system

Abstract

The system according to the embodiment aims to efficiently share information between departments and manage performance taking into account external factors. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an external factor acquisition unit, and a forecast creation unit. The collection unit collects information from each department. The analysis unit analyzes the information collected by the collection unit. The external factor acquisition unit acquires information from external data sources. The forecast creation unit creates a forecast based on the data obtained by the analysis unit and the external factor acquisition unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately consider information sharing between departments or the influence of external factors when managing performance across an organization, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently share information between departments and manage performance taking into account external factors. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an external factor acquisition unit, and a forecast creation unit. The collection unit collects information from each department. The analysis unit analyzes the information collected by the collection unit. The external factor acquisition unit acquires information from external data sources. The forecast creation unit creates a forecast based on the data obtained by the analysis unit and the external factor acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently share information between departments and manage performance taking into account external factors. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A performance management system according to an embodiment of the present invention collects and analyzes information from each department and creates a forecast taking external factors into account. The performance management system efficiently manages performance across the entire organization by collecting information from each department, analyzing it using an AI chatbot, acquiring information from external data sources, and creating a forecast. For example, the performance management system collects information such as sales data from the sales department and campaign data from the marketing department. The AI ​​chatbot then analyzes the collected data and measures the effectiveness of each measure. Furthermore, the performance management system acquires external factors such as disaster information from external data sources and reflects their impact on the organization's performance. Finally, the performance management system uses machine learning to predict future forecasts based on past data and current conditions. This promotes information sharing between departments and enables highly accurate forecasts and effectiveness measurement that take into account factors outside the department. This allows the performance management system to efficiently manage performance across the entire organization. For example, the sales department can predict future sales based on the forecast provided by the AI ​​chatbot and plan appropriate measures. Furthermore, the marketing department can accurately measure the effectiveness of campaigns and incorporate the results into future campaigns.

[0029] The performance management system according to the embodiment includes a collection unit, an analysis unit, an external factor acquisition unit, and a forecast creation unit. The collection unit collects information from each department. The information from each department includes, for example, sales data from the sales department and campaign data from the marketing department, but is not limited to these examples. The collection unit, for example, collects sales data from the sales department. The collection unit can also collect campaign data from the marketing department. The collection unit can also collect data from other departments. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, uses a machine learning algorithm to measure the effectiveness of each measure. For example, the analysis unit analyzes the extent to which a specific campaign has affected sales. The analysis unit can also use a machine learning algorithm to measure the effectiveness of other measures. The analysis unit can also use a machine learning algorithm to predict the effectiveness of measures. The external factor acquisition unit acquires information from an external data source. For example, the external factor acquisition unit acquires disaster information from an external data source. For example, the external factor acquisition unit can acquire weather data. The external factor acquisition unit can also acquire economic indicators. The external factor acquisition unit can also acquire information from other external data sources. The forecast creation unit creates a forecast based on the data acquired by the analysis unit and the external factor acquisition unit. The forecast creation unit predicts future forecasts based on, for example, past data and the current situation. For example, the forecast creation unit uses a machine learning algorithm to predict future sales. The forecast creation unit can also use a machine learning algorithm to predict future market trends. The forecast creation unit can also use a machine learning algorithm to create other forecasts. This allows the performance management system according to the embodiment to efficiently manage performance across an entire organization. For example, the performance management system can efficiently manage performance across an entire organization by collecting and analyzing information from each department and creating forecasts taking external factors into consideration.

[0030] The collection unit can collect sales data from the sales department or campaign data from the marketing department. The collection unit, for example, collects sales data from the sales department. Sales data includes, for example, monthly sales and sales by product, but is not limited to these examples. The collection unit can also collect campaign data from the marketing department. Campaign data includes, for example, the campaign period and target customers, but is not limited to these examples. The collection unit can also collect data from other departments. This allows for more detailed performance management by collecting specific data from each department.

[0031] The analysis unit can measure the effectiveness of each measure using a machine learning algorithm. The analysis unit, for example, uses a machine learning algorithm to analyze the extent to which a specific campaign has affected sales. For example, the analysis unit measures the effectiveness of a campaign using regression analysis. The analysis unit can also measure the effectiveness of a campaign using a classification algorithm. The analysis unit can also measure the effectiveness of a campaign using a clustering algorithm. In this way, by using a machine learning algorithm, the effectiveness of each measure can be measured with high accuracy.

[0032] The external factor acquisition unit can acquire disaster information from an external data source. The external factor acquisition unit acquires, for example, earthquake information from the external data source. Earthquake information includes, for example, the date and time of the earthquake occurrence, the epicenter, and seismic intensity, but is not limited to these examples. The external factor acquisition unit can also acquire flood information from the external data source. Flood information includes, for example, the date and time of the flood occurrence, the damage situation, etc., but is not limited to these examples. The external factor acquisition unit can also acquire other disaster information. In this way, by acquiring disaster information from an external data source, it becomes possible to manage performance taking into account factors outside the department.

[0033] The forecast creation unit can predict future forecasts based on past data and current conditions. The forecast creation unit, for example, predicts future sales based on past sales data and current market conditions. For example, the forecast creation unit uses a machine learning algorithm to predict future sales based on past sales data and current market conditions. The forecast creation unit can also predict future campaign effectiveness based on past campaign data and current market conditions. The forecast creation unit can also predict other forecasts based on past data and current conditions. This makes it possible to create highly accurate forecasts by predicting future forecasts based on past data and current conditions.

[0034] The collection unit can analyze the past data submission history of each department and select an appropriate collection method. The collection unit, for example, analyzes the format of data submitted by each department in the past and selects the most efficient collection method. For example, the collection unit analyzes the past data submission history and selects the optimal collection method. The collection unit can also analyze the data submission frequency of each department and set an optimal collection schedule. For example, the collection unit analyzes the past data submission frequency and set an optimal collection schedule. The collection unit can also evaluate the quality of data based on the data submission history of each department and select the optimal collection method. For example, the collection unit evaluates the quality of data based on the past data submission history and selects the optimal collection method. In this way, by analyzing the past data submission history, the optimal collection method can be selected and efficient information collection becomes possible.

[0035] When collecting information, the collection unit can perform filtering based on the current projects and areas of interest of each department. For example, the collection unit collects only data related to the current projects of each department. For example, the collection unit filters and collects data related to the current projects. The collection unit can also prioritize collecting highly relevant data based on the areas of interest of each department. For example, the collection unit prioritizes collecting highly relevant data based on the areas of interest. The collection unit can also filter and collect necessary data taking into account the progress of projects in each department. For example, the collection unit filters and collects necessary data taking into account the progress of projects. In this way, by filtering based on the current projects and areas of interest of each department, highly relevant information can be efficiently collected.

[0036] When collecting information, the collection unit can select the optimal collection means according to the input method of each department. For example, if each department uses voice input, the collection unit collects voice data. For example, the collection unit collects voice data from departments using voice input. Furthermore, if each department uses text input, the collection unit can also collect text data. For example, the collection unit collects text data from departments using text input. Furthermore, if each department uses image input, the collection unit can also collect image data. For example, the collection unit collects image data from departments using image input. This enables efficient information collection by selecting the optimal collection means according to the input method of each department.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each department. The collection unit, for example, prioritizes collecting region-specific information based on the location of each department. For example, the collection unit prioritizes collecting region-specific information by taking into account the geographical location information. The collection unit can also prioritize collecting related market data by taking into account the geographical location information of each department. For example, the collection unit prioritizes collecting related market data by taking into account the geographical location information. The collection unit can also prioritize collecting external factors such as disaster information based on the geographical location information of each department. For example, the collection unit prioritizes collecting external factors such as disaster information based on the geographical location information. In this way, by prioritizedly collecting highly relevant information by taking into account the geographical location information of each department, it is possible to efficiently collect region-specific information.

[0038] When collecting information, the collection department can analyze the social media activities of each department and collect specific information. For example, the collection department analyzes the content of posts on social media by each department and collects related market data. For example, the collection department analyzes social media activities and collects related market data. The collection department can also collect customer feedback based on the social media activities of each department. For example, the collection department collects customer feedback based on the social media activities. The collection department can also analyze social media trends of each department and collect related information. For example, the collection department analyzes social media trends and collects related information. In this way, by analyzing the social media activities of each department, related market data and customer feedback can be collected efficiently.

[0039] When collecting information, the collection unit can customize the collection method by reflecting past feedback from each department. The collection unit, for example, improves the collection method based on past feedback from each department. For example, the collection unit improves the collection method based on past feedback. The collection unit can also adjust the type of data to be collected by reflecting past feedback from each department. For example, the collection unit adjusts the type of data to be collected by reflecting past feedback. The collection unit can also optimize the collection frequency by referring to past feedback from each department. For example, the collection unit optimizes the collection frequency by referring to past feedback. In this way, the collection method can be optimized by reflecting past feedback from each department, enabling efficient information collection.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the measure. For example, the analysis unit performs a detailed analysis on a measure with a high level of importance. For example, the analysis unit performs a detailed analysis on a measure with a high level of importance based on the importance of the measure. The analysis unit can also perform a simplified analysis on a measure with a low level of importance. For example, the analysis unit performs a simplified analysis on a measure with a low level of importance based on the importance of the measure. The analysis unit can also optimally allocate analysis resources according to the importance of the measure. For example, the analysis unit optimally allocates analysis resources based on the importance of the measure. In this way, by adjusting the level of detail of the analysis based on the importance of the measure, resources can be optimally allocated and efficient analysis can be performed.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the measure. For example, the analysis unit applies a marketing-specific analysis algorithm to a marketing measure. For example, the analysis unit applies a marketing-specific analysis algorithm to a marketing measure depending on the category of the measure. The analysis unit can also apply a sales-specific analysis algorithm to a sales measure. For example, the analysis unit applies a sales-specific analysis algorithm to a sales measure depending on the category of the measure. The analysis unit can also apply a human resources-specific analysis algorithm to a human resources measure. For example, the analysis unit applies a human resources-specific analysis algorithm to a human resources measure depending on the category of the measure. In this way, by applying different analysis algorithms depending on the category of the measure, optimal analysis can be performed for each measure.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of each department. The analysis unit, for example, optimizes the analysis algorithm based on past analysis results of each department. For example, the analysis unit optimizes the analysis algorithm based on past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results of each department. For example, the analysis unit improves the accuracy of the analysis by referring to past analysis results. The analysis unit can also analyze past analysis results of each department and identify areas for improvement in the analysis. For example, the analysis unit analyzes past analysis results and identifies areas for improvement in the analysis. In this way, the accuracy of the analysis can be improved by referring to past analysis results of each department.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the implementation timing of measures. For example, if the implementation timing of a measure is approaching, the analysis unit performs analysis preferentially. For example, the analysis unit performs analysis preferentially for measures whose implementation timing is approaching based on the implementation timing of the measure. Furthermore, the analysis unit can postpone analysis for measures whose implementation timing is further away if the implementation timing of the measure is far away. For example, the analysis unit postpones analysis for measures whose implementation timing is further away based on the implementation timing of the measure. Furthermore, the analysis unit can optimally allocate analysis resources according to the implementation timing of the measure. For example, the analysis unit optimally allocates analysis resources based on the implementation timing of the measure. In this way, by determining the priority of analysis based on the implementation timing of the measure, timely analysis is possible.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of measures. For example, when the relevance of measures is high, the analysis unit performs analysis preferentially. For example, the analysis unit performs analysis preferentially on measures with high relevance based on the relevance of measures. Furthermore, when the relevance of measures is low, the analysis unit can postpone analysis. For example, the analysis unit postpones analysis on measures with low relevance based on the relevance of measures. Furthermore, the analysis unit can optimally adjust the order of analysis according to the relevance of measures. For example, the analysis unit optimally adjusts the order of analysis based on the relevance of measures. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of measures.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the level of expertise of each department. For example, if the level of expertise of each department is high, the analysis unit provides analysis results that use a lot of technical terminology. For example, the analysis unit provides analysis results that use a lot of technical terminology to departments with a high level of expertise according to the level of expertise. Furthermore, the analysis unit can also provide analysis results that avoid technical terminology when the level of expertise of each department is low. For example, the analysis unit provides analysis results that avoid technical terminology to departments with a low level of expertise according to the level of expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the level of expertise of each department. For example, the analysis unit adjusts the way in which the analysis results are expressed according to the level of expertise. In this way, by adjusting the use of technical terminology in the analysis according to the level of expertise of each department, it is possible to provide analysis results that are easy to understand.

[0046] When acquiring external factors, the external factor acquisition unit can predict current external factors by referring to past external factor data. The external factor acquisition unit, for example, refers to past disaster data to predict current disaster risk. For example, the external factor acquisition unit refers to past disaster data to predict current disaster risk. The external factor acquisition unit can also predict current market trends by referring to past market data. For example, the external factor acquisition unit refers to past market data to predict current market trends. The external factor acquisition unit can also predict current weather by referring to past weather data. For example, the external factor acquisition unit refers to past weather data to predict current weather. In this way, by referring to past external factor data, current external factors can be predicted with high accuracy.

[0047] When acquiring external factors, the external factor acquisition unit can evaluate the reliability of the external data source and select data to acquire. For example, the external factor acquisition unit evaluates the reliability of the external data source and prioritizes acquiring highly reliable data. For example, the external factor acquisition unit evaluates the reliability of the external data source and prioritizes acquiring highly reliable data. The external factor acquisition unit can also evaluate the reliability of the external data source and exclude low-reliability data. For example, the external factor acquisition unit evaluates the reliability of the external data source and excludes low-reliability data. The external factor acquisition unit can also evaluate the reliability of the external data source and acquire only highly reliable data. For example, the external factor acquisition unit evaluates the reliability of the external data source and acquires only highly reliable data. In this way, highly reliable data can be efficiently acquired by evaluating the reliability of the external data source.

[0048] When acquiring external factors, the external factor acquisition unit can adjust the timing of acquisition based on the update frequency of the external data source. For example, if the update frequency of the external data source is high, the external factor acquisition unit acquires data frequently. For example, the external factor acquisition unit acquires data frequently when the update frequency is high based on the update frequency of the external data source. The external factor acquisition unit can also reduce the frequency of data acquisition when the update frequency of the external data source is low. For example, the external factor acquisition unit reduces the frequency of data acquisition when the update frequency is low based on the update frequency of the external data source. The external factor acquisition unit can also optimally adjust the timing of data acquisition according to the update frequency of the external data source. For example, the external factor acquisition unit optimally adjusts the timing of data acquisition based on the update frequency of the external data source. As a result, by adjusting the timing of acquisition based on the update frequency of the external data source, the latest data can be efficiently acquired.

[0049] When acquiring external factors, the external factor acquisition unit can prioritize acquiring highly relevant external factors by taking geographical distribution into consideration. The external factor acquisition unit, for example, prioritizes acquiring external factors from geographically close regions. For example, the external factor acquisition unit prioritizes acquiring external factors from geographically close regions by taking geographical distribution into consideration. The external factor acquisition unit can also prioritize acquiring market data that is highly geographically relevant. For example, the external factor acquisition unit prioritizes acquiring market data that is highly geographically relevant by taking geographical distribution into consideration. The external factor acquisition unit can also prioritize acquiring disaster information that is highly geographically relevant. For example, the external factor acquisition unit prioritizes acquiring disaster information that is highly geographically relevant by taking geographical distribution into consideration. In this way, by prioritizing acquiring highly relevant external factors by taking geographical distribution into consideration, it is possible to efficiently acquire region-specific information.

[0050] The external factor acquisition unit can improve the accuracy of acquisition by referring to related literature when acquiring external factors. The external factor acquisition unit, for example, improves the accuracy of acquiring external factors by referring to related literature. For example, the external factor acquisition unit improves the accuracy of acquiring external factors by referring to related literature. The external factor acquisition unit can also evaluate the reliability of the external factors based on the related literature. For example, the external factor acquisition unit evaluates the reliability of the external factors based on the related literature. The external factor acquisition unit can also optimize the method of acquiring external factors by referring to related literature. For example, the external factor acquisition unit optimizes the method of acquiring external factors by referring to related literature. In this way, the accuracy of acquiring external factors can be improved by referring to related literature.

[0051] When acquiring external factors, the external factor acquisition unit can select external factors to acquire taking market value into consideration. The external factor acquisition unit, for example, prioritizes acquiring external factors with high market value. For example, the external factor acquisition unit prioritizes acquiring external factors with high market value taking market value into consideration. The external factor acquisition unit can also exclude external factors with low market value. For example, the external factor acquisition unit excludes external factors with low market value taking market value into consideration. The external factor acquisition unit can also select external factors to acquire based on market value. For example, the external factor acquisition unit selects external factors to acquire based on market value. In this way, by selecting external factors to acquire taking market value into consideration, important external factors can be acquired efficiently.

[0052] When creating a forecast, the forecast creation unit can adjust the level of detail of the forecast based on past data and the current situation. The forecast creation unit, for example, creates a detailed forecast based on past data and the current situation. For example, the forecast creation unit creates a detailed forecast based on past data and the current situation. The forecast creation unit can also create a simplified forecast based on past data and the current situation. For example, the forecast creation unit creates a simplified forecast based on past data and the current situation. The forecast creation unit can also improve the accuracy of the forecast based on past data and the current situation. For example, the forecast creation unit improves the accuracy of the forecast based on past data and the current situation. In this way, by adjusting the level of detail of the forecast based on past data and the current situation, it is possible to provide a highly accurate forecast.

[0053] When creating a forecast, the forecast creation unit can improve the accuracy of the forecast by referring to the past forecast results of each department. The forecast creation unit, for example, improves the accuracy of the forecast based on the past forecast results of each department. For example, the forecast creation unit improves the accuracy of the forecast based on the past forecast results. The forecast creation unit can also identify areas for improvement in the forecast by referring to the past forecast results of each department. For example, the forecast creation unit can identify areas for improvement in the forecast by referring to the past forecast results. The forecast creation unit can also analyze the past forecast results of each department and optimize the accuracy of the forecast. For example, the forecast creation unit analyzes the past forecast results and optimizes the accuracy of the forecast. In this way, the accuracy of the forecast can be improved by referring to the past forecast results of each department.

[0054] The forecast creation unit can predict the forecast taking into account the influence of external factors when creating the forecast. The forecast creation unit, for example, predicts the forecast taking into account the influence of external factors. For example, the forecast creation unit predicts the forecast taking into account the influence of external factors. The forecast creation unit can also improve the accuracy of the forecast based on the influence of external factors. For example, the forecast creation unit improves the accuracy of the forecast based on the influence of external factors. The forecast creation unit can also analyze the influence of external factors and optimize the forecast accuracy. For example, the forecast creation unit analyzes the influence of external factors and optimizes the forecast accuracy. In this way, by taking the influence of external factors into account, it is possible to provide a more realistic forecast.

[0055] When creating a forecast, the forecast creation unit can determine the priority of the forecasts based on the implementation timing of the measures. For example, if the implementation timing of a measure is approaching, the forecast creation unit creates the forecast with priority. For example, the forecast creation unit creates a forecast with priority for measures whose implementation timing is near, based on the implementation timing of the measure. The forecast creation unit can also postpone creating a forecast for measures whose implementation timing is far away, based on the implementation timing of the measure. For example, the forecast creation unit postpones creating a forecast for measures whose implementation timing is far away, based on the implementation timing of the measure. The forecast creation unit can also optimally allocate forecast resources according to the implementation timing of the measure. For example, the forecast creation unit optimally allocates forecast resources based on the implementation timing of the measure. In this way, by determining the priority of the forecasts based on the implementation timing of the measure, it is possible to provide a timely forecast.

[0056] The forecast creation unit can adjust the order of forecasts based on the relevance of measures when creating forecasts. For example, if the relevance of measures is high, the forecast creation unit creates forecasts with priority. For example, the forecast creation unit creates forecasts with priority for highly relevant measures based on the relevance of measures. The forecast creation unit can also postpone creating forecasts for measures with low relevance when the relevance of measures is low. For example, the forecast creation unit postpones creating forecasts for measures with low relevance based on the relevance of measures. The forecast creation unit can also optimally adjust the order of forecasts according to the relevance of measures. For example, the forecast creation unit optimally adjusts the order of forecasts based on the relevance of measures. As a result, by adjusting the order of forecasts based on the relevance of measures, efficient forecast creation is possible.

[0057] When creating a forecast, the forecast creation unit can adjust the use of technical terminology in the forecast depending on the level of expertise of each department. For example, if the level of expertise of each department is high, the forecast creation unit provides a forecast that uses a lot of technical terminology. For example, the forecast creation unit provides a forecast that uses a lot of technical terminology to departments with a high level of expertise depending on the level of expertise. In addition, the forecast creation unit can also provide a forecast that avoids technical terminology when the level of expertise of each department is low. For example, the forecast creation unit provides a forecast that avoids technical terminology to departments with a low level of expertise depending on the level of expertise. In addition, the forecast creation unit can adjust the way the forecast is expressed depending on the level of expertise of each department. For example, the forecast creation unit adjusts the way the forecast is expressed depending on the level of expertise. In this way, by adjusting the use of technical terminology in the forecast depending on the level of expertise of each department, it is possible to provide a forecast that is easy to understand.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The performance management system can further include a real-time data collection unit. The real-time data collection unit collects data from each department in real time and immediately sends it to the analysis unit. For example, every time sales data from the sales department is updated, the real-time data collection unit immediately collects the data and sends it to the analysis unit. Similarly, when new campaign data is added from the marketing department, the real-time data collection unit can also immediately collect the data and send it to the analysis unit. Furthermore, the real-time data collection unit can also collect information from external data sources in real time and send it to the external factor acquisition unit. This allows the performance management system to quickly create forecasts based on the latest data and more efficiently manage performance across the entire organization.

[0060] The analysis unit may include a visualization unit for visualizing the analysis results. The visualization unit displays the analysis results in a visual format, such as a graph or chart, based on the data provided by the analysis unit. For example, the analysis results of sales data may be displayed in a bar graph. The analysis results of campaign effectiveness may also be displayed in a pie chart. Furthermore, the visualization unit may provide a dashboard that can be customized by the user, allowing the user to check the necessary information at a glance. For example, the user may check the analysis results of sales data and campaign effectiveness on a single dashboard. In this way, visual display of the analysis results allows the user to deepen their understanding of the data and make quick decisions.

[0061] The collection unit may include an automation unit for automating the data collection process of each department. The automation unit automates data collection from each department, reducing manual data entry. For example, sales data from the sales department may be automatically collected and sent to the collection unit. Campaign data from the marketing department may also be automatically collected and sent to the collection unit. Furthermore, the automation unit may automatically collect information from external data sources and send it to the external factor acquisition unit. This automates the data collection process, reduces errors caused by manual data entry, and enables efficient information collection.

[0062] The analysis unit may include a sharing unit for sharing the analysis results. The sharing unit shares the analysis results with each department, promoting information sharing between departments. For example, the analysis results of sales data from the sales department are shared with the marketing department through the sharing unit. The analysis results of the effectiveness of campaigns from the marketing department can also be shared with the sales department through the sharing unit. Furthermore, the sharing unit can also share analysis results that take into account the influence of external factors with each department. This allows the analysis results to be shared with each department, promoting information sharing between departments and efficiently managing performance across the organization.

[0063] The external factor acquisition unit may include a reliability evaluation unit for evaluating the reliability of the external data source. The reliability evaluation unit evaluates the reliability of the external data source and prioritizes acquisition of highly reliable data. For example, the reliability evaluation unit evaluates the reliability of the external data source and prioritizes acquisition of highly reliable data. The reliability evaluation unit may also evaluate the reliability of the external data source and exclude low-reliability data. For example, the reliability evaluation unit evaluates the reliability of the external data source and excludes low-reliability data. Furthermore, the reliability evaluation unit may evaluate the reliability of the external data source and acquire only highly reliable data. In this way, highly reliable data can be efficiently acquired by evaluating the reliability of the external data source.

[0064] When creating a forecast, the forecast creation department can adjust the use of technical terminology in the forecast according to the level of expertise of each department. For example, if the level of expertise of each department is high, the forecast creation department provides a forecast that uses a lot of technical terminology. For example, the forecast creation department provides a forecast that uses a lot of technical terminology to departments with a high level of expertise according to the level of expertise. Also, if the level of expertise of each department is low, the forecast creation department can provide a forecast that avoids technical terminology. For example, the forecast creation department provides a forecast that avoids technical terminology to departments with a low level of expertise according to the level of expertise. Also, the forecast creation department can adjust the way the forecast is expressed according to the level of expertise of each department. For example, the forecast creation department adjusts the way the forecast is expressed according to the level of expertise. In this way, by adjusting the use of technical terminology in the forecast according to the level of expertise of each department, it is possible to provide a forecast that is easy to understand.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection department collects information from each department. Information from each department includes sales data from the sales department and campaign data from the marketing department. For example, the collection department can collect sales data from the sales department and campaign data from the marketing department. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses machine learning algorithms to measure the effectiveness of each measure and analyze the extent to which a specific campaign affected sales. It can also measure the effectiveness of other measures and predict the effectiveness of measures. Step 3: The external factor acquisition unit acquires information from external data sources. For example, the external factor acquisition unit can acquire disaster information, weather data, economic indicators, etc. Step 4: The forecast creation unit creates forecasts based on the data obtained by the analysis unit and the external factor acquisition unit. The forecast creation unit predicts future forecasts based on past data and current conditions, and can use machine learning algorithms to predict future sales and market trends.

[0067] (Example 2) A performance management system according to an embodiment of the present invention collects and analyzes information from each department and creates a forecast taking external factors into account. The performance management system efficiently manages performance across the entire organization by collecting information from each department, analyzing it using an AI chatbot, acquiring information from external data sources, and creating a forecast. For example, the performance management system collects information such as sales data from the sales department and campaign data from the marketing department. The AI ​​chatbot then analyzes the collected data and measures the effectiveness of each measure. Furthermore, the performance management system acquires external factors such as disaster information from external data sources and reflects their impact on the organization's performance. Finally, the performance management system uses machine learning to predict future forecasts based on past data and current conditions. This promotes information sharing between departments and enables highly accurate forecasts and effectiveness measurement that take into account factors outside the department. This allows the performance management system to efficiently manage performance across the entire organization. For example, the sales department can predict future sales based on the forecast provided by the AI ​​chatbot and plan appropriate measures. Furthermore, the marketing department can accurately measure the effectiveness of campaigns and incorporate the results into future campaigns.

[0068] The performance management system according to the embodiment includes a collection unit, an analysis unit, an external factor acquisition unit, and a forecast creation unit. The collection unit collects information from each department. The information from each department includes, for example, sales data from the sales department and campaign data from the marketing department, but is not limited to these examples. The collection unit, for example, collects sales data from the sales department. The collection unit can also collect campaign data from the marketing department. The collection unit can also collect data from other departments. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, uses a machine learning algorithm to measure the effectiveness of each measure. For example, the analysis unit analyzes the extent to which a specific campaign has affected sales. The analysis unit can also use a machine learning algorithm to measure the effectiveness of other measures. The analysis unit can also use a machine learning algorithm to predict the effectiveness of measures. The external factor acquisition unit acquires information from an external data source. For example, the external factor acquisition unit acquires disaster information from an external data source. For example, the external factor acquisition unit can acquire weather data. The external factor acquisition unit can also acquire economic indicators. The external factor acquisition unit can also acquire information from other external data sources. The forecast creation unit creates a forecast based on the data acquired by the analysis unit and the external factor acquisition unit. The forecast creation unit predicts future forecasts based on, for example, past data and the current situation. For example, the forecast creation unit uses a machine learning algorithm to predict future sales. The forecast creation unit can also use a machine learning algorithm to predict future market trends. The forecast creation unit can also use a machine learning algorithm to create other forecasts. This allows the performance management system according to the embodiment to efficiently manage performance across an entire organization. For example, the performance management system can efficiently manage performance across an entire organization by collecting and analyzing information from each department and creating forecasts taking external factors into consideration.

[0069] The collection unit can collect sales data from the sales department or campaign data from the marketing department. The collection unit, for example, collects sales data from the sales department. Sales data includes, for example, monthly sales and sales by product, but is not limited to these examples. The collection unit can also collect campaign data from the marketing department. Campaign data includes, for example, the campaign period and target customers, but is not limited to these examples. The collection unit can also collect data from other departments. This allows for more detailed performance management by collecting specific data from each department.

[0070] The analysis unit can measure the effectiveness of each measure using a machine learning algorithm. The analysis unit, for example, uses a machine learning algorithm to analyze the extent to which a specific campaign has affected sales. For example, the analysis unit measures the effectiveness of a campaign using regression analysis. The analysis unit can also measure the effectiveness of a campaign using a classification algorithm. The analysis unit can also measure the effectiveness of a campaign using a clustering algorithm. In this way, by using a machine learning algorithm, the effectiveness of each measure can be measured with high accuracy.

[0071] The external factor acquisition unit can acquire disaster information from an external data source. The external factor acquisition unit acquires, for example, earthquake information from the external data source. Earthquake information includes, for example, the date and time of the earthquake occurrence, the epicenter, and seismic intensity, but is not limited to these examples. The external factor acquisition unit can also acquire flood information from the external data source. Flood information includes, for example, the date and time of the flood occurrence, the damage situation, etc., but is not limited to these examples. The external factor acquisition unit can also acquire other disaster information. In this way, by acquiring disaster information from an external data source, it becomes possible to manage performance taking into account factors outside the department.

[0072] The forecast creation unit can predict future forecasts based on past data and current conditions. The forecast creation unit, for example, predicts future sales based on past sales data and current market conditions. For example, the forecast creation unit uses a machine learning algorithm to predict future sales based on past sales data and current market conditions. The forecast creation unit can also predict future campaign effectiveness based on past campaign data and current market conditions. The forecast creation unit can also predict other forecasts based on past data and current conditions. This makes it possible to create highly accurate forecasts by predicting future forecasts based on past data and current conditions.

[0073] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the user's emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of information collection to reduce the burden on the user. For example, the collection unit estimates the user's emotions and reduces the frequency of information collection when the user is feeling stressed. The collection unit can also increase the frequency of information collection and collect more detailed data when the user is relaxed. For example, the collection unit estimates the user's emotions and increases the frequency of information collection when the user is relaxed. The collection unit can also quickly time information collection and immediately obtain necessary data when the user is in a hurry. For example, the collection unit estimates the user's emotions and quickly time information collection when the user is in a hurry. This reduces the burden on the user by adjusting the timing of information collection according to the user's emotions, enabling efficient information collection. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The collection unit can analyze the past data submission history of each department and select an appropriate collection method. The collection unit, for example, analyzes the format of data submitted by each department in the past and selects the most efficient collection method. For example, the collection unit analyzes the past data submission history and selects the optimal collection method. The collection unit can also analyze the data submission frequency of each department and set an optimal collection schedule. For example, the collection unit analyzes the past data submission frequency and set an optimal collection schedule. The collection unit can also evaluate the quality of data based on the data submission history of each department and select the optimal collection method. For example, the collection unit evaluates the quality of data based on the past data submission history and selects the optimal collection method. In this way, by analyzing the past data submission history, the optimal collection method can be selected and efficient information collection becomes possible.

[0075] When collecting information, the collection unit can perform filtering based on the current projects and areas of interest of each department. For example, the collection unit collects only data related to the current projects of each department. For example, the collection unit filters and collects data related to the current projects. The collection unit can also prioritize collecting highly relevant data based on the areas of interest of each department. For example, the collection unit prioritizes collecting highly relevant data based on the areas of interest. The collection unit can also filter and collect necessary data taking into account the progress of projects in each department. For example, the collection unit filters and collects necessary data taking into account the progress of projects. In this way, by filtering based on the current projects and areas of interest of each department, highly relevant information can be efficiently collected.

[0076] When collecting information, the collection unit can select the optimal collection means according to the input method of each department. For example, if each department uses voice input, the collection unit collects voice data. For example, the collection unit collects voice data from departments using voice input. Furthermore, if each department uses text input, the collection unit can also collect text data. For example, the collection unit collects text data from departments using text input. Furthermore, if each department uses image input, the collection unit can also collect image data. For example, the collection unit collects image data from departments using image input. This enables efficient information collection by selecting the optimal collection means according to the input method of each department.

[0077] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting information of high importance. For example, the collection unit estimates the user's emotions and prioritizes collecting information of high importance when the user is feeling stressed. The collection unit can also prioritize collecting detailed information when the user is relaxed. For example, the collection unit estimates the user's emotions and prioritizes collecting detailed information when the user is relaxed. The collection unit can also prioritize collecting information that can be collected quickly when the user is in a hurry. For example, the collection unit estimates the user's emotions and prioritizes collecting information that can be collected quickly when the user is in a hurry. In this way, by determining the priority of information to be collected according to the user's emotions, important information can be collected efficiently. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each department. The collection unit, for example, prioritizes collecting region-specific information based on the location of each department. For example, the collection unit prioritizes collecting region-specific information by taking into account the geographical location information. The collection unit can also prioritize collecting related market data by taking into account the geographical location information of each department. For example, the collection unit prioritizes collecting related market data by taking into account the geographical location information. The collection unit can also prioritize collecting external factors such as disaster information based on the geographical location information of each department. For example, the collection unit prioritizes collecting external factors such as disaster information based on the geographical location information. In this way, by prioritizedly collecting highly relevant information by taking into account the geographical location information of each department, it is possible to efficiently collect region-specific information.

[0079] When collecting information, the collection department can analyze the social media activities of each department and collect specific information. For example, the collection department analyzes the content of posts on social media by each department and collects related market data. For example, the collection department analyzes social media activities and collects related market data. The collection department can also collect customer feedback based on the social media activities of each department. For example, the collection department collects customer feedback based on the social media activities. The collection department can also analyze social media trends of each department and collect related information. For example, the collection department analyzes social media trends and collects related information. In this way, by analyzing the social media activities of each department, related market data and customer feedback can be collected efficiently.

[0080] When collecting information, the collection unit can customize the collection method by reflecting past feedback from each department. The collection unit, for example, improves the collection method based on past feedback from each department. For example, the collection unit improves the collection method based on past feedback. The collection unit can also adjust the type of data to be collected by reflecting past feedback from each department. For example, the collection unit adjusts the type of data to be collected by reflecting past feedback. The collection unit can also optimize the collection frequency by referring to past feedback from each department. For example, the collection unit optimizes the collection frequency by referring to past feedback. In this way, the collection method can be optimized by reflecting past feedback from each department, enabling efficient information collection.

[0081] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. For example, if the user is in a hurry, the analysis unit can provide a summary analysis result. By adjusting the way the analysis is presented based on the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the measure. For example, the analysis unit performs a detailed analysis on a measure with a high level of importance. For example, the analysis unit performs a detailed analysis on a measure with a high level of importance based on the importance of the measure. The analysis unit can also perform a simplified analysis on a measure with a low level of importance. For example, the analysis unit performs a simplified analysis on a measure with a low level of importance based on the importance of the measure. The analysis unit can also optimally allocate analysis resources according to the importance of the measure. For example, the analysis unit optimally allocates analysis resources based on the importance of the measure. In this way, by adjusting the level of detail of the analysis based on the importance of the measure, resources can be optimally allocated and efficient analysis can be performed.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the measure. For example, the analysis unit applies a marketing-specific analysis algorithm to a marketing measure. For example, the analysis unit applies a marketing-specific analysis algorithm to a marketing measure depending on the category of the measure. The analysis unit can also apply a sales-specific analysis algorithm to a sales measure. For example, the analysis unit applies a sales-specific analysis algorithm to a sales measure depending on the category of the measure. The analysis unit can also apply a human resources-specific analysis algorithm to a human resources measure. For example, the analysis unit applies a human resources-specific analysis algorithm to a human resources measure depending on the category of the measure. In this way, by applying different analysis algorithms depending on the category of the measure, optimal analysis can be performed for each measure.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of each department. The analysis unit, for example, optimizes the analysis algorithm based on past analysis results of each department. For example, the analysis unit optimizes the analysis algorithm based on past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results of each department. For example, the analysis unit improves the accuracy of the analysis by referring to past analysis results. The analysis unit can also analyze past analysis results of each department and identify areas for improvement in the analysis. For example, the analysis unit analyzes past analysis results and identifies areas for improvement in the analysis. In this way, the accuracy of the analysis can be improved by referring to past analysis results of each department.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit can estimate the user's emotions and provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, the analysis unit can estimate the user's emotions and provide a detailed analysis result when the user is relaxed. The analysis unit can also provide an analysis result with added visually stimulating effects when the user is excited. For example, the analysis unit can estimate the user's emotions and provide an analysis result with added visually stimulating effects when the user is excited. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing an optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the implementation timing of measures. For example, if the implementation timing of a measure is approaching, the analysis unit performs analysis preferentially. For example, the analysis unit performs analysis preferentially for measures whose implementation timing is approaching based on the implementation timing of the measure. Furthermore, the analysis unit can postpone analysis for measures whose implementation timing is further away if the implementation timing of the measure is far away. For example, the analysis unit postpones analysis for measures whose implementation timing is further away based on the implementation timing of the measure. Furthermore, the analysis unit can optimally allocate analysis resources according to the implementation timing of the measure. For example, the analysis unit optimally allocates analysis resources based on the implementation timing of the measure. In this way, by determining the priority of analysis based on the implementation timing of the measure, timely analysis is possible.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of measures. For example, when the relevance of measures is high, the analysis unit performs analysis preferentially. For example, the analysis unit performs analysis preferentially on measures with high relevance based on the relevance of measures. Furthermore, when the relevance of measures is low, the analysis unit can postpone analysis. For example, the analysis unit postpones analysis on measures with low relevance based on the relevance of measures. Furthermore, the analysis unit can optimally adjust the order of analysis according to the relevance of measures. For example, the analysis unit optimally adjusts the order of analysis based on the relevance of measures. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of measures.

[0088] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the level of expertise of each department. For example, if the level of expertise of each department is high, the analysis unit provides analysis results that use a lot of technical terminology. For example, the analysis unit provides analysis results that use a lot of technical terminology to departments with a high level of expertise according to the level of expertise. Furthermore, the analysis unit can also provide analysis results that avoid technical terminology when the level of expertise of each department is low. For example, the analysis unit provides analysis results that avoid technical terminology to departments with a low level of expertise according to the level of expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the level of expertise of each department. For example, the analysis unit adjusts the way in which the analysis results are expressed according to the level of expertise. In this way, by adjusting the use of technical terminology in the analysis according to the level of expertise of each department, it is possible to provide analysis results that are easy to understand.

[0089] The external factor acquisition unit can estimate the user's emotions and adjust the timing of external factor acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the external factor acquisition unit reduces the frequency of external factor acquisition to reduce the burden. For example, the external factor acquisition unit estimates the user's emotions and reduces the frequency of external factor acquisition when the user is feeling stressed. The external factor acquisition unit can also increase the frequency of external factor acquisition and collect more detailed data when the user is relaxed. For example, the external factor acquisition unit estimates the user's emotions and increases the frequency of external factor acquisition when the user is relaxed. The external factor acquisition unit can also quickly acquire external factors and immediately acquire necessary data when the user is in a hurry. For example, the external factor acquisition unit estimates the user's emotions and quickly acquires external factors when the user is in a hurry. This enables efficient data acquisition by adjusting the timing of external factor acquisition according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] When acquiring external factors, the external factor acquisition unit can predict current external factors by referring to past external factor data. The external factor acquisition unit, for example, refers to past disaster data to predict current disaster risk. For example, the external factor acquisition unit refers to past disaster data to predict current disaster risk. The external factor acquisition unit can also predict current market trends by referring to past market data. For example, the external factor acquisition unit refers to past market data to predict current market trends. The external factor acquisition unit can also predict current weather by referring to past weather data. For example, the external factor acquisition unit refers to past weather data to predict current weather. In this way, by referring to past external factor data, current external factors can be predicted with high accuracy.

[0091] When acquiring external factors, the external factor acquisition unit can evaluate the reliability of the external data source and select data to acquire. For example, the external factor acquisition unit evaluates the reliability of the external data source and prioritizes acquiring highly reliable data. For example, the external factor acquisition unit evaluates the reliability of the external data source and prioritizes acquiring highly reliable data. The external factor acquisition unit can also evaluate the reliability of the external data source and exclude low-reliability data. For example, the external factor acquisition unit evaluates the reliability of the external data source and excludes low-reliability data. The external factor acquisition unit can also evaluate the reliability of the external data source and acquire only highly reliable data. For example, the external factor acquisition unit evaluates the reliability of the external data source and acquires only highly reliable data. In this way, highly reliable data can be efficiently acquired by evaluating the reliability of the external data source.

[0092] When acquiring external factors, the external factor acquisition unit can adjust the timing of acquisition based on the update frequency of the external data source. For example, if the update frequency of the external data source is high, the external factor acquisition unit acquires data frequently. For example, the external factor acquisition unit acquires data frequently when the update frequency is high based on the update frequency of the external data source. The external factor acquisition unit can also reduce the frequency of data acquisition when the update frequency of the external data source is low. For example, the external factor acquisition unit reduces the frequency of data acquisition when the update frequency is low based on the update frequency of the external data source. The external factor acquisition unit can also optimally adjust the timing of data acquisition according to the update frequency of the external data source. For example, the external factor acquisition unit optimally adjusts the timing of data acquisition based on the update frequency of the external data source. As a result, by adjusting the timing of acquisition based on the update frequency of the external data source, the latest data can be efficiently acquired.

[0093] The external factor acquisition unit can estimate the user's emotions and determine the priority of external factors to be acquired based on the estimated user emotions. For example, when the user is feeling stressed, the external factor acquisition unit prioritizes acquiring external factors with high importance. For example, when the user is feeling stressed, the external factor acquisition unit estimates the user's emotions and prioritizes acquiring external factors with high importance. The external factor acquisition unit can also prioritize acquiring detailed external factors when the user is relaxed. For example, the external factor acquisition unit estimates the user's emotions and prioritizes acquiring detailed external factors when the user is relaxed. The external factor acquisition unit can also prioritize acquiring external factors that can be acquired quickly when the user is in a hurry. For example, the external factor acquisition unit estimates the user's emotions and prioritizes acquiring external factors that can be acquired quickly when the user is in a hurry. In this way, by determining the priority of external factors to be acquired according to the user's emotions, important external factors can be efficiently acquired. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] When acquiring external factors, the external factor acquisition unit can prioritize acquiring highly relevant external factors by taking geographical distribution into consideration. The external factor acquisition unit, for example, prioritizes acquiring external factors from geographically close regions. For example, the external factor acquisition unit prioritizes acquiring external factors from geographically close regions by taking geographical distribution into consideration. The external factor acquisition unit can also prioritize acquiring market data that is highly geographically relevant. For example, the external factor acquisition unit prioritizes acquiring market data that is highly geographically relevant by taking geographical distribution into consideration. The external factor acquisition unit can also prioritize acquiring disaster information that is highly geographically relevant. For example, the external factor acquisition unit prioritizes acquiring disaster information that is highly geographically relevant by taking geographical distribution into consideration. In this way, by prioritizing acquiring highly relevant external factors by taking geographical distribution into consideration, it is possible to efficiently acquire region-specific information.

[0095] The external factor acquisition unit can improve the accuracy of acquisition by referring to related literature when acquiring external factors. The external factor acquisition unit, for example, improves the accuracy of acquiring external factors by referring to related literature. For example, the external factor acquisition unit improves the accuracy of acquiring external factors by referring to related literature. The external factor acquisition unit can also evaluate the reliability of the external factors based on the related literature. For example, the external factor acquisition unit evaluates the reliability of the external factors based on the related literature. The external factor acquisition unit can also optimize the method of acquiring external factors by referring to related literature. For example, the external factor acquisition unit optimizes the method of acquiring external factors by referring to related literature. In this way, the accuracy of acquiring external factors can be improved by referring to related literature.

[0096] When acquiring external factors, the external factor acquisition unit can select external factors to acquire taking market value into consideration. The external factor acquisition unit, for example, prioritizes acquiring external factors with high market value. For example, the external factor acquisition unit prioritizes acquiring external factors with high market value taking market value into consideration. The external factor acquisition unit can also exclude external factors with low market value. For example, the external factor acquisition unit excludes external factors with low market value taking market value into consideration. The external factor acquisition unit can also select external factors to acquire based on market value. For example, the external factor acquisition unit selects external factors to acquire based on market value. In this way, by selecting external factors to acquire taking market value into consideration, important external factors can be acquired efficiently.

[0097] The forecast creation unit can estimate the user's emotions and adjust the way the forecast is expressed based on the estimated user emotions. For example, if the user is nervous, the forecast creation unit provides a simple, highly visible forecast. For example, the forecast creation unit estimates the user's emotions and provides a simple, highly visible forecast when the user is nervous. The forecast creation unit can also provide a detailed forecast when the user is relaxed. For example, the forecast creation unit estimates the user's emotions and provides a detailed forecast when the user is relaxed. The forecast creation unit can also provide a forecast that focuses on the main points when the user is in a hurry. For example, the forecast creation unit estimates the user's emotions and provides a forecast that focuses on the main points when the user is in a hurry. This allows the forecast to be easily understood by adjusting the way the forecast is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] When creating a forecast, the forecast creation unit can adjust the level of detail of the forecast based on past data and the current situation. The forecast creation unit, for example, creates a detailed forecast based on past data and the current situation. For example, the forecast creation unit creates a detailed forecast based on past data and the current situation. The forecast creation unit can also create a simplified forecast based on past data and the current situation. For example, the forecast creation unit creates a simplified forecast based on past data and the current situation. The forecast creation unit can also improve the accuracy of the forecast based on past data and the current situation. For example, the forecast creation unit improves the accuracy of the forecast based on past data and the current situation. In this way, by adjusting the level of detail of the forecast based on past data and the current situation, it is possible to provide a highly accurate forecast.

[0099] When creating a forecast, the forecast creation unit can improve the accuracy of the forecast by referring to the past forecast results of each department. The forecast creation unit, for example, improves the accuracy of the forecast based on the past forecast results of each department. For example, the forecast creation unit improves the accuracy of the forecast based on the past forecast results. The forecast creation unit can also identify areas for improvement in the forecast by referring to the past forecast results of each department. For example, the forecast creation unit can identify areas for improvement in the forecast by referring to the past forecast results. The forecast creation unit can also analyze the past forecast results of each department and optimize the accuracy of the forecast. For example, the forecast creation unit analyzes the past forecast results and optimizes the accuracy of the forecast. In this way, the accuracy of the forecast can be improved by referring to the past forecast results of each department.

[0100] The forecast creation unit can predict the forecast taking into account the influence of external factors when creating the forecast. The forecast creation unit, for example, predicts the forecast taking into account the influence of external factors. For example, the forecast creation unit predicts the forecast taking into account the influence of external factors. The forecast creation unit can also improve the accuracy of the forecast based on the influence of external factors. For example, the forecast creation unit improves the accuracy of the forecast based on the influence of external factors. The forecast creation unit can also analyze the influence of external factors and optimize the forecast accuracy. For example, the forecast creation unit analyzes the influence of external factors and optimizes the forecast accuracy. In this way, by taking the influence of external factors into account, it is possible to provide a more realistic forecast.

[0101] The forecast creation unit can estimate the user's emotions and adjust the length of the forecast based on the estimated user emotions. For example, if the user is in a hurry, the forecast creation unit provides a short, concise forecast. For example, the forecast creation unit estimates the user's emotions and provides a short, concise forecast when the user is in a hurry. The forecast creation unit can also provide a detailed forecast when the user is relaxed. For example, the forecast creation unit estimates the user's emotions and provides a detailed forecast when the user is relaxed. The forecast creation unit can also provide a forecast with a visually stimulating effect when the user is excited. For example, the forecast creation unit estimates the user's emotions and provides a forecast with a visually stimulating effect when the user is excited. This allows the length of the forecast to be adjusted according to the user's emotions, thereby providing an optimal forecast for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] When creating a forecast, the forecast creation unit can determine the priority of the forecasts based on the implementation timing of the measures. For example, if the implementation timing of a measure is approaching, the forecast creation unit creates the forecast with priority. For example, the forecast creation unit creates a forecast with priority for measures whose implementation timing is near, based on the implementation timing of the measure. The forecast creation unit can also postpone creating a forecast for measures whose implementation timing is far away, based on the implementation timing of the measure. For example, the forecast creation unit postpones creating a forecast for measures whose implementation timing is far away, based on the implementation timing of the measure. The forecast creation unit can also optimally allocate forecast resources according to the implementation timing of the measure. For example, the forecast creation unit optimally allocates forecast resources based on the implementation timing of the measure. In this way, by determining the priority of the forecasts based on the implementation timing of the measure, it is possible to provide a timely forecast.

[0103] The forecast creation unit can adjust the order of forecasts based on the relevance of measures when creating forecasts. For example, if the relevance of measures is high, the forecast creation unit creates forecasts with priority. For example, the forecast creation unit creates forecasts with priority for highly relevant measures based on the relevance of measures. The forecast creation unit can also postpone creating forecasts for measures with low relevance when the relevance of measures is low. For example, the forecast creation unit postpones creating forecasts for measures with low relevance based on the relevance of measures. The forecast creation unit can also optimally adjust the order of forecasts according to the relevance of measures. For example, the forecast creation unit optimally adjusts the order of forecasts based on the relevance of measures. As a result, by adjusting the order of forecasts based on the relevance of measures, efficient forecast creation is possible.

[0104] When creating a forecast, the forecast creation unit can adjust the use of technical terminology in the forecast depending on the level of expertise of each department. For example, if the level of expertise of each department is high, the forecast creation unit provides a forecast that uses a lot of technical terminology. For example, the forecast creation unit provides a forecast that uses a lot of technical terminology to departments with a high level of expertise depending on the level of expertise. In addition, the forecast creation unit can also provide a forecast that avoids technical terminology when the level of expertise of each department is low. For example, the forecast creation unit provides a forecast that avoids technical terminology to departments with a low level of expertise depending on the level of expertise. In addition, the forecast creation unit can adjust the way the forecast is expressed depending on the level of expertise of each department. For example, the forecast creation unit adjusts the way the forecast is expressed depending on the level of expertise. In this way, by adjusting the use of technical terminology in the forecast depending on the level of expertise of each department, it is possible to provide a forecast that is easy to understand. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, external factor acquisition unit, and forecast creation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects information from each department. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The external factor acquisition unit acquires information from an external data source via the communication I / F 44 of the smart device 14, for example. The forecast creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a forecast based on the analyzed data and external factors. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, external factor acquisition unit, and forecast creation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects information from each department. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The external factor acquisition unit acquires information from an external data source via, for example, the communication I / F 44 of the smart glasses 214. The forecast creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a forecast based on the analyzed data and external factors. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, external factor acquisition unit, and forecast creation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects information from each department. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The external factor acquisition unit acquires information from an external data source via the communication I / F 44 of the headset type terminal 314, for example. The forecast creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a forecast based on the analyzed data and external factors. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, external factor acquisition unit, and forecast creation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects information from each department. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The external factor acquisition unit acquires information from an external data source via the communication I / F 44 of the robot 414, for example. The forecast creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a forecast based on the analyzed data and external factors.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The performance management system can further include a real-time data collection unit. The real-time data collection unit collects data from each department in real time and immediately sends it to the analysis unit. For example, every time sales data from the sales department is updated, the real-time data collection unit immediately collects the data and sends it to the analysis unit. Similarly, when new campaign data is added from the marketing department, the real-time data collection unit can also immediately collect the data and send it to the analysis unit. Furthermore, the real-time data collection unit can also collect information from external data sources in real time and send it to the external factor acquisition unit. This allows the performance management system to quickly create forecasts based on the latest data and more efficiently manage performance across the entire organization.

[0107] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects only data of high importance, thereby reducing the burden on the user. For example, the collection unit estimates the user's emotions and collects only data of high importance when the user is feeling stressed. The collection unit can also collect detailed data when the user is relaxed. For example, the collection unit estimates the user's emotions and collects detailed data when the user is relaxed. The collection unit can also prioritize collecting data that can be collected quickly when the user is in a hurry. For example, the collection unit estimates the user's emotions and prioritizes collecting data that can be collected quickly when the user is in a hurry. This enables efficient information collection by adjusting the type of data to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] The analysis unit may include a visualization unit for visualizing the analysis results. The visualization unit displays the analysis results in a visual format, such as a graph or chart, based on the data provided by the analysis unit. For example, the analysis results of sales data may be displayed in a bar graph. The analysis results of campaign effectiveness may also be displayed in a pie chart. Furthermore, the visualization unit may provide a dashboard that can be customized by the user, allowing the user to check the necessary information at a glance. For example, the user may check the analysis results of sales data and campaign effectiveness on a single dashboard. In this way, visual display of the analysis results allows the user to deepen their understanding of the data and make quick decisions.

[0109] The external factor acquisition unit can estimate the user's emotions and adjust the frequency of external factor acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the external factor acquisition unit can reduce the acquisition frequency to reduce the user's burden. For example, the external factor acquisition unit can estimate the user's emotions and reduce the acquisition frequency if the user is feeling stressed. Also, if the user is relaxed, the external factor acquisition unit can increase the acquisition frequency to collect more detailed data. For example, the external factor acquisition unit can estimate the user's emotions and increase the acquisition frequency if the user is relaxed. Also, if the user is in a hurry, the external factor acquisition unit can prioritize collecting data that can be acquired quickly. For example, the external factor acquisition unit can estimate the user's emotions and prioritize collecting data that can be acquired quickly if the user is in a hurry. This enables efficient data collection by adjusting the acquisition frequency of external factors according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] The forecast creation unit can estimate the user's emotions and adjust the way the forecast is expressed based on the estimated user's emotions. For example, if the user is nervous, the forecast creation unit provides a simple, highly visible forecast. For example, the forecast creation unit estimates the user's emotions and provides a simple, highly visible forecast when the user is nervous. Furthermore, if the user is relaxed, the forecast creation unit can also provide a detailed forecast. For example, the forecast creation unit estimates the user's emotions and provides a detailed forecast when the user is relaxed. Furthermore, if the user is in a hurry, the forecast creation unit can also provide a forecast that focuses on the main points. For example, the forecast creation unit estimates the user's emotions and provides a forecast that focuses on the main points when the user is in a hurry. This allows the forecast to be easily understood by adjusting the way the forecast is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] The collection unit may include an automation unit for automating the data collection process of each department. The automation unit automates data collection from each department, reducing manual data entry. For example, sales data from the sales department may be automatically collected and sent to the collection unit. Campaign data from the marketing department may also be automatically collected and sent to the collection unit. Furthermore, the automation unit may automatically collect information from external data sources and send it to the external factor acquisition unit. This automates the data collection process, reduces errors caused by manual data entry, and enables efficient information collection.

[0112] The analysis unit may include a sharing unit for sharing the analysis results. The sharing unit shares the analysis results with each department, promoting information sharing between departments. For example, the analysis results of sales data from the sales department are shared with the marketing department through the sharing unit. The analysis results of the effectiveness of campaigns from the marketing department can also be shared with the sales department through the sharing unit. Furthermore, the sharing unit can also share analysis results that take into account the influence of external factors with each department. This allows the analysis results to be shared with each department, promoting information sharing between departments and efficiently managing performance across the organization.

[0113] The external factor acquisition unit may include a reliability evaluation unit for evaluating the reliability of the external data source. The reliability evaluation unit evaluates the reliability of the external data source and prioritizes acquisition of highly reliable data. For example, the reliability evaluation unit evaluates the reliability of the external data source and prioritizes acquisition of highly reliable data. The reliability evaluation unit may also evaluate the reliability of the external data source and exclude low-reliability data. For example, the reliability evaluation unit evaluates the reliability of the external data source and excludes low-reliability data. Furthermore, the reliability evaluation unit may evaluate the reliability of the external data source and acquire only highly reliable data. In this way, highly reliable data can be efficiently acquired by evaluating the reliability of the external data source.

[0114] When creating a forecast, the forecast creation department can adjust the use of technical terminology in the forecast according to the level of expertise of each department. For example, if the level of expertise of each department is high, the forecast creation department provides a forecast that uses a lot of technical terminology. For example, the forecast creation department provides a forecast that uses a lot of technical terminology to departments with a high level of expertise according to the level of expertise. Also, if the level of expertise of each department is low, the forecast creation department can provide a forecast that avoids technical terminology. For example, the forecast creation department provides a forecast that avoids technical terminology to departments with a low level of expertise according to the level of expertise. Also, the forecast creation department can adjust the way the forecast is expressed according to the level of expertise of each department. For example, the forecast creation department adjusts the way the forecast is expressed according to the level of expertise. In this way, by adjusting the use of technical terminology in the forecast according to the level of expertise of each department, it is possible to provide a forecast that is easy to understand.

[0115] The forecast creation unit can estimate the user's emotions and adjust the length of the forecast based on the estimated user emotions. For example, if the user is in a hurry, the forecast creation unit provides a short, concise forecast. For example, the forecast creation unit estimates the user's emotions and provides a short, concise forecast when the user is in a hurry. Also, if the user is relaxed, the forecast creation unit can provide a detailed forecast. For example, the forecast creation unit estimates the user's emotions and provides a detailed forecast when the user is relaxed. Also, if the user is excited, the forecast creation unit can provide a forecast with a visually stimulating effect. For example, the forecast creation unit estimates the user's emotions and provides a forecast with a visually stimulating effect when the user is excited. In this way, by adjusting the length of the forecast according to the user's emotions, it is possible to provide an optimal forecast for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection department collects information from each department. Information from each department includes sales data from the sales department and campaign data from the marketing department. For example, the collection department can collect sales data from the sales department and campaign data from the marketing department. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses machine learning algorithms to measure the effectiveness of each measure and analyze the extent to which a specific campaign affected sales. It can also measure the effectiveness of other measures and predict the effectiveness of measures. Step 3: The external factor acquisition unit acquires information from external data sources. For example, the external factor acquisition unit can acquire disaster information, weather data, economic indicators, etc. Step 4: The forecast creation unit creates forecasts based on the data obtained by the analysis unit and the external factor acquisition unit. The forecast creation unit predicts future forecasts based on past data and current conditions, and can use machine learning algorithms to predict future sales and market trends.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] 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.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] 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.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] 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.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0181] 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.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects information from each department, an analysis unit that analyzes the information collected by the collection unit; an external factor acquisition unit that acquires information from an external data source; a forecast creation unit that creates a forecast based on the data obtained by the analysis unit and the external factor acquisition unit. A system characterized by:

2. The collecting unit Collect sales data from the sales department or campaign data from the marketing department 2. The system of claim 1.

3. The analysis unit Use machine learning algorithms to measure the effectiveness of each measure 2. The system of claim 1.

4. The external factor acquisition unit Obtaining disaster information from external data sources 2. The system of claim 1.

5. The forecast creation unit Predict future prospects based on past data and current conditions 2. The system of claim 1.

6. The collecting unit Estimate user emotions and adjust the timing of information collection based on the user emotions 2. The system of claim 1.

7. The collecting unit Analyze each department's past data submission history and select the appropriate collection method 2. The system of claim 1.

8. The collecting unit When gathering information, filter it based on each department's current projects and areas of interest.

2. The system of claim 1.

9. The collecting unit When collecting information, select the most appropriate collection method depending on the input method of each department.

2. The system of claim 1.

10. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

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