System

Abstract

An object of a system according to an embodiment is to analyze data in a company and propose an effective sales strategy.SOLUTION: A system according to an embodiment includes a data collection unit, a data analysis unit, and a sales strategy proposal unit. The data collection unit collects various data in the company. The data analysis unit analyzes the data collected by the data collection unit. The sales strategy proposal unit proposes a sales strategy on the basis of the result analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not yet been able to effectively utilize data within a company to formulate sales strategies, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze data within a company and propose effective sales strategies. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, and a sales strategy proposal unit. The data collection unit collects various data within the company. The data analysis unit analyzes the data collected by the data collection unit. The sales strategy proposal unit proposes a sales strategy based on the results of the analysis by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data within a company and propose effective sales strategies. [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 sales support system according to an embodiment of the present invention is a system that accumulates data within a company and supports sales activities based on that data. This sales support system uses AI to collect and analyze various data within the company and optimizes sales strategies and marketing activities based on the results. As a result, the sales support system can optimize sales activities based on the company's data and provide an efficient sales strategy.

[0029] A sales support system according to an embodiment includes a data collection unit, a data analysis unit, and a sales strategy proposal unit. The data collection unit collects various data within a company, such as sales data, customer data, inventory data, and marketing data. The data collection unit can also acquire data from external data sources using an API. For example, the data collection unit automatically collects data from each department within the company and stores it in a central database. The data analysis unit analyzes the data collected by the data collection unit. For example, the data analysis unit can analyze customer purchase histories using a machine learning algorithm to identify which products are popular with which customers. The data analysis unit can also analyze inventory data to predict which products are in short supply. The data analysis unit can also analyze marketing data to evaluate the effectiveness of campaigns. The sales strategy proposal unit proposes a sales strategy based on the results of the analysis by the data analysis unit. For example, the sales strategy proposal unit indicates which products should be proposed to a specific customer group and when. The sales strategy proposal unit can also propose which products should be prioritized for sale based on inventory status. Furthermore, the sales strategy proposal unit supports the design of new campaigns based on the results of past marketing campaigns. As a result, the sales support system according to the embodiment can optimize sales activities based on data within the company and provide efficient sales strategies. For example, the sales support system supports product proposals tailored to customer needs, streamlining inventory management, and implementing effective marketing campaigns. This is expected to improve the efficiency and effectiveness of a company's sales activities.

[0030] The data collection unit collects employee behavioral data and internal communication data, enabling an understanding of trends across the organization. For example, the data collection unit collects employee behavioral data and stores information such as arrival times, break times, and work progress in a database. This makes it possible to understand employee working styles and work efficiency. The data collection unit also collects internal communication data and analyzes the content of email exchanges and chats to evaluate the flow of information and the quality of communication within the organization. Furthermore, the data collection unit integrates employee behavioral data and internal communication data to understand trends across the organization. For example, it can analyze project progress and team cooperation. This allows an understanding of trends across the organization and improves work efficiency.

[0031] The data collection unit can evaluate the reliability of data and automatically filter out unreliable data. The data collection unit, for example, evaluates the source of the data and the collection method, and automatically filters out unreliable data. For example, it excludes manually entered data and incomplete data. The data collection unit also adds a function to check the consistency and integrity of data to evaluate the reliability of the data. For example, it can compare data from different data sources and check for inconsistencies. Furthermore, when filtering out unreliable data, the data collection unit calculates a data quality score and excludes data below a certain score. For example, it can evaluate the data based on the missing data rate or error rate. This makes it possible to exclude unreliable data and improve data quality.

[0032] The data collection department can collect external market data and competitor data to enable analysis from a broader perspective. For example, the data collection department collects external market data and integrates it with internal company data for analysis. For example, it collects publicly available market reports and statistical data to understand industry trends and market demand. The data collection department also collects competitor data and compares it with the company's performance. For example, it can analyze competitors' sales data and marketing activities to review its own strategy. Furthermore, the data collection department can integrate external data and internal data to perform analysis from a broader perspective. For example, it can optimize sales strategies by taking external economic indicators and consumer trends into account. This allows it to collect external market data and competitor data to enable analysis from a broader perspective.

[0033] The data collection unit can collect voice data and image data to collect multimodal data. For example, the data collection unit collects voice data and analyzes the contents of telephone conversations and meetings with customers. For example, customer requests and complaints can be extracted from the voice data and reflected in sales strategies. The data collection unit also collects image data and uses it for product quality control and marketing material analysis. For example, it can evaluate the effectiveness of product appearance inspections and advertising images. Furthermore, the data collection unit integrates voice data and image data to perform multimodal data analysis. For example, it can analyze customer facial expressions and tone of voice to evaluate customer satisfaction. This makes it possible to collect voice data and image data to collect multimodal data.

[0034] The data analysis unit can analyze past sales data and future market trend forecast data to develop future sales strategies. The data analysis unit, for example, analyzes past sales data to predict future market trends. For example, it develops an algorithm to predict future demand based on past sales patterns. The data analysis unit also collects market trend forecast data and reflects it in sales strategies. For example, it can analyze industry trend reports and economic forecast data to develop future sales plans. Furthermore, the data analysis unit integrates past sales data with future market trend forecast data to develop future sales strategies. For example, it can plan the timing of product launches and marketing campaigns based on the forecast data. In this way, it is possible to analyze past sales data and future market trend forecast data to develop future sales strategies.

[0035] The data analysis unit can provide customized insights for each department, thereby optimizing each department. The data analysis unit, for example, customizes the analysis results for each department and provides insights for each department. For example, it provides the sales department with sales data analysis results and the marketing department with campaign effectiveness analysis results. The data analysis unit also analyzes data for each department to provide insights tailored to the needs of each department. For example, it can provide the inventory management department with inventory data analysis results to support inventory optimization. Furthermore, the data analysis unit builds a system that provides customized insights to optimize each department. For example, it can provide analysis results based on each department's KPIs to support performance improvement for each department. This makes it possible to provide customized insights for each department and optimize each department.

[0036] The data analysis unit can analyze social media data and analyze real-time customer responses. For example, the data analysis unit collects social media data and analyzes real-time customer responses. For example, it analyzes posts on Twitter and Facebook to understand customer opinions and impressions. The data analysis unit also analyzes social media data to identify customer trends and interests. For example, it can evaluate customer responses to specific products or brands. Furthermore, the data analysis unit develops real-time marketing strategies based on social media data. For example, it can adjust promotions and campaigns depending on customer responses. This makes it possible to analyze social media data and analyze real-time customer responses.

[0037] The data analysis unit can display the insights it provides as a visual dashboard, making them easy to understand intuitively. The data analysis unit, for example, displays insights provided by AI as a visual dashboard, making them easy for users to understand intuitively. For example, it visualizes the analysis results using graphs and charts. The data analysis unit also uses the visual dashboard to monitor data fluctuations in real time. For example, it can display fluctuations in sales data and inventory data in real time. Furthermore, the data analysis unit provides a visual dashboard that can be customized by the user, making it easy to access required information. For example, filtering functions and drill-down functions can be added. This allows the insights it provides to be displayed as a visual dashboard, making them easy to understand intuitively.

[0038] The sales strategy proposal unit can provide a strategy optimized for each region, taking into account the characteristics of each region. For example, the sales strategy proposal unit analyzes sales data for each region and proposes a sales strategy that suits the characteristics of each region. For example, it creates a product sales plan taking into account consumer behavior and market needs for each region. The sales strategy proposal unit also proposes a marketing strategy that takes into account the characteristics of each region. For example, it can carry out promotions that are tailored to the culture and customs of each region. Furthermore, the sales strategy proposal unit proposes an inventory management strategy that reflects the characteristics of each region. For example, it can optimize inventory based on demand forecasts for each region. This makes it possible to provide a strategy optimized for each region, taking into account the characteristics of each region.

[0039] The sales strategy proposal unit can provide timely strategies by taking into account external factors such as seasons and events. For example, the sales strategy proposal unit analyzes seasonal sales data and proposes sales strategies according to the season. For example, it plans product lineups to match summer and winter demand. The sales strategy proposal unit also proposes sales strategies that take into account external factors such as events and holidays. For example, it can plan promotions for Christmas and New Year. Furthermore, the sales strategy proposal unit proposes inventory management strategies according to seasons and events. For example, it can adjust inventory for specific seasons or events. This makes it possible to provide timely strategies by taking into account external factors such as seasons and events.

[0040] The Sales Strategy Proposal Department can incorporate success stories from other industries and utilize best practices from other industries. For example, the Sales Strategy Proposal Department collects success stories from other industries and incorporates them into its sales strategy. For example, it refers to marketing methods and sales strategies from other industries. The Sales Strategy Proposal Department also utilizes best practices from other industries to optimize its sales strategy. For example, it can introduce new sales methods based on success stories from other industries. Furthermore, the Sales Strategy Proposal Department improves its sales strategy based on success stories from other industries. For example, it can incorporate customer engagement methods from other industries. This makes it possible to incorporate success stories from other industries and utilize best practices from other industries.

[0041] The sales strategy proposal department can verify in advance using a simulation tool and select the optimal strategy. For example, the sales strategy proposal department can verify a sales strategy proposed by AI using a simulation tool and select the optimal strategy. For example, it can simulate a sales scenario and predict the effects. The sales strategy proposal department can also use a simulation tool to compare the effects of different sales strategies. For example, it can simulate multiple strategies and select the most effective strategy. Furthermore, the sales strategy proposal department can adjust the sales strategy based on the simulation results. For example, it can optimize the strategy based on the data obtained from the simulation. This makes it possible to verify in advance using a simulation tool and select the optimal strategy.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data collection unit collects data from IoT devices and can understand product usage in real time. For example, it collects data from smart home appliances and wearable devices to monitor product usage frequency and performance. The data collection unit can also predict product failures and propose maintenance schedules based on data from IoT devices. Furthermore, the data collection unit can analyze data from IoT devices and propose product improvements and new functions. This allows the data collection unit to understand product usage in real time.

[0044] The data analysis unit can use natural language processing technology to analyze customer feedback and reviews and identify areas for improvement in products and services. For example, it can analyze online reviews and free-form comment sections in surveys to extract customer opinions and requests. The data analysis unit can also classify customer feedback and identify common problems and areas for improvement. Furthermore, the data analysis unit can propose improvements to products and services based on customer feedback. This makes it possible to analyze customer feedback using natural language processing technology and identify areas for improvement in products and services.

[0045] The data collection unit can collect environmental data and evaluate the environmental impact of a product. For example, it can monitor the energy consumption during the manufacturing process and use of a product to evaluate its impact on the environment. The data collection unit can also suggest eco-friendly improvements to the product based on the environmental data. Furthermore, the data collection unit can analyze the environmental data and develop strategies to minimize the environmental impact throughout the product's life cycle. This allows the collection of environmental data and the evaluation of the product's environmental impact.

[0046] The data collection department collects social media data and can grasp real-time customer reactions. For example, it collects posts from Twitter and Facebook and monitors customer opinions and feedback in real time. The data collection department can also identify customer trends and interests based on social media data. Furthermore, the data collection department can analyze social media data and develop real-time marketing strategies. This allows it to collect social media data and grasp real-time customer reactions.

[0047] The data collection unit can collect external weather data and reflect it in sales strategies. For example, it can predict product demand and adjust sales plans according to weather fluctuations. The data collection unit can also optimize seasonal sales strategies based on weather data. Furthermore, the data collection unit can analyze weather data and propose sales promotion measures under specific weather conditions. This allows external weather data to be collected and reflected in sales strategies.

[0048] The data collection unit can collect customer purchase history data and make personalized product proposals. For example, it can propose products that match the customer's preferences and needs based on past purchase history. The data collection unit can also analyze purchase history data and identify customer purchasing patterns. Furthermore, the data collection unit can develop marketing strategies that match the customer's lifestyle and preferences based on the purchase history data. This makes it possible to collect customer purchase history data and make personalized product proposals.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The data collection department collects various data within the company. For example, it collects sales data, customer data, inventory data, marketing data, etc. The data collection department can also obtain data from external data sources using APIs. For example, the data collection department can automatically collect data from each department within the company and store it in a central database. Step 2: The data analysis unit analyzes the data collected by the data collection unit. For example, the data analysis unit may use machine learning algorithms to analyze customer purchase histories to identify which products are popular with which customers. The data analysis unit may also analyze inventory data to predict which products are in short supply. Furthermore, the data analysis unit may analyze marketing data to evaluate which campaigns were effective. Step 3: The sales strategy proposal department proposes a sales strategy based on the results of the analysis by the data analysis department. For example, the sales strategy proposal department indicates which products should be proposed to a specific customer group and when. The sales strategy proposal department can also suggest which products should be sold preferentially depending on the inventory situation. Furthermore, the sales strategy proposal department supports the design of new campaigns based on the results of past marketing campaigns.

[0051] (Example 2) A sales support system according to an embodiment of the present invention is a system that accumulates data within a company and supports sales activities based on that data. This sales support system uses AI to collect and analyze various data within the company and optimizes sales strategies and marketing activities based on the results. As a result, the sales support system can optimize sales activities based on the company's data and provide an efficient sales strategy.

[0052] A sales support system according to an embodiment includes a data collection unit, a data analysis unit, and a sales strategy proposal unit. The data collection unit collects various data within a company, such as sales data, customer data, inventory data, and marketing data. The data collection unit can also acquire data from external data sources using an API. For example, the data collection unit automatically collects data from each department within the company and stores it in a central database. The data analysis unit analyzes the data collected by the data collection unit. For example, the data analysis unit can analyze customer purchase histories using a machine learning algorithm to identify which products are popular with which customers. The data analysis unit can also analyze inventory data to predict which products are in short supply. The data analysis unit can also analyze marketing data to evaluate the effectiveness of campaigns. The sales strategy proposal unit proposes a sales strategy based on the results of the analysis by the data analysis unit. For example, the sales strategy proposal unit indicates which products should be proposed to a specific customer group and when. The sales strategy proposal unit can also propose which products should be prioritized for sale based on inventory status. Furthermore, the sales strategy proposal unit supports the design of new campaigns based on the results of past marketing campaigns. As a result, the sales support system according to the embodiment can optimize sales activities based on data within the company and provide efficient sales strategies. For example, the sales support system supports product proposals tailored to customer needs, streamlining inventory management, and implementing effective marketing campaigns. This is expected to improve the efficiency and effectiveness of a company's sales activities.

[0053] The data collection unit collects employee behavioral data and internal communication data, enabling an understanding of trends across the organization. For example, the data collection unit collects employee behavioral data and stores information such as arrival times, break times, and work progress in a database. This makes it possible to understand employee working styles and work efficiency. The data collection unit also collects internal communication data and analyzes the content of email exchanges and chats to evaluate the flow of information and the quality of communication within the organization. Furthermore, the data collection unit integrates employee behavioral data and internal communication data to understand trends across the organization. For example, it can analyze project progress and team cooperation. This allows an understanding of trends across the organization and improves work efficiency.

[0054] The data collection unit can evaluate the reliability of data and automatically filter out unreliable data. The data collection unit, for example, evaluates the source of the data and the collection method, and automatically filters out unreliable data. For example, it excludes manually entered data and incomplete data. The data collection unit also adds a function to check the consistency and integrity of data to evaluate the reliability of the data. For example, it can compare data from different data sources and check for inconsistencies. Furthermore, when filtering out unreliable data, the data collection unit calculates a data quality score and excludes data below a certain score. For example, it can evaluate the data based on the missing data rate or error rate. This makes it possible to exclude unreliable data and improve data quality.

[0055] The data collection unit uses the emotion estimation function to collect employee emotion data and understand employee motivation and stress levels. For example, the data collection unit uses the emotion estimation function to analyze employees' facial expressions and voices and collect emotion data in real time. For example, the data collection unit monitors employee emotions using a camera or microphone. The data collection unit also collects employee emotion data and builds a system to evaluate motivation and stress levels. For example, it can calculate emotion scores and generate reports periodically. Furthermore, the data collection unit suggests measures to improve employee motivation and reduce stress based on the emotion data. For example, it can recommend counseling or training for employees with low emotion scores. In this way, employee emotion data can be collected and motivation and stress levels can be understood.

[0056] The data collection department can collect external market data and competitor data to enable analysis from a broader perspective. For example, the data collection department collects external market data and integrates it with internal company data for analysis. For example, it collects publicly available market reports and statistical data to understand industry trends and market demand. The data collection department also collects competitor data and compares it with the company's performance. For example, it can analyze competitors' sales data and marketing activities to review its own strategy. Furthermore, the data collection department can integrate external data and internal data to perform analysis from a broader perspective. For example, it can optimize sales strategies by taking external economic indicators and consumer trends into account. This allows it to collect external market data and competitor data to enable analysis from a broader perspective.

[0057] The data collection unit can collect voice data and image data to collect multimodal data. For example, the data collection unit collects voice data and analyzes the contents of telephone conversations and meetings with customers. For example, customer requests and complaints can be extracted from the voice data and reflected in sales strategies. The data collection unit also collects image data and uses it for product quality control and marketing material analysis. For example, it can evaluate the effectiveness of product appearance inspections and advertising images. Furthermore, the data collection unit integrates voice data and image data to perform multimodal data analysis. For example, it can analyze customer facial expressions and tone of voice to evaluate customer satisfaction. This makes it possible to collect voice data and image data to collect multimodal data.

[0058] The data collection unit uses the emotion estimation function to collect customer emotion data, enabling a real-time understanding of customer satisfaction and dissatisfaction. The data collection unit, for example, uses the emotion estimation function to analyze the customer's facial expressions and voice to collect emotion data in real time. For example, the data collection unit monitors customer emotions using a camera or microphone. The data collection unit also collects customer emotion data and builds a system to evaluate customer satisfaction and dissatisfaction. For example, it can calculate emotion scores and generate reports periodically. Furthermore, the data collection unit proposes measures to improve customer satisfaction and remedy dissatisfaction based on the emotion data. For example, it can provide special services or follow-ups to customers with low emotion scores. This makes it possible to collect customer emotion data and understand customer satisfaction and dissatisfaction in real time.

[0059] The data analysis unit can analyze past sales data and future market trend forecast data to develop future sales strategies. The data analysis unit, for example, analyzes past sales data to predict future market trends. For example, it develops an algorithm to predict future demand based on past sales patterns. The data analysis unit also collects market trend forecast data and reflects it in sales strategies. For example, it can analyze industry trend reports and economic forecast data to develop future sales plans. Furthermore, the data analysis unit integrates past sales data with future market trend forecast data to develop future sales strategies. For example, it can plan the timing of product launches and marketing campaigns based on the forecast data. In this way, it is possible to analyze past sales data and future market trend forecast data to develop future sales strategies.

[0060] The data analysis unit can provide customized insights for each department, thereby optimizing each department. The data analysis unit, for example, customizes the analysis results for each department and provides insights for each department. For example, it provides the sales department with sales data analysis results and the marketing department with campaign effectiveness analysis results. The data analysis unit also analyzes data for each department to provide insights tailored to the needs of each department. For example, it can provide the inventory management department with inventory data analysis results to support inventory optimization. Furthermore, the data analysis unit builds a system that provides customized insights to optimize each department. For example, it can provide analysis results based on each department's KPIs to support performance improvement for each department. This makes it possible to provide customized insights for each department and optimize each department.

[0061] The data analysis unit can analyze customer emotion data using the emotion estimation function and provide insights based on the customer's emotions. The data analysis unit, for example, uses the emotion estimation function to analyze customer emotion data and provide insights based on the customer's emotions. For example, customer satisfaction and dissatisfaction points are evaluated based on the customer's emotion score. The data analysis unit also analyzes customer emotion data and proposes emotion-based marketing strategies. For example, special promotions can be offered to customers with positive emotions. Furthermore, the data analysis unit provides product suggestions and service improvement measures tailored to the customer's emotions based on the emotion data. For example, customer support can be strengthened for customers with low emotion scores. In this way, customer emotion data can be analyzed and insights based on the customer's emotions can be provided.

[0062] The data analysis unit can analyze social media data and analyze real-time customer responses. For example, the data analysis unit collects social media data and analyzes real-time customer responses. For example, it analyzes posts on Twitter and Facebook to understand customer opinions and impressions. The data analysis unit also analyzes social media data to identify customer trends and interests. For example, it can evaluate customer responses to specific products or brands. Furthermore, the data analysis unit develops real-time marketing strategies based on social media data. For example, it can adjust promotions and campaigns depending on customer responses. This makes it possible to analyze social media data and analyze real-time customer responses.

[0063] The data analysis unit can display the insights it provides as a visual dashboard, making them easy to understand intuitively. The data analysis unit, for example, displays insights provided by AI as a visual dashboard, making them easy for users to understand intuitively. For example, it visualizes the analysis results using graphs and charts. The data analysis unit also uses the visual dashboard to monitor data fluctuations in real time. For example, it can display fluctuations in sales data and inventory data in real time. Furthermore, the data analysis unit provides a visual dashboard that can be customized by the user, making it easy to access required information. For example, filtering functions and drill-down functions can be added. This allows the insights it provides to be displayed as a visual dashboard, making them easy to understand intuitively.

[0064] The data analysis unit can analyze employee emotion data using the emotion estimation function and propose measures to improve employee motivation. The data analysis unit, for example, uses the emotion estimation function to analyze employee emotion data and propose measures to improve motivation. For example, the data analysis unit evaluates employee stress levels based on emotion scores. The data analysis unit also analyzes employee emotion data and proposes emotion-based motivation improvement measures. For example, leadership training can be provided to employees with positive emotions. Furthermore, the data analysis unit can propose employee benefits and support programs tailored to the employee's emotions based on the emotion data. For example, mental health support can be provided to employees with low emotion scores. In this way, employee emotion data can be analyzed and measures to improve motivation can be proposed.

[0065] The sales strategy proposal unit can provide a strategy optimized for each region, taking into account the characteristics of each region. For example, the sales strategy proposal unit analyzes sales data for each region and proposes a sales strategy that suits the characteristics of each region. For example, it creates a product sales plan taking into account consumer behavior and market needs for each region. The sales strategy proposal unit also proposes a marketing strategy that takes into account the characteristics of each region. For example, it can carry out promotions that are tailored to the culture and customs of each region. Furthermore, the sales strategy proposal unit proposes an inventory management strategy that reflects the characteristics of each region. For example, it can optimize inventory based on demand forecasts for each region. This makes it possible to provide a strategy optimized for each region, taking into account the characteristics of each region.

[0066] The sales strategy proposal unit can provide timely strategies by taking into account external factors such as seasons and events. For example, the sales strategy proposal unit analyzes seasonal sales data and proposes sales strategies according to the season. For example, it plans product lineups to match summer and winter demand. The sales strategy proposal unit also proposes sales strategies that take into account external factors such as events and holidays. For example, it can plan promotions for Christmas and New Year. Furthermore, the sales strategy proposal unit proposes inventory management strategies according to seasons and events. For example, it can adjust inventory for specific seasons or events. This makes it possible to provide timely strategies by taking into account external factors such as seasons and events.

[0067] The sales strategy proposal unit can propose a sales strategy tailored to the customer's emotions based on the customer's emotion data using the emotion estimation function. The sales strategy proposal unit, for example, uses the emotion estimation function to analyze the customer's emotion data and propose a sales strategy based on the emotions. For example, a special offer is provided to customers with positive emotions. The sales strategy proposal unit also makes product proposals tailored to the customer's emotions based on the customer's emotion data. For example, related products can be proposed to customers with high emotion scores. Furthermore, the sales strategy proposal unit proposes a marketing campaign tailored to the customer's emotions based on the emotion data. For example, a special promotion can be carried out for customers with low emotion scores. In this way, a sales strategy tailored to the customer's emotions can be proposed based on the customer's emotion data.

[0068] The Sales Strategy Proposal Department can incorporate success stories from other industries and utilize best practices from other industries. For example, the Sales Strategy Proposal Department collects success stories from other industries and incorporates them into its sales strategy. For example, it refers to marketing methods and sales strategies from other industries. The Sales Strategy Proposal Department also utilizes best practices from other industries to optimize its sales strategy. For example, it can introduce new sales methods based on success stories from other industries. Furthermore, the Sales Strategy Proposal Department improves its sales strategy based on success stories from other industries. For example, it can incorporate customer engagement methods from other industries. This makes it possible to incorporate success stories from other industries and utilize best practices from other industries.

[0069] The sales strategy proposal department can verify in advance using a simulation tool and select the optimal strategy. For example, the sales strategy proposal department can verify a sales strategy proposed by AI using a simulation tool and select the optimal strategy. For example, it can simulate a sales scenario and predict the effects. The sales strategy proposal department can also use a simulation tool to compare the effects of different sales strategies. For example, it can simulate multiple strategies and select the most effective strategy. Furthermore, the sales strategy proposal department can adjust the sales strategy based on the simulation results. For example, it can optimize the strategy based on the data obtained from the simulation. This makes it possible to verify in advance using a simulation tool and select the optimal strategy.

[0070] The sales strategy proposal unit can propose sales strategies that increase employee motivation based on employee emotion data using the emotion estimation function. The sales strategy proposal unit, for example, uses the emotion estimation function to analyze employee emotion data and propose measures to improve motivation. For example, the sales strategy proposal unit evaluates employee stress levels based on emotion scores. The sales strategy proposal unit also proposes motivation improvement measures tailored to the emotions based on the employee emotion data. For example, leadership training can be provided to employees with positive emotions. Furthermore, the sales strategy proposal unit proposes welfare benefits and support programs tailored to the employee emotions based on the emotion data. For example, mental health support can be provided to employees with low emotion scores. This makes it possible to propose sales strategies that increase employee motivation based on employee emotion data.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The data collection unit collects data from IoT devices and can understand product usage in real time. For example, it collects data from smart home appliances and wearable devices to monitor product usage frequency and performance. The data collection unit can also predict product failures and propose maintenance schedules based on data from IoT devices. Furthermore, the data collection unit can analyze data from IoT devices and propose product improvements and new functions. This allows the data collection unit to understand product usage in real time.

[0073] The data analysis unit can use natural language processing technology to analyze customer feedback and reviews and identify areas for improvement in products and services. For example, it can analyze online reviews and free-form comment sections in surveys to extract customer opinions and requests. The data analysis unit can also classify customer feedback and identify common problems and areas for improvement. Furthermore, the data analysis unit can propose improvements to products and services based on customer feedback. This makes it possible to analyze customer feedback using natural language processing technology and identify areas for improvement in products and services.

[0074] The data collection unit can collect environmental data and evaluate the environmental impact of a product. For example, it can monitor the energy consumption during the manufacturing process and use of a product to evaluate its impact on the environment. The data collection unit can also suggest eco-friendly improvements to the product based on the environmental data. Furthermore, the data collection unit can analyze the environmental data and develop strategies to minimize the environmental impact throughout the product's life cycle. This allows the collection of environmental data and the evaluation of the product's environmental impact.

[0075] The data analysis unit can analyze employee emotion data using the emotion estimation function and provide insights based on employee emotions. For example, the data analysis unit can evaluate employee motivation and stress levels based on employee emotion scores. The data analysis unit can also analyze employee emotion data and propose emotion-based business improvement measures. Furthermore, the data analysis unit can provide employee benefits and support programs tailored to employee emotions based on the emotion data. This makes it possible to analyze employee emotion data and provide insights based on employee emotions.

[0076] The data collection unit can collect customer emotion data using the emotion estimation function and provide customer support based on the customer's emotions. For example, the priority of support can be determined based on the customer's emotion score. The data collection unit can also collect customer emotion data and suggest a support method based on the emotion. Furthermore, the data collection unit can perform follow-up tailored to the customer's emotions based on the emotion data. This makes it possible to collect customer emotion data and provide customer support based on the customer's emotions.

[0077] The data collection unit can collect employee emotion data using the emotion estimation function and provide feedback based on the employee's emotions. For example, the content and timing of feedback can be adjusted based on the employee's emotion score. The data collection unit can also collect employee emotion data and suggest a feedback method based on the emotion. Furthermore, the data collection unit can provide feedback tailored to the employee's emotions based on the emotion data. This makes it possible to collect employee emotion data and provide feedback based on the employee's emotions.

[0078] The data collection unit can collect customer emotion data using the emotion estimation function and propose a marketing strategy based on the customer's emotions. For example, the content and timing of a marketing campaign can be adjusted based on the customer's emotion score. The data collection unit can also collect customer emotion data and propose a promotion method based on the emotion. Furthermore, the data collection unit can plan a marketing strategy tailored to the customer's emotions based on the emotion data. This makes it possible to collect customer emotion data and propose a marketing strategy based on the customer's emotions.

[0079] The data collection department collects social media data and can grasp real-time customer reactions. For example, it collects posts from Twitter and Facebook and monitors customer opinions and feedback in real time. The data collection department can also identify customer trends and interests based on social media data. Furthermore, the data collection department can analyze social media data and develop real-time marketing strategies. This allows it to collect social media data and grasp real-time customer reactions.

[0080] The data collection unit can collect external weather data and reflect it in sales strategies. For example, it can predict product demand and adjust sales plans according to weather fluctuations. The data collection unit can also optimize seasonal sales strategies based on weather data. Furthermore, the data collection unit can analyze weather data and propose sales promotion measures under specific weather conditions. This allows external weather data to be collected and reflected in sales strategies.

[0081] The data collection unit can collect customer purchase history data and make personalized product proposals. For example, it can propose products that match the customer's preferences and needs based on past purchase history. The data collection unit can also analyze purchase history data and identify customer purchasing patterns. Furthermore, the data collection unit can develop marketing strategies that match the customer's lifestyle and preferences based on the purchase history data. This makes it possible to collect customer purchase history data and make personalized product proposals.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The data collection department collects various data within the company. For example, it collects sales data, customer data, inventory data, marketing data, etc. The data collection department can also obtain data from external data sources using APIs. For example, the data collection department can automatically collect data from each department within the company and store it in a central database. Step 2: The data analysis unit analyzes the data collected by the data collection unit. For example, the data analysis unit may use machine learning algorithms to analyze customer purchase histories to identify which products are popular with which customers. The data analysis unit may also analyze inventory data to predict which products are in short supply. Furthermore, the data analysis unit may analyze marketing data to evaluate which campaigns were effective. Step 3: The sales strategy proposal department proposes a sales strategy based on the results of the analysis by the data analysis department. For example, the sales strategy proposal department indicates which products should be proposed to a specific customer group and when. The sales strategy proposal department can also suggest which products should be sold preferentially depending on the inventory situation. Furthermore, the sales strategy proposal department supports the design of new campaigns based on the results of past marketing campaigns.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects various data within the company; a data analysis unit that analyzes the data collected by the data collection unit; a sales strategy proposal unit that proposes a sales strategy based on the results of the analysis by the data analysis unit. A system characterized by:

2. The data collection unit Collect employee behavioral data and internal communication data to understand trends across the organization 2. The system of claim 1.

3. The data collection unit Evaluate the reliability of data and automatically filter out unreliable data 2. The system of claim 1.

4. The data collection unit Collect employee sentiment data to understand employee motivation and stress levels 2. The system of claim 1.

5. The data collection unit Collect external market and competitor data to enable broader analysis 2. The system of claim 1.

6. The data collection unit Collect audio and image data, and collect multimodal data.

2. The system of claim 1.

7. The data collection unit Collect customer sentiment data to understand customer satisfaction and dissatisfaction in real time 2. The system of claim 1.

8. The data analysis unit Analyze past sales data and future market trend forecast data to develop future sales strategies 2. The system of claim 1.

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