System

Abstract

An object of a system according to an embodiment is to calculate an expected contract rate of a business customer list and optimally sort the list.SOLUTION: A system includes a collection unit, a calculation unit, and a sort unit. The collection part collects information for each item of the business destination list. The calculation unit calculates an expected contract rate based on the information collected by the collection unit. The sorting unit sorts the business customer list based on the expected contract rate calculated by the calculation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not efficiently calculate the expected success rate of a sales list and do not adequately sort the list optimally, and there is room for improvement.

[0005] The system according to the embodiment aims to calculate the expected success rate of a sales list and sort the list optimally. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a calculation unit, and a sorting unit. The collection unit collects information for each item in the sales contact list. The calculation unit calculates a predicted success rate based on the information collected by the collection unit. The sorting unit sorts the sales contact list based on the predicted success rate calculated by the calculation unit. [Effects of the Invention]

[0007] An embodiment of the system can calculate the expected success rate of a sales listing and optimally sort the listing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sales target list sorting system according to an embodiment of the present invention sorts a sales target list by expected success rate. This system significantly improves the efficiency of sales activities by collecting information for each item on the sales target list, calculating the expected success rate using AI, and sorting the list using a binary search algorithm. For example, for each item on the sales target list, information such as past sales data, customer behavior history, and customer attribute information is collected. The collected information is then input into AI to calculate the expected success rate. The AI ​​learns from past data and estimates the expected success rate for each sales target. In calculating the expected success rate, the AI ​​takes into account customer behavior patterns and past sales history. After the expected success rate is calculated, the sales target list is sorted using a binary search algorithm. This allows sales representatives to approach sales targets with the highest expected success rate first, improving the efficiency of sales activities. As a result, the sales target list sorting system can reduce low-productivity labor costs by several tenths. With conventional tools, salespeople had to manually check the list and select contacts to approach, but with this system, the AI ​​automatically calculates the expected conversion rate and suggests the optimal contacts to approach, reducing the burden on salespeople. Furthermore, the AI ​​continuously learns and can improve the accuracy of the expected conversion rate. Every time new data is added, the AI ​​learns from that data and reflects it in the calculation of the expected rate. This improves the accuracy of the system and further improves the efficiency of sales activities.

[0029] A sales target list sorting system according to an embodiment includes a collection unit, a calculation unit, and a sorting unit. The collection unit collects information for each item in the sales target list. The collected information includes, but is not limited to, past sales contract data, customer behavioral history, and customer attribute information. The collection unit can collect, for example, customer contact information, purchase history, and interests. The collection unit can also use AI to collect information such as customer behavior patterns and past sales contract history. The calculation unit calculates a predicted sales contract rate based on the information collected by the collection unit. The calculation unit calculates the predicted sales contract rate using a machine learning algorithm such as a random forest or a neural network. For example, a random forest is an ensemble learning method that uses multiple decision trees to make predictions, and a neural network is a method of learning data using multiple artificial neurons. The calculation unit can use AI to calculate the predicted sales contract rate by taking into account customer behavioral patterns and past sales contract history. The sorting unit sorts the sales target list based on the predicted sales contract rate calculated by the calculation unit. The sorting unit sorts the sales contact list in descending order of predicted success rate using, for example, a binary search algorithm. The binary search algorithm is an algorithm for efficiently sorting lists, and can sort the list in descending order of predicted success rate. As a result, the sales contact list sorting system according to the embodiment can improve the efficiency of sales activities. For example, a sales representative can approach sales contacts in descending order of predicted success rate, thereby improving the efficiency of sales activities. Furthermore, the calculation unit can continuously learn and improve the accuracy of the predicted success rate each time new data is added. This improves the accuracy of the system and further improves the efficiency of sales activities.

[0030] The collection unit can collect information including past contract data, customer behavior history, and customer attribute information. For example, the collection unit collects past contract data. The past contract data includes, but is not limited to, the contract date, the contract amount, and the details leading to the contract. The collection unit can also collect customer behavior history. The customer behavior history includes, but is not limited to, website browsing history, purchase history, and inquiry history. The collection unit can also collect customer attribute information. The customer attribute information includes, but is not limited to, age, gender, occupation, and place of residence. By collecting past contract data, customer behavior history, and attribute information, the accuracy of the contract prediction rate can be improved. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input customer behavior history into AI and have the AI ​​analyze the behavioral patterns.

[0031] The calculation unit can calculate the predicted closing rate using a machine learning algorithm such as a random forest or a neural network. The calculation unit calculates the predicted closing rate using, for example, a random forest. Random forest is an ensemble learning method that uses multiple decision trees to make predictions, and the prediction results of each decision tree are integrated to make a final prediction. The calculation unit can also calculate the predicted closing rate using a neural network. A neural network is a method that uses multiple artificial neurons to learn data and extracts complex patterns from input data to make predictions. Furthermore, the calculation unit can calculate the predicted closing rate by combining a random forest and a neural network. For example, the prediction results from the random forest are input into a neural network to calculate the final predicted closing rate. This allows the use of a machine learning algorithm to improve the calculation accuracy of the predicted closing rate. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input customer behavior history into AI and have the AI ​​calculate the predicted closing rate.

[0032] The sorting unit can sort the list of prospects in descending order of predicted success rate using a binary search algorithm. The sorting unit, for example, sorts the list of prospects using a binary search algorithm. The binary search algorithm is an algorithm for efficiently sorting a list and can rearrange the list in descending order of predicted success rate. For example, the sorting unit places prospects with high predicted success rates at the top of the list and prospects with low predicted success rates at the bottom of the list. The sorting unit can also use a binary search algorithm to compare predicted success rates with the midpoint of the list as a reference to efficiently sort the list. Furthermore, the sorting unit can also sort a portion of the list using a binary search algorithm. For example, a portion of the list is extracted based on specific conditions and only that portion is sorted. In this way, the binary search algorithm can efficiently sort the list of prospects. Some or all of the above-described processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input the predicted success rate into AI and have the AI ​​sort the list.

[0033] The calculation unit continuously learns, improving the accuracy of the predicted closing rate each time new data is added. The calculation unit continuously learns, for example, using online learning. Online learning is a learning method that updates the model each time new data is added, thereby improving the accuracy of the predicted closing rate in real time. The calculation unit can also continuously learn using batch learning. Batch learning is a method that learns by collecting new data at regular intervals, improving the accuracy of the predicted closing rate periodically. Furthermore, the calculation unit can efficiently perform continuous learning by devising a method for incorporating new data. For example, the calculation unit establishes a mechanism for automatically collecting new data and incorporating it into the model. This improves the accuracy of the predicted closing rate through continuous learning. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input new data into the AI ​​and have the AI ​​update the model.

[0034] The collection unit can evaluate the reliability of past contract data and prioritize collecting highly reliable data. For example, to evaluate the reliability of past contract data, the collection unit considers the source, consistency, update frequency, etc. of the data. For example, the collection unit prioritizes collecting highly reliable data and filters out unreliable data. The collection unit can also collect information necessary for calculating a predicted contract rate based on the highly reliable data. For example, the collection unit improves the accuracy of the predicted contract rate by preferentially collecting highly reliable data. In this way, the accuracy of the predicted contract rate can be improved by preferentially collecting highly reliable data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past contract data into AI and have the AI ​​evaluate the reliability.

[0035] The collection unit can monitor customer behavioral history in real time and collect the latest information. For example, the collection unit can monitor customer website visit history in real time and collect the latest behavioral data. For example, the collection unit can track customer social media activity in real time to understand their latest interests. The collection unit can also update customer purchase history in real time and collect the latest purchasing patterns. This allows for collecting the latest information in real time, thereby improving the accuracy of the predicted conversion rate. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input customer behavioral history into AI and have the AI ​​perform real-time monitoring.

[0036] The collection unit can finely classify customer attribute information and improve the accuracy of information collection based on specific attributes. The collection unit, for example, finely classifies customer attribute information such as age, gender, and occupation, and collects information based on specific attributes. For example, the collection unit analyzes customer purchase histories and behavioral patterns for each attribute to collect highly accurate information. The collection unit can also collect information optimal for a target demographic based on the customer attribute information. In this way, by finely classifying customer attribute information, the accuracy of information collection can be improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer attribute information into AI and have the AI ​​collect information for each attribute.

[0037] The collection unit can collect contract data for each region, taking into account the geographical location information of the customer. The collection unit, for example, collects contract data for each region based on the geographical location information of the customer. For example, the collection unit analyzes the contract data for each region and identifies trends specific to the region. The collection unit can also collect data for calculating a predicted contract rate for each region based on the geographical location information. In this way, by collecting contract data for each region, it is possible to identify trends specific to the region. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the customer into AI and cause the AI ​​to collect contract data for each region.

[0038] The collection unit can collect customer behavioral history from social media and use it for analysis. For example, the collection unit collects customer social media posts and analyzes the behavioral history. For example, the collection unit collects customer check-in information on social media and understands behavioral patterns. The collection unit can also analyze customer friendships on social media and identify influential people. In this way, by collecting behavioral history from social media, it is possible to understand the interests of customers. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into AI and have the AI ​​analyze the behavioral history.

[0039] The collection unit can customize the type of information to be collected by reflecting past customer feedback. The collection unit, for example, analyzes past customer feedback and customizes the type of information to be collected. For example, the collection unit prioritizes collecting important information based on customer feedback. The collection unit can also improve the accuracy of the information to be collected by reflecting customer feedback. In this way, the accuracy of the information to be collected can be improved by reflecting customer feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer feedback data into AI and have the AI ​​customize the information.

[0040] The calculation unit can analyze the customer's purchase history and behavioral patterns in detail when calculating the expected success rate. For example, the calculation unit calculates the expected success rate by analyzing the customer's past purchase history in detail. For example, the calculation unit calculates the expected success rate by analyzing the customer's behavioral patterns in detail. The calculation unit can also calculate the expected success rate by combining the customer's purchase history and behavioral patterns. This allows for a detailed analysis of the customer's purchase history and behavioral patterns, thereby improving the accuracy of the expected success rate. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, AI. For example, the calculation unit can input the customer's purchase history data into AI and have the AI ​​analyze the behavioral patterns.

[0041] The calculation unit can improve accuracy by combining different machine learning algorithms when calculating the predicted closing rate. The calculation unit, for example, calculates the predicted closing rate by combining a random forest and a neural network. For example, the calculation unit calculates the predicted closing rate by combining a support vector machine and a decision tree. The calculation unit can also improve the accuracy of the predicted closing rate by combining different machine learning algorithms. In this way, the accuracy of the predicted closing rate can be improved by combining different machine learning algorithms. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input different machine learning algorithms into AI and cause the AI ​​to calculate the predicted closing rate.

[0042] When calculating the predicted closing rate, the calculation unit can optimize the prediction model based on trends in past closing data. The calculation unit, for example, analyzes trends in past closing data and optimizes the prediction model. For example, the calculation unit optimizes the prediction model taking into account seasonal fluctuations in the closing data. The calculation unit can also optimize the prediction model taking into account long-term trends in the closing data. This allows for improved accuracy of the prediction model by taking into account trends in past closing data. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input past closing data into AI and have the AI ​​analyze the trends and optimize the prediction model.

[0043] When calculating the expected contract rate, the calculation unit can calculate the expected contract rate for each region taking into account the geographical location information of the customer. The calculation unit, for example, calculates the expected contract rate for each region based on the geographical location information of the customer. For example, the calculation unit analyzes contract data for each region and calculates an expected contract rate specific to that region. The calculation unit can also optimize the expected contract rate for each region based on the geographical location information. In this way, by calculating the expected contract rate for each region, it is possible to grasp trends specific to that region. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the geographical location information of the customer into AI and cause the AI ​​to calculate the expected contract rate for each region.

[0044] When calculating the predicted conversion rate, the calculation unit can analyze the customer's social media activity and correct the predicted rate. The calculation unit, for example, analyzes the content of the customer's social media posts and corrects the predicted conversion rate. For example, the calculation unit corrects the predicted conversion rate based on the customer's check-in information on social media. The calculation unit can also analyze the customer's friendships on social media and correct the predicted conversion rate. In this way, the accuracy of the predicted conversion rate can be improved by analyzing social media activity. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input social media data into AI and have the AI ​​correct the predicted conversion rate.

[0045] When calculating the expected contract rate, the calculation unit can adjust the expected contract rate by taking the customer's market value into consideration. The calculation unit, for example, evaluates the customer's market value and adjusts the expected contract rate. For example, the calculation unit adjusts the expected contract rate based on the customer's purchasing power. The calculation unit can also adjust the expected contract rate by combining the customer's market value and purchase history. In this way, the accuracy of the expected contract rate can be improved by taking the customer's market value into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input customer market value data into AI and have the AI ​​adjust the expected contract rate.

[0046] When sorting, the sorting unit can rearrange the list taking into consideration not only the predicted closing rate but also the priority of the customer. The sorting unit, for example, rearranges the list by combining the predicted closing rate and the priority of the customer. For example, the sorting unit evaluates the priority of the customer and rearranges the list together with the predicted closing rate. The sorting unit can also rearrange the list in descending order of predicted closing rate based on the priority of the customer. This makes it possible to create a more effective sales target list by taking customer priority into consideration. Some or all of the above-described processing in the sorting unit may be performed using, or without, AI. For example, the sorting unit can input customer priority data into AI and have the AI ​​rearrange the list.

[0047] The sorting unit can apply different sorting algorithms based on customer attribute information when sorting. The sorting unit applies different sorting algorithms based on attribute information such as the customer's age, gender, and occupation. For example, the sorting unit analyzes the customer's purchase history and behavioral patterns for each attribute and applies the optimal sorting algorithm. The sorting unit can also sort the list in descending order of predicted success rate based on the customer's attribute information. In this way, by applying a sorting algorithm based on the customer's attribute information, a more accurate list can be created. Some or all of the above-described processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input customer attribute information into AI and have the AI ​​apply the optimal sorting algorithm.

[0048] When sorting, the sorting unit can optimize the list by taking into account trends in past sales data. For example, the sorting unit analyzes trends in past sales data and optimizes the list in descending order of predicted sales rate. For example, the sorting unit optimizes the list by taking into account seasonal fluctuations in sales data. The sorting unit can also optimize the list by taking into account long-term trends in sales data. This makes it possible to create a more effective list by taking into account trends in past sales data. Some or all of the above-described processing in the sorting unit may be performed using, or without, AI. For example, the sorting unit can input past sales data into AI and have the AI ​​analyze the trends and optimize the list.

[0049] When sorting, the sorting unit can create a list for each region, taking into account the geographical location information of the customer. For example, the sorting unit calculates the expected closing rate for each region based on the geographical location information of the customer and creates a list. For example, the sorting unit analyzes closing data for each region and creates a list specific to that region. The sorting unit can also create a list in descending order of the expected closing rate for each region based on the geographical location information. In this way, by creating a list for each region, it is possible to grasp trends specific to that region. Some or all of the above-mentioned processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input the geographical location information of the customer into AI and have the AI ​​create a list for each region.

[0050] The sorting unit can analyze the customer's social media activity and correct the list when sorting. For example, the sorting unit analyzes the customer's social media posts and corrects the predicted conversion rate to create a list. For example, the sorting unit corrects the predicted conversion rate based on the customer's social media check-in information to create a list. The sorting unit can also analyze the customer's social media friendships and correct the predicted conversion rate to create a list. In this way, the accuracy of the predicted conversion rate can be improved by analyzing social media activity. Some or all of the above-mentioned processing in the sorting unit may be performed using, or without, AI, for example. For example, the sorting unit can input social media data into AI and have the AI ​​correct the predicted conversion rate.

[0051] The sorting unit can adjust the list taking into account the market value of the customer when sorting. For example, the sorting unit evaluates the market value of the customer and adjusts the expected success rate to create the list. For example, the sorting unit adjusts the expected success rate based on the customer's purchasing power to create the list. The sorting unit can also combine the customer's market value and purchase history to create the list and adjust the expected success rate. In this way, by taking the customer's market value into consideration, the accuracy of the expected success rate can be improved. Some or all of the above-mentioned processing in the sorting unit may be performed using, or without, AI. For example, the sorting unit can input customer market value data into AI and have the AI ​​adjust the expected success rate.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The collection unit can monitor customers' social media activities in real time and collect the latest behavioral data. For example, the collection unit can collect customers' social media posts to understand their behavioral patterns. The collection unit can also collect customers' social media check-in information to understand their interests. Furthermore, the collection unit can analyze customers' social media friendships and identify influential people. By collecting behavioral history from social media, it is possible to understand customers' interests and improve the accuracy of the conversion prediction rate. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input social media data into AI and have the AI ​​analyze the behavioral history.

[0054] The calculation unit can calculate the predicted closing rate by combining different machine learning algorithms. For example, the calculation unit calculates the predicted closing rate by combining a random forest and a neural network. For example, the calculation unit calculates the predicted closing rate by combining a support vector machine and a decision tree. The calculation unit can also improve the accuracy of the predicted closing rate by combining different machine learning algorithms. In this way, the accuracy of the predicted closing rate can be improved by combining different machine learning algorithms. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input different machine learning algorithms into AI and cause the AI ​​to calculate the predicted closing rate.

[0055] The sorting unit can create a list for each region taking into account the geographical location information of the customer. For example, the sorting unit calculates the expected closing rate for each region based on the geographical location information of the customer and creates a list. For example, the sorting unit analyzes closing data for each region and creates a list specific to that region. The sorting unit can also create a list in descending order of the expected closing rate for each region based on the geographical location information. In this way, by creating a list for each region, it is possible to grasp trends specific to that region. Some or all of the above-described processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input the geographical location information of the customer into AI and have the AI ​​create a list for each region.

[0056] When sorting, the sorting unit can rearrange the list taking into consideration not only the predicted closing rate but also the priority of the customer. For example, the sorting unit rearranges the list by combining the predicted closing rate and the priority of the customer. For example, the sorting unit evaluates the priority of the customer and rearranges the list together with the predicted closing rate. The sorting unit can also rearrange the list in descending order of predicted closing rate based on the priority of the customer. In this way, by taking the priority of the customer into consideration, a more effective sales target list can be created. Some or all of the above-described processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input customer priority data into AI and have the AI ​​rearrange the list.

[0057] The collection unit can customize the type of information to be collected by reflecting past customer feedback. For example, the collection unit analyzes past customer feedback and customizes the type of information to be collected. For example, the collection unit prioritizes collecting important information based on customer feedback. The collection unit can also improve the accuracy of the information to be collected by reflecting customer feedback. In this way, the accuracy of the information to be collected can be improved by reflecting customer feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer feedback data into AI and have the AI ​​customize the information.

[0058] When calculating the expected contract rate, the calculation unit can adjust the expected contract rate by taking into account the market value of the customer. For example, the calculation unit evaluates the market value of the customer and adjusts the expected contract rate. For example, the calculation unit adjusts the expected contract rate based on the customer's purchasing power. The calculation unit can also adjust the expected contract rate by combining the customer's market value and purchase history. In this way, the accuracy of the expected contract rate can be improved by taking the customer's market value into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input customer market value data into AI and have the AI ​​adjust the expected contract rate.

[0059] The sorting unit can analyze the customer's social media activity and correct the list when sorting. For example, the sorting unit analyzes the customer's social media posts and corrects the predicted conversion rate to create a list. For example, the sorting unit can correct the predicted conversion rate based on the customer's social media check-in information to create a list. The sorting unit can also analyze the customer's social media friendships and correct the predicted conversion rate to create a list. In this way, the accuracy of the predicted conversion rate can be improved by analyzing social media activity. Some or all of the above-mentioned processing in the sorting unit may be performed using, or without, AI, for example. For example, the sorting unit can input social media data into AI and have the AI ​​correct the predicted conversion rate.

[0060] The collection unit can collect contract data for each region, taking into account the geographical location information of the customer. For example, the collection unit collects contract data for each region based on the geographical location information of the customer. For example, the collection unit analyzes the contract data for each region to understand trends specific to the region. The collection unit can also collect data for calculating a predicted contract rate for each region based on the geographical location information. In this way, by collecting contract data for each region, it is possible to understand trends specific to the region. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the customer into AI and cause the AI ​​to collect contract data for each region.

[0061] When calculating the predicted closing rate, the calculation unit can optimize the prediction model based on trends in past closing data. For example, the calculation unit analyzes trends in past closing data and optimizes the prediction model. For example, the calculation unit optimizes the prediction model taking into account seasonal fluctuations in the closing data. The calculation unit can also optimize the prediction model taking into account long-term trends in the closing data. This allows for improved accuracy of the prediction model by taking into account trends in past closing data. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input past closing data into AI and have the AI ​​analyze the trends and optimize the prediction model.

[0062] The collection unit can finely classify customer attribute information and improve the accuracy of information collection based on specific attributes. For example, the collection unit can finely classify customer attribute information such as age, gender, and occupation, and collect information based on specific attributes. For example, the collection unit can analyze customer purchase histories and behavioral patterns for each attribute to collect highly accurate information. The collection unit can also collect information optimal for a target demographic based on customer attribute information. In this way, by finely classifying customer attribute information, the accuracy of information collection can be improved. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input customer attribute information into AI and have the AI ​​collect information for each attribute.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection department collects information for each item on the sales target list. The collected information includes past sales data, customer behavior history, customer attribute information, customer contact information, purchase history, and interests. The collection department can also use AI to collect information such as customer behavior patterns and past sales history. Step 2: The calculation unit calculates the predicted success rate based on the information collected by the collection unit. The calculation unit calculates the predicted success rate using machine learning algorithms such as random forests and neural networks. This makes it possible to calculate the predicted success rate by taking into account customer behavior patterns, past success history, etc. Step 3: The sorting unit sorts the list of sales contacts based on the predicted success rate calculated by the calculation unit. The sorting unit uses a binary search algorithm to sort the list of sales contacts in descending order of predicted success rate. This allows sales representatives to approach sales contacts in descending order of predicted success rate, improving the efficiency of sales activities.

[0065] (Example 2) A sales target list sorting system according to an embodiment of the present invention sorts a sales target list by expected success rate. This system significantly improves the efficiency of sales activities by collecting information for each item on the sales target list, calculating the expected success rate using AI, and sorting the list using a binary search algorithm. For example, for each item on the sales target list, information such as past sales data, customer behavior history, and customer attribute information is collected. The collected information is then input into AI to calculate the expected success rate. The AI ​​learns from past data and estimates the expected success rate for each sales target. In calculating the expected success rate, the AI ​​takes into account customer behavior patterns and past sales history. After the expected success rate is calculated, the sales target list is sorted using a binary search algorithm. This allows sales representatives to approach sales targets with the highest expected success rate first, improving the efficiency of sales activities. As a result, the sales target list sorting system can reduce low-productivity labor costs by several tenths. With conventional tools, salespeople had to manually check the list and select contacts to approach, but with this system, the AI ​​automatically calculates the expected conversion rate and suggests the optimal contacts to approach, reducing the burden on salespeople. Furthermore, the AI ​​continuously learns and can improve the accuracy of the expected conversion rate. Every time new data is added, the AI ​​learns from that data and reflects it in the calculation of the expected rate. This improves the accuracy of the system and further improves the efficiency of sales activities.

[0066] A sales target list sorting system according to an embodiment includes a collection unit, a calculation unit, and a sorting unit. The collection unit collects information for each item in the sales target list. The collected information includes, but is not limited to, past sales contract data, customer behavioral history, and customer attribute information. The collection unit can collect, for example, customer contact information, purchase history, and interests. The collection unit can also use AI to collect information such as customer behavior patterns and past sales contract history. The calculation unit calculates a predicted sales contract rate based on the information collected by the collection unit. The calculation unit calculates the predicted sales contract rate using a machine learning algorithm such as a random forest or a neural network. For example, a random forest is an ensemble learning method that uses multiple decision trees to make predictions, and a neural network is a method of learning data using multiple artificial neurons. The calculation unit can use AI to calculate the predicted sales contract rate by taking into account customer behavioral patterns and past sales contract history. The sorting unit sorts the sales target list based on the predicted sales contract rate calculated by the calculation unit. The sorting unit sorts the sales contact list in descending order of predicted success rate using, for example, a binary search algorithm. The binary search algorithm is an algorithm for efficiently sorting lists, and can sort the list in descending order of predicted success rate. As a result, the sales contact list sorting system according to the embodiment can improve the efficiency of sales activities. For example, a sales representative can approach sales contacts in descending order of predicted success rate, thereby improving the efficiency of sales activities. Furthermore, the calculation unit can continuously learn and improve the accuracy of the predicted success rate each time new data is added. This improves the accuracy of the system and further improves the efficiency of sales activities.

[0067] The collection unit can collect information including past contract data, customer behavior history, and customer attribute information. For example, the collection unit collects past contract data. The past contract data includes, but is not limited to, the contract date, the contract amount, and the details leading to the contract. The collection unit can also collect customer behavior history. The customer behavior history includes, but is not limited to, website browsing history, purchase history, and inquiry history. The collection unit can also collect customer attribute information. The customer attribute information includes, but is not limited to, age, gender, occupation, and place of residence. By collecting past contract data, customer behavior history, and attribute information, the accuracy of the contract prediction rate can be improved. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input customer behavior history into AI and have the AI ​​analyze the behavioral patterns.

[0068] The calculation unit can calculate the predicted closing rate using a machine learning algorithm such as a random forest or a neural network. The calculation unit calculates the predicted closing rate using, for example, a random forest. Random forest is an ensemble learning method that uses multiple decision trees to make predictions, and the prediction results of each decision tree are integrated to make a final prediction. The calculation unit can also calculate the predicted closing rate using a neural network. A neural network is a method that uses multiple artificial neurons to learn data and extracts complex patterns from input data to make predictions. Furthermore, the calculation unit can calculate the predicted closing rate by combining a random forest and a neural network. For example, the prediction results from the random forest are input into a neural network to calculate the final predicted closing rate. This allows the use of a machine learning algorithm to improve the calculation accuracy of the predicted closing rate. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input customer behavior history into AI and have the AI ​​calculate the predicted closing rate.

[0069] The sorting unit can sort the list of prospects in descending order of predicted success rate using a binary search algorithm. The sorting unit, for example, sorts the list of prospects using a binary search algorithm. The binary search algorithm is an algorithm for efficiently sorting a list and can rearrange the list in descending order of predicted success rate. For example, the sorting unit places prospects with high predicted success rates at the top of the list and prospects with low predicted success rates at the bottom of the list. The sorting unit can also use a binary search algorithm to compare predicted success rates with the midpoint of the list as a reference to efficiently sort the list. Furthermore, the sorting unit can also sort a portion of the list using a binary search algorithm. For example, a portion of the list is extracted based on specific conditions and only that portion is sorted. In this way, the binary search algorithm can efficiently sort the list of prospects. Some or all of the above-described processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input the predicted success rate into AI and have the AI ​​sort the list.

[0070] The calculation unit continuously learns, improving the accuracy of the predicted closing rate each time new data is added. The calculation unit continuously learns, for example, using online learning. Online learning is a learning method that updates the model each time new data is added, thereby improving the accuracy of the predicted closing rate in real time. The calculation unit can also continuously learn using batch learning. Batch learning is a method that learns by collecting new data at regular intervals, improving the accuracy of the predicted closing rate periodically. Furthermore, the calculation unit can efficiently perform continuous learning by devising a method for incorporating new data. For example, the calculation unit establishes a mechanism for automatically collecting new data and incorporating it into the model. This improves the accuracy of the predicted closing rate through continuous learning. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input new data into the AI ​​and have the AI ​​update the model.

[0071] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions captured by a camera and estimates emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of information collection to reduce the user's burden. Also, if the user is relaxed, the collection unit can increase the frequency of information collection and collect detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting important information and process it quickly. This reduces the user's burden by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0072] The collection unit can evaluate the reliability of past contract data and prioritize collecting highly reliable data. For example, to evaluate the reliability of past contract data, the collection unit considers the source, consistency, update frequency, etc. of the data. For example, the collection unit prioritizes collecting highly reliable data and filters out unreliable data. The collection unit can also collect information necessary for calculating a predicted contract rate based on the highly reliable data. For example, the collection unit improves the accuracy of the predicted contract rate by preferentially collecting highly reliable data. In this way, the accuracy of the predicted contract rate can be improved by preferentially collecting highly reliable data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past contract data into AI and have the AI ​​evaluate the reliability.

[0073] The collection unit can monitor customer behavioral history in real time and collect the latest information. For example, the collection unit can monitor customer website visit history in real time and collect the latest behavioral data. For example, the collection unit can track customer social media activity in real time to understand their latest interests. The collection unit can also update customer purchase history in real time and collect the latest purchasing patterns. This allows for collecting the latest information in real time, thereby improving the accuracy of the predicted conversion rate. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input customer behavioral history into AI and have the AI ​​perform real-time monitoring.

[0074] The collection unit can finely classify customer attribute information and improve the accuracy of information collection based on specific attributes. The collection unit, for example, finely classifies customer attribute information such as age, gender, and occupation, and collects information based on specific attributes. For example, the collection unit analyzes customer purchase histories and behavioral patterns for each attribute to collect highly accurate information. The collection unit can also collect information optimal for a target demographic based on the customer attribute information. In this way, by finely classifying customer attribute information, the accuracy of information collection can be improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer attribute information into AI and have the AI ​​collect information for each attribute.

[0075] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions captured by a camera and estimates emotions. For example, when the user is stressed, the collection unit prioritizes collecting important information, thereby reducing the user's burden. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information and provide highly accurate data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. This reduces the user's burden by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] The collection unit can collect contract data for each region, taking into account the geographical location information of the customer. The collection unit, for example, collects contract data for each region based on the geographical location information of the customer. For example, the collection unit analyzes the contract data for each region and identifies trends specific to the region. The collection unit can also collect data for calculating a predicted contract rate for each region based on the geographical location information. In this way, by collecting contract data for each region, it is possible to identify trends specific to the region. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the customer into AI and cause the AI ​​to collect contract data for each region.

[0077] The collection unit can collect customer behavioral history from social media and use it for analysis. For example, the collection unit collects customer social media posts and analyzes the behavioral history. For example, the collection unit collects customer check-in information on social media and understands behavioral patterns. The collection unit can also analyze customer friendships on social media and identify influential people. In this way, by collecting behavioral history from social media, it is possible to understand the interests of customers. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into AI and have the AI ​​analyze the behavioral history.

[0078] The collection unit can customize the type of information to be collected by reflecting past customer feedback. The collection unit, for example, analyzes past customer feedback and customizes the type of information to be collected. For example, the collection unit prioritizes collecting important information based on customer feedback. The collection unit can also improve the accuracy of the information to be collected by reflecting customer feedback. In this way, the accuracy of the information to be collected can be improved by reflecting customer feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer feedback data into AI and have the AI ​​customize the information.

[0079] The calculation unit can estimate the user's emotions and adjust the calculation method for the expected success rate based on the estimated user emotions. The calculation unit, for example, uses facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions captured by a camera and estimates emotions. For example, if the user is stressed, the calculation unit calculates the expected success rate using a simple calculation method. Alternatively, if the user is relaxed, the calculation unit can calculate the expected success rate using detailed data. Furthermore, if the user is in a hurry, the calculation unit can calculate the expected success rate using a method that allows for quick calculation. This allows for adjusting the calculation method according to the user's emotions, thereby improving the calculation accuracy of the expected success rate. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or without AI. For example, the calculation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0080] The calculation unit can analyze the customer's purchase history and behavioral patterns in detail when calculating the expected success rate. For example, the calculation unit calculates the expected success rate by analyzing the customer's past purchase history in detail. For example, the calculation unit calculates the expected success rate by analyzing the customer's behavioral patterns in detail. The calculation unit can also calculate the expected success rate by combining the customer's purchase history and behavioral patterns. This allows for a detailed analysis of the customer's purchase history and behavioral patterns, thereby improving the accuracy of the expected success rate. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, AI. For example, the calculation unit can input the customer's purchase history data into AI and have the AI ​​analyze the behavioral patterns.

[0081] The calculation unit can improve accuracy by combining different machine learning algorithms when calculating the predicted closing rate. The calculation unit, for example, calculates the predicted closing rate by combining a random forest and a neural network. For example, the calculation unit calculates the predicted closing rate by combining a support vector machine and a decision tree. The calculation unit can also improve the accuracy of the predicted closing rate by combining different machine learning algorithms. In this way, the accuracy of the predicted closing rate can be improved by combining different machine learning algorithms. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input different machine learning algorithms into AI and cause the AI ​​to calculate the predicted closing rate.

[0082] When calculating the predicted closing rate, the calculation unit can optimize the prediction model based on trends in past closing data. The calculation unit, for example, analyzes trends in past closing data and optimizes the prediction model. For example, the calculation unit optimizes the prediction model taking into account seasonal fluctuations in the closing data. The calculation unit can also optimize the prediction model taking into account long-term trends in the closing data. This allows for improved accuracy of the prediction model by taking into account trends in past closing data. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input past closing data into AI and have the AI ​​analyze the trends and optimize the prediction model.

[0083] The calculation unit can estimate the user's emotions and adjust the display method of the expected closing rate based on the estimated user emotions. The calculation unit, for example, uses facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions captured by a camera and estimates emotions. For example, if the user is stressed, the calculation unit provides a simple, highly visible display method. If the user is relaxed, the calculation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the calculation unit can also provide a display method that focuses on the main points. This improves visibility by adjusting the display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the calculation unit can be performed using AI, for example, or without AI. For example, the calculation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0084] When calculating the expected contract rate, the calculation unit can calculate the expected contract rate for each region taking into account the geographical location information of the customer. The calculation unit, for example, calculates the expected contract rate for each region based on the geographical location information of the customer. For example, the calculation unit analyzes contract data for each region and calculates an expected contract rate specific to that region. The calculation unit can also optimize the expected contract rate for each region based on the geographical location information. In this way, by calculating the expected contract rate for each region, it is possible to grasp trends specific to that region. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the geographical location information of the customer into AI and cause the AI ​​to calculate the expected contract rate for each region.

[0085] When calculating the predicted conversion rate, the calculation unit can analyze the customer's social media activity and correct the predicted rate. The calculation unit, for example, analyzes the content of the customer's social media posts and corrects the predicted conversion rate. For example, the calculation unit corrects the predicted conversion rate based on the customer's check-in information on social media. The calculation unit can also analyze the customer's friendships on social media and correct the predicted conversion rate. In this way, the accuracy of the predicted conversion rate can be improved by analyzing social media activity. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input social media data into AI and have the AI ​​correct the predicted conversion rate.

[0086] When calculating the expected contract rate, the calculation unit can adjust the expected contract rate by taking the customer's market value into consideration. The calculation unit, for example, evaluates the customer's market value and adjusts the expected contract rate. For example, the calculation unit adjusts the expected contract rate based on the customer's purchasing power. The calculation unit can also adjust the expected contract rate by combining the customer's market value and purchase history. In this way, the accuracy of the expected contract rate can be improved by taking the customer's market value into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input customer market value data into AI and have the AI ​​adjust the expected contract rate.

[0087] The sorting unit can estimate the user's emotions and adjust the sorting criteria based on the estimated user emotions. The sorting unit, for example, uses facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions captured by a camera and estimates emotions. For example, if the user is stressed, the sorting unit can sort the list using simple sorting criteria. Alternatively, if the user is relaxed, the sorting unit can sort the list using detailed sorting criteria. Furthermore, if the user is in a hurry, the sorting unit can sort the list using criteria that allow for quick sorting. This reduces the burden on the user by adjusting the sorting criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sorting unit can be performed using, for example, an AI, or without an AI. For example, the sorting unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0088] When sorting, the sorting unit can rearrange the list taking into consideration not only the predicted closing rate but also the priority of the customer. The sorting unit, for example, rearranges the list by combining the predicted closing rate and the priority of the customer. For example, the sorting unit evaluates the priority of the customer and rearranges the list together with the predicted closing rate. The sorting unit can also rearrange the list in descending order of predicted closing rate based on the priority of the customer. This makes it possible to create a more effective sales target list by taking customer priority into consideration. Some or all of the above-described processing in the sorting unit may be performed using, or without, AI. For example, the sorting unit can input customer priority data into AI and have the AI ​​rearrange the list.

[0089] The sorting unit can apply different sorting algorithms based on customer attribute information when sorting. The sorting unit applies different sorting algorithms based on attribute information such as the customer's age, gender, and occupation. For example, the sorting unit analyzes the customer's purchase history and behavioral patterns for each attribute and applies the optimal sorting algorithm. The sorting unit can also sort the list in descending order of predicted success rate based on the customer's attribute information. In this way, by applying a sorting algorithm based on the customer's attribute information, a more accurate list can be created. Some or all of the above-described processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input customer attribute information into AI and have the AI ​​apply the optimal sorting algorithm.

[0090] When sorting, the sorting unit can optimize the list by taking into account trends in past sales data. For example, the sorting unit analyzes trends in past sales data and optimizes the list in descending order of predicted sales rate. For example, the sorting unit optimizes the list by taking into account seasonal fluctuations in sales data. The sorting unit can also optimize the list by taking into account long-term trends in sales data. This makes it possible to create a more effective list by taking into account trends in past sales data. Some or all of the above-described processing in the sorting unit may be performed using, or without, AI. For example, the sorting unit can input past sales data into AI and have the AI ​​analyze the trends and optimize the list.

[0091] The sorting unit can estimate the user's emotions and adjust the display method of the sorted results based on the estimated user emotions. The sorting unit, for example, uses facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions captured by a camera and estimates emotions. For example, if the user is stressed, the sorting unit can provide a simple, highly visible display method. If the user is relaxed, the sorting unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the sorting unit can also provide a display method that focuses on the main points. This improves visibility by adjusting the display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the sorting unit can be performed using AI, for example, or without AI. For example, the sorting unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0092] When sorting, the sorting unit can create a list for each region, taking into account the geographical location information of the customer. For example, the sorting unit calculates the expected closing rate for each region based on the geographical location information of the customer and creates a list. For example, the sorting unit analyzes closing data for each region and creates a list specific to that region. The sorting unit can also create a list in descending order of the expected closing rate for each region based on the geographical location information. In this way, by creating a list for each region, it is possible to grasp trends specific to that region. Some or all of the above-mentioned processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input the geographical location information of the customer into AI and have the AI ​​create a list for each region.

[0093] The sorting unit can analyze the customer's social media activity and correct the list when sorting. For example, the sorting unit analyzes the customer's social media posts and corrects the predicted conversion rate to create a list. For example, the sorting unit corrects the predicted conversion rate based on the customer's social media check-in information to create a list. The sorting unit can also analyze the customer's social media friendships and correct the predicted conversion rate to create a list. In this way, the accuracy of the predicted conversion rate can be improved by analyzing social media activity. Some or all of the above-mentioned processing in the sorting unit may be performed using, or without, AI, for example. For example, the sorting unit can input social media data into AI and have the AI ​​correct the predicted conversion rate.

[0094] The sorting unit can adjust the list taking into account the market value of the customer when sorting. For example, the sorting unit evaluates the market value of the customer and adjusts the expected success rate to create the list. For example, the sorting unit adjusts the expected success rate based on the customer's purchasing power to create the list. The sorting unit can also combine the customer's market value and purchase history to create the list and adjust the expected success rate. In this way, by taking the customer's market value into consideration, the accuracy of the expected success rate can be improved. Some or all of the above-mentioned processing in the sorting unit may be performed using, or without, AI. For example, the sorting unit can input customer market value data into AI and have the AI ​​adjust the expected success rate. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, calculation unit, and sort unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect information for each item in the sales contact list using the camera 42 or microphone 38B of the smart device 14. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates a predicted contract rate based on the collected information. The sorting unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and sorts the sales contact list based on the calculated predicted contract rate. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, calculation unit, and sort unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect information for each item in the sales contact list using the camera 42 or the microphone 238 of the smart glasses 214. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates a predicted closing rate based on the collected information. The sorting unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and sorts the sales contact list based on the calculated predicted closing rate. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, calculation unit, and sort unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect information for each item in the sales contact list using the camera 42 or microphone 238 of the headset terminal 314. The calculation unit is realized by the specific processing unit 290 of the data processing device 12, and calculates a predicted contract rate based on the collected information. The sorting unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and sorts the sales contact list based on the calculated predicted contract rate. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, calculation unit, and sort unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information for each item in the sales contact list using the camera 42 or microphone 238 of the robot 414. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates a predicted contract rate based on the collected information. The sorting unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and sorts the sales contact list based on the calculated predicted contract rate.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The collection unit can monitor customers' social media activities in real time and collect the latest behavioral data. For example, the collection unit can collect customers' social media posts to understand their behavioral patterns. The collection unit can also collect customers' social media check-in information to understand their interests. Furthermore, the collection unit can analyze customers' social media friendships and identify influential people. By collecting behavioral history from social media, it is possible to understand customers' interests and improve the accuracy of the conversion prediction rate. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input social media data into AI and have the AI ​​analyze the behavioral history.

[0097] The calculation unit can calculate the predicted closing rate by combining different machine learning algorithms. For example, the calculation unit calculates the predicted closing rate by combining a random forest and a neural network. For example, the calculation unit calculates the predicted closing rate by combining a support vector machine and a decision tree. The calculation unit can also improve the accuracy of the predicted closing rate by combining different machine learning algorithms. In this way, the accuracy of the predicted closing rate can be improved by combining different machine learning algorithms. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input different machine learning algorithms into AI and cause the AI ​​to calculate the predicted closing rate.

[0098] The sorting unit can create a list for each region taking into account the geographical location information of the customer. For example, the sorting unit calculates the expected closing rate for each region based on the geographical location information of the customer and creates a list. For example, the sorting unit analyzes closing data for each region and creates a list specific to that region. The sorting unit can also create a list in descending order of the expected closing rate for each region based on the geographical location information. In this way, by creating a list for each region, it is possible to grasp trends specific to that region. Some or all of the above-described processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input the geographical location information of the customer into AI and have the AI ​​create a list for each region.

[0099] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, the collection unit uses facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions captured by a camera and estimates emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of information collection to reduce the user's burden. Also, if the user is relaxed, the collection unit can increase the frequency of information collection and collect detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting important information and process it quickly. This reduces the user's burden by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0100] The calculation unit can estimate the user's emotions and adjust the calculation method for the expected success rate based on the estimated user emotions. For example, the calculation unit uses facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions captured by a camera and estimates emotions. For example, if the user is stressed, the calculation unit calculates the expected success rate using a simple calculation method. Alternatively, if the user is relaxed, the calculation unit can calculate the expected success rate using detailed data. Furthermore, if the user is in a hurry, the calculation unit can calculate the expected success rate using a method that allows for quick calculation. This allows for adjusting the calculation method according to the user's emotions, thereby improving the calculation accuracy of the expected success rate. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or without AI. For example, the calculation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0101] The sorting unit can estimate the user's emotions and adjust the sorting criteria based on the estimated user emotions. For example, the sorting unit uses facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions captured by a camera and estimates emotions. For example, if the user is stressed, the sorting unit can sort the list using simple sorting criteria. Alternatively, if the user is relaxed, the sorting unit can sort the list using detailed sorting criteria. Furthermore, if the user is in a hurry, the sorting unit can sort the list using criteria that allow for quick sorting. This reduces the burden on the user by adjusting the sorting criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sorting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sorting unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0102] When sorting, the sorting unit can rearrange the list taking into consideration not only the predicted closing rate but also the priority of the customer. For example, the sorting unit rearranges the list by combining the predicted closing rate and the priority of the customer. For example, the sorting unit evaluates the priority of the customer and rearranges the list together with the predicted closing rate. The sorting unit can also rearrange the list in descending order of predicted closing rate based on the priority of the customer. In this way, by taking the priority of the customer into consideration, a more effective sales target list can be created. Some or all of the above-described processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input customer priority data into AI and have the AI ​​rearrange the list.

[0103] The collection unit can customize the type of information to be collected by reflecting past customer feedback. For example, the collection unit analyzes past customer feedback and customizes the type of information to be collected. For example, the collection unit prioritizes collecting important information based on customer feedback. The collection unit can also improve the accuracy of the information to be collected by reflecting customer feedback. In this way, the accuracy of the information to be collected can be improved by reflecting customer feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer feedback data into AI and have the AI ​​customize the information.

[0104] When calculating the expected contract rate, the calculation unit can adjust the expected contract rate by taking into account the market value of the customer. For example, the calculation unit evaluates the market value of the customer and adjusts the expected contract rate. For example, the calculation unit adjusts the expected contract rate based on the customer's purchasing power. The calculation unit can also adjust the expected contract rate by combining the customer's market value and purchase history. In this way, the accuracy of the expected contract rate can be improved by taking the customer's market value into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input customer market value data into AI and have the AI ​​adjust the expected contract rate.

[0105] The sorting unit can estimate the user's emotions and adjust the display method of the sorted results based on the estimated user emotions. For example, the sorting unit uses facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology analyzes the user's facial expressions captured by a camera and estimates emotions. For example, the sorting unit provides a simple, highly visible display method when the user is stressed. The sorting unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the sorting unit can provide a display method that focuses on the main points when the user is in a hurry. This improves visibility by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sorting unit can be performed using AI, for example, or without AI. For example, the sorting unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0106] The sorting unit can analyze the customer's social media activity and correct the list when sorting. For example, the sorting unit analyzes the customer's social media posts and corrects the predicted conversion rate to create a list. For example, the sorting unit can correct the predicted conversion rate based on the customer's social media check-in information to create a list. The sorting unit can also analyze the customer's social media friendships and correct the predicted conversion rate to create a list. In this way, the accuracy of the predicted conversion rate can be improved by analyzing social media activity. Some or all of the above-mentioned processing in the sorting unit may be performed using, or without, AI, for example. For example, the sorting unit can input social media data into AI and have the AI ​​correct the predicted conversion rate.

[0107] The collection unit can collect contract data for each region, taking into account the geographical location information of the customer. For example, the collection unit collects contract data for each region based on the geographical location information of the customer. For example, the collection unit analyzes the contract data for each region to understand trends specific to the region. The collection unit can also collect data for calculating a predicted contract rate for each region based on the geographical location information. In this way, by collecting contract data for each region, it is possible to understand trends specific to the region. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the customer into AI and cause the AI ​​to collect contract data for each region.

[0108] When calculating the predicted closing rate, the calculation unit can optimize the prediction model based on trends in past closing data. For example, the calculation unit analyzes trends in past closing data and optimizes the prediction model. For example, the calculation unit optimizes the prediction model taking into account seasonal fluctuations in the closing data. The calculation unit can also optimize the prediction model taking into account long-term trends in the closing data. This allows for improved accuracy of the prediction model by taking into account trends in past closing data. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input past closing data into AI and have the AI ​​analyze the trends and optimize the prediction model.

[0109] The calculation unit can estimate the user's emotions and adjust the display method of the expected closing rate based on the estimated user emotions. For example, the calculation unit uses facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology analyzes the user's facial expressions captured by a camera and estimates emotions. For example, if the user is stressed, the calculation unit provides a simple, highly visible display method. If the user is relaxed, the calculation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the calculation unit can also provide a display method that focuses on the main points. This improves visibility by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the calculation unit can be performed using AI, for example, or without AI. For example, the calculation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0110] The collection unit can finely classify customer attribute information and improve the accuracy of information collection based on specific attributes. For example, the collection unit can finely classify customer attribute information such as age, gender, and occupation, and collect information based on specific attributes. For example, the collection unit can analyze customer purchase histories and behavioral patterns for each attribute to collect highly accurate information. The collection unit can also collect information optimal for a target demographic based on customer attribute information. In this way, by finely classifying customer attribute information, the accuracy of information collection can be improved. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input customer attribute information into AI and have the AI ​​collect information for each attribute.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection department collects information for each item on the sales target list. The collected information includes past sales data, customer behavior history, customer attribute information, customer contact information, purchase history, and interests. The collection department can also use AI to collect information such as customer behavior patterns and past sales history. Step 2: The calculation unit calculates the predicted success rate based on the information collected by the collection unit. The calculation unit calculates the predicted success rate using machine learning algorithms such as random forests and neural networks. This makes it possible to calculate the predicted success rate by taking into account customer behavior patterns, past success history, etc. Step 3: The sorting unit sorts the list of sales contacts based on the predicted success rate calculated by the calculation unit. The sorting unit uses a binary search algorithm to sort the list of sales contacts in descending order of predicted success rate. This allows sales representatives to approach sales contacts in descending order of predicted success rate, improving the efficiency of sales activities.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information for each item on the sales target list; a calculation unit that calculates a contract prediction rate based on the information collected by the collection unit; a sorting unit that sorts the sales destination list based on the expected contract rate calculated by the calculation unit. A system characterized by:

2. The collecting unit Collect information including past contract data, customer behavior history, and customer attribute information 2. The system of claim 1.

3. The calculation unit Calculate predicted win rates using random forest or neural network machine learning algorithms 2. The system of claim 1.

4. The sorting unit Use a binary search algorithm to sort the list of prospects in descending order of expected success rate.

2. The system of claim 1.

5. The calculation unit Continuously learns and improves the accuracy of win predictions with each new piece of data 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Evaluate the reliability of past sales data and prioritize collection of reliable data 2. The system of claim 1.

8. The collecting unit Monitor customer behavior in real time and collect the latest information 2. The system of claim 1.

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