System

Abstract

An object of a system according to an embodiment is to analyze sales data and automatically determine a winning ratio for each presented amount.SOLUTION: A system includes a data acquisition unit, an analysis unit, an input unit, and a determination unit. The data-fetching part fetches the past business date by using the generated AI. The analysis part analyzes the business data fetched by the data fetching part. The input unit inputs product information and customer information. The determination unit determines a winning rate for each presented amount based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, salespeople often relied on experience and intuition when deciding on the price to offer, making it difficult to set the optimal price.

[0005] The system according to the embodiment aims to analyze sales data and automatically determine the winning rate for each offered amount. [Means for solving the problem]

[0006] The system according to the embodiment includes a data import unit, an analysis unit, an input unit, and a determination unit. The data import unit imports past sales data using a generation AI. The analysis unit analyzes the sales data imported by the data import unit. The input unit inputs product information and customer information. The determination unit determines the winning rate for each bid amount based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze sales data and automatically determine the winning rate for each offered amount. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The pricing support system according to an embodiment of the present invention uses a generation AI to analyze past sales data, and automatically determines the winning rate for each proposed price based on the input of product information and customer information, and presents optimal terms. This allows even inexperienced salespeople to present optimal terms, preventing profit losses due to excessive discounts.

[0029] A pricing support system according to an embodiment includes a data capture unit, an analysis unit, an input unit, and a determination unit. The data capture unit captures past sales data using a generation AI. For example, the data capture unit collects sales data and customer information entered by salespeople. The data capture unit can also acquire transaction histories from a database. The data capture unit can also capture unstructured data such as salespeople's notes and conversation records. The analysis unit analyzes the sales data captured by the data capture unit. For example, the analysis unit can analyze data trends using statistical analysis. The analysis unit can also identify conditions that lead to orders using machine learning algorithms. The analysis unit can also analyze unstructured data using natural language processing technology. The input unit inputs product information and customer information. For example, the input unit provides an interface for users to input product specifications and prices. The input unit can also provide fields for inputting information such as the customer's industry and purpose. The input unit can also present past success stories and failure stories in real time to improve input accuracy. The determination unit determines the winning rate for each bid amount based on the data analyzed by the analysis unit. For example, the determination unit calculates the winning rate for each bid amount using a generation AI. The determination unit can also perform analysis taking into account competitors' price trends and market trends. The determination unit can also visualize the winning rate for each bid amount and allow users to intuitively understand it using graphs and charts. This allows the pricing support system according to the embodiment to enable even inexperienced salespeople to present optimal terms and avoid profit losses due to excessive discounts. For example, if a slight reduction in the bid amount significantly increases the winning rate, the system sets the optimal price based on that information. The system can also analyze the extent to which excessive discounts will reduce profits based on past data and propose an appropriate discount amount.

[0030] The data capture unit can analyze unstructured data contained in sales data and extract more detailed conditions. For example, the data capture unit performs text analysis on salesperson's notes and conversation records to extract important keywords and phrases. For example, it analyzes customer reactions and requests to identify factors that lead to orders. The data capture unit also analyzes unstructured data using natural language processing technology to extract customer needs and interests. For example, it understands customer purchasing intent and information about competitors from conversation records. The data capture unit also analyzes salesperson's notes and conversation records to identify communication patterns with customers. For example, it analyzes successful and unsuccessful sales talks to derive optimal sales techniques. In this way, more detailed conditions can be extracted by analyzing unstructured data.

[0031] The data acquisition unit performs an analysis based on the sales data and at least one external factor, either seasonal or economic conditions, to identify factors that cause fluctuations in the quoted price. The data acquisition unit, for example, combines past sales data with seasonal data to analyze seasonal order trends. For example, it identifies products for which demand increases during specific seasons and sets optimal prices. The data acquisition unit also imports economic indicator data to analyze the impact of economic conditions on sales performance. For example, it analyzes how the state of the economy affects the probability of receiving an order. The data acquisition unit also combines past sales data with weather data to analyze the impact of weather on orders. For example, it identifies products for which orders increase on rainy days and reflects this in pricing. In this way, by performing an analysis that takes into account external factors such as seasonality and economic conditions, it is possible to identify factors that cause fluctuations in the quoted price.

[0032] The data import unit imports sales data from different industries and regions and can perform analysis from a cross-industry perspective. For example, the data import unit imports sales data from different industries and analyzes common success patterns. For example, it compares sales methods between the manufacturing and service industries and derives the optimal method. The data import unit also imports sales data from each region and analyzes success factors specific to that region. For example, it compares sales performance in urban and rural areas and sets prices according to the region. The data import unit also integrates data from different industries and regions and proposes new sales strategies from a cross-industry perspective. For example, it applies success stories from other industries to its own company. In this way, by importing sales data from different industries and regions, it is possible to perform analysis from a cross-industry perspective.

[0033] When importing sales data, the data import unit analyzes multimodal information including at least one of image and audio data, allowing for the extraction of more diversified conditions. For example, the data import unit analyzes image data included in the sales data to identify the impact that the visual elements of a product have on orders. For example, it analyzes the impact that the quality of a product image has on the probability of receiving an order. The data import unit also analyzes audio data to identify the impact that a salesperson's tone and speaking style have on orders. For example, it analyzes whether a particular tone or speaking style leads to success. The data import unit also integrates image and audio data with text data to optimize sales strategies based on multimodal information. For example, it makes proposals that take both visual and audio data into consideration. In this way, by analyzing multimodal information including image and audio data, more diversified conditions can be extracted.

[0034] The input unit uses a generation AI to present past success stories and failure stories in real time when product information and customer information are entered, improving the accuracy of input. For example, when product information and customer information are entered, the input unit presents past success stories in real time to serve as a reference for input. For example, it displays successful cases in the same industry. The input unit also presents past failure stories as input to serve as a reference for avoiding the same mistakes. For example, it displays cases of past lost sales and points to note. The input unit also uses a generation AI to analyze past data and automatically present success stories and failure stories based on the input content. For example, it displays the case that is closest to the input content. This allows past success stories and failure stories to be presented in real time, improving the accuracy of input.

[0035] The input unit can analyze a customer's past purchasing history and behavioral patterns to generate a more detailed customer profile. For example, the input unit analyzes a customer's past purchasing history to identify purchasing trends and preferences. For example, it identifies customers who frequently purchase specific products and reflects this in the profile. The input unit also analyzes a customer's behavioral patterns to identify purchasing intentions and interests. For example, it generates a profile based on website browsing history and click data. The input unit also builds a system that integrates a customer's past data to generate a detailed customer profile. For example, it integrates purchase history, behavioral patterns, and survey results. In this way, a more detailed customer profile can be generated by analyzing a customer's past purchasing history and behavioral patterns.

[0036] When inputting product information and customer information, the input unit can incorporate at least one of information from different languages ​​or cultural spheres and perform analysis from a global perspective. The input unit, for example, incorporates product information in different languages ​​and analyzes it from a global perspective. For example, it analyzes product information in English or Chinese and sets optimal prices. The input unit also incorporates customer information from different cultural spheres and performs analysis taking cultural background into consideration. For example, it identifies products preferred in specific cultural spheres and reflects this in proposals. The input unit also analyzes product information and customer information from a global perspective and sets prices appropriate for the international market. For example, it makes proposals that take into consideration the market needs of each country. In this way, by incorporating information from different languages ​​and cultural spheres, analysis can be performed from a global perspective.

[0037] The input unit can convert the input information into a visual note or mind map to make it easier to understand visually. The input unit, for example, converts product information and customer information into a visual note and displays it visually. For example, it shows the product's features and customer profile using diagrams and icons. The input unit also converts the input information into a mind map format to visually organize related keywords and concepts. For example, it makes it possible to understand the relationship between the product and the customer at a glance. The input unit also develops tools that automatically generate visual notes and mind maps to enable users to easily visualize information. For example, it provides a function for visualizing information using drag and drop. This allows the input information to be converted into a visual note or mind map to make it easier to understand visually.

[0038] When the judgment unit uses the generation AI to determine the win rate for each bid amount, it can make a more detailed win rate prediction based on success patterns learned from past data. For example, the judgment unit has the generation AI learn past data and make a detailed prediction of the win rate for each bid amount. For example, it calculates the win rate based on success patterns in a specific price range. The judgment unit also analyzes past success patterns and builds a system that predicts the win rate for each bid amount. For example, it extracts commonalities between successful cases and reflects them in the win rate prediction. The judgment unit also has the generation AI predict the win rate for each bid amount in real time based on past data. For example, it instantly displays the win rate for the input bid amount. This allows for a more detailed win rate prediction based on success patterns learned from past data.

[0039] When determining the winning rate for each bid amount, the determination unit can perform an analysis based on at least one of the price trends of competitors or market trends. For example, the determination unit collects competitors' price trends in real time and reflects them in the winning rate for each bid amount. For example, the determination unit recalculates the winning rate if a competitor's price drops. The determination unit also imports market trend data and analyzes the winning rate for each bid amount. For example, the determination unit analyzes the impact of specific market trends on the probability of receiving an order. The determination unit also integrates competitors' price trends and market trends to build a system that predicts the winning rate for each bid amount. For example, the determination unit proposes an optimal bid amount depending on the price fluctuations of competitors. This allows for a more accurate determination of the winning rate for each bid amount by performing an analysis that takes into account competitors' price trends and market trends.

[0040] When determining the winning rate for each bid amount, the determination unit can integrate at least one of data from different industries or applications to perform analysis from a more multifaceted perspective. The determination unit, for example, integrates data from different industries and performs a multifaceted analysis of the winning rate for each bid amount. For example, it compares data from the manufacturing and service industries to determine optimal pricing. The determination unit also integrates data from different applications to build a system that analyzes the winning rate for each bid amount. For example, it integrates data from medical and industrial applications to determine optimal pricing. The determination unit also predicts the winning rate for each bid amount from a multifaceted perspective based on data from different industries and applications. For example, it applies successful cases from other industries to its own company. In this way, by integrating data from different industries and applications, it is possible to perform analysis from a more multifaceted perspective.

[0041] The determination unit visualizes the win rate for each offered amount, allowing the user to intuitively understand using a graph or chart. The determination unit, for example, visualizes the win rate for each offered amount using a graph or chart, allowing the user to intuitively understand. For example, the determination unit displays the win rate for each price range using a bar graph. The determination unit also displays the win rate for each offered amount using a heat map, visually indicating the optimal price range. For example, price ranges with a high win rate are highlighted in color. The determination unit also visualizes the win rate for each offered amount in real time, allowing the user to instantly understand. For example, the graph is updated every time the price setting is changed. In this way, by visualizing the win rate for each offered amount, the user can intuitively understand.

[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 acquisition unit performs an analysis based on the sales data and at least one external factor, either seasonal or economic conditions, to identify factors that cause fluctuations in the quoted price. The data acquisition unit, for example, combines past sales data with seasonal data to analyze seasonal order trends. For example, it identifies products for which demand increases during specific seasons and sets optimal prices. The data acquisition unit also imports economic indicator data to analyze the impact of economic conditions on sales performance. For example, it analyzes how the state of the economy affects the probability of receiving an order. The data acquisition unit also combines past sales data with weather data to analyze the impact of weather on orders. For example, it identifies products for which orders increase on rainy days and reflects this in pricing. In this way, by performing an analysis that takes into account external factors such as seasonality and economic conditions, it is possible to identify factors that cause fluctuations in the quoted price.

[0044] The data import unit imports sales data from different industries and regions and can perform analysis from a cross-industry perspective. For example, the data import unit imports sales data from different industries and analyzes common success patterns. For example, it compares sales methods between the manufacturing and service industries and derives the optimal method. The data import unit also imports sales data from each region and analyzes success factors specific to that region. For example, it compares sales performance in urban and rural areas and sets prices according to the region. The data import unit also integrates data from different industries and regions and proposes new sales strategies from a cross-industry perspective. For example, it applies success stories from other industries to its own company. In this way, by importing sales data from different industries and regions, it is possible to perform analysis from a cross-industry perspective.

[0045] When importing sales data, the data import unit analyzes multimodal information including at least one of image and audio data, allowing for the extraction of more diversified conditions. For example, the data import unit analyzes image data included in the sales data to identify the impact that the visual elements of a product have on orders. For example, it analyzes the impact that the quality of a product image has on the probability of receiving an order. The data import unit also analyzes audio data to identify the impact that a salesperson's tone and speaking style have on orders. For example, it analyzes whether a particular tone or speaking style leads to success. The data import unit also integrates image and audio data with text data to optimize sales strategies based on multimodal information. For example, it makes proposals that take both visual and audio data into consideration. In this way, by analyzing multimodal information including image and audio data, more diversified conditions can be extracted.

[0046] The input unit uses a generation AI to present past success stories and failure stories in real time when product information and customer information are entered, improving the accuracy of input. For example, when product information and customer information are entered, the input unit presents past success stories in real time to serve as a reference for input. For example, it displays successful cases in the same industry. The input unit also presents past failure stories as input to serve as a reference for avoiding the same mistakes. For example, it displays cases of past lost sales and points to note. The input unit also uses a generation AI to analyze past data and automatically present success stories and failure stories based on the input content. For example, it displays the case that is closest to the input content. This allows past success stories and failure stories to be presented in real time, improving the accuracy of input.

[0047] The input unit can analyze a customer's past purchasing history and behavioral patterns to generate a more detailed customer profile. For example, the input unit analyzes a customer's past purchasing history to identify purchasing trends and preferences. For example, it identifies customers who frequently purchase specific products and reflects this in the profile. The input unit also analyzes a customer's behavioral patterns to identify purchasing intentions and interests. For example, it generates a profile based on website browsing history and click data. The input unit also builds a system that integrates a customer's past data to generate a detailed customer profile. For example, it integrates purchase history, behavioral patterns, and survey results. In this way, a more detailed customer profile can be generated by analyzing a customer's past purchasing history and behavioral patterns.

[0048] When inputting product information and customer information, the input unit can incorporate at least one of information from different languages ​​or cultural spheres and perform analysis from a global perspective. The input unit, for example, incorporates product information in different languages ​​and analyzes it from a global perspective. For example, it analyzes product information in English or Chinese and sets optimal prices. The input unit also incorporates customer information from different cultural spheres and performs analysis taking cultural background into consideration. For example, it identifies products preferred in specific cultural spheres and reflects this in proposals. The input unit also analyzes product information and customer information from a global perspective and sets prices appropriate for the international market. For example, it makes proposals that take into consideration the market needs of each country. In this way, by incorporating information from different languages ​​and cultural spheres, analysis can be performed from a global perspective.

[0049] The input unit can convert the input information into a visual note or mind map to make it easier to understand visually. The input unit, for example, converts product information and customer information into a visual note and displays it visually. For example, it shows the product's features and customer profile using diagrams and icons. The input unit also converts the input information into a mind map format to visually organize related keywords and concepts. For example, it makes it possible to understand the relationship between the product and the customer at a glance. The input unit also develops tools that automatically generate visual notes and mind maps to enable users to easily visualize information. For example, it provides a function for visualizing information using drag and drop. This allows the input information to be converted into a visual note or mind map to make it easier to understand visually.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data capture unit uses generation AI to capture past sales data. For example, it collects sales data and customer information entered by salespeople and retrieves transaction history from a database. It can also capture unstructured data such as salespeople's notes and conversation records. Step 2: The analysis unit analyzes the sales data captured by the data capture unit. For example, it uses statistical analysis to analyze data trends and machine learning algorithms to identify conditions that lead to orders. It can also analyze unstructured data using natural language processing technology. Step 3: The input section inputs product information and customer information. For example, it provides an interface for users to input product specifications and prices, and fields for inputting information such as the customer's industry and use. It can also display past success stories and failure stories in real time to improve input accuracy. Step 4: The judgment unit determines the winning rate for each bid amount based on the data analyzed by the analysis unit. For example, it uses generation AI to calculate the winning rate for each bid amount and performs analysis taking into account the price trends of competitors and market trends. It can also visualize the winning rate for each bid amount and use graphs and charts to help users intuitively understand it.

[0052] (Example 2) The pricing support system according to an embodiment of the present invention uses a generation AI to analyze past sales data, and automatically determines the winning rate for each proposed price based on the input of product information and customer information, and presents optimal terms. This allows even inexperienced salespeople to present optimal terms, preventing profit losses due to excessive discounts.

[0053] A pricing support system according to an embodiment includes a data capture unit, an analysis unit, an input unit, and a determination unit. The data capture unit captures past sales data using a generation AI. For example, the data capture unit collects sales data and customer information entered by salespeople. The data capture unit can also acquire transaction histories from a database. The data capture unit can also capture unstructured data such as salespeople's notes and conversation records. The analysis unit analyzes the sales data captured by the data capture unit. For example, the analysis unit can analyze data trends using statistical analysis. The analysis unit can also identify conditions that lead to orders using machine learning algorithms. The analysis unit can also analyze unstructured data using natural language processing technology. The input unit inputs product information and customer information. For example, the input unit provides an interface for users to input product specifications and prices. The input unit can also provide fields for inputting information such as the customer's industry and purpose. The input unit can also present past success stories and failure stories in real time to improve input accuracy. The determination unit determines the winning rate for each bid amount based on the data analyzed by the analysis unit. For example, the determination unit calculates the winning rate for each bid amount using a generation AI. The determination unit can also perform analysis taking into account competitors' price trends and market trends. The determination unit can also visualize the winning rate for each bid amount and allow users to intuitively understand it using graphs and charts. This allows the pricing support system according to the embodiment to enable even inexperienced salespeople to present optimal terms and avoid profit losses due to excessive discounts. For example, if a slight reduction in the bid amount significantly increases the winning rate, the system sets the optimal price based on that information. The system can also analyze the extent to which excessive discounts will reduce profits based on past data and propose an appropriate discount amount.

[0054] The data capture unit can analyze unstructured data contained in sales data and extract more detailed conditions. For example, the data capture unit performs text analysis on salesperson's notes and conversation records to extract important keywords and phrases. For example, it analyzes customer reactions and requests to identify factors that lead to orders. The data capture unit also analyzes unstructured data using natural language processing technology to extract customer needs and interests. For example, it understands customer purchasing intent and information about competitors from conversation records. The data capture unit also analyzes salesperson's notes and conversation records to identify communication patterns with customers. For example, it analyzes successful and unsuccessful sales talks to derive optimal sales techniques. In this way, more detailed conditions can be extracted by analyzing unstructured data.

[0055] The data acquisition unit performs an analysis based on the sales data and at least one external factor, either seasonal or economic conditions, to identify factors that cause fluctuations in the quoted price. The data acquisition unit, for example, combines past sales data with seasonal data to analyze seasonal order trends. For example, it identifies products for which demand increases during specific seasons and sets optimal prices. The data acquisition unit also imports economic indicator data to analyze the impact of economic conditions on sales performance. For example, it analyzes how the state of the economy affects the probability of receiving an order. The data acquisition unit also combines past sales data with weather data to analyze the impact of weather on orders. For example, it identifies products for which orders increase on rainy days and reflects this in pricing. In this way, by performing an analysis that takes into account external factors such as seasonality and economic conditions, it is possible to identify factors that cause fluctuations in the quoted price.

[0056] The data acquisition unit can use the emotion estimation function to analyze customers' emotional responses from sales data and identify emotionally positive conditions. The data acquisition unit, for example, analyzes customers' emotional responses contained in past sales data and identifies conditions that result in strong positive emotions. For example, it extracts proposal content that pleases the customer. The data acquisition unit also uses the emotion estimation function to calculate a customer's emotion score and identify sales methods that result in high positive emotions. For example, it analyzes pricing that satisfies the customer. The data acquisition unit also identifies emotionally positive conditions based on the customer's emotional response data and reflects these in sales strategies. For example, it reproduces proposals that the customer favorably accepted. In this way, emotionally positive conditions can be identified by analyzing the customer's emotional responses.

[0057] The data import unit imports sales data from different industries and regions and can perform analysis from a cross-industry perspective. For example, the data import unit imports sales data from different industries and analyzes common success patterns. For example, it compares sales methods between the manufacturing and service industries and derives the optimal method. The data import unit also imports sales data from each region and analyzes success factors specific to that region. For example, it compares sales performance in urban and rural areas and sets prices according to the region. The data import unit also integrates data from different industries and regions and proposes new sales strategies from a cross-industry perspective. For example, it applies success stories from other industries to its own company. In this way, by importing sales data from different industries and regions, it is possible to perform analysis from a cross-industry perspective.

[0058] When importing sales data, the data import unit analyzes multimodal information including at least one of image and audio data, allowing for the extraction of more diversified conditions. For example, the data import unit analyzes image data included in the sales data to identify the impact that the visual elements of a product have on orders. For example, it analyzes the impact that the quality of a product image has on the probability of receiving an order. The data import unit also analyzes audio data to identify the impact that a salesperson's tone and speaking style have on orders. For example, it analyzes whether a particular tone or speaking style leads to success. The data import unit also integrates image and audio data with text data to optimize sales strategies based on multimodal information. For example, it makes proposals that take both visual and audio data into consideration. In this way, by analyzing multimodal information including image and audio data, more diversified conditions can be extracted.

[0059] The data acquisition unit uses the emotion estimation function to analyze the emotional state of the salesperson and make a proposal at the most emotionally appropriate timing. The data acquisition unit, for example, analyzes the emotional state of the salesperson in real time and makes a proposal when positive emotions are strong. For example, a proposal is made when the salesperson is feeling confident. The data acquisition unit also uses the emotion estimation function to calculate the emotional score of the salesperson and identify the optimal timing for making a proposal. For example, a proposal is made when the salesperson is relaxed. The data acquisition unit also builds a system that adjusts the timing of the proposal based on the emotional state of the salesperson. For example, a proposal is refrained from when the salesperson is feeling stressed. In this way, by analyzing the emotional state of the salesperson, a proposal can be made at the emotionally optimal timing.

[0060] The input unit uses a generation AI to present past success stories and failure stories in real time when product information and customer information are entered, improving the accuracy of input. For example, when product information and customer information are entered, the input unit presents past success stories in real time to serve as a reference for input. For example, it displays successful cases in the same industry. The input unit also presents past failure stories as input to serve as a reference for avoiding the same mistakes. For example, it displays cases of past lost sales and points to note. The input unit also uses a generation AI to analyze past data and automatically present success stories and failure stories based on the input content. For example, it displays the case that is closest to the input content. This allows past success stories and failure stories to be presented in real time, improving the accuracy of input.

[0061] The input unit can analyze a customer's past purchasing history and behavioral patterns to generate a more detailed customer profile. For example, the input unit analyzes a customer's past purchasing history to identify purchasing trends and preferences. For example, it identifies customers who frequently purchase specific products and reflects this in the profile. The input unit also analyzes a customer's behavioral patterns to identify purchasing intentions and interests. For example, it generates a profile based on website browsing history and click data. The input unit also builds a system that integrates a customer's past data to generate a detailed customer profile. For example, it integrates purchase history, behavioral patterns, and survey results. In this way, a more detailed customer profile can be generated by analyzing a customer's past purchasing history and behavioral patterns.

[0062] The input unit can use the emotion estimation function to analyze the emotional state of a customer in real time and suggest product information that is most emotionally appropriate. For example, the input unit can analyze the emotional state of a customer in real time and suggest product information that is most appropriate when the customer is feeling strongly positive. For example, it can suggest a new product when the customer is excited. The input unit can also use the emotion estimation function to calculate the customer's emotion score and identify product information that is most emotionally appropriate. For example, it can suggest a high-priced product when the customer is relaxed. The input unit can also build a system that adjusts the timing of product information suggestions based on the customer's emotional state. For example, it can refrain from suggestions when the customer is feeling stressed. In this way, by analyzing the customer's emotional state in real time, it is possible to suggest product information that is most emotionally appropriate.

[0063] When inputting product information and customer information, the input unit can incorporate at least one of information from different languages ​​or cultural spheres and perform analysis from a global perspective. The input unit, for example, incorporates product information in different languages ​​and analyzes it from a global perspective. For example, it analyzes product information in English or Chinese and sets optimal prices. The input unit also incorporates customer information from different cultural spheres and performs analysis taking cultural background into consideration. For example, it identifies products preferred in specific cultural spheres and reflects this in proposals. The input unit also analyzes product information and customer information from a global perspective and sets prices appropriate for the international market. For example, it makes proposals that take into consideration the market needs of each country. In this way, by incorporating information from different languages ​​and cultural spheres, analysis can be performed from a global perspective.

[0064] The input unit can convert the input information into a visual note or mind map to make it easier to understand visually. The input unit, for example, converts product information and customer information into a visual note and displays it visually. For example, it shows the product's features and customer profile using diagrams and icons. The input unit also converts the input information into a mind map format to visually organize related keywords and concepts. For example, it makes it possible to understand the relationship between the product and the customer at a glance. The input unit also develops tools that automatically generate visual notes and mind maps to enable users to easily visualize information. For example, it provides a function for visualizing information using drag and drop. This allows the input information to be converted into a visual note or mind map to make it easier to understand visually.

[0065] The input unit uses the emotion estimation function to collect the user's emotional reactions to the input information, thereby improving the accuracy of the input. The input unit, for example, collects the user's emotional reactions to the input product information or customer information in real time and improves the accuracy of the input based on that data. For example, it prioritizes the adoption of information with a high number of positive reactions. The input unit also uses the emotion estimation function to analyze the user's emotional score for the input information and reconsiders the input content if there are a high number of negative reactions. The input unit also identifies areas for improvement in the input based on the user's emotional reaction data and builds a system to improve the accuracy of the input. For example, it makes suggestions to correct parts with low emotional scores. In this way, the accuracy of the input can be improved by collecting the user's emotional reactions to the input information.

[0066] When the judgment unit uses the generation AI to determine the win rate for each bid amount, it can make a more detailed win rate prediction based on success patterns learned from past data. For example, the judgment unit has the generation AI learn past data and make a detailed prediction of the win rate for each bid amount. For example, it calculates the win rate based on success patterns in a specific price range. The judgment unit also analyzes past success patterns and builds a system that predicts the win rate for each bid amount. For example, it extracts commonalities between successful cases and reflects them in the win rate prediction. The judgment unit also has the generation AI predict the win rate for each bid amount in real time based on past data. For example, it instantly displays the win rate for the input bid amount. This allows for a more detailed win rate prediction based on success patterns learned from past data.

[0067] When determining the winning rate for each bid amount, the determination unit can perform an analysis based on at least one of the price trends of competitors or market trends. For example, the determination unit collects competitors' price trends in real time and reflects them in the winning rate for each bid amount. For example, the determination unit recalculates the winning rate if a competitor's price drops. The determination unit also imports market trend data and analyzes the winning rate for each bid amount. For example, the determination unit analyzes the impact of specific market trends on the probability of receiving an order. The determination unit also integrates competitors' price trends and market trends to build a system that predicts the winning rate for each bid amount. For example, the determination unit proposes an optimal bid amount depending on the price fluctuations of competitors. This allows for a more accurate determination of the winning rate for each bid amount by performing an analysis that takes into account competitors' price trends and market trends.

[0068] The determination unit uses the emotion estimation function to analyze the customer's emotional response to the proposed price and propose an emotionally optimal proposed price. The determination unit, for example, analyzes the customer's emotional response in real time and identifies the proposed price that evokes strong positive emotions. For example, it proposes the optimal proposed price based on the price range that pleases the customer. The determination unit also uses the emotion estimation function to calculate the customer's emotional score and identify the emotionally optimal proposed price. For example, it analyzes the price to propose when the customer is relaxed. The determination unit also builds a system that adjusts the proposed price based on the customer's emotional response data. For example, it proposes a lower price when the customer is feeling stressed. In this way, by analyzing the customer's emotional response, it is possible to propose an emotionally optimal proposed price.

[0069] When determining the winning rate for each bid amount, the determination unit can integrate at least one of data from different industries or applications to perform analysis from a more multifaceted perspective. The determination unit, for example, integrates data from different industries and performs a multifaceted analysis of the winning rate for each bid amount. For example, it compares data from the manufacturing and service industries to determine optimal pricing. The determination unit also integrates data from different applications to build a system that analyzes the winning rate for each bid amount. For example, it integrates data from medical and industrial applications to determine optimal pricing. The determination unit also predicts the winning rate for each bid amount from a multifaceted perspective based on data from different industries and applications. For example, it applies successful cases from other industries to its own company. In this way, by integrating data from different industries and applications, it is possible to perform analysis from a more multifaceted perspective.

[0070] The determination unit visualizes the win rate for each offered amount, allowing the user to intuitively understand using a graph or chart. The determination unit, for example, visualizes the win rate for each offered amount using a graph or chart, allowing the user to intuitively understand. For example, the determination unit displays the win rate for each price range using a bar graph. The determination unit also displays the win rate for each offered amount using a heat map, visually indicating the optimal price range. For example, price ranges with a high win rate are highlighted in color. The determination unit also visualizes the win rate for each offered amount in real time, allowing the user to instantly understand. For example, the graph is updated every time the price setting is changed. In this way, by visualizing the win rate for each offered amount, the user can intuitively understand.

[0071] The determination unit uses the emotion estimation function to analyze the emotional state of the salesperson regarding the proposed amount, and can make a proposal at the emotionally optimal timing. The determination unit, for example, analyzes the emotional state of the salesperson in real time and makes a proposal at a timing when positive emotions are strong. For example, the proposal is made when the salesperson is confident. The determination unit also uses the emotion estimation function to calculate the emotional score of the salesperson and identify the optimal timing to make a proposal. For example, the proposal is made when the salesperson is relaxed. The determination unit also builds a system that adjusts the timing of the proposal based on the emotional state of the salesperson. For example, the proposal is refrained from when the salesperson is feeling stressed. In this way, by analyzing the emotional state of the salesperson, a proposal can be made at the emotionally optimal timing.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The data acquisition unit performs an analysis based on the sales data and at least one external factor, either seasonal or economic conditions, to identify factors that cause fluctuations in the quoted price. The data acquisition unit, for example, combines past sales data with seasonal data to analyze seasonal order trends. For example, it identifies products for which demand increases during specific seasons and sets optimal prices. The data acquisition unit also imports economic indicator data to analyze the impact of economic conditions on sales performance. For example, it analyzes how the state of the economy affects the probability of receiving an order. The data acquisition unit also combines past sales data with weather data to analyze the impact of weather on orders. For example, it identifies products for which orders increase on rainy days and reflects this in pricing. In this way, by performing an analysis that takes into account external factors such as seasonality and economic conditions, it is possible to identify factors that cause fluctuations in the quoted price.

[0074] The data import unit imports sales data from different industries and regions and can perform analysis from a cross-industry perspective. For example, the data import unit imports sales data from different industries and analyzes common success patterns. For example, it compares sales methods between the manufacturing and service industries and derives the optimal method. The data import unit also imports sales data from each region and analyzes success factors specific to that region. For example, it compares sales performance in urban and rural areas and sets prices according to the region. The data import unit also integrates data from different industries and regions and proposes new sales strategies from a cross-industry perspective. For example, it applies success stories from other industries to its own company. In this way, by importing sales data from different industries and regions, it is possible to perform analysis from a cross-industry perspective.

[0075] When importing sales data, the data import unit analyzes multimodal information including at least one of image and audio data, allowing for the extraction of more diversified conditions. For example, the data import unit analyzes image data included in the sales data to identify the impact that the visual elements of a product have on orders. For example, it analyzes the impact that the quality of a product image has on the probability of receiving an order. The data import unit also analyzes audio data to identify the impact that a salesperson's tone and speaking style have on orders. For example, it analyzes whether a particular tone or speaking style leads to success. The data import unit also integrates image and audio data with text data to optimize sales strategies based on multimodal information. For example, it makes proposals that take both visual and audio data into consideration. In this way, by analyzing multimodal information including image and audio data, more diversified conditions can be extracted.

[0076] The data acquisition unit can use the emotion estimation function to analyze customers' emotional responses from sales data and identify emotionally positive conditions. The data acquisition unit, for example, analyzes customers' emotional responses contained in past sales data and identifies conditions that result in strong positive emotions. For example, it extracts proposal content that pleases the customer. The data acquisition unit also uses the emotion estimation function to calculate a customer's emotion score and identify sales methods that result in high positive emotions. For example, it analyzes pricing that satisfies the customer. The data acquisition unit also identifies emotionally positive conditions based on the customer's emotional response data and reflects these in sales strategies. For example, it reproduces proposals that the customer favorably accepted. In this way, emotionally positive conditions can be identified by analyzing the customer's emotional responses.

[0077] The data acquisition unit uses the emotion estimation function to analyze the emotional state of the salesperson and make a proposal at the most emotionally appropriate timing. The data acquisition unit, for example, analyzes the emotional state of the salesperson in real time and makes a proposal when positive emotions are strong. For example, a proposal is made when the salesperson is feeling confident. The data acquisition unit also uses the emotion estimation function to calculate the emotional score of the salesperson and identify the optimal timing for making a proposal. For example, a proposal is made when the salesperson is relaxed. The data acquisition unit also builds a system that adjusts the timing of the proposal based on the emotional state of the salesperson. For example, a proposal is refrained from when the salesperson is feeling stressed. In this way, by analyzing the emotional state of the salesperson, a proposal can be made at the emotionally optimal timing.

[0078] The input unit uses a generation AI to present past success stories and failure stories in real time when product information and customer information are entered, improving the accuracy of input. For example, when product information and customer information are entered, the input unit presents past success stories in real time to serve as a reference for input. For example, it displays successful cases in the same industry. The input unit also presents past failure stories as input to serve as a reference for avoiding the same mistakes. For example, it displays cases of past lost sales and points to note. The input unit also uses a generation AI to analyze past data and automatically present success stories and failure stories based on the input content. For example, it displays the case that is closest to the input content. This allows past success stories and failure stories to be presented in real time, improving the accuracy of input.

[0079] The input unit can analyze a customer's past purchasing history and behavioral patterns to generate a more detailed customer profile. For example, the input unit analyzes a customer's past purchasing history to identify purchasing trends and preferences. For example, it identifies customers who frequently purchase specific products and reflects this in the profile. The input unit also analyzes a customer's behavioral patterns to identify purchasing intentions and interests. For example, it generates a profile based on website browsing history and click data. The input unit also builds a system that integrates a customer's past data to generate a detailed customer profile. For example, it integrates purchase history, behavioral patterns, and survey results. In this way, a more detailed customer profile can be generated by analyzing a customer's past purchasing history and behavioral patterns.

[0080] The input unit can use the emotion estimation function to analyze the emotional state of a customer in real time and suggest product information that is most emotionally appropriate. For example, the input unit can analyze the emotional state of a customer in real time and suggest product information that is most appropriate when the customer is feeling strongly positive. For example, it can suggest a new product when the customer is excited. The input unit can also use the emotion estimation function to calculate the customer's emotion score and identify product information that is most emotionally appropriate. For example, it can suggest a high-priced product when the customer is relaxed. The input unit can also build a system that adjusts the timing of product information suggestions based on the customer's emotional state. For example, it can refrain from suggestions when the customer is feeling stressed. In this way, by analyzing the customer's emotional state in real time, it is possible to suggest product information that is most emotionally appropriate.

[0081] When inputting product information and customer information, the input unit can incorporate at least one of information from different languages ​​or cultural spheres and perform analysis from a global perspective. The input unit, for example, incorporates product information in different languages ​​and analyzes it from a global perspective. For example, it analyzes product information in English or Chinese and sets optimal prices. The input unit also incorporates customer information from different cultural spheres and performs analysis taking cultural background into consideration. For example, it identifies products preferred in specific cultural spheres and reflects this in proposals. The input unit also analyzes product information and customer information from a global perspective and sets prices appropriate for the international market. For example, it makes proposals that take into consideration the market needs of each country. In this way, by incorporating information from different languages ​​and cultural spheres, analysis can be performed from a global perspective.

[0082] The input unit can convert the input information into a visual note or mind map to make it easier to understand visually. The input unit, for example, converts product information and customer information into a visual note and displays it visually. For example, it shows the product's features and customer profile using diagrams and icons. The input unit also converts the input information into a mind map format to visually organize related keywords and concepts. For example, it makes it possible to understand the relationship between the product and the customer at a glance. The input unit also develops tools that automatically generate visual notes and mind maps to enable users to easily visualize information. For example, it provides a function for visualizing information using drag and drop. This allows the input information to be converted into a visual note or mind map to make it easier to understand visually.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The data capture unit uses generation AI to capture past sales data. For example, it collects sales data and customer information entered by salespeople and retrieves transaction history from a database. It can also capture unstructured data such as salespeople's notes and conversation records. Step 2: The analysis unit analyzes the sales data captured by the data capture unit. For example, it uses statistical analysis to analyze data trends and machine learning algorithms to identify conditions that lead to orders. It can also analyze unstructured data using natural language processing technology. Step 3: The input section inputs product information and customer information. For example, it provides an interface for users to input product specifications and prices, and fields for inputting information such as the customer's industry and use. It can also display past success stories and failure stories in real time to improve input accuracy. Step 4: The judgment unit determines the winning rate for each bid amount based on the data analyzed by the analysis unit. For example, it uses generation AI to calculate the winning rate for each bid amount and performs analysis taking into account the price trends of competitors and market trends. It can also visualize the winning rate for each bid amount and use graphs and charts to help users intuitively understand it.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 AI 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 import section that imports past sales data using generation AI; an analysis unit that analyzes the sales data acquired by the data acquisition unit; an input unit for inputting product information and customer information; a determination unit that determines the winning rate for each offered amount based on the data analyzed by the analysis unit. A system characterized by:

2. The data acquisition unit Analyze the unstructured data contained in the sales data and extract more detailed conditions.

2. The system of claim 1.

3. The data acquisition unit Based on the sales data, an analysis is performed based on at least one external factor of the season or economic conditions to identify factors that cause fluctuations in the offered price.

2. The system of claim 1.

4. The data acquisition unit Analyze customer emotional responses from the sales data and identify emotionally positive conditions 2. The system of claim 1.

5. The data acquisition unit Import sales data from different industries and regions and analyze it from a cross-industry perspective 2. The system of claim 1.

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