using AI.

[0072] The prediction unit can predict future sales and profits based on current and future trends, the economic situation by industry, the investment situation, and the trends of rival companies. The prediction unit predicts current and future trends based on, for example, economic indicators, industry reports, expert predictions, etc. For example, the prediction unit predicts current and future trends based on economic indicators. The prediction unit can also predict current and future trends based on industry reports. The prediction unit can also predict current and future trends based on expert predictions. For example, the prediction unit predicts future sales and profits taking into account the current economic situation and the economic trends of the industry. The prediction unit can also make predictions that reflect the competitive environment by taking into account new products and market entry trends of rival companies. This makes it possible to make more accurate sales and profit predictions by taking into account current and future trends, the economic situation by industry, and the trends of rival companies. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input economic indicators and industry reports into AI and predict future sales and profits using AI.

[0073] The providing unit can provide the prediction results in a form that can be utilized for business strategies, financial plans, and sales strategies. The providing unit provides the prediction results in, for example, a report format, a dashboard, an alert function, and the like. For example, the providing unit provides the prediction results in a report format, and provides them in a form that can be utilized for business strategies, financial plans, and sales strategies. The providing unit can also provide the prediction results in a dashboard so that the prediction results can be confirmed in real time. Furthermore, the providing unit can provide the prediction results in an alert function, and can immediately notify important prediction results. This supports corporate decision-making by utilizing the prediction results in business strategies, financial plans, and sales strategies. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the prediction results to AI and provide the prediction results using AI.

[0074] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is stressed, the collection unit automatically collects data to reduce the burden on the user. In addition, when the user is relaxed, the collection unit can also allow the user to select the timing of data collection. Furthermore, when the user is in a hurry, the collection unit can quickly collect data and provide the results quickly. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI, estimate the emotion using the generation AI, and adjust the timing of data collection.

[0075] The collection unit can analyze the past data collection history and select an appropriate collection method. The collection unit, for example, selects the most efficient collection method from the past data collection history. The collection unit can also optimize the collection frequency based on the past data collection history. Furthermore, the collection unit can analyze the past data collection history and propose a method for improving the quality of data. For example, the collection unit selects an optimal collection method based on the past data collection history and improves the efficiency of data collection. As a result, the optimal collection method can be selected by analyzing the past data collection history, and the efficiency of data collection can be improved. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into AI and select the optimal collection method using AI.

[0076] The collection unit can perform filtering based on the company's current business situation and market trends when collecting data. For example, the collection unit preferentially collects important data based on the company's current business situation. The collection unit can also collect highly relevant data in consideration of market trends. Furthermore, the collection unit can combine the company's business situation and market trends to select the optimal data collection method. For example, the collection unit preferentially collects important data based on the company's financial report and industry report. As a result, highly relevant data can be collected by filtering data based on the company's business situation and market trends. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the company's business situation and market trends into AI and filter the data using AI.

[0077] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects important data. In addition, the collection unit can also collect detailed data when the user is relaxed. Furthermore, when the user is in a hurry, the collection unit can also prioritize data that can be collected quickly. In this way, important data can be preferentially collected by determining the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI, estimate the emotion using the generation AI, and determine the priority of the data.

[0078] The collection unit can collect highly relevant data preferentially in consideration of the geographical location information of the company when collecting data. The collection unit, for example, collects region-specific data based on the location of the company. The collection unit can also collect highly relevant market data in consideration of the geographical location information. Furthermore, the collection unit can select an optimal data collection method based on the geographical location information of the company. For example, the collection unit collects region-specific data based on the location of the company and grasps the market trends of the region. In this way, region-specific data can be collected by considering the geographical location information of the company. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the company into AI and collect highly relevant data using AI.

[0079] The collection unit can analyze the social media activity of the company and collect relevant data when collecting data. The collection unit, for example, collects relevant data based on the social media activity of the company. The collection unit can also analyze social media trends and collect important data. Furthermore, the collection unit can select an optimal data collection method in consideration of the social media activity of the company. For example, the collection unit collects relevant data based on the social media activity of the company and grasps social media trends. In this way, highly relevant data can be collected by analyzing the social media activity of the company. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media activity of the company into AI and collect related data using AI.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can also provide an analysis result that covers the main points. In this way, by adjusting the expression method of the analysis according to the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI, estimate the emotion using the generation AI, and adjust the expression method of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimize analysis resources according to the importance of the data. For example, the analysis unit evaluates the importance of the data based on the business impact and the reliability of the data, and adjusts the level of detail of the analysis. As a result, efficient data analysis is possible by adjusting the level of detail of the analysis according to the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and adjust the level of detail of the analysis using AI.

[0082] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an industry-specific analysis algorithm to industry-specific data. The analysis unit can also apply a product-specific analysis algorithm to product-specific data. The analysis unit can also apply a scale-specific analysis algorithm to scale-specific data. For example, the analysis unit applies a manufacturing-specific analysis algorithm to manufacturing data, and applies a retail-specific analysis algorithm to retail data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into AI and apply an appropriate analysis algorithm using AI.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit performs a short and to the point analysis. In addition, the analysis unit can also perform a detailed analysis when the user is relaxed. Furthermore, when the user is excited, the analysis unit can also perform a visually stimulating analysis. In this way, by adjusting the length of the analysis according to the user's emotion, it is possible to provide an optimal analysis result for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI, estimate the emotion using the generation AI, and adjust the length of the analysis.

[0084] The analysis unit can determine the analysis priority based on the collection time of the data during the analysis. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also prioritize the analysis of important data based on past data. Furthermore, the analysis unit can optimize the analysis resources according to the collection time of the data. For example, the analysis unit prioritizes the analysis of the latest data, and prioritizes the analysis of important data based on past data. In this way, the analysis priority can be determined based on the collection time of the data, so that the latest data can be analyzed with priority. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to AI and determine the analysis priority using AI.

[0085] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can optimize analysis resources according to the relevance of data. For example, the analysis unit evaluates the relevance of data using correlation analysis or causal analysis and adjusts the order of analysis. This enables efficient data analysis by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI and adjust the order of analysis using AI.

[0086] The prediction unit can estimate the user's emotion and adjust the prediction method based on the estimated user's emotion. For example, when the user is relaxed, the prediction unit provides a detailed prediction method. In addition, when the user is in a hurry, the prediction unit can also provide a simplified prediction method. In addition, when the user is excited, the prediction unit can also provide a visually stimulating prediction method. In this way, by adjusting the prediction method according to the user's emotion, it is possible to provide an optimal prediction result for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data into the generation AI, estimate the emotion using the generation AI, and adjust the prediction method.

[0087] The prediction unit can adjust the prediction algorithm by referring to past prediction data at the time of prediction. The prediction unit optimizes the prediction algorithm based on, for example, past prediction data. The prediction unit can also analyze past prediction data and improve prediction accuracy. Furthermore, the prediction unit can select an optimal prediction method by referring to past prediction data. For example, the prediction unit optimizes the prediction algorithm based on past sales prediction data and improves prediction accuracy. As a result, the prediction algorithm can be optimized and prediction accuracy can be improved by referring to past prediction data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input past prediction data into AI and adjust the prediction algorithm using AI.

[0088] The prediction unit can customize the means of prediction based on the current business situation of the company at the time of prediction. The prediction unit selects an optimal prediction means based on, for example, the current business situation of the company. The prediction unit can also adjust the level of detail of the prediction according to the business situation. Furthermore, the prediction unit can optimize the resources of the prediction in consideration of the business situation of the company. For example, the prediction unit selects an optimal prediction means based on the financial report and performance indicators of the company and adjusts the level of detail of the prediction. As a result, by customizing the means of prediction based on the current business situation of the company, more appropriate prediction results can be provided. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the business situation of the company into AI and customize the means of prediction using AI.

[0089] The prediction unit can estimate the user's emotion and determine the priority of prediction based on the estimated user's emotion. For example, when the user is feeling stressed, the prediction unit prioritizes important predictions. In addition, the prediction unit can also perform detailed predictions when the user is relaxed. Furthermore, the prediction unit can perform predictions quickly when the user is in a hurry. This allows important predictions to be prioritized by determining the priority of predictions according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data into the generation AI, estimate the emotion using the generation AI, and determine the priority of predictions.

[0090] The prediction unit can select an appropriate prediction method in consideration of the geographical location information of the company at the time of prediction. The prediction unit selects a region-specific prediction method based on, for example, the location of the company. The prediction unit can also provide an optimal prediction method in consideration of the geographical location information. Furthermore, the prediction unit can optimize prediction resources based on the geographical location information of the company. For example, the prediction unit selects a region-specific prediction method based on the location of the company and grasps the market trends of the region. In this way, a region-specific prediction method can be provided by considering the geographical location information of the company. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the geographical location information of the company into AI and select the optimal prediction method using AI.

[0091] At the time of prediction, the prediction unit can analyze the social media activity of the company and propose a means of prediction. The prediction unit can, for example, propose an optimal prediction means based on the social media activity of the company. The prediction unit can also analyze social media trends and improve the accuracy of prediction. Furthermore, the prediction unit can optimize prediction resources in consideration of the social media activity of the company. For example, the prediction unit proposes an optimal prediction means based on the social media activity of the company and grasps social media trends. In this way, the optimal prediction means can be proposed by analyzing the social media activity of the company. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the social media activity of the company into AI and propose a means of prediction using AI.

[0092] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. In addition, when the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to provide information that is easy for the user to understand by adjusting the display method of information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, estimate the emotion using the generation AI, and adjust the display method of the information.

[0093] The providing unit can select an appropriate providing method by referring to the user's past usage history at the time of providing. The providing unit selects an optimal providing method based on the user's past usage history, for example. The providing unit can also analyze the past usage history and provide optimal information to the user. Furthermore, the providing unit can optimize the providing resources by referring to the user's past usage history. For example, the providing unit selects an optimal providing method based on past access logs and purchase history, and provides information useful to the user. As a result, by referring to the user's past usage history, the optimal information providing method can be selected and information useful to the user can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history into AI and select the optimal providing method using AI.

[0094] The providing unit can customize information based on the current needs of the user at the time of providing. The providing unit provides optimal information based on the current needs of the user, for example. The providing unit can also adjust the level of detail of the information according to the needs of the user. Furthermore, the providing unit can optimize information resources in consideration of the current needs of the user. For example, the providing unit grasps the current needs of the user based on survey results and behavioral data, and provides optimal information. As a result, the information can be customized based on the current needs of the user, thereby providing optimal information for the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the current needs of the user into AI and customize the information using AI.

[0095] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide important information with priority. In addition, the providing unit can also provide detailed information when the user is relaxed. Furthermore, when the user is in a hurry, the providing unit can also prioritize information that can be provided quickly. In this way, important information can be provided with priority by determining the priority of information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, estimate the emotion using the generation AI, and determine the priority of information.

[0096] The providing unit can select the optimal providing method in consideration of the geographical location information of the user at the time of providing. The providing unit provides area-specific information based on, for example, the user's location. The providing unit can also provide highly relevant information in consideration of the geographical location information. Furthermore, the providing unit can select the optimal providing method based on the user's geographical location information. For example, the providing unit provides area-specific information based on the user's location and grasps the market trend of the area. Thereby, it is possible to provide area-specific information by considering the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI and select the optimal providing method using AI.

[0097] The providing unit can analyze the user's social media activity and propose a means of providing information at the time of providing. The providing unit can propose an optimal means of providing information based on, for example, the user's social media activity. The providing unit can also analyze social media trends and provide important information. Furthermore, the providing unit can select an optimal information providing method in consideration of the user's social media activity. For example, the providing unit proposes an optimal means of providing information based on the user's social media activity and grasps social media trends. In this way, the optimal means of providing information can be proposed by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI and propose a means of providing information using AI.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The collection department can collect employee satisfaction data of companies in addition to the company's past sales and profit data. For example, by collecting employee survey results, turnover rates, employee performance data, etc. and inputting this data into AI, it is possible to analyze the impact of employee satisfaction on sales and profits. Furthermore, the collection department can propose measures to improve employee motivation based on the employee satisfaction data. This allows companies to increase sales and profits by improving employee satisfaction.

[0100] The collection department can collect customer satisfaction data of companies in addition to their past sales and profit data. For example, by collecting customer survey results, customer feedback, and customer repeat rates, and inputting this data into AI, it is possible to analyze the impact of customer satisfaction on sales and profits. Furthermore, the collection department can propose measures to improve customer satisfaction based on the customer satisfaction data. This allows companies to increase sales and profits by improving customer satisfaction.

[0101] The analysis unit can analyze the brand value data of the company in addition to the collected data. For example, it can analyze data such as brand awareness, brand loyalty, and brand image, and understand the impact of these data on sales and profits. Furthermore, the analysis unit can propose improvements to the brand strategy based on the brand value data. This allows companies to increase their sales and profits by improving their brand value.

[0102] The analysis unit can analyze the company's supply chain data in addition to the collected data. For example, it can analyze data such as supply chain efficiency, costs, and lead times, and understand the impact of these data on sales and profits. Furthermore, the analysis unit can propose supply chain optimization measures based on the supply chain data. This allows companies to increase sales and profits by improving the efficiency of their supply chains.

[0103] The forecasting department can forecast future sales and profits based on the company's innovation activity data, in addition to current and future trends. For example, the forecasting department can forecast the impact of innovation activities on sales and profits based on data such as the company's research and development investment, the development status of new products, and the status of patent acquisition. Furthermore, the forecasting department can make predictions that take into account future market trends and the competitive environment based on the innovation activity data. This allows companies to improve their future sales and profits by strengthening their innovation activities.

[0104] When providing the prediction result, the providing unit can estimate the user's emotion and adjust the format of the information to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, concise and to the point information can be provided. When the user is relaxed, detailed information can be provided. Furthermore, when the user is excited, visually appealing information can be provided. In this way, the format of information can be adjusted according to the user's emotion, thereby providing optimal information for the user.

[0105] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, data collection can be performed quickly to provide the results quickly. In this way, the frequency of data collection can be adjusted according to the user's emotions, thereby reducing the burden on the user.

[0106] The analysis unit can estimate the user's emotions and adjust the visualization method of the analysis based on the estimated user's emotions. For example, if the user is nervous, a simple graph or chart with high visibility can be provided. Also, if the user is relaxed, a complex graph or chart containing detailed data can be provided. Furthermore, if the user is excited, visually stimulating animations and interactive visualizations can be provided. In this way, by adjusting the visualization method of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0107] The prediction unit can estimate the user's emotions and adjust the accuracy of the prediction based on the estimated user's emotions. For example, if the user is feeling stressed, a simplified prediction can be provided to reduce the burden on the user. Also, if the user is relaxed, a detailed prediction can be provided. Furthermore, if the user is in a hurry, a quick prediction can be made and the result can be provided quickly. In this way, by adjusting the accuracy of the prediction according to the user's emotions, it is possible to provide the optimal prediction result for the user.

[0108] The providing unit can estimate the user's emotions and determine the priority order of information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, important information can be provided preferentially. Also, if the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, information that can be provided quickly can be given priority. In this way, by determining the priority order of information according to the user's emotions, important information can be provided preferentially.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects the company's past sales and profit data. The company's past sales and profit data includes, but is not limited to, monthly data, quarterly data, and annual data. The collection unit collects, for example, sales and profit data for the past five years and allows the AI ​​to learn from it. The collection unit can also collect information on customers by industry, size, and product type. For example, information on manufacturing, retail, large companies, small and medium-sized enterprises, and specific product categories is collected. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts trends. Trends are extracted by methods such as, but not limited to, moving averages, seasonal adjustments, and regression analysis. For example, the analysis unit analyzes the increase / decrease trends in sales to customers in a particular industry or size. The analysis unit can also analyze sales trends by product type to determine which products are likely to sell at what times. Step 3: The forecasting unit forecasts future sales and profits based on the trends extracted by the analysis unit and current and future trends. The current and future trends are forecast based on, for example, economic indicators, industry reports, expert forecasts, etc., but are not limited to such examples. For example, the forecasting unit forecasts future sales and profits taking into account the current economic situation and the business trends of the industry. The forecasting unit can also make forecasts that reflect the competitive environment by taking into account new products and market entry trends of rival companies. Step 4: The provision unit provides the prediction results obtained by the prediction unit. For example, the provision unit provides the prediction results in a form that can be used for management strategies, financial plans, and sales strategies. For example, the provision unit can provide the prediction results in a report format, a dashboard, an alert function, or the like.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned collection unit, analysis unit, prediction unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14, and collects past sales and profit data of a company. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and extracts trends. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts future sales and profits based on the analyzed trend and current and future trends. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the prediction result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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, the RAM 30, and the 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.

[0118] 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.

[0119] 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 the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] 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 performed in a secure state.

[0122] Fig. 4 shows an example of 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.

[0123] 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.

[0124] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0128] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned collection unit, analysis unit, prediction unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214, and collects past sales and profit data of a company. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and extracts trends. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts future sales and profits based on the analyzed trend and current and future trends. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the prediction result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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, the RAM 30, and the 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.

[0134] 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.

[0135] 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 the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] 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 performed in a secure state.

[0138] 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.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned collection unit, analysis unit, prediction unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314, and collects past sales and profit data of a company. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and extracts trends. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts future sales and profits based on the analyzed trend and current and future trends. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the prediction result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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, the RAM 30, and the 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.

[0150] 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.

[0151] 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 the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] 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 performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0155] 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.

[0156] 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.

[0157] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0159] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 a voice indicating a user input for 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.

[0161] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0163] Each of the multiple elements including the above-mentioned collection unit, analysis unit, prediction unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414, and collects past sales and profit data of a company. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and extracts trends. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts future sales and profits based on the analyzed trend and current and future trends. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the prediction result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] 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 Universal Serial Bus (USB) 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.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0177] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection department that collects past sales and profit data; an analysis unit that analyzes the data collected by the collection unit and extracts trends; a prediction unit for predicting future sales and profits based on the trends extracted by the analysis unit and current and future trends; A providing unit that provides the prediction result obtained by the prediction unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect information on customers by industry, size, and product 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected data to extract trends in sales increases and decreases for customers in specific industries and sizes. 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze sales trends by product type to understand which products are most likely to sell at which times 2. The system of claim 1, (Appendix 5) The prediction unit is Predict future sales and profits based on current and future trends, industry economic conditions, investment trends, and competitor trends 2. The system of claim 1, (Appendix 6) The providing unit is Provide prediction results in a form that can be used for business strategies, financial plans, and sales strategies 2. The system of claim 1, (Appendix 7) The collecting unit includes: Using a method for estimating user emotion, the timing of data collection is adjusted based on the estimated user emotion. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze past data collection history and select appropriate collection methods 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting data, filter it based on the company's current business situation and market trends. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Using a method for estimating user emotions, a priority is determined for data to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, prioritize collection of relevant data by considering the geographic location of the company 2. The system of claim 1, (Appendix 12) The collecting unit includes: During data collection, analyze the company's social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Using a method for estimating user emotions, the presentation of the analysis is adjusted based on the estimated user emotions. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) The analysis unit is Using a method for estimating user emotion, the length of the analysis is adjusted based on the estimated user emotion. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 19) The prediction unit is Using a method for estimating user sentiment, the method for predicting is adjusted based on the estimated user sentiment. 2. The system of claim 1, (Appendix 20) The prediction unit is When making predictions, look at past forecast data to adjust the forecasting algorithm 2. The system of claim 1, (Appendix 21) The prediction unit is When forecasting, customize the forecasting methodology based on the company's current operating conditions 2. The system of claim 1, (Appendix 22) The prediction unit is Using a method for estimating user sentiment, the system prioritizes predictions based on the estimated user sentiment. 2. The system of claim 1, (Appendix 23) The prediction unit is When forecasting, consider the geographic location of the company to select the appropriate forecasting method 2. The system of claim 1, (Appendix 24) The prediction unit is When forecasting, we analyze the social media activity of companies to suggest a means of prediction. 2. The system of claim 1, (Appendix 25) The providing unit is Using a method for estimating user emotion, a method for displaying information to be provided is adjusted based on the estimated user emotion. 2. The system of claim 1, (Appendix 26) The providing unit is When providing the service, the appropriate method of providing the service is selected by referring to the user's past usage history. 2. The system of claim 1, (Appendix 27) The providing unit is Customize information based on the user's current needs at the time of delivery 2. The system of claim 1, (Appendix 28) The providing unit is Using a method for estimating user emotions, the priority of information to be provided is determined based on the estimated user emotions. 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, the optimal method of providing the information is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The providing unit is At the time of delivery, analyze the user's social media activity to suggest ways to deliver information 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects past sales and profit data; an analysis unit that analyzes the data collected by the collection unit and extracts trends; a prediction unit that predicts future sales and profits based on the trends extracted by the analysis unit and current and future trends; A providing unit that provides the prediction result obtained by the prediction unit. A system characterized by:

2. The collecting unit includes: Collect information on customers by industry, size, and product 2. The system of claim 1 .

3. The analysis unit is Analyze the collected data to extract trends in sales increases and decreases for customers in specific industries and sizes.

2. The system of claim 1 .

4. The analysis unit is Analyze sales trends by product type to understand which products are most likely to sell at which times 2. The system of claim 1 .

5. The prediction unit is Predict future sales and profits based on current and future trends, industry economic conditions, investment trends, and competitor trends 2. The system of claim 1 .

6. The providing unit is Provide prediction results in a form that can be used for business strategies, financial plans, and sales strategies 2. The system of claim 1 .

7. The collecting unit includes: Using a method for estimating user emotion, the timing of data collection is adjusted based on the estimated user emotion.

2. The system of claim 1 .

8. The collecting unit includes: Analyze past data collection history and select appropriate collection methods 2. The system of claim 1 .

9. The collecting unit includes: When collecting data, filter it based on the company's current business situation and market trends.

2. The system of claim 1 .

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