System

Abstract

To provide a system according to an embodiment that predicts the order accuracy and proposes an optimal appointment.SOLUTION: A system comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer data, conversation data, and emotion data. The analysis unit analyzes the data collected by the collection unit, and predicts the accuracy of order accuracy. The proposal unit proposes an appointment on the basis of the accuracy of order accuracy predicted by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the conventional technology, there is a risk of making a wasteful approach to customers with a low probability of receiving an order, and losing customers with a high probability of receiving an order.

[0005] The system according to the embodiment aims to predict the probability of receiving an order and propose optimal appointments. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer data, conversation data, and emotion data. The analysis unit analyzes the data collected by the collection unit and predicts an order probability. The proposal unit proposes an appointment based on the order probability predicted by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can predict the probability of receiving an order and suggest optimal appointments. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The sales support and management system according to the embodiment of the present invention is a system that predicts the probability of receiving an order using customer data, conversation data, and emotion data, and proposes an appointment based on the prediction. This system collects customer data, conversation data, and emotion data, and predicts the probability of receiving an order by analyzing the collected data. Furthermore, the system proposes an appointment based on the predicted probability of receiving an order. This mechanism makes it possible to avoid wasteful approaches to customers with low probability of receiving an order, and to effectively approach customers with high probability of receiving an order. For example, data such as customer attribute information (new, continuing, size, location, etc.), conversation content, and the other party's reaction (facial expression, emotion) are collected. This data is collected by AI. Next, the collected data is analyzed to predict the probability of receiving an order. The AI ​​analyzes the collected data and predicts the customer's probability of receiving an order. For example, it is possible to determine how positive a customer is about receiving an order from the conversation content and the other party's reaction. Furthermore, the system proposes an appointment based on the predicted probability of receiving an order. The AI ​​proposes appointments in order of the customers with high probability of receiving an order. For example, it is possible to suggest that an appointment be set as soon as possible for customers with high probability of receiving an order. In addition, the priority of sales can be determined based on the probability of receiving an order, the price of the product, the attributes of the customer, the sales target of the sales representative, and the schedule of the sales representative. AI determines the priority of sales based on this information and proposes the optimal appointment. For example, it can preferentially propose an appointment to a customer with a high probability of receiving an order and a high price of the product. Furthermore, it can estimate the timing when the likelihood of receiving an order will increase based on the probability of receiving an order, the price of the product, and the attributes of the customer. AI estimates the timing when the likelihood of receiving an order will increase based on this information and proposes an appointment at that timing. For example, if a customer tends to be positive about receiving an order at a certain time, it can propose an appointment at that time. Finally, it is also possible to evaluate sales representatives based on the probability of receiving an order. AI evaluates the performance of sales representatives based on the probability of receiving an order and provides appropriate feedback. For example, it can highly evaluate sales representatives who have made effective approaches to customers with a high probability of receiving an order.This system is expected to improve the efficiency of sales activities and the success rate. Sales representatives can effectively approach customers who are likely to place an order, and avoid unnecessary approaches. It can also be used to evaluate sales representatives, which contributes to improving the quality of sales activities. This allows the sales support and management system to predict the success rate of an order using customer data, conversation data, and emotion data, and propose appointments based on that prediction.

[0029] The sales support and management system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer data, conversation data, and emotion data. The customer data includes, for example, basic information of the customer, purchase history, inquiry history, etc., but is not limited to such examples. The conversation data includes, for example, recorded telephone data, chat logs, etc., but is not limited to such examples. The emotion data includes, for example, voice tone analysis, facial expression recognition, etc., but is not limited to such examples. The collection unit, for example, acquires basic information of the customer from a database. The collection unit can also automatically collect recorded telephone data. Furthermore, the collection unit can also capture the facial expression of the customer with a camera and collect emotion data using facial expression recognition technology. The analysis unit analyzes the data collected by the collection unit and predicts the probability of receiving an order. The analysis unit predicts the probability of receiving an order of the customer, for example, using a machine learning algorithm. Furthermore, the analysis unit can also analyze the conversation data and predict the probability of receiving an order of the customer using natural language processing technology. Furthermore, the analysis unit can also analyze the emotion data of the customer and predict the probability of receiving an order using facial expression recognition technology. For example, the analysis unit uses a machine learning algorithm to predict the customer's order probability based on past purchase history and inquiry history. The natural language processing technology analyzes conversation data and evaluates the customer's interests and purchasing motivation. The facial expression recognition technology estimates emotions from the customer's facial expressions and predicts the order probability. The proposal unit proposes an appointment based on the order probability predicted by the analysis unit. The proposal unit proposes appointments, for example, in order of the customer with the highest order probability. The proposal unit can also determine the priority of sales based on the order probability, the price of the product, the customer's attributes, the sales target of the sales representative, and the schedule of the sales representative, and propose the optimal appointment. Furthermore, the proposal unit can estimate the timing when the order probability is high based on the order probability, the price of the product, and the customer's attributes, and propose an appointment at that timing. For example, the proposal unit suggests setting an appointment as soon as possible for a customer with a high order probability. The proposal unit preferentially proposes appointments for customers with a high order probability and a high price of the product.If a customer has a tendency to be positive about receiving an order at a particular time, the suggestion unit suggests an appointment at that time. As a result, the sales support and management system according to the embodiment can predict the order probability using customer data, conversation data, and emotion data, and suggest an appointment based on the prediction. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest an appointment using an AI model that inputs the order probability predicted by the analysis unit and outputs an appointment.

[0030] The collection unit collects customer data, conversation data, and emotion data. The customer data includes, for example, basic information of the customer, purchase history, inquiry history, etc., but is not limited to such examples. Specifically, the basic information of the customer includes personal information such as name, address, contact information, age, and gender. The purchase history includes details of products and services purchased in the past, purchase date and time, purchase amount, etc. The inquiry history includes the content, date and time, and response status of inquiries made by the customer in the past. These data are important for understanding the behavior and preferences of the customer. The conversation data includes, for example, telephone recording data, chat log, etc., but is not limited to such examples. The telephone recording data is used to record the content of the conversation between the customer and the sales representative and to analyze it later. The chat log is used to record the exchange in online chat or messaging app and to understand the customer's questions and requests. The emotion data includes, for example, voice tone analysis, facial expression recognition, etc., but is not limited to such examples. Voice tone analysis is a technology that analyzes the tone and pitch of the customer's voice to estimate the emotional state. Facial expression recognition is a technology that analyzes the facial expressions of customers captured by a camera and estimates emotions such as joy, anger, and sadness. The collection unit, for example, acquires basic information about the customer from a database. The collection unit can also automatically collect recorded telephone call data. Furthermore, the collection unit can capture the facial expressions of the customer with a camera and collect emotion data using facial expression recognition technology. This allows the collection unit to collect a wide range of data from various data sources and grasp the behavior and emotions of customers in detail. The collected data is stored in a central database and made accessible to the analysis unit and the proposal unit. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit and predicts the probability of receiving an order. The analysis unit predicts the probability of receiving an order from a customer, for example, using a machine learning algorithm. Specifically, the analysis unit learns the behavioral patterns and purchasing tendencies of customers based on past purchase histories and inquiry histories, and predicts the probability of receiving an order in the future. For example, the analysis unit predicts the probability that a customer who has purchased a specific product in the past will purchase the same product again, or the probability that a customer who has made a specific inquiry will subsequently purchase the product. The analysis unit can also analyze conversation data using natural language processing technology and predict the probability of receiving an order from a customer. The natural language processing technology analyzes the content of a conversation with a customer and evaluates the customer's interest and purchasing motivation. For example, if a customer frequently asks questions about a specific product, it is determined that the customer is highly interested in the product, and the probability of receiving an order is predicted to be high. Furthermore, the analysis unit can also analyze the customer's emotional data using facial expression recognition technology and predict the probability of receiving an order. The facial expression recognition technology estimates emotions from the customer's facial expression and evaluates the emotions the customer has toward the product. For example, if a customer has a happy expression when looking at a product, it is determined that the customer is highly motivated to purchase the product, and the probability of receiving an order is predicted to be high. This allows the analysis unit to analyze collected data from multiple angles and predict the likelihood of customer orders with high accuracy. Furthermore, the analysis unit can use past data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past purchase data, it can predict purchasing trends during specific seasons or events and develop future sales strategies. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The proposal unit proposes an appointment based on the order probability predicted by the analysis unit. The proposal unit, for example, proposes an appointment in the order of the customer with the highest order probability. Specifically, the customers are prioritized based on the order probability predicted by the analysis unit, and appointments are set for customers with the highest order probability. The proposal unit can also determine the priority of sales based on the order probability, the price of the product, the customer's attributes, the sales target of the sales representative, and the sales representative's schedule, and propose the optimal appointment. For example, for a customer with a high order probability and a high price of the product, the proposal unit preferentially proposes an appointment and develops an optimal strategy for achieving the sales target of the sales representative. Furthermore, the proposal unit can estimate the timing when the order probability is high based on the order probability, the price of the product, and the customer's attributes, and propose an appointment at that timing. For example, if a customer has a tendency to be positive about orders at a specific time, the proposal unit proposes an appointment at that time. In this way, the proposal unit can determine the timing when the customer's purchasing motivation is high and support effective sales activities. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose an appointment using an AI model that inputs the order probability predicted by the analysis unit and outputs an appointment. Specifically, the AI ​​model considers multiple factors such as the order probability, the price of the product, the attributes of the customer, and the schedule of the sales representative to propose an optimal appointment. This allows the proposal unit to set appointments efficiently and effectively and maximize the results of sales activities. Furthermore, the proposal unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposal content. For example, the proposal algorithm is adjusted based on the results of the appointment and the customer's reaction, and more accurate proposals are made. Furthermore, the proposal unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using email, SMS, telephone, etc. in combination. This allows the proposal unit to quickly and reliably propose appointments to the user, improving the efficiency and effectiveness of sales activities.

[0033] The suggestion unit may include a setting unit that sets the appointment proposed by the suggestion unit. For example, the setting unit automatically sets the date, time, location, purpose, etc. of the proposed appointment. The setting unit may also add the proposed appointment to a calendar and send a notification. For example, the setting unit automatically adds the date and time of the proposed appointment to a calendar and sends a notification. Furthermore, the setting unit may also automatically set the location of the proposed appointment and provide map information. For example, the setting unit automatically sets the location of the proposed appointment and provides map information. This allows the proposed appointment to be automatically set. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit may set the appointment using an AI model that inputs information of the appointment proposed by the suggestion unit and outputs the setting of the appointment.

[0034] The suggestion unit can suggest appointments to customers in the order of the order probability. For example, the suggestion unit suggests to a customer with a high order probability that an appointment be set as soon as possible. The suggestion unit can also preferentially suggest appointments to customers with a high order probability. For example, the suggestion unit preferentially suggests appointments to customers with a high order probability. This makes it possible to preferentially suggest appointments to customers with a high order probability. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can suggest appointments using an AI model that inputs information about customers with a high order probability and outputs appointments.

[0035] The proposal unit may include a determination processing unit that determines the priority of sales based on the order probability, the price of the product, the attributes of the customer, the sales target of the sales representative, and the schedule of the sales representative. For example, the determination processing unit preferentially proposes an appointment to a customer with a high order probability and a high price of the product. The determination processing unit may also propose an optimal appointment based on the attributes of the customer and the schedule of the sales representative. For example, the determination processing unit proposes an optimal appointment based on the attributes of the customer and the schedule of the sales representative. This makes it possible to determine the priority of sales and propose an optimal appointment. A part or all of the above-mentioned processing in the determination processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination processing unit may determine the priority of sales using an AI model that inputs information on the order probability, the price of the product, the attributes of the customer, the sales target of the sales representative, and the schedule of the sales representative, and outputs the priority of sales.

[0036] The suggestion unit may include an estimation processing unit that estimates the timing when the likelihood of receiving an order will increase based on the order probability, the price of the product, and the attributes of the customer. For example, if a customer has a tendency to be positive about receiving an order at a specific time, the estimation processing unit suggests an appointment at that time. The estimation processing unit may also estimate the timing when the likelihood of receiving an order will increase based on the price of the product and the attributes of the customer. This makes it possible to estimate the timing when the likelihood of receiving an order will increase and propose an appointment at that timing. A part or all of the above-mentioned processing in the estimation processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation processing unit may estimate the timing of the expected order using an AI model that receives information on the order probability, the price of the product, and the attributes of the customer and outputs the timing of the expected order.

[0037] The analysis unit may include an evaluation unit that evaluates the sales representative based on the order probability predicted by the analysis unit. For example, the evaluation unit highly evaluates a sales representative who makes an effective approach to a customer with a high order probability. The evaluation unit may also evaluate a sales representative who avoids a wasteful approach to a customer with a low order probability. This makes it possible to evaluate the performance of the sales representative and provide appropriate feedback. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit may evaluate the sales representative using an AI model that receives information on the order probability predicted by the analysis unit as input and outputs an evaluation of the sales representative.

[0038] The collection unit can analyze the customer's past behavior history and select the optimal data collection method. For example, the collection unit preferentially uses a data collection method that the customer has frequently used in the past. The collection unit can also select the most effective data collection method from the customer's past behavior history. Furthermore, the collection unit can analyze the customer's behavior pattern and collect data at the optimal timing. This makes it possible to select the optimal data collection method based on the customer's past behavior history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can select the data collection method using an AI model that inputs the customer's past behavior history and outputs the optimal data collection method.

[0039] The collection unit can perform filtering based on the customer's current project or field of interest when collecting data. For example, the collection unit preferentially collects data related to the project the customer is currently working on. The collection unit can also collect highly relevant data based on the customer's field of interest. Furthermore, the collection unit can filter and collect necessary data according to the progress of the customer's current project. This makes it possible to collect highly relevant data based on the customer's current project or field of interest. 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 collect data using an AI model that inputs information on the customer's current project or field of interest and outputs filtered data.

[0040] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the customer when collecting data. For example, the collection unit preferentially collects data related to a region based on the location of the customer. The collection unit can also select an optimal data collection method in consideration of the geographical location information of the customer. Furthermore, the collection unit can also preferentially collect market data related to the location of the customer. This makes it possible to collect highly relevant data based on the geographical location information of the customer. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that inputs the geographical location information of the customer and outputs highly relevant data.

[0041] The collection unit can analyze the social media activity of the customer at the time of data collection and collect related data. For example, the collection unit collects data related to topics of interest from the social media activity of the customer. The collection unit can also analyze the customer's comments on social media and collect related data. Furthermore, the collection unit can select the optimal timing for data collection based on the frequency of the customer's social media activity. This makes it possible to collect related data based on the customer's social media activity. 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 collect data using an AI model that receives information on the customer's social media activity as input and outputs related data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This makes it possible to adjust the level of detail of the analysis according to the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that inputs the importance of the data and outputs the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice data. This makes it possible to apply an optimal analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can apply an analysis algorithm using an AI model that inputs the category of data and outputs an optimal analysis algorithm.

[0044] The analysis unit can determine the priority of the analysis based on the submission time of the data during the analysis. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also postpone the analysis of data submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the submission time. This makes it possible to determine the priority of the analysis based on the submission time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can determine the priority of the analysis using an AI model that inputs the submission time of the data and outputs the priority of the analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can optimally allocate analysis resources based on the relevance of data. This makes it possible to adjust the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the order of analysis using an AI model that inputs the relevance of data and outputs the order of analysis.

[0046] The proposal unit can adjust the level of detail of the proposal based on the degree of order probability at the time of proposal. For example, when the order probability is high, the proposal unit makes a detailed proposal. Also, when the order probability is low, the proposal unit can make a simplified proposal. Furthermore, the proposal unit can optimally allocate resources of the proposal according to the order probability. This makes it possible to adjust the level of detail of the proposal according to the order probability. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or may be performed without using AI. For example, the proposal unit can adjust the level of detail of the proposal using an AI model that inputs the order probability and outputs the level of detail of the proposal.

[0047] The proposal unit can apply different proposal algorithms depending on the attributes of the customer when making a proposal. For example, the proposal unit applies a first proposal algorithm to a new customer. The proposal unit can also apply a repeat proposal algorithm to a continuing customer. Furthermore, the proposal unit can also apply a special proposal algorithm to a large-scale customer. This makes it possible to apply an optimal proposal algorithm depending on the attributes of the customer. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can apply a proposal algorithm using an AI model that inputs customer attributes and outputs an optimal proposal algorithm.

[0048] The suggestion unit can determine the priority of the proposal based on the past reactions of the customer at the time of the proposal. For example, the suggestion unit prioritizes the proposal to customers who have responded favorably in the past. The suggestion unit can also postpone the proposal to customers who have responded negatively in the past. Furthermore, the suggestion unit can optimally allocate resources for the proposal based on the past reactions of the customer. This makes it possible to determine the priority of the proposal based on the past reactions of the customer. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can determine the priority of the proposal using an AI model that inputs the past reactions of the customer and outputs the priority of the proposal.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the customer at the time of proposal. For example, the proposal unit prioritizes proposals to customers with high relevance. The proposal unit can also postpone proposals to customers with low relevance. Furthermore, the proposal unit can optimally allocate proposal resources based on the relevance of the customer. This makes it possible to adjust the order of proposals based on the relevance of the customer. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can adjust the order of proposals using an AI model that inputs the relevance of the customer and outputs the order of proposals.

[0050] The setting unit can select the optimal setting method by referring to the past schedule history of the customer when setting an appointment. The setting unit can select the optimal setting method by referring to the past schedule history of the customer when setting an appointment. For example, the setting unit can suggest the optimal appointment time from the past schedule history of the customer. The setting unit can also preferentially suggest a time period that the customer has set in the past. Furthermore, the setting unit can analyze the schedule history of the customer and select the most efficient appointment setting method. This makes it possible to select the optimal appointment setting method based on the past schedule history of the customer. A part or all of the above-mentioned processing in the setting unit can be performed, for example, using AI or can be performed without using AI. For example, the setting unit can select the appointment setting method using an AI model that inputs the past schedule history of the customer and outputs the optimal appointment setting method.

[0051] The setting unit can select the optimal setting method in consideration of the geographical location information of the customer when setting an appointment. For example, the setting unit suggests an optimal appointment location based on the location of the customer. The setting unit can also suggest an optimal appointment time in consideration of the geographical location information of the customer. Furthermore, the setting unit can select the optimal appointment setting method in consideration of a schedule related to the location of the customer. In this way, the optimal appointment setting method can be selected based on the geographical location information of the customer. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit can select the appointment setting method using an AI model that inputs the geographical location information of the customer and outputs the optimal appointment setting method.

[0052] The determination processing unit can select the optimal determination method by referring to the customer's past behavior history at the time of determination. For example, the determination processing unit determines the optimal sales priority from the customer's past behavior history. The determination processing unit can also preferentially determine sales to which the customer has responded favorably in the past. Furthermore, the determination processing unit can analyze the customer's behavior history and determine the most effective sales priority. This makes it possible to determine the optimal sales priority based on the customer's past behavior history. A part or all of the above-mentioned processing in the determination processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination processing unit can determine the priority of a sales using an AI model that inputs the customer's past behavior history and outputs the optimal sales priority.

[0053] The determination processing unit can select the optimal determination method by considering the geographical location information of the customer at the time of determination. For example, the determination processing unit determines the optimal sales priority based on the location of the customer. The determination processing unit can also determine the optimal sales priority by considering the geographical location information of the customer. Furthermore, the determination processing unit can also determine the optimal sales priority by considering the behavioral history related to the location of the customer. In this way, the optimal sales priority can be determined based on the geographical location information of the customer. A part or all of the above-mentioned processing in the determination processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination processing unit can determine the priority of the sales using an AI model that inputs the geographical location information of the customer and outputs the optimal sales priority.

[0054] The estimation processing unit can select an optimal estimation method by referring to the customer's past behavior history at the time of estimation. For example, the estimation processing unit estimates the optimal expected timing of order receipt from the customer's past behavior history. The estimation processing unit can also preferentially estimate the timing at which the customer showed a favorable response in the past. Furthermore, the estimation processing unit can analyze the customer's behavior history and estimate the most effective expected timing of order receipt. This makes it possible to estimate the optimal expected timing of order receipt based on the customer's past behavior history. A part or all of the above-mentioned processing in the estimation processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation processing unit can estimate the expected timing of order receipt using an AI model that inputs the customer's past behavior history and outputs the optimal expected timing of order receipt.

[0055] The estimation processing unit can select an optimal estimation method by considering the geographical location information of the customer at the time of estimation. For example, the estimation processing unit estimates the optimal expected timing of order receipt based on the location of the customer. The estimation processing unit can also estimate the optimal expected timing of order receipt by considering the geographical location information of the customer. Furthermore, the estimation processing unit can also estimate the optimal expected timing of order receipt by considering the behavior history related to the location of the customer. In this way, it is possible to estimate the optimal expected timing of order receipt based on the geographical location information of the customer. A part or all of the above-mentioned processing in the estimation processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation processing unit can estimate the expected timing of order receipt using an AI model that inputs the geographical location information of the customer and outputs the optimal expected timing of order receipt.

[0056] The evaluation unit can select the optimal evaluation method by referring to the sales representative's past performance history at the time of evaluation. For example, the evaluation unit selects the optimal evaluation criteria from the sales representative's past performance history. The evaluation unit can also preferentially evaluate sales to which the sales representative has responded favorably in the past. Furthermore, the evaluation unit can analyze the performance history of the sales representative and select the most effective evaluation criteria. This makes it possible to select the optimal evaluation criteria based on the sales representative's past performance history. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit can select the evaluation criteria using an AI model that inputs the sales representative's past performance history and outputs the optimal evaluation criteria.

[0057] The evaluation unit can select the optimal evaluation method by considering the geographic location information of the sales representative at the time of evaluation. For example, the evaluation unit selects the optimal evaluation criteria based on the location of the sales representative. The evaluation unit can also select the optimal evaluation criteria by considering the geographic location information of the sales representative. Furthermore, the evaluation unit can also select the optimal evaluation criteria by considering the performance history related to the location of the sales representative. This makes it possible to select the optimal evaluation criteria based on the geographic location information of the sales representative. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit can select the evaluation criteria using an AI model that inputs the geographic location information of the sales representative and outputs the optimal evaluation criteria.

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

[0059] The collection department monitors the social media activities of customers in real time, and can immediately grasp the interests and trends of customers. For example, the collection department collects information immediately when a customer mentions a specific product or service. The collection department can also analyze the number of followers and engagement rate of customers on social media to identify influential customers. Furthermore, the collection department can grasp the interests of customers in detail based on the frequency of their social media activities and the contents of their posts. This makes it possible to develop more effective sales strategies based on the social media activities of customers.

[0060] The analysis unit can predict a customer's purchasing pattern based on the customer's purchasing history and identify products that are likely to be purchased next. For example, the analysis unit can analyze data on products and services purchased in the past by the customer and suggest similar products. The analysis unit can also analyze the customer's purchasing frequency and purchase timing and predict the timing of the next purchase. Furthermore, the analysis unit can grasp the customer's preferences and needs in detail based on the customer's purchasing history and make personalized suggestions. This makes it possible to develop a more effective sales strategy based on the customer's purchasing history.

[0061] The suggestion unit can analyze past feedback from customers and make suggestions to improve customer satisfaction. For example, the suggestion unit can analyze feedback provided by customers in the past and identify areas for improvement. The suggestion unit can also make suggestions for improving services and products based on customer feedback. Furthermore, the suggestion unit can make suggestions based on customer feedback in accordance with customer needs and expectations. This makes it possible to make suggestions to improve customer satisfaction.

[0062] The proposal unit can make proposals according to the characteristics of each region, taking into account the geographical location information of the customer. For example, the proposal unit can make proposals that reflect the needs and trends specific to each region, based on the location of the customer. The proposal unit can also analyze market data for each region and make optimal proposals. Furthermore, the proposal unit can make proposals that take into account the competitive situation for each region, based on the geographical location information of the customer. This makes it possible to make more effective proposals based on the geographical location information of the customer.

[0063] The suggestion unit can make suggestions according to the customer's lifestyle and preferences based on the customer's past purchasing history. For example, the suggestion unit can analyze data on products and services purchased by the customer in the past and make suggestions that suit the customer's lifestyle. The suggestion unit can also make personalized suggestions based on the customer's preferences and interests. Furthermore, the suggestion unit can make suggestions according to the customer's life stage based on the customer's purchasing history. This makes it possible to make more effective suggestions according to the customer's lifestyle and preferences.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The collection unit collects customer data, conversation data, and emotion data. The customer data includes, for example, basic information of the customer, purchase history, inquiry history, etc. The conversation data includes, for example, telephone recording data, chat logs, etc. The emotion data includes, for example, voice tone analysis, facial expression recognition, etc. The collection unit obtains the customer's basic information from a database, automatically collects telephone recording data, captures the customer's facial expressions with a camera, and collects emotion data using facial expression recognition technology. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the likelihood of receiving an order. The analysis unit predicts the likelihood of receiving an order using a machine learning algorithm, analyzes conversation data using natural language processing technology, and evaluates the customer's interests and purchasing intent. Furthermore, the analysis unit analyzes the customer's emotional data using facial expression recognition technology, and predicts the likelihood of receiving an order. Step 3: The proposal unit proposes appointments based on the order probability predicted by the analysis unit. The proposal unit proposes appointments in descending order of order probability, determines sales priorities based on the order probability, product price, customer attributes, sales targets of sales representatives, and sales representative schedules, and proposes optimal appointments. Furthermore, it estimates the timing when the likelihood of receiving an order will increase based on the order probability, product price, and customer attributes, and proposes appointments at that timing.

[0066] (Example 2) The sales support and management system according to the embodiment of the present invention is a system that predicts the probability of receiving an order using customer data, conversation data, and emotion data, and proposes an appointment based on the prediction. This system collects customer data, conversation data, and emotion data, and predicts the probability of receiving an order by analyzing the collected data. Furthermore, the system proposes an appointment based on the predicted probability of receiving an order. This mechanism makes it possible to avoid wasteful approaches to customers with low probability of receiving an order, and to effectively approach customers with high probability of receiving an order. For example, data such as customer attribute information (new, continuing, size, location, etc.), conversation content, and the other party's reaction (facial expression, emotion) are collected. This data is collected by AI. Next, the collected data is analyzed to predict the probability of receiving an order. The AI ​​analyzes the collected data and predicts the customer's probability of receiving an order. For example, it is possible to determine how positive a customer is about receiving an order from the conversation content and the other party's reaction. Furthermore, the system proposes an appointment based on the predicted probability of receiving an order. The AI ​​proposes appointments in order of the customers with high probability of receiving an order. For example, it is possible to suggest that an appointment be set as soon as possible for customers with high probability of receiving an order. In addition, the priority of sales can be determined based on the probability of receiving an order, the price of the product, the attributes of the customer, the sales target of the sales representative, and the schedule of the sales representative. AI determines the priority of sales based on this information and proposes the optimal appointment. For example, it can preferentially propose an appointment to a customer with a high probability of receiving an order and a high price of the product. Furthermore, it can estimate the timing when the likelihood of receiving an order will increase based on the probability of receiving an order, the price of the product, and the attributes of the customer. AI estimates the timing when the likelihood of receiving an order will increase based on this information and proposes an appointment at that timing. For example, if a customer tends to be positive about receiving an order at a certain time, it can propose an appointment at that time. Finally, it is also possible to evaluate sales representatives based on the probability of receiving an order. AI evaluates the performance of sales representatives based on the probability of receiving an order and provides appropriate feedback. For example, it can highly evaluate sales representatives who have made effective approaches to customers with a high probability of receiving an order.This system is expected to improve the efficiency of sales activities and the success rate. Sales representatives can effectively approach customers who are likely to place an order, and avoid unnecessary approaches. It can also be used to evaluate sales representatives, which contributes to improving the quality of sales activities. This allows the sales support and management system to predict the success rate of an order using customer data, conversation data, and emotion data, and propose appointments based on that prediction.

[0067] The sales support and management system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer data, conversation data, and emotion data. The customer data includes, for example, basic information of the customer, purchase history, inquiry history, etc., but is not limited to such examples. The conversation data includes, for example, recorded telephone data, chat logs, etc., but is not limited to such examples. The emotion data includes, for example, voice tone analysis, facial expression recognition, etc., but is not limited to such examples. The collection unit, for example, acquires basic information of the customer from a database. The collection unit can also automatically collect recorded telephone data. Furthermore, the collection unit can also capture the facial expression of the customer with a camera and collect emotion data using facial expression recognition technology. The analysis unit analyzes the data collected by the collection unit and predicts the probability of receiving an order. The analysis unit predicts the probability of receiving an order of the customer, for example, using a machine learning algorithm. Furthermore, the analysis unit can also analyze the conversation data and predict the probability of receiving an order of the customer using natural language processing technology. Furthermore, the analysis unit can also analyze the emotion data of the customer and predict the probability of receiving an order using facial expression recognition technology. For example, the analysis unit uses a machine learning algorithm to predict the customer's order probability based on past purchase history and inquiry history. The natural language processing technology analyzes conversation data and evaluates the customer's interests and purchasing motivation. The facial expression recognition technology estimates emotions from the customer's facial expressions and predicts the order probability. The proposal unit proposes an appointment based on the order probability predicted by the analysis unit. The proposal unit proposes appointments, for example, in order of the customer with the highest order probability. The proposal unit can also determine the priority of sales based on the order probability, the price of the product, the customer's attributes, the sales target of the sales representative, and the schedule of the sales representative, and propose the optimal appointment. Furthermore, the proposal unit can estimate the timing when the order probability is high based on the order probability, the price of the product, and the customer's attributes, and propose an appointment at that timing. For example, the proposal unit suggests setting an appointment as soon as possible for a customer with a high order probability. The proposal unit preferentially proposes appointments for customers with a high order probability and a high price of the product.If a customer has a tendency to be positive about receiving an order at a particular time, the suggestion unit suggests an appointment at that time. As a result, the sales support and management system according to the embodiment can predict the order probability using customer data, conversation data, and emotion data, and suggest an appointment based on the prediction. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest an appointment using an AI model that inputs the order probability predicted by the analysis unit and outputs an appointment.

[0068] The collection unit collects customer data, conversation data, and emotion data. The customer data includes, for example, basic information of the customer, purchase history, inquiry history, etc., but is not limited to such examples. Specifically, the basic information of the customer includes personal information such as name, address, contact information, age, and gender. The purchase history includes details of products and services purchased in the past, purchase date and time, purchase amount, etc. The inquiry history includes the content, date and time, and response status of inquiries made by the customer in the past. These data are important for understanding the behavior and preferences of the customer. The conversation data includes, for example, telephone recording data, chat log, etc., but is not limited to such examples. The telephone recording data is used to record the content of the conversation between the customer and the sales representative and to analyze it later. The chat log is used to record the exchange in online chat or messaging app and to understand the customer's questions and requests. The emotion data includes, for example, voice tone analysis, facial expression recognition, etc., but is not limited to such examples. Voice tone analysis is a technology that analyzes the tone and pitch of the customer's voice to estimate the emotional state. Facial expression recognition is a technology that analyzes the facial expressions of customers captured by a camera and estimates emotions such as joy, anger, and sadness. The collection unit, for example, acquires basic information about the customer from a database. The collection unit can also automatically collect recorded telephone call data. Furthermore, the collection unit can capture the facial expressions of the customer with a camera and collect emotion data using facial expression recognition technology. This allows the collection unit to collect a wide range of data from various data sources and grasp the behavior and emotions of customers in detail. The collected data is stored in a central database and made accessible to the analysis unit and the proposal unit. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0069] The analysis unit analyzes the data collected by the collection unit and predicts the probability of receiving an order. The analysis unit predicts the probability of receiving an order from a customer, for example, using a machine learning algorithm. Specifically, the analysis unit learns the behavioral patterns and purchasing tendencies of customers based on past purchase histories and inquiry histories, and predicts the probability of receiving an order in the future. For example, the analysis unit predicts the probability that a customer who has purchased a specific product in the past will purchase the same product again, or the probability that a customer who has made a specific inquiry will subsequently purchase the product. The analysis unit can also analyze conversation data using natural language processing technology and predict the probability of receiving an order from a customer. The natural language processing technology analyzes the content of a conversation with a customer and evaluates the customer's interest and purchasing motivation. For example, if a customer frequently asks questions about a specific product, it is determined that the customer is highly interested in the product, and the probability of receiving an order is predicted to be high. Furthermore, the analysis unit can also analyze the customer's emotional data using facial expression recognition technology and predict the probability of receiving an order. The facial expression recognition technology estimates emotions from the customer's facial expression and evaluates the emotions the customer has toward the product. For example, if a customer has a happy expression when looking at a product, it is determined that the customer is highly motivated to purchase the product, and the probability of receiving an order is predicted to be high. This allows the analysis unit to analyze collected data from multiple angles and predict the likelihood of customer orders with high accuracy. Furthermore, the analysis unit can use past data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past purchase data, it can predict purchasing trends during specific seasons or events and develop future sales strategies. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0070] The proposal unit proposes an appointment based on the order probability predicted by the analysis unit. The proposal unit, for example, proposes an appointment in the order of the customer with the highest order probability. Specifically, the customers are prioritized based on the order probability predicted by the analysis unit, and appointments are set for customers with the highest order probability. The proposal unit can also determine the priority of sales based on the order probability, the price of the product, the customer's attributes, the sales target of the sales representative, and the sales representative's schedule, and propose the optimal appointment. For example, for a customer with a high order probability and a high price of the product, the proposal unit preferentially proposes an appointment and develops an optimal strategy for achieving the sales target of the sales representative. Furthermore, the proposal unit can estimate the timing when the order probability is high based on the order probability, the price of the product, and the customer's attributes, and propose an appointment at that timing. For example, if a customer has a tendency to be positive about orders at a specific time, the proposal unit proposes an appointment at that time. In this way, the proposal unit can determine the timing when the customer's purchasing motivation is high and support effective sales activities. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose an appointment using an AI model that inputs the order probability predicted by the analysis unit and outputs an appointment. Specifically, the AI ​​model considers multiple factors such as the order probability, the price of the product, the attributes of the customer, and the schedule of the sales representative to propose an optimal appointment. This allows the proposal unit to set appointments efficiently and effectively and maximize the results of sales activities. Furthermore, the proposal unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposal content. For example, the proposal algorithm is adjusted based on the results of the appointment and the customer's reaction, and more accurate proposals are made. Furthermore, the proposal unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using email, SMS, telephone, etc. in combination. This allows the proposal unit to quickly and reliably propose appointments to the user, improving the efficiency and effectiveness of sales activities.

[0071] The suggestion unit may include a setting unit that sets the appointment proposed by the suggestion unit. For example, the setting unit automatically sets the date, time, location, purpose, etc. of the proposed appointment. The setting unit may also add the proposed appointment to a calendar and send a notification. For example, the setting unit automatically adds the date and time of the proposed appointment to a calendar and sends a notification. Furthermore, the setting unit may also automatically set the location of the proposed appointment and provide map information. For example, the setting unit automatically sets the location of the proposed appointment and provides map information. This allows the proposed appointment to be automatically set. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit may set the appointment using an AI model that inputs information of the appointment proposed by the suggestion unit and outputs the setting of the appointment.

[0072] The suggestion unit can suggest appointments to customers in the order of the order probability. For example, the suggestion unit suggests to a customer with a high order probability that an appointment be set as soon as possible. The suggestion unit can also preferentially suggest appointments to customers with a high order probability. For example, the suggestion unit preferentially suggests appointments to customers with a high order probability. This makes it possible to preferentially suggest appointments to customers with a high order probability. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can suggest appointments using an AI model that inputs information about customers with a high order probability and outputs appointments.

[0073] The proposal unit may include a determination processing unit that determines the priority of sales based on the order probability, the price of the product, the attributes of the customer, the sales target of the sales representative, and the schedule of the sales representative. For example, the determination processing unit preferentially proposes an appointment to a customer with a high order probability and a high price of the product. The determination processing unit may also propose an optimal appointment based on the attributes of the customer and the schedule of the sales representative. For example, the determination processing unit proposes an optimal appointment based on the attributes of the customer and the schedule of the sales representative. This makes it possible to determine the priority of sales and propose an optimal appointment. A part or all of the above-mentioned processing in the determination processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination processing unit may determine the priority of sales using an AI model that inputs information on the order probability, the price of the product, the attributes of the customer, the sales target of the sales representative, and the schedule of the sales representative, and outputs the priority of sales.

[0074] The suggestion unit may include an estimation processing unit that estimates the timing when the likelihood of receiving an order will increase based on the order probability, the price of the product, and the attributes of the customer. For example, if a customer has a tendency to be positive about receiving an order at a specific time, the estimation processing unit suggests an appointment at that time. The estimation processing unit may also estimate the timing when the likelihood of receiving an order will increase based on the price of the product and the attributes of the customer. This makes it possible to estimate the timing when the likelihood of receiving an order will increase and propose an appointment at that timing. A part or all of the above-mentioned processing in the estimation processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation processing unit may estimate the timing of the expected order using an AI model that receives information on the order probability, the price of the product, and the attributes of the customer and outputs the timing of the expected order.

[0075] The analysis unit may include an evaluation unit that evaluates the sales representative based on the order probability predicted by the analysis unit. For example, the evaluation unit highly evaluates a sales representative who makes an effective approach to a customer with a high order probability. The evaluation unit may also evaluate a sales representative who avoids a wasteful approach to a customer with a low order probability. This makes it possible to evaluate the performance of the sales representative and provide appropriate feedback. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit may evaluate the sales representative using an AI model that receives information on the order probability predicted by the analysis unit as input and outputs an evaluation of the sales representative.

[0076] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection and collects data when the user is relaxed. Also, when the user is relaxed, the collection unit can increase the frequency of data collection and collect detailed data. Furthermore, when the user is in a hurry, the collection unit can collect necessary data in a short time. This makes it possible to adjust the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0077] The collection unit can analyze the customer's past behavior history and select the optimal data collection method. For example, the collection unit preferentially uses a data collection method that the customer has frequently used in the past. The collection unit can also select the most effective data collection method from the customer's past behavior history. Furthermore, the collection unit can analyze the customer's behavior pattern and collect data at the optimal timing. This makes it possible to select the optimal data collection method based on the customer's past behavior history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can select the data collection method using an AI model that inputs the customer's past behavior history and outputs the optimal data collection method.

[0078] The collection unit can perform filtering based on the customer's current project or field of interest when collecting data. For example, the collection unit preferentially collects data related to the project the customer is currently working on. The collection unit can also collect highly relevant data based on the customer's field of interest. Furthermore, the collection unit can filter and collect necessary data according to the progress of the customer's current project. This makes it possible to collect highly relevant data based on the customer's current project or field of interest. 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 collect data using an AI model that inputs information on the customer's current project or field of interest and outputs filtered data.

[0079] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects data with a high degree of importance. In addition, when the user is relaxed, the collection unit can also preferentially collect detailed data. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect data that can be collected quickly. This makes it possible to determine the priority of data to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0080] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the customer when collecting data. For example, the collection unit preferentially collects data related to a region based on the location of the customer. The collection unit can also select an optimal data collection method in consideration of the geographical location information of the customer. Furthermore, the collection unit can also preferentially collect market data related to the location of the customer. This makes it possible to collect highly relevant data based on the geographical location information of the customer. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that inputs the geographical location information of the customer and outputs highly relevant data.

[0081] The collection unit can analyze the social media activity of the customer at the time of data collection and collect related data. For example, the collection unit collects data related to topics of interest from the social media activity of the customer. The collection unit can also analyze the customer's comments on social media and collect related data. Furthermore, the collection unit can select the optimal timing for data collection based on the frequency of the customer's social media activity. This makes it possible to collect related data based on the customer's social media activity. 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 collect data using an AI model that receives information on the customer's social media activity as input and outputs related data.

[0082] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, when the user is stressed, the analysis unit uses a simplified analysis algorithm. Also, when the user is relaxed, the analysis unit can use a detailed analysis algorithm. Furthermore, when the user is in a hurry, the analysis unit can use an analysis algorithm for quickly producing results. This allows the analysis algorithm to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This makes it possible to adjust the level of detail of the analysis according to the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that inputs the importance of the data and outputs the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice data. This makes it possible to apply an optimal analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can apply an analysis algorithm using an AI model that inputs the category of data and outputs an optimal analysis algorithm.

[0085] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0086] The analysis unit can determine the priority of the analysis based on the submission time of the data during the analysis. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also postpone the analysis of data submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the submission time. This makes it possible to determine the priority of the analysis based on the submission time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can determine the priority of the analysis using an AI model that inputs the submission time of the data and outputs the priority of the analysis.

[0087] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can optimally allocate analysis resources based on the relevance of data. This makes it possible to adjust the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the order of analysis using an AI model that inputs the relevance of data and outputs the order of analysis.

[0088] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit makes a simple and easy-to-understand suggestion. In addition, when the user is relaxed, the suggestion unit can also make a suggestion including detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also make a suggestion that focuses on the main points. This makes it possible to adjust the way of expressing the suggestion according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute the emotion estimation.

[0089] The proposal unit can adjust the level of detail of the proposal based on the degree of order probability at the time of proposal. For example, when the order probability is high, the proposal unit makes a detailed proposal. Also, when the order probability is low, the proposal unit can make a simplified proposal. Furthermore, the proposal unit can optimally allocate resources of the proposal according to the order probability. This makes it possible to adjust the level of detail of the proposal according to the order probability. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or may be performed without using AI. For example, the proposal unit can adjust the level of detail of the proposal using an AI model that inputs the order probability and outputs the level of detail of the proposal.

[0090] The proposal unit can apply different proposal algorithms depending on the attributes of the customer when making a proposal. For example, the proposal unit applies a first proposal algorithm to a new customer. The proposal unit can also apply a repeat proposal algorithm to a continuing customer. Furthermore, the proposal unit can also apply a special proposal algorithm to a large-scale customer. This makes it possible to apply an optimal proposal algorithm depending on the attributes of the customer. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can apply a proposal algorithm using an AI model that inputs customer attributes and outputs an optimal proposal algorithm.

[0091] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit makes a short and to the point suggestion. Also, when the user is relaxed, the suggestion unit can make a longer suggestion including a detailed explanation. Furthermore, when the user is in a hurry, the suggestion unit can make a quick suggestion. This allows the length of the suggestion to be adjusted according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0092] The suggestion unit can determine the priority of the proposal based on the past reactions of the customer at the time of the proposal. For example, the suggestion unit prioritizes the proposal to customers who have responded favorably in the past. The suggestion unit can also postpone the proposal to customers who have responded negatively in the past. Furthermore, the suggestion unit can optimally allocate resources for the proposal based on the past reactions of the customer. This makes it possible to determine the priority of the proposal based on the past reactions of the customer. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can determine the priority of the proposal using an AI model that inputs the past reactions of the customer and outputs the priority of the proposal.

[0093] The proposal unit can adjust the order of proposals based on the relevance of the customer at the time of proposal. For example, the proposal unit prioritizes proposals to customers with high relevance. The proposal unit can also postpone proposals to customers with low relevance. Furthermore, the proposal unit can optimally allocate proposal resources based on the relevance of the customer. This makes it possible to adjust the order of proposals based on the relevance of the customer. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can adjust the order of proposals using an AI model that inputs the relevance of the customer and outputs the order of proposals.

[0094] The setting unit can estimate the user's emotion and adjust the appointment setting method based on the estimated user's emotion. For example, the setting unit provides a simple and quick appointment setting method when the user is stressed. The setting unit can also provide detailed setting options when the user is relaxed. Furthermore, the setting unit can set the appointment by prioritizing voice input when the user is in a hurry. This makes it possible to adjust the appointment setting method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the setting unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0095] The setting unit can select the optimal setting method by referring to the past schedule history of the customer when setting an appointment. The setting unit can select the optimal setting method by referring to the past schedule history of the customer when setting an appointment. For example, the setting unit can suggest the optimal appointment time from the past schedule history of the customer. The setting unit can also preferentially suggest a time period that the customer has set in the past. Furthermore, the setting unit can analyze the schedule history of the customer and select the most efficient appointment setting method. This makes it possible to select the optimal appointment setting method based on the past schedule history of the customer. A part or all of the above-mentioned processing in the setting unit can be performed, for example, using AI or can be performed without using AI. For example, the setting unit can select the appointment setting method using an AI model that inputs the past schedule history of the customer and outputs the optimal appointment setting method.

[0096] The setting unit can estimate the user's emotion and determine the priority of appointments based on the estimated user's emotion. For example, when the user is feeling stressed, the setting unit can prioritize appointments with high importance. In addition, when the user is relaxed, the setting unit can also prioritize detailed appointments. Furthermore, when the user is in a hurry, the setting unit can also prioritize appointments that can be set quickly. This makes it possible to determine the priority of appointments according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the setting unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0097] The setting unit can select the optimal setting method in consideration of the geographical location information of the customer when setting an appointment. For example, the setting unit suggests an optimal appointment location based on the location of the customer. The setting unit can also suggest an optimal appointment time in consideration of the geographical location information of the customer. Furthermore, the setting unit can select the optimal appointment setting method in consideration of a schedule related to the location of the customer. In this way, the optimal appointment setting method can be selected based on the geographical location information of the customer. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit can select the appointment setting method using an AI model that inputs the geographical location information of the customer and outputs the optimal appointment setting method.

[0098] The determination processing unit can estimate the user's emotions and adjust the criteria for determining the priority of sales based on the estimated user's emotions. In addition, the determination processing unit can also prioritize detailed sales when the user is relaxed. Furthermore, the determination processing unit can also prioritize sales that can be quickly determined when the user is in a hurry. This makes it possible to adjust the criteria for determining the priority of sales according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination processing unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0099] The determination processing unit can select the optimal determination method by referring to the customer's past behavior history at the time of determination. For example, the determination processing unit determines the optimal sales priority from the customer's past behavior history. The determination processing unit can also preferentially determine sales to which the customer has responded favorably in the past. Furthermore, the determination processing unit can analyze the customer's behavior history and determine the most effective sales priority. This makes it possible to determine the optimal sales priority based on the customer's past behavior history. A part or all of the above-mentioned processing in the determination processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination processing unit can determine the priority of a sales using an AI model that inputs the customer's past behavior history and outputs the optimal sales priority.

[0100] The determination processing unit can estimate the user's emotion and adjust the order of determining the priority of sales based on the estimated user's emotion. In addition, the determination processing unit can also prioritize detailed sales when the user is relaxed. Furthermore, the determination processing unit can also prioritize sales that can be quickly determined when the user is in a hurry. This makes it possible to adjust the order of determining the priority of sales according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination processing unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0101] The determination processing unit can select the optimal determination method by considering the geographical location information of the customer at the time of determination. For example, the determination processing unit determines the optimal sales priority based on the location of the customer. The determination processing unit can also determine the optimal sales priority by considering the geographical location information of the customer. Furthermore, the determination processing unit can also determine the optimal sales priority by considering the behavioral history related to the location of the customer. In this way, the optimal sales priority can be determined based on the geographical location information of the customer. A part or all of the above-mentioned processing in the determination processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination processing unit can determine the priority of the sales using an AI model that inputs the geographical location information of the customer and outputs the optimal sales priority.

[0102] The estimation processing unit can estimate the user's emotion and adjust the method of estimating the timing when the order likelihood increases based on the estimated user's emotion. For example, when the user is stressed, the estimation processing unit estimates the order likelihood at a timing when the user is relaxed. In addition, when the user is relaxed, the estimation processing unit can also estimate the order likelihood based on detailed data. Furthermore, when the user is in a hurry, the estimation processing unit can quickly estimate the order likelihood. This makes it possible to adjust the method of estimating the timing when the order likelihood increases according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation processing unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0103] The estimation processing unit can select an optimal estimation method by referring to the customer's past behavior history at the time of estimation. For example, the estimation processing unit estimates the optimal expected timing of order receipt from the customer's past behavior history. The estimation processing unit can also preferentially estimate the timing at which the customer showed a favorable response in the past. Furthermore, the estimation processing unit can analyze the customer's behavior history and estimate the most effective expected timing of order receipt. This makes it possible to estimate the optimal expected timing of order receipt based on the customer's past behavior history. A part or all of the above-mentioned processing in the estimation processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation processing unit can estimate the expected timing of order receipt using an AI model that inputs the customer's past behavior history and outputs the optimal expected timing of order receipt.

[0104] The estimation processing unit can estimate the user's emotion and adjust the order of estimating the timing when the order likelihood increases based on the estimated user's emotion. For example, when the user is stressed, the estimation processing unit preferentially estimates the order likelihood with a high degree of importance. In addition, when the user is relaxed, the estimation processing unit can also preferentially estimate the order likelihood that can be quickly estimated when the user is in a hurry. This makes it possible to adjust the order of estimating the timing when the order likelihood increases according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation processing unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0105] The estimation processing unit can select an optimal estimation method by considering the geographical location information of the customer at the time of estimation. For example, the estimation processing unit estimates the optimal expected timing of order receipt based on the location of the customer. The estimation processing unit can also estimate the optimal expected timing of order receipt by considering the geographical location information of the customer. Furthermore, the estimation processing unit can also estimate the optimal expected timing of order receipt by considering the behavior history related to the location of the customer. In this way, it is possible to estimate the optimal expected timing of order receipt based on the geographical location information of the customer. A part or all of the above-mentioned processing in the estimation processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation processing unit can estimate the expected timing of order receipt using an AI model that inputs the geographical location information of the customer and outputs the optimal expected timing of order receipt.

[0106] The evaluation unit can estimate the user's emotion and adjust the evaluation criteria of the salesperson based on the estimated user's emotion. For example, when the user is stressed, the evaluation unit prioritizes evaluation of sales with high importance. In addition, when the user is relaxed, the evaluation unit can also prioritize evaluation of detailed sales. Furthermore, when the user is in a hurry, the evaluation unit can also prioritize evaluation of sales that can be quickly evaluated. This makes it possible to adjust the evaluation criteria of the salesperson according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0107] The evaluation unit can select the optimal evaluation method by referring to the sales representative's past performance history at the time of evaluation. For example, the evaluation unit selects the optimal evaluation criteria from the sales representative's past performance history. The evaluation unit can also preferentially evaluate sales to which the sales representative has responded favorably in the past. Furthermore, the evaluation unit can analyze the performance history of the sales representative and select the most effective evaluation criteria. This makes it possible to select the optimal evaluation criteria based on the sales representative's past performance history. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit can select the evaluation criteria using an AI model that inputs the sales representative's past performance history and outputs the optimal evaluation criteria.

[0108] The evaluation unit can estimate the user's emotion and determine the priority of the salesperson's evaluation based on the estimated user's emotion. For example, when the user is stressed, the evaluation unit prioritizes evaluation of sales with high importance. In addition, when the user is relaxed, the evaluation unit can also prioritize evaluation of detailed sales. Furthermore, when the user is in a hurry, the evaluation unit can also prioritize evaluation of sales that can be quickly evaluated. This makes it possible to determine the priority of the salesperson's evaluation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0109] The evaluation unit can select the optimal evaluation method by considering the geographic location information of the sales representative at the time of evaluation. For example, the evaluation unit selects the optimal evaluation criteria based on the location of the sales representative. The evaluation unit can also select the optimal evaluation criteria by considering the geographic location information of the sales representative. Furthermore, the evaluation unit can also select the optimal evaluation criteria by considering the performance history related to the location of the sales representative. This makes it possible to select the optimal evaluation criteria based on the geographic location information of the sales representative. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit can select the evaluation criteria using an AI model that inputs the geographic location information of the sales representative and outputs the optimal evaluation criteria.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The collection department monitors the social media activities of customers in real time, and can immediately grasp the interests and trends of customers. For example, the collection department collects information immediately when a customer mentions a specific product or service. The collection department can also analyze the number of followers and engagement rate of customers on social media to identify influential customers. Furthermore, the collection department can grasp the interests of customers in detail based on the frequency of their social media activities and the contents of their posts. This makes it possible to develop more effective sales strategies based on the social media activities of customers.

[0112] The analysis unit can predict a customer's purchasing pattern based on the customer's purchasing history and identify products that are likely to be purchased next. For example, the analysis unit can analyze data on products and services purchased in the past by the customer and suggest similar products. The analysis unit can also analyze the customer's purchasing frequency and purchase timing and predict the timing of the next purchase. Furthermore, the analysis unit can grasp the customer's preferences and needs in detail based on the customer's purchasing history and make personalized suggestions. This makes it possible to develop a more effective sales strategy based on the customer's purchasing history.

[0113] The suggestion unit can analyze past feedback from customers and make suggestions to improve customer satisfaction. For example, the suggestion unit can analyze feedback provided by customers in the past and identify areas for improvement. The suggestion unit can also make suggestions for improving services and products based on customer feedback. Furthermore, the suggestion unit can make suggestions based on customer feedback in accordance with customer needs and expectations. This makes it possible to make suggestions to improve customer satisfaction.

[0114] The proposal unit can make proposals according to the characteristics of each region, taking into account the geographical location information of the customer. For example, the proposal unit can make proposals that reflect the needs and trends specific to each region, based on the location of the customer. The proposal unit can also analyze market data for each region and make optimal proposals. Furthermore, the proposal unit can make proposals that take into account the competitive situation for each region, based on the geographical location information of the customer. This makes it possible to make more effective proposals based on the geographical location information of the customer.

[0115] The suggestion unit can make suggestions according to the customer's lifestyle and preferences based on the customer's past purchasing history. For example, the suggestion unit can analyze data on products and services purchased by the customer in the past and make suggestions that suit the customer's lifestyle. The suggestion unit can also make personalized suggestions based on the customer's preferences and interests. Furthermore, the suggestion unit can make suggestions according to the customer's life stage based on the customer's purchasing history. This makes it possible to make more effective suggestions according to the customer's lifestyle and preferences.

[0116] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the display method of the analysis results can be adjusted according to the user's emotions.

[0117] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand suggestion can be made. If the user is relaxed, a suggestion including detailed information can be made. Furthermore, if the user is in a hurry, a suggestion that focuses on the main points can be made. In this way, the way in which suggestions are expressed can be adjusted according to the user's emotions.

[0118] The setting unit can estimate the user's emotion and adjust the appointment setting method based on the estimated user's emotion. For example, if the user is stressed, a simple and quick appointment setting method can be provided. Also, if the user is relaxed, detailed setting options can be provided. Furthermore, if the user is in a hurry, the appointment can be set by prioritizing voice input. In this way, the appointment setting method can be adjusted according to the user's emotion.

[0119] The determination processing unit can estimate the user's emotions and adjust the criteria for determining the priority of sales based on the estimated user's emotions. For example, if the user is stressed, sales with high importance can be prioritized. Also, if the user is relaxed, detailed sales can be prioritized. Furthermore, if the user is in a hurry, sales that can be quickly determined can be prioritized. In this way, the criteria for determining the priority of sales can be adjusted according to the user's emotions.

[0120] The estimation processing unit can estimate the user's emotions and adjust the method of estimating the timing when the likelihood of receiving an order will increase based on the estimated user's emotions. For example, if the user is feeling stressed, the likelihood of receiving an order is estimated when the user is relaxed. Also, if the user is relaxed, the likelihood of receiving an order can be estimated based on detailed data. Furthermore, if the user is in a hurry, the likelihood of receiving an order can be estimated quickly. This makes it possible to adjust the method of estimating the timing when the likelihood of receiving an order will increase according to the user's emotions.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The collection unit collects customer data, conversation data, and emotion data. The customer data includes, for example, basic information of the customer, purchase history, inquiry history, etc. The conversation data includes, for example, telephone recording data, chat logs, etc. The emotion data includes, for example, voice tone analysis, facial expression recognition, etc. The collection unit obtains the customer's basic information from a database, automatically collects telephone recording data, captures the customer's facial expressions with a camera, and collects emotion data using facial expression recognition technology. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the likelihood of receiving an order. The analysis unit predicts the likelihood of receiving an order using a machine learning algorithm, analyzes conversation data using natural language processing technology, and evaluates the customer's interests and purchasing intent. Furthermore, the analysis unit analyzes the customer's emotional data using facial expression recognition technology, and predicts the likelihood of receiving an order. Step 3: The proposal unit proposes appointments based on the order probability predicted by the analysis unit. The proposal unit proposes appointments in descending order of order probability, determines sales priorities based on the order probability, product price, customer attributes, sales targets of sales representatives, and sales representative schedules, and proposes optimal appointments. Furthermore, it estimates the timing when the likelihood of receiving an order will increase based on the order probability, product price, and customer attributes, and proposes appointments at that timing.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0124] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0125] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] For example, the collection unit can collect facial expressions and voices of customers using the camera 42 and microphone 38B of the smart device 14, and transmit them to the data processing device 12 by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict the probability of receiving an order. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes an appointment based on the predicted probability of receiving an order. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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, the RAM 30, and the 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.

[0130] 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.

[0131] 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 the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] 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 performed in a secure state.

[0134] Fig. 4 shows an example of 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.

[0135] 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.

[0136] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0140] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] For example, the collection unit can collect facial expressions and voices of customers using the camera 42 and microphone 238 of the smart glasses 214, and transmit them to the data processing device 12 by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict the probability of receiving an order. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes an appointment based on the predicted probability of receiving an order. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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, the RAM 30, and the 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.

[0146] 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.

[0147] 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 the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0149] 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 performed in a secure state.

[0150] 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.

[0151] 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.

[0152] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0154] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] 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 voice indicating a user input for 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.

[0156] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] For example, the collection unit can collect facial expressions and voices of customers using the camera 42 and microphone 238 of the headset terminal 314, and transmit them to the data processing device 12 by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict the probability of receiving an order. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes an appointment based on the predicted probability of receiving an order. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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, the RAM 30, and the 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.

[0162] 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.

[0163] 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 the voice according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0165] 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 performed in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0167] 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.

[0168] 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.

[0169] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0171] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0172] 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 a voice indicating a user input for 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.

[0173] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0174] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0175] For example, the collection unit can collect facial expressions and voices of customers using the camera 42 and microphone 238 of the robot 414, and transmit them to the data processing device 12 by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict the probability of receiving an order. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes an appointment based on the predicted probability of receiving an order. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0177] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0179] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0181] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0183] 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.

[0184] 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 Universal Serial Bus (USB) 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.

[0185] In addition, 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 upon request from the data processing device 12.

[0186] 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.

[0187] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0188] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0189] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0190] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0192] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0194] (Appendix 1) A collection unit that collects customer data, conversation data, and emotion data; an analysis unit that analyzes the data collected by the collection unit and predicts an order probability; A proposal unit that proposes an appointment based on the order probability predicted by the analysis unit. A system characterized by: (Appendix 2) a setting unit for setting the appointment proposed by the suggestion unit; 2. The system of claim 1, (Appendix 3) The suggestion unit, Specify specific criteria for proposing the above appointments to customers in order of the probability of receiving an order. 2. The system of claim 1, (Appendix 4) The suggestion unit, a determination processing unit that indicates specific criteria for determining the priority of sales based on the probability of receiving an order, the price of a commodity, the attributes of a customer, the sales target of a sales representative, and the schedule of the sales representative; The determination processing unit proposes the appointment based on the determination result. 2. The system of claim 1, (Appendix 5) The suggestion unit, an estimation processing unit that estimates a timing for indicating a specific criterion for increasing the likelihood of receiving an order based on the order probability, the price of the product, and the attributes of the customer; The estimation processing unit proposes the appointment at the estimated timing. 2. The system of claim 1, (Appendix 6) and an evaluation unit that evaluates salespeople based on the order probability predicted by the analysis unit. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze customer past behavior and choose the best data collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: Filter data based on the customer's current projects and interests 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: Considering the geographic location of customers when collecting data, prioritize collecting the most relevant data 2. The system of claim 1, (Appendix 12) The collecting unit includes: Analyze your customers' social media activity during data collection and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is Adjust the level of analysis detail based on the importance of the data during analysis 2. The system of claim 1, (Appendix 15) The analysis unit is Apply different analysis algorithms depending on the category of data during analysis 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is Prioritize analyses based on when data are submitted 2. The system of claim 1, (Appendix 18) The analysis unit is Adjust the order of analysis based on data relevance during analysis 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, Adjust the details of the proposal based on the probability of winning 2. The system of claim 1, (Appendix 21) The suggestion unit, Apply different proposal algorithms depending on customer attributes when making proposals 2. The system of claim 1, (Appendix 22) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The suggestion unit, Prioritize proposals based on past customer responses when making proposals 2. The system of claim 1, (Appendix 24) The suggestion unit, Adjust the order of suggestions based on customer relevance at suggestion time 2. The system of claim 1, (Appendix 25) The setting unit is Inferring a user's emotion and adjusting an appointment setting method based on the inferred user's emotion 3. The system of claim 2, (Appendix 26) The setting unit is When setting appointments, refer to the customer's past schedule history to select the optimal setting method 3. The system of claim 2, (Appendix 27) The setting unit is To estimate a user's emotion and prioritize appointments based on the estimated user's emotion. 3. The system of claim 2, (Appendix 28) The setting unit is Consider the geographic location of the customer when setting up an appointment to determine the best method 3. The system of claim 2, (Appendix 29) The determination processing unit: Inferring user emotions and adjusting criteria for determining sales priorities based on the estimated user emotions 5. The system of claim 4. (Appendix 30) The determination processing unit: When making a judgment, the optimal judgment method is selected by referring to the customer's past behavioral history. 5. The system of claim 4. (Appendix 31) The determination processing unit: Inferring user emotions and adjusting the order of determining sales priorities based on the estimated user emotions 5. The system of claim 4. (Appendix 32) The determination processing unit: Select the optimal judgment method by considering the customer's geographic location information when making judgments. 5. The system of claim 4. (Appendix 33) The estimation processing unit: To estimate a user's emotion and adjust a method for estimating a timing when an order is likely to increase based on the estimated user's emotion. 6. The system of claim 5, (Appendix 34) The estimation processing unit: When making an estimation, the optimal estimation method is selected by referring to the customer's past behavior history. 6. The system of claim 5, (Appendix 35) The estimation processing unit: To estimate a user's emotion and adjust an order for estimating a timing when an order is likely to increase based on the estimated user's emotion. 6. The system of claim 5, (Appendix 36) The estimation processing unit: Select the optimal estimation method by taking into account the customer's geographic location information during estimation 6. The system of claim 5, (Appendix 37) The evaluation unit is Infer user sentiment and adjust sales representatives' evaluation criteria based on the inferred user sentiment 7. The system of claim 6, (Appendix 38) The evaluation unit is Use salespeople's past performance history during evaluation to determine the best evaluation method 7. The system of claim 6, (Appendix 39) The evaluation unit is Infer user sentiment and prioritize salespeople's ratings based on the inferred user sentiment 7. The system of claim 6, (Appendix 40) The evaluation unit is Consider the geographic location of salespeople during evaluation to determine the optimal evaluation method 7. The system of claim 6, [Explanation of symbols]

[0195] 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 customer data, conversation data, and emotion data; an analysis unit that analyzes the data collected by the collection unit and predicts an order probability; A proposal unit that proposes an appointment based on the order probability predicted by the analysis unit. A system characterized by:

2. a setting unit for setting the appointment proposed by the suggestion unit; 2. The system of claim 1 .

3. The suggestion unit, A specific criterion for proposing the appointment to customers in order of the order probability is specified.

2. The system of claim 1 .

4. The suggestion unit, a determination processing unit that indicates specific criteria for determining the priority of sales based on the order probability, the price of the product, the attributes of the customer, the sales target of the sales representative, and the schedule of the sales representative; The determination processing unit proposes the appointment based on a result of the determination.

2. The system of claim 1 .

5. The suggestion unit, an estimation processing unit that estimates a timing for indicating a specific criterion for increasing the likelihood of receiving an order based on the order probability, a price of a commodity, and an attribute of a customer; The estimation processing unit proposes the appointment at the estimated timing.

2. The system of claim 1 .

6. and an evaluation unit that evaluates salespeople based on the order probability predicted by the analysis unit.

2. The system of claim 1 .

7. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

8. The collecting unit includes: Analyze customer past behavior and choose the best data collection method 2. The system of claim 1 .

9. The collecting unit includes: Filter data based on the customer's current projects and interests 2. The system of claim 1 .

10. The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1 .

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