System

Abstract

An object of a system according to an embodiment is to analyze lead data and existing customer data and optimize sales activities.SOLUTION: A system includes a data collection unit, a preprocessing unit, an analysis unit, a scoring unit, and a presentation unit. The data collection unit collects lead data or existing customer data. The preprocessing part preprocesses the data collected by the data collection part. The analysis unit analyzes the data pre-processed by the pre-processing unit and predicts purchase intention or demand of the customer. The scoring unit performs scoring of the read based on the result predicted by the analysis unit. The presentation unit presents the priority of the lead scored by the scoring unit to the salesperson.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have room for improvement in terms of effectively utilizing lead data and existing customer data to predict customer purchasing intent and demand and optimize sales activities.

[0005] The system according to the embodiment aims to analyze lead data and existing customer data and optimize sales activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a preprocessing unit, an analysis unit, a scoring unit, and a presentation unit. The data collection unit collects lead data or existing customer data. The preprocessing unit preprocesses the data collected by the data collection unit. The analysis unit analyzes the data preprocessed by the preprocessing unit and predicts customer purchasing intentions or demand. The scoring unit scores leads based on the results predicted by the analysis unit. The presentation unit presents the priorities of the leads scored by the scoring unit to a sales representative. [Effects of the Invention]

[0007] The system according to the embodiment can analyze lead data and existing customer data to optimize sales activities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A lead scoring system according to an embodiment of the present invention is a system that learns lead data and existing customer data to predict customer purchasing intent and demand. This system presents high-priority leads to sales representatives, supporting more effective sales activities. As a result, the lead scoring system allows sales representatives to focus their approaches on high-priority leads, improving the efficiency and effectiveness of sales activities.

[0029] The lead scoring system according to the embodiment includes a data collection unit, a preprocessing unit, an analysis unit, a scoring unit, and a presentation unit. The data collection unit collects lead data or existing customer data. For example, it collects data such as customers' past purchase history, website visit history, and email open rates. The data collection unit can also collect social media activity and online reviews. The preprocessing unit preprocesses the collected data. For example, it cleanses, normalizes, and filters the data. The preprocessing unit can cleanse the collected data in real time to ensure that the latest data is always available for analysis. The preprocessing unit can use an emotion estimation function to estimate emotions from the content of customers' emails and chats and incorporate the emotion data into preprocessing. The analysis unit analyzes the preprocessed data and predicts customers' purchasing intent or demand. For example, the generation AI uses a machine learning algorithm to analyze customer behavior patterns and purchase history to identify customers with high purchasing intent. The analysis unit can automatically try different machine learning algorithms and select the optimal model. Furthermore, the analysis unit can use the emotion estimation function to analyze changes in customer emotions and incorporate the impact of emotional fluctuations on purchasing intent into the model. The scoring unit scores leads based on the results predicted by the analysis unit. For example, the generation AI analyzes newly collected lead data and scores each lead's purchasing intent. The scoring unit can also automatically provide feedback on the lead scoring results to continuously improve the accuracy of the model. Furthermore, the scoring unit can use the emotion estimation function to incorporate lead emotional data into scoring and prioritize emotionally positive leads. The presentation unit presents the priorities of leads scored by the scoring unit to sales representatives. For example, the generation AI lists leads with high scores and notifies the sales representative. The presentation unit can also predict the optimal timing to approach high-priority leads. Furthermore, the presentation unit can use the emotion estimation function to analyze the emotional state of leads and present leads at the emotionally optimal timing.As a result, the lead scoring system according to the embodiment allows sales representatives to focus on approaching high-priority leads, thereby improving the efficiency and effectiveness of sales activities. For example, the generation AI suggests optimal approaches and proposals to sales representatives. The generation AI can analyze the sales representative's past success stories and propose optimal sales strategies. The generation AI can also monitor the results of sales activities in real time and modify strategies as necessary. This allows sales representatives to conduct effective sales activities.

[0030] The data collection unit can collect customers' social media activities or online reviews and use them to predict their purchasing intent. For example, the data collection unit uses a generation AI to collect customers' social media activities (e.g., Facebook or Twitter posts) and use them to predict their purchasing intent. This makes it possible to understand customers' interests and concerns in real time. The data collection unit also collects reviews from online review sites (e.g., Amazon or Yelp) and the generation AI analyzes their content. This allows customers' evaluations of products and services to be reflected in the prediction of their purchasing intent. The data collection unit also uses a generation AI to collect customers' social media engagement (e.g., the number of likes and shares) and use them to predict their purchasing intent. This makes it possible to quantitatively evaluate the customer's level of interest. As a result, by collecting customers' social media activities and online reviews, the accuracy of purchasing intent predictions can be improved.

[0031] The preprocessing unit can cleanse collected data in real time, ensuring that the latest data is always available for analysis. For example, the preprocessing unit cleanses data collected by the generation AI in real time to remove duplicate or inaccurate data. This improves the quality of the data used for analysis. The preprocessing unit also enables the generation AI to automatically complete missing values ​​in the data and convert it into a format suitable for analysis. For example, it completes missing values ​​with the mean or median. The preprocessing unit also enables the generation AI to detect outliers in the data and take appropriate action. For example, it removes outliers or replaces them with appropriate values. This allows the quality of the data used for analysis to be improved by cleansing the data in real time.

[0032] The data collection unit can also collect customers' offline activities and integrate them into data analysis. For example, the data collection unit uses generation AI to collect customers' in-store purchase histories and integrate them with online data. This makes it possible to understand customers' overall purchasing behavior. The data collection unit also collects event participation histories, and generation AI integrates that data into the analysis. For example, the type and frequency of events that a customer has participated in can be reflected in the analysis. The data collection unit also uses generation AI to collect customers' offline activity data in real time and integrate it with online data. This makes it possible to use the latest offline activity data for analysis. This makes it possible to understand customers' overall purchasing behavior by collecting customers' offline activity data and integrating it into data analysis.

[0033] The data collection unit collects data from different industries and can predict purchasing intent from a cross-industry perspective. The data collection unit, for example, uses generation AI to collect data from different industries and predict customer purchasing intent from a cross-industry perspective. For example, data from the fashion industry and the technology industry is integrated. The data collection unit also analyzes data from different industries and identifies common purchasing patterns. For example, it analyzes products and services commonly purchased by customers from different industries. The data collection unit also uses generation AI to collect data from different industries in real time and predict purchasing intent from a cross-industry perspective. This allows the latest industry data to be used for analysis. This allows data from different industries to be collected and purchasing intent to be predicted from a cross-industry perspective, thereby improving prediction accuracy.

[0034] The analysis unit can analyze customer life events and predict changes in purchasing intent. For example, the generation AI collects customer life event data and analyzes the impact of events such as marriage or moving on purchasing intent. For example, it predicts changes in purchasing patterns after marriage. The analysis unit also analyzes customer life event data and incorporates the impact of specific events on purchasing intent into a model. For example, it predicts the intent to purchase new furniture or home appliances after moving. The analysis unit also uses the generation AI to collect customer life event data in real time and predict changes in purchasing intent. This allows the latest life event data to be used in analysis. This makes it possible to predict changes in purchasing intent by analyzing customer life events, thereby achieving more accurate lead scoring.

[0035] The analysis unit can automatically try different machine learning algorithms and select the optimal model. In the analysis unit, for example, the generation AI automatically tries multiple machine learning algorithms and selects the optimal model. For example, it tries random forests, support vector machines, neural networks, etc. In addition, the analysis unit has the generation AI evaluate the performance of each algorithm and select the model with the highest accuracy. For example, it evaluates the accuracy of the model using cross-validation. In addition, the analysis unit has the generation AI try different machine learning algorithms in real time and select the optimal model. This makes it possible to select the optimal model based on the latest data. As a result, by automatically trying different machine learning algorithms, the optimal model can be selected and analysis accuracy can be improved.

[0036] The analysis unit can not only predict a customer's purchasing intent, but also their lifetime value (LTV). For example, the generation AI in the analysis unit analyzes a customer's purchase history data and predicts lifetime value (LTV). For example, it predicts future purchase amounts based on past purchasing patterns. The analysis unit also analyzes customer behavior data and builds a model to predict LTV. For example, it calculates LTV based on a customer's repeat purchase rate and average purchase amount. The analysis unit also builds a model in which the generation AI simultaneously predicts a customer's purchasing intent and LTV. This makes it possible to grasp the overall value of a customer. By simultaneously predicting a customer's purchasing intent and lifetime value (LTV), it is possible to grasp the overall value of a customer.

[0037] The analysis unit can analyze data from different regions and cultural spheres and reflect regional differences in purchasing intent in the model. In the analysis unit, for example, the generation AI collects data from different regions and analyzes regional differences in purchasing intent. For example, differences in purchasing patterns between urban and rural areas are reflected in the model. The analysis unit also analyzes data from different cultural spheres and incorporates cultural differences in purchasing intent into the model. For example, differences in purchasing behavior between Asia and Europe. The analysis unit also allows the generation AI to collect data from each region and cultural sphere in real time and reflect differences in purchasing intent in the model. This allows the latest regional data to be used in the analysis. This allows data from different regions and cultural spheres to be analyzed and regional differences in purchasing intent to be reflected in the model, making it possible to make predictions that take into account regional purchasing patterns.

[0038] In addition to scoring leads, the scoring department can also predict changes in a lead's future purchasing intent. For example, the scoring department uses a generation AI to analyze lead data and predict changes in future purchasing intent. For example, it predicts future purchasing intent based on a lead's past behavioral patterns. The scoring department also incorporates future fluctuations in purchasing intent into a model based on the lead scoring results. For example, it predicts purchasing intent when a lead's score is high and when it is low. The scoring department also uses a generation AI to collect lead data in real time and predict changes in future purchasing intent. This allows the latest lead data to be used for analysis. This makes it possible to create long-term sales strategies by predicting changes in a lead's future purchasing intent.

[0039] The scoring unit can automatically feed back lead scoring results and continuously improve the accuracy of the model. For example, the scoring unit automatically feeds back lead scoring results from the generation AI and improves the accuracy of the model. For example, it adjusts the model parameters based on the scoring results. The scoring unit also builds a system that regularly feeds back lead scoring results and continuously improves the accuracy of the model. For example, it retrains the model based on monthly scoring results. The scoring unit also improves the accuracy of the model by feeding back lead scoring results from the generation AI in real time. This makes it possible to adjust the model based on the latest scoring results. This makes it possible to continuously improve the accuracy of the model by feeding back lead scoring results.

[0040] In addition to scoring leads, the scoring unit can also evaluate the cross-selling and up-selling potential of leads. For example, the scoring unit uses generation AI to analyze lead data and evaluate the cross-selling and up-selling potential. For example, it predicts the possibility of additional purchases based on the lead's past purchasing history. The scoring unit also incorporates the cross-selling and up-selling potential into a model based on the lead scoring results. For example, it suggests additional purchases if the lead's score is high. The scoring unit also uses generation AI to collect lead data in real time and evaluate the cross-selling and up-selling potential. This allows the latest lead data to be used for analysis. This makes it possible to not miss additional sales opportunities by evaluating the cross-selling and up-selling potential of leads.

[0041] The scoring unit can analyze lead data from different industries and build a lead scoring model for each industry. For example, the generation AI collects lead data from different industries and builds a lead scoring model for each industry. For example, it analyzes lead data from the IT industry and the manufacturing industry. The scoring unit also analyzes lead data from different industries and incorporates industry-specific purchasing patterns into the model. For example, it reflects differences in purchasing cycles and purchasing intent between industries. The scoring unit also collects lead data from different industries in real time using the generation AI and builds a lead scoring model for each industry. This allows the latest industry data to be used for analysis. By analyzing lead data from different industries and building a lead scoring model for each industry, it becomes possible to perform scoring that takes into account industry-specific purchasing patterns.

[0042] The presentation unit can predict the optimal timing to approach high-priority leads. For example, the generation AI analyzes lead data and predicts the optimal timing to approach. For example, it identifies the optimal time to contact based on the lead's past behavioral patterns. The presentation unit also incorporates the optimal timing to approach into a model based on the lead's scoring results. For example, it approaches the lead when their score is high. The generation AI also collects lead data in real time and predicts the optimal timing to approach. This allows the latest lead data to be used for analysis. This makes it possible to predict the optimal timing to approach leads effectively.

[0043] The presentation unit updates the lead priorities in real time and can provide sales representatives with the latest information. For example, the presentation unit allows the generation AI to update the lead scoring results in real time and provide sales representatives with the latest information. For example, it immediately notifies them when the lead score changes. The presentation unit also updates the lead priorities in real time and builds a system that allows sales representatives to always be aware of the latest lead information. For example, it displays the lead priorities in real time using a dashboard. The presentation unit also allows the generation AI to collect lead data in real time and update the priorities. This allows sales activities to be carried out based on the latest lead data. This allows the lead priorities to be updated in real time and sales representatives to be provided with the latest information, thereby supporting effective sales activities.

[0044] The presentation unit can suggest the optimal approach method for high-priority leads. For example, the generation AI in the presentation unit analyzes lead data and suggests the optimal approach method. For example, it determines whether email, phone call, or visit is optimal based on the lead's past responses. The presentation unit also incorporates the optimal approach method into a model based on the lead scoring results. For example, it suggests a visit if the lead has a high score. The generation AI in the presentation unit also collects lead data in real time and suggests the optimal approach method. This allows the latest lead data to be used for analysis. This makes it possible to suggest the optimal approach method, enabling an effective approach to leads.

[0045] The presentation department shares lead priorities among different sales teams and departments, enabling the entire team to conduct effective sales activities. For example, the presentation department builds a system in which the generation AI shares lead priorities among different sales teams and departments. For example, lead information is shared using a cloud-based platform. The presentation department also shares lead priorities to enable the entire team to conduct effective sales activities. For example, if a lead has a high score, multiple teams will work together to approach it. The presentation department also has the generation AI collect lead data in real time and share it among different sales teams and departments. This allows sales activities to be based on the latest lead data. This allows the entire team to conduct effective sales activities by sharing lead priorities.

[0046] The presentation unit can analyze the sales representative's past success stories and propose the optimal sales strategy. For example, the generation AI in the presentation unit collects the sales representative's past success stories and proposes the optimal sales strategy. For example, it identifies the optimal approach method based on past success patterns. The presentation unit also analyzes the sales representative's success stories and incorporates common success factors into a model. For example, it identifies the factors that lead to success in a specific approach method or timing. The presentation unit also uses the generation AI to collect sales representative data in real time and propose the optimal sales strategy. This allows sales activities to be conducted based on the latest success stories. This makes it possible to propose the optimal sales strategy by analyzing the sales representative's past success stories and improve the effectiveness of sales activities.

[0047] The presentation unit can monitor the results of sales activities in real time and revise the strategy as necessary. In the presentation unit, for example, the generation AI monitors the results of sales activities in real time and revise the strategy as necessary. For example, the strategy is adjusted based on the performance data of sales representatives. The presentation unit also builds a system that analyzes the results of sales activities in real time and proposes effective strategies. For example, the strategy is revised based on the closing rate and lead responses. In addition, the presentation unit has the generation AI collect sales activity data in real time and revise the strategy as necessary. This allows for effective sales activities to be carried out based on the latest sales data. This makes it possible to maximize the effectiveness of sales activities by monitoring the results of sales activities in real time and revising the strategy as necessary.

[0048] The presentation unit can assign the most suitable leads based on the skills and experience of sales representatives. For example, the generation AI in the presentation unit collects skill and experience data on sales representatives and assigns the most suitable leads. For example, it assigns leads in a particular industry to representatives who are knowledgeable in that industry. The presentation unit also analyzes past performance data on sales representatives and builds a system to assign the most suitable leads. For example, it assigns important leads to representatives with a high closing rate. The generation AI in the presentation unit also collects data on sales representatives in real time and assigns the most suitable leads. This allows for effective lead assignment based on the latest representative data. This maximizes the effectiveness of sales activities by assigning the most suitable leads based on the sales representative's skills and experience.

[0049] The presentation department promotes information sharing between different sales teams and departments, enabling the entire team to carry out effective sales activities. For example, the presentation department builds a system in which the generation AI promotes information sharing between sales teams and departments. For example, sales data is shared using a cloud-based platform. The presentation department also promotes information sharing between sales teams and departments, enabling the entire team to carry out effective sales activities. For example, lead scoring results and success stories are shared. The presentation department also has the generation AI collect sales data in real time and share it between different teams and departments. This allows effective sales activities to be carried out based on the latest sales data. This promotes information sharing, enabling the entire team to carry out effective sales activities.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] Lead scoring systems can also collect customer health data and predict purchasing intent based on their health status. For example, by collecting heart rate and sleep data from wearable devices, it can predict that purchasing intent will increase when a customer's health status is good. It can also collect diet and exercise data from health apps to evaluate the purchasing intent of customers who lead a healthy lifestyle. It can also collect health checkup results and suggest specific products and services to customers whose health status has improved. By utilizing customer health data, it is possible to make more accurate predictions of purchasing intent.

[0052] Lead scoring systems can also collect data on a customer's hobbies and interests and use it to predict purchase intent. For example, data on the hobby clubs and events a customer participates in can be collected to predict purchase intent for products and services related to those hobbies. It can also collect data on the content a customer views online and the newsletters they subscribe to to evaluate purchase intent based on their areas of interest. It can also collect data on the accounts and groups a customer follows on social media to predict purchase intent for products and services related to topics of interest. This makes it possible to predict purchase intent based on a customer's hobbies and interests.

[0053] Lead scoring systems can also collect data on customers' geographical movements and use it to predict purchasing intent. For example, by collecting smartphone location information and analyzing customers' frequent locations and movement patterns, it is possible to predict purchasing intent in a specific area. It is also possible to collect transportation usage data and evaluate purchasing intent based on the frequency of commuting and travel. Furthermore, it is possible to collect data on events customers attend and travel destinations and predict purchasing intent in that area. By utilizing customer geographical movement data, it is possible to make more accurate predictions of purchasing intent.

[0054] Lead scoring systems can also collect customer financial data and use it to predict purchasing intent. For example, they can collect credit card usage history and bank account transaction history to evaluate a customer's spending patterns and purchasing power. They can also collect a customer's investment portfolio and asset status to predict that purchasing intent will increase when they have financial leeway. They can also collect data on a customer's loans and credit score to evaluate purchasing intent based on creditworthiness. By utilizing a customer's financial data, it is possible to make more accurate predictions of purchasing intent.

[0055] Lead scoring systems can also collect customer education data and use it to predict purchasing intent. For example, data on a customer's educational background and qualifications can be collected to evaluate purchasing intent based on their level of education. Data on online courses and seminars attended by customers can also be collected to predict the purchasing intent of customers with a high level of motivation to learn. Furthermore, data on the educational or research institutions to which customers belong can be collected to evaluate the purchasing intent of customers with an interest in a particular field. By utilizing customer education data, more accurate purchasing intent predictions can be made.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data collection department collects lead or existing customer data. For example, data on customers' past purchase history, website visit history, email open rates, etc. The data collection department can also collect social media activity and online reviews. Step 2: The preprocessing section preprocesses the collected data. For example, it cleanses, normalizes, and filters the data. The preprocessing section can also cleanse the collected data in real time, ensuring that the latest data is always available for analysis. Furthermore, the preprocessing section can use an emotion estimation function to estimate emotions from the content of customer emails and chats, and incorporate that emotion data into preprocessing. Step 3: The analysis unit analyzes the preprocessed data and predicts customer purchasing intent or demand. For example, the generative AI uses a machine learning algorithm to analyze customer behavior patterns and purchase history to identify customers with high purchasing intent. The analysis unit can also automatically try different machine learning algorithms and select the optimal model. Furthermore, the analysis unit can use an emotion estimation function to analyze changes in customer emotions and incorporate the impact of emotional fluctuations on purchasing intent into the model. Step 4: The scoring unit scores leads based on the results predicted by the analysis unit. For example, the generation AI analyzes newly collected lead data and scores each lead's purchasing intent. The scoring unit can also automatically provide feedback on the lead scoring results to continuously improve the accuracy of the model. Furthermore, the scoring unit can use the emotion estimation function to incorporate lead emotion data into the scoring and prioritize emotionally positive leads. Step 5: The presentation unit presents the priority of the leads scored by the scoring unit to the sales representative. For example, the generation AI lists high-scoring leads and notifies the sales representative. The presentation unit can also predict the optimal timing to approach high-priority leads. Furthermore, the presentation unit can use an emotion estimation function to analyze the emotional state of the lead and present the lead at the emotionally optimal timing.

[0058] (Example 2) A lead scoring system according to an embodiment of the present invention is a system that learns lead data and existing customer data to predict customer purchasing intent and demand. This system presents high-priority leads to sales representatives, supporting more effective sales activities. As a result, the lead scoring system allows sales representatives to focus their approaches on high-priority leads, improving the efficiency and effectiveness of sales activities.

[0059] The lead scoring system according to the embodiment includes a data collection unit, a preprocessing unit, an analysis unit, a scoring unit, and a presentation unit. The data collection unit collects lead data or existing customer data. For example, it collects data such as customers' past purchase history, website visit history, and email open rates. The data collection unit can also collect social media activity and online reviews. The preprocessing unit preprocesses the collected data. For example, it cleanses, normalizes, and filters the data. The preprocessing unit can cleanse the collected data in real time to ensure that the latest data is always available for analysis. The preprocessing unit can use an emotion estimation function to estimate emotions from the content of customers' emails and chats and incorporate the emotion data into preprocessing. The analysis unit analyzes the preprocessed data and predicts customers' purchasing intent or demand. For example, the generation AI uses a machine learning algorithm to analyze customer behavior patterns and purchase history to identify customers with high purchasing intent. The analysis unit can automatically try different machine learning algorithms and select the optimal model. Furthermore, the analysis unit can use the emotion estimation function to analyze changes in customer emotions and incorporate the impact of emotional fluctuations on purchasing intent into the model. The scoring unit scores leads based on the results predicted by the analysis unit. For example, the generation AI analyzes newly collected lead data and scores each lead's purchasing intent. The scoring unit can also automatically provide feedback on the lead scoring results to continuously improve the accuracy of the model. Furthermore, the scoring unit can use the emotion estimation function to incorporate lead emotional data into scoring and prioritize emotionally positive leads. The presentation unit presents the priorities of leads scored by the scoring unit to sales representatives. For example, the generation AI lists leads with high scores and notifies the sales representative. The presentation unit can also predict the optimal timing to approach high-priority leads. Furthermore, the presentation unit can use the emotion estimation function to analyze the emotional state of leads and present leads at the emotionally optimal timing.As a result, the lead scoring system according to the embodiment allows sales representatives to focus on approaching high-priority leads, thereby improving the efficiency and effectiveness of sales activities. For example, the generation AI suggests optimal approaches and proposals to sales representatives. The generation AI can analyze the sales representative's past success stories and propose optimal sales strategies. The generation AI can also monitor the results of sales activities in real time and modify strategies as necessary. This allows sales representatives to conduct effective sales activities.

[0060] The data collection unit can collect customers' social media activities or online reviews and use them to predict their purchasing intent. For example, the data collection unit uses a generation AI to collect customers' social media activities (e.g., Facebook or Twitter posts) and use them to predict their purchasing intent. This makes it possible to understand customers' interests and concerns in real time. The data collection unit also collects reviews from online review sites (e.g., Amazon or Yelp) and the generation AI analyzes their content. This allows customers' evaluations of products and services to be reflected in the prediction of their purchasing intent. The data collection unit also uses a generation AI to collect customers' social media engagement (e.g., the number of likes and shares) and use them to predict their purchasing intent. This makes it possible to quantitatively evaluate the customer's level of interest. As a result, by collecting customers' social media activities and online reviews, the accuracy of purchasing intent predictions can be improved.

[0061] The preprocessing unit can cleanse collected data in real time, ensuring that the latest data is always available for analysis. For example, the preprocessing unit cleanses data collected by the generation AI in real time to remove duplicate or inaccurate data. This improves the quality of the data used for analysis. The preprocessing unit also enables the generation AI to automatically complete missing values ​​in the data and convert it into a format suitable for analysis. For example, it completes missing values ​​with the mean or median. The preprocessing unit also enables the generation AI to detect outliers in the data and take appropriate action. For example, it removes outliers or replaces them with appropriate values. This allows the quality of the data used for analysis to be improved by cleansing the data in real time.

[0062] The preprocessing unit can use the emotion estimation function to estimate emotions from the content of customer emails and chat messages and incorporate the emotion data into preprocessing. In the preprocessing unit, for example, the generation AI analyzes the content of customer emails and chat messages and calculates an emotion score using the emotion estimation function. For example, it classifies emotions into positive, negative, and neutral. The preprocessing unit also uses the emotion estimation function to quantify the intensity of emotions from the content of customer emails and chat messages and incorporates the data into preprocessing. For example, it quantifies the emotion score and stores it in a database. The preprocessing unit also allows the generation AI to analyze the content of customer emails and chat messages in real time and incorporates the emotion estimation data into preprocessing. This allows the latest emotion data to be used in analysis. Incorporating customer emotion data into preprocessing improves the accuracy of the data used for analysis.

[0063] The data collection unit can also collect customers' offline activities and integrate them into data analysis. For example, the data collection unit uses generation AI to collect customers' in-store purchase histories and integrate them with online data. This makes it possible to understand customers' overall purchasing behavior. The data collection unit also collects event participation histories, and generation AI integrates that data into the analysis. For example, the type and frequency of events that a customer has participated in can be reflected in the analysis. The data collection unit also uses generation AI to collect customers' offline activity data in real time and integrate it with online data. This makes it possible to use the latest offline activity data for analysis. This makes it possible to understand customers' overall purchasing behavior by collecting customers' offline activity data and integrating it into data analysis.

[0064] The data collection unit collects data from different industries and can predict purchasing intent from a cross-industry perspective. The data collection unit, for example, uses generation AI to collect data from different industries and predict customer purchasing intent from a cross-industry perspective. For example, data from the fashion industry and the technology industry is integrated. The data collection unit also analyzes data from different industries and identifies common purchasing patterns. For example, it analyzes products and services commonly purchased by customers from different industries. The data collection unit also uses generation AI to collect data from different industries in real time and predict purchasing intent from a cross-industry perspective. This allows the latest industry data to be used for analysis. This allows data from different industries to be collected and purchasing intent to be predicted from a cross-industry perspective, thereby improving prediction accuracy.

[0065] The preprocessing unit can use the emotion estimation function to estimate emotions from facial expressions or voice when a customer visits a website and use the data for preprocessing. For example, the preprocessing unit has the generation AI analyze the customer's facial expressions when visiting a website and calculate an emotion score using the emotion estimation function. For example, the preprocessing unit analyzes the customer's facial expressions in real time using a camera. The preprocessing unit also uses voice analysis technology to estimate emotions from the voice when the customer visits a website and uses the data for preprocessing. For example, the preprocessing unit analyzes the tone of the customer's voice using a microphone. The preprocessing unit also collects facial expression and voice data when the generation AI visits a website in real time and incorporates the emotion estimation data into preprocessing. This allows the latest emotion data to be used for analysis. As a result, by using facial expression and voice data when the customer visits a website for preprocessing, the accuracy of the data used for analysis can be improved.

[0066] The analysis unit can analyze customer life events and predict changes in purchasing intent. For example, the generation AI collects customer life event data and analyzes the impact of events such as marriage or moving on purchasing intent. For example, it predicts changes in purchasing patterns after marriage. The analysis unit also analyzes customer life event data and incorporates the impact of specific events on purchasing intent into a model. For example, it predicts the intent to purchase new furniture or home appliances after moving. The analysis unit also uses the generation AI to collect customer life event data in real time and predict changes in purchasing intent. This allows the latest life event data to be used in analysis. This makes it possible to predict changes in purchasing intent by analyzing customer life events, thereby achieving more accurate lead scoring.

[0067] The analysis unit can automatically try different machine learning algorithms and select the optimal model. In the analysis unit, for example, the generation AI automatically tries multiple machine learning algorithms and selects the optimal model. For example, it tries random forests, support vector machines, neural networks, etc. In addition, the analysis unit has the generation AI evaluate the performance of each algorithm and select the model with the highest accuracy. For example, it evaluates the accuracy of the model using cross-validation. In addition, the analysis unit has the generation AI try different machine learning algorithms in real time and select the optimal model. This makes it possible to select the optimal model based on the latest data. As a result, by automatically trying different machine learning algorithms, the optimal model can be selected and analysis accuracy can be improved.

[0068] The analysis unit can use the emotion estimation function to analyze changes in customers' emotions and incorporate the impact of emotional fluctuations on purchasing willingness into a model. In the analysis unit, for example, the generation AI collects customer emotion data and analyzes the impact of emotional changes on purchasing willingness. For example, it predicts purchasing willingness during periods of strong positive emotions. The analysis unit also uses the emotion estimation function to quantify changes in customers' emotions and incorporate that data into a model. For example, it quantifies emotion scores and reflects them in the model. The analysis unit also uses the generation AI to collect customer emotion data in real time and incorporates the impact of emotional changes on purchasing willingness into a model. This allows the latest emotion data to be used in the analysis. This allows for more accurate predictions to be achieved by analyzing changes in customers' emotions and incorporating the impact of emotional fluctuations on purchasing willingness into a model.

[0069] The analysis unit can not only predict a customer's purchasing intent, but also their lifetime value (LTV). For example, the generation AI in the analysis unit analyzes a customer's purchase history data and predicts lifetime value (LTV). For example, it predicts future purchase amounts based on past purchasing patterns. The analysis unit also analyzes customer behavior data and builds a model to predict LTV. For example, it calculates LTV based on a customer's repeat purchase rate and average purchase amount. The analysis unit also builds a model in which the generation AI simultaneously predicts a customer's purchasing intent and LTV. This makes it possible to grasp the overall value of a customer. By simultaneously predicting a customer's purchasing intent and lifetime value (LTV), it is possible to grasp the overall value of a customer.

[0070] The analysis unit can analyze data from different regions and cultural spheres and reflect regional differences in purchasing intent in the model. In the analysis unit, for example, the generation AI collects data from different regions and analyzes regional differences in purchasing intent. For example, differences in purchasing patterns between urban and rural areas are reflected in the model. The analysis unit also analyzes data from different cultural spheres and incorporates cultural differences in purchasing intent into the model. For example, differences in purchasing behavior between Asia and Europe. The analysis unit also allows the generation AI to collect data from each region and cultural sphere in real time and reflect differences in purchasing intent in the model. This allows the latest regional data to be used in the analysis. This allows data from different regions and cultural spheres to be analyzed and regional differences in purchasing intent to be reflected in the model, making it possible to make predictions that take into account regional purchasing patterns.

[0071] The analysis unit can use the emotion estimation function to analyze customer emotion data and propose a personalized marketing strategy based on emotion. For example, the generation AI collects customer emotion data and proposes a personalized marketing strategy based on emotion. For example, the analysis unit runs a specific promotion for customers with strong positive emotions. The analysis unit also uses the emotion estimation function to analyze customer emotion data and generate emotion-based marketing messages. For example, it sends customized messages according to the emotion score. The analysis unit also uses the generation AI to collect customer emotion data in real time and proposes an emotion-based marketing strategy. This allows the latest emotion data to be used for analysis. This makes it possible to analyze customer emotion data and propose a personalized marketing strategy based on emotion, thereby enabling an effective approach to customers.

[0072] In addition to scoring leads, the scoring department can also predict changes in a lead's future purchasing intent. For example, the scoring department uses a generation AI to analyze lead data and predict changes in future purchasing intent. For example, it predicts future purchasing intent based on a lead's past behavioral patterns. The scoring department also incorporates future fluctuations in purchasing intent into a model based on the lead scoring results. For example, it predicts purchasing intent when a lead's score is high and when it is low. The scoring department also uses a generation AI to collect lead data in real time and predict changes in future purchasing intent. This allows the latest lead data to be used for analysis. This makes it possible to create long-term sales strategies by predicting changes in a lead's future purchasing intent.

[0073] The scoring unit can automatically feed back lead scoring results and continuously improve the accuracy of the model. For example, the scoring unit automatically feeds back lead scoring results from the generation AI and improves the accuracy of the model. For example, it adjusts the model parameters based on the scoring results. The scoring unit also builds a system that regularly feeds back lead scoring results and continuously improves the accuracy of the model. For example, it retrains the model based on monthly scoring results. The scoring unit also improves the accuracy of the model by feeding back lead scoring results from the generation AI in real time. This makes it possible to adjust the model based on the latest scoring results. This makes it possible to continuously improve the accuracy of the model by feeding back lead scoring results.

[0074] The scoring unit can use the emotion estimation function to incorporate the emotional data of leads into scoring and prioritize emotionally positive leads. For example, the scoring unit uses the generation AI to collect emotional data of leads and calculates an emotion score using the emotion estimation function. For example, it prioritizes scoring leads with strong positive emotions. The scoring unit also uses the emotion estimation function to incorporate the emotional data of leads into scoring and prioritizes emotionally positive leads. For example, it quantifies the emotion score and reflects it in the scoring. The scoring unit also uses the generation AI to collect emotional data of leads in real time and builds a scoring model that prioritizes emotionally positive leads. This allows the latest emotional data to be used in analysis. This makes it possible to improve the closing rate by prioritizing emotionally positive leads.

[0075] In addition to scoring leads, the scoring unit can also evaluate the cross-selling and up-selling potential of leads. For example, the scoring unit uses generation AI to analyze lead data and evaluate the cross-selling and up-selling potential. For example, it predicts the possibility of additional purchases based on the lead's past purchasing history. The scoring unit also incorporates the cross-selling and up-selling potential into a model based on the lead scoring results. For example, it suggests additional purchases if the lead's score is high. The scoring unit also uses generation AI to collect lead data in real time and evaluate the cross-selling and up-selling potential. This allows the latest lead data to be used for analysis. This makes it possible to not miss additional sales opportunities by evaluating the cross-selling and up-selling potential of leads.

[0076] The scoring unit can analyze lead data from different industries and build a lead scoring model for each industry. For example, the generation AI collects lead data from different industries and builds a lead scoring model for each industry. For example, it analyzes lead data from the IT industry and the manufacturing industry. The scoring unit also analyzes lead data from different industries and incorporates industry-specific purchasing patterns into the model. For example, it reflects differences in purchasing cycles and purchasing intent between industries. The scoring unit also collects lead data from different industries in real time using the generation AI and builds a lead scoring model for each industry. This allows the latest industry data to be used for analysis. By analyzing lead data from different industries and building a lead scoring model for each industry, it becomes possible to perform scoring that takes into account industry-specific purchasing patterns.

[0077] The scoring unit can use the emotion estimation function to analyze the emotional data of leads and perform emotion-based lead scoring. For example, the scoring unit uses the generation AI to collect the emotional data of leads and calculates an emotion score using the emotion estimation function. For example, it prioritizes scoring leads with strong positive emotions. The scoring unit also uses the emotion estimation function to analyze the emotional data of leads and perform emotion-based lead scoring. For example, it quantifies the emotion score and reflects it in the scoring. The scoring unit also uses the generation AI to collect the emotional data of leads in real time and builds an emotion-based lead scoring model. This allows the latest emotional data to be used for analysis. As a result, emotion-based lead scoring can be performed to prioritize approaching emotionally positive leads.

[0078] The presentation unit can predict the optimal timing to approach high-priority leads. For example, the generation AI analyzes lead data and predicts the optimal timing to approach. For example, it identifies the optimal time to contact based on the lead's past behavioral patterns. The presentation unit also incorporates the optimal timing to approach into a model based on the lead's scoring results. For example, it approaches the lead when their score is high. The generation AI also collects lead data in real time and predicts the optimal timing to approach. This allows the latest lead data to be used for analysis. This makes it possible to predict the optimal timing to approach leads effectively.

[0079] The presentation unit updates the lead priorities in real time and can provide sales representatives with the latest information. For example, the presentation unit allows the generation AI to update the lead scoring results in real time and provide sales representatives with the latest information. For example, it immediately notifies them when the lead score changes. The presentation unit also updates the lead priorities in real time and builds a system that allows sales representatives to always be aware of the latest lead information. For example, it displays the lead priorities in real time using a dashboard. The presentation unit also allows the generation AI to collect lead data in real time and update the priorities. This allows sales activities to be carried out based on the latest lead data. This allows the lead priorities to be updated in real time and sales representatives to be provided with the latest information, thereby supporting effective sales activities.

[0080] The presentation unit can use the emotion estimation function to analyze the emotional state of the lead and present the lead at the emotionally optimal timing. For example, the presentation unit uses the generation AI to collect emotional data of the lead and calculate an emotion score using the emotion estimation function. For example, the presentation unit presents the lead when positive emotions are strong. The presentation unit also uses the emotion estimation function to analyze the emotional state of the lead and presents the lead at the emotionally optimal timing. For example, the emotion score is quantified and reflected in the lead presentation. The presentation unit also uses the generation AI to collect emotional data of the lead in real time and presents the lead at the emotionally optimal timing. This allows the latest emotion data to be used for analysis. This makes it possible to improve the closing rate by presenting the lead at the emotionally optimal timing.

[0081] The presentation unit can suggest the optimal approach method for high-priority leads. For example, the generation AI in the presentation unit analyzes lead data and suggests the optimal approach method. For example, it determines whether email, phone call, or visit is optimal based on the lead's past responses. The presentation unit also incorporates the optimal approach method into a model based on the lead scoring results. For example, it suggests a visit if the lead has a high score. The generation AI in the presentation unit also collects lead data in real time and suggests the optimal approach method. This allows the latest lead data to be used for analysis. This makes it possible to suggest the optimal approach method, enabling an effective approach to leads.

[0082] The presentation department shares lead priorities among different sales teams and departments, enabling the entire team to conduct effective sales activities. For example, the presentation department builds a system in which the generation AI shares lead priorities among different sales teams and departments. For example, lead information is shared using a cloud-based platform. The presentation department also shares lead priorities to enable the entire team to conduct effective sales activities. For example, if a lead has a high score, multiple teams will work together to approach it. The presentation department also has the generation AI collect lead data in real time and share it among different sales teams and departments. This allows sales activities to be based on the latest lead data. This allows the entire team to conduct effective sales activities by sharing lead priorities.

[0083] The presentation unit can use the emotion estimation function to analyze the emotional data of leads and present high-priority leads based on their emotions. For example, the presentation unit uses the generation AI to collect emotional data of leads and calculate an emotion score using the emotion estimation function. For example, the presentation unit preferentially presents leads with strong positive emotions. The presentation unit also uses the emotion estimation function to analyze the emotional data of leads and present high-priority leads based on their emotions. For example, the emotion score is quantified and reflected in the lead presentation. The presentation unit also uses the generation AI to collect emotional data of leads in real time and present high-priority leads based on their emotions. This allows the latest emotional data to be used for analysis. This makes it possible to improve the closing rate by presenting high-priority leads based on their emotions.

[0084] The presentation unit can analyze the sales representative's past success stories and propose the optimal sales strategy. For example, the generation AI in the presentation unit collects the sales representative's past success stories and proposes the optimal sales strategy. For example, it identifies the optimal approach method based on past success patterns. The presentation unit also analyzes the sales representative's success stories and incorporates common success factors into a model. For example, it identifies the factors that lead to success in a specific approach method or timing. The presentation unit also uses the generation AI to collect sales representative data in real time and propose the optimal sales strategy. This allows sales activities to be conducted based on the latest success stories. This makes it possible to propose the optimal sales strategy by analyzing the sales representative's past success stories and improve the effectiveness of sales activities.

[0085] The presentation unit can monitor the results of sales activities in real time and revise the strategy as necessary. In the presentation unit, for example, the generation AI monitors the results of sales activities in real time and revise the strategy as necessary. For example, the strategy is adjusted based on the performance data of sales representatives. The presentation unit also builds a system that analyzes the results of sales activities in real time and proposes effective strategies. For example, the strategy is revised based on the closing rate and lead responses. In addition, the presentation unit has the generation AI collect sales activity data in real time and revise the strategy as necessary. This allows for effective sales activities to be carried out based on the latest sales data. This makes it possible to maximize the effectiveness of sales activities by monitoring the results of sales activities in real time and revising the strategy as necessary.

[0086] The presentation unit can use the emotion estimation function to analyze the emotional state of the lead and propose an emotion-based sales approach. For example, the presentation unit uses the generation AI to collect emotional data on the lead and calculate an emotion score using the emotion estimation function. For example, the presentation unit proposes a specific approach to a lead with strong positive emotions. The presentation unit also uses the emotion estimation function to analyze the emotional state of the lead and propose an emotion-based sales approach. For example, the emotion score is quantified and reflected in the approach method. The presentation unit also uses the generation AI to collect emotional data on the lead in real time and propose an emotion-based sales approach. This allows the latest emotion data to be used for analysis. This makes it possible to propose an emotion-based sales approach, thereby enabling an effective approach to leads.

[0087] The presentation unit can assign the most suitable leads based on the skills and experience of sales representatives. For example, the generation AI in the presentation unit collects skill and experience data on sales representatives and assigns the most suitable leads. For example, it assigns leads in a particular industry to representatives who are knowledgeable in that industry. The presentation unit also analyzes past performance data on sales representatives and builds a system to assign the most suitable leads. For example, it assigns important leads to representatives with a high closing rate. The generation AI in the presentation unit also collects data on sales representatives in real time and assigns the most suitable leads. This allows for effective lead assignment based on the latest representative data. This maximizes the effectiveness of sales activities by assigning the most suitable leads based on the sales representative's skills and experience.

[0088] The presentation department promotes information sharing between different sales teams and departments, enabling the entire team to carry out effective sales activities. For example, the presentation department builds a system in which the generation AI promotes information sharing between sales teams and departments. For example, sales data is shared using a cloud-based platform. The presentation department also promotes information sharing between sales teams and departments, enabling the entire team to carry out effective sales activities. For example, lead scoring results and success stories are shared. The presentation department also has the generation AI collect sales data in real time and share it between different teams and departments. This allows effective sales activities to be carried out based on the latest sales data. This promotes information sharing, enabling the entire team to carry out effective sales activities.

[0089] The presentation unit can use the emotion estimation function to analyze the emotional data of leads and support sales activities based on their emotions. For example, the presentation unit has the generation AI collect emotional data of leads and calculate an emotion score using the emotion estimation function. For example, the presentation unit suggests specific sales activities to leads with strong positive emotions. The presentation unit also uses the emotion estimation function to analyze the emotional data of leads and support sales activities based on their emotions. For example, the emotion score is quantified and reflected in sales activities. The presentation unit also has the generation AI collect emotional data of leads in real time and support sales activities based on their emotions. This allows the latest emotional data to be used for analysis. This allows for support in sales activities based on emotions, making it possible to approach leads more effectively.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] Lead scoring systems can also collect customer health data and predict purchasing intent based on their health status. For example, by collecting heart rate and sleep data from wearable devices, it can predict that purchasing intent will increase when a customer's health status is good. It can also collect diet and exercise data from health apps to evaluate the purchasing intent of customers who lead a healthy lifestyle. It can also collect health checkup results and suggest specific products and services to customers whose health status has improved. By utilizing customer health data, it is possible to make more accurate predictions of purchasing intent.

[0092] Lead scoring systems can also collect data on a customer's hobbies and interests and use it to predict purchase intent. For example, data on the hobby clubs and events a customer participates in can be collected to predict purchase intent for products and services related to those hobbies. It can also collect data on the content a customer views online and the newsletters they subscribe to to evaluate purchase intent based on their areas of interest. It can also collect data on the accounts and groups a customer follows on social media to predict purchase intent for products and services related to topics of interest. This makes it possible to predict purchase intent based on a customer's hobbies and interests.

[0093] Lead scoring systems can also collect data on customers' geographical movements and use it to predict purchasing intent. For example, by collecting smartphone location information and analyzing customers' frequent locations and movement patterns, it is possible to predict purchasing intent in a specific area. It is also possible to collect transportation usage data and evaluate purchasing intent based on the frequency of commuting and travel. Furthermore, it is possible to collect data on events customers attend and travel destinations and predict purchasing intent in that area. By utilizing customer geographical movement data, it is possible to make more accurate predictions of purchasing intent.

[0094] Lead scoring systems can also collect customer financial data and use it to predict purchasing intent. For example, they can collect credit card usage history and bank account transaction history to evaluate a customer's spending patterns and purchasing power. They can also collect a customer's investment portfolio and asset status to predict that purchasing intent will increase when they have financial leeway. They can also collect data on a customer's loans and credit score to evaluate purchasing intent based on creditworthiness. By utilizing a customer's financial data, it is possible to make more accurate predictions of purchasing intent.

[0095] Lead scoring systems can also collect customer education data and use it to predict purchasing intent. For example, data on a customer's educational background and qualifications can be collected to evaluate purchasing intent based on their level of education. Data on online courses and seminars attended by customers can also be collected to predict the purchasing intent of customers with a high level of motivation to learn. Furthermore, data on the educational or research institutions to which customers belong can be collected to evaluate the purchasing intent of customers with an interest in a particular field. By utilizing customer education data, more accurate purchasing intent predictions can be made.

[0096] The lead scoring system can also use customer emotion estimation functions to analyze a customer's stress level and evaluate the impact of stress on purchasing intent. For example, stress levels can be estimated from the content of a customer's emails and chats, and it can be predicted that purchasing intent will increase when stress is low. Stress levels can also be analyzed from a customer's social media posts and comments, and it can be determined that purchasing intent will decrease when stress is high. Furthermore, stress levels can be estimated from a customer's voice data and facial expression data, and specific products and services can be suggested when stress is low. In this way, analyzing a customer's stress level makes it possible to predict purchasing intent with greater accuracy.

[0097] The lead scoring system can also use customer emotion estimation functions to analyze a customer's happiness level and evaluate the impact that happiness level has on purchasing intent. For example, it can estimate happiness levels from the content of a customer's emails and chats, and predict that purchasing intent will increase when happiness levels are high. It can also analyze happiness levels from a customer's social media posts and comments, and evaluate that purchasing intent increases when happiness levels are high. It can also estimate happiness levels from a customer's voice data and facial expression data, and suggest specific products or services when happiness levels are high. In this way, analyzing customer happiness levels makes it possible to predict purchasing intent with greater accuracy.

[0098] The lead scoring system can also use customer emotion estimation functions to analyze a customer's excitement level and evaluate the impact that excitement level has on purchasing intent. For example, excitement level can be estimated from the content of a customer's emails and chats, and it can predict that purchasing intent will increase when excitement levels are high. It can also analyze excitement levels from customers' social media posts and comments, and evaluate that purchasing intent increases when excitement levels are high. Furthermore, excitement levels can be estimated from customer voice data and facial expression data, and specific products and services can be suggested when excitement levels are high. In this way, analyzing customer excitement levels makes it possible to predict purchasing intent with greater accuracy.

[0099] The lead scoring system can also use customer emotion estimation functions to analyze a customer's fatigue level and evaluate the impact that fatigue level has on purchasing intent. For example, fatigue levels can be estimated from the content of a customer's emails and chats, and it can be predicted that purchasing intent will increase when fatigue levels are low. Fatigue levels can also be analyzed from a customer's social media posts and comments, and it can be determined that purchasing intent will decrease when fatigue levels are high. Furthermore, fatigue levels can be estimated from a customer's voice data and facial expression data, and specific products or services can be suggested when fatigue levels are low. In this way, analyzing customer fatigue levels makes it possible to predict purchasing intent with greater accuracy.

[0100] The lead scoring system can also use customer emotion estimation functions to analyze customer motivation and evaluate the impact that motivation has on purchasing intent. For example, it can estimate motivation from the content of a customer's emails and chats, and predict that purchasing intent will increase when motivation is high. It can also analyze motivation from a customer's social media posts and comments, and evaluate that purchasing intent increases when motivation is high. It can also estimate motivation from a customer's voice data and facial expression data, and suggest specific products or services when motivation is high. By analyzing customer motivation in this way, it becomes possible to predict purchasing intent with greater accuracy.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The data collection department collects lead or existing customer data. For example, data on customers' past purchase history, website visit history, email open rates, etc. The data collection department can also collect social media activity and online reviews. Step 2: The preprocessing section preprocesses the collected data. For example, it cleanses, normalizes, and filters the data. The preprocessing section can also cleanse the collected data in real time, ensuring that the latest data is always available for analysis. Furthermore, the preprocessing section can use an emotion estimation function to estimate emotions from the content of customer emails and chats, and incorporate that emotion data into preprocessing. Step 3: The analysis unit analyzes the preprocessed data and predicts customer purchasing intent or demand. For example, the generative AI uses a machine learning algorithm to analyze customer behavior patterns and purchase history to identify customers with high purchasing intent. The analysis unit can also automatically try different machine learning algorithms and select the optimal model. Furthermore, the analysis unit can use an emotion estimation function to analyze changes in customer emotions and incorporate the impact of emotional fluctuations on purchasing intent into the model. Step 4: The scoring unit scores leads based on the results predicted by the analysis unit. For example, the generation AI analyzes newly collected lead data and scores each lead's purchasing intent. The scoring unit can also automatically provide feedback on the lead scoring results to continuously improve the accuracy of the model. Furthermore, the scoring unit can use the emotion estimation function to incorporate lead emotion data into the scoring and prioritize emotionally positive leads. Step 5: The presentation unit presents the priority of the leads scored by the scoring unit to the sales representative. For example, the generation AI lists high-scoring leads and notifies the sales representative. The presentation unit can also predict the optimal timing to approach high-priority leads. Furthermore, the presentation unit can use an emotion estimation function to analyze the emotional state of the lead and present the lead at the emotionally optimal timing.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

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

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects lead data or existing customer data; a preprocessing unit that preprocesses the data collected by the data collection unit; an analysis unit that analyzes the data preprocessed by the preprocessing unit and predicts customer purchasing intentions or demands; a scoring unit that scores the read based on the results predicted by the analysis unit; a presentation unit that presents the priorities of the leads scored by the scoring unit to a sales representative. A system characterized by:

2. The data collection unit Collecting customer social media activity or online reviews to help predict said purchase intent 2. The system of claim 1.

3. The pre-treatment unit Cleanse collected data in real time, ensuring the latest data is always available for analysis 2. The system of claim 1.

4. The pre-treatment unit Estimate emotions from customer emails and chat content and incorporate that emotion data into preprocessing.

2. The system of claim 1.

5. The data collection unit Collect customer offline activities and integrate them into data analysis 2. The system of claim 1.

6. The data collection unit Collect data from different industries and predict purchasing intentions from a cross-industry perspective 2. The system of claim 1.

7. The pre-treatment unit Estimate emotions from facial expressions or voice when customers visit a website and use that data for preprocessing.

2. The system of claim 1.

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