system

Abstract

We provide the system. [Solution] Means of aggregating information, A means of integrating aggregated information and evaluating the probability of a commercial transaction with a potential buyer, A means of creating and presenting a list of potential buyers based on the evaluation results, A means of providing individually tailored product proposals, A system that includes this.

Description

Technical Field

[0001] The technology of this disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is difficult for the sales team to effectively identify leads that are likely to lead to a deal from a large amount of customer data, and it is also required to quickly formulate an optimal approach based on customer needs. Furthermore, it takes a lot of time to determine the actions to be taken at each stage of the negotiation, which is a factor reducing the success rate of closing a deal. In such a situation, it is necessary to develop a system for improving sales efficiency.

Means for Solving the Problems

[0005] This invention provides a system for collecting and integrating market data, social network data, and customer relationship management system data. This makes it possible to evaluate the likelihood of a potential customer closing a deal and display it as a list. It also includes means for estimating customer needs by analyzing past purchase history and market trend data, and enables sales teams to quickly implement effective strategies by monitoring progress at each stage of the closing process and suggesting the next actions based on past success stories.

[0006] "Market data" refers to data that includes market trends, competitive landscape, and related industry information.

[0007] "Social network data" refers to data that includes user behavior, interests, comments, and sharing information on social media platforms.

[0008] "Customer relationship management system data" refers to data collected to manage customer interactions, purchase history, inquiry history, contract information, and other related information.

[0009] A "potential customer" is an individual or company that does not currently have a business relationship with us but may purchase our products or services in the future.

[0010] "Closing probability" is an indicator that shows the degree to which a customer or lead is likely to close a deal and sign a contract.

[0011] "Past purchase history" refers to records of products and services that a customer has purchased in the past.

[0012] "Market trend data" refers to data that shows changes, trends, and consumer behavior in an industry or the overall market.

[0013] "Each stage of a sales negotiation" is a concept that refers to each stage in the sales process leading up to the conclusion of a contract with a customer.

[0014] A "success story" is a specific example or case study of a method that was used in the past to address similar situations or challenges and achieved the desired results. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered 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), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is an automated system for streamlining the sales process and improving the closing rate. This system primarily consists of three elements: a server, terminals, and users.

[0037] Data collection and integration:

[0038] The server collects market data, social network data, and customer relationship management system data. This data is obtained from multiple sources and integrated to create detailed profiles of potential customers.

[0039] Assessing the likelihood of a potential customer converting into a customer:

[0040] The server uses machine learning algorithms to evaluate the likelihood of each potential customer converting, based on the integrated data. Based on the evaluation results, the server creates a list in order of priority and displays it on the terminal.

[0041] Predicting customer needs:

[0042] The server analyzes past purchase history data and market trend data to predict each customer's purchasing needs. These analysis results are then provided to the user via their terminal.

[0043] Sales negotiation progress management and action proposals:

[0044] The server monitors the progress of the sales negotiation in real time. This allows for the suggestion of the optimal action at each stage of the negotiation, based on past success stories. The terminal notifies the user of these suggestions, and the user takes the next action accordingly.

[0045] Deal prediction model:

[0046] The server utilizes Business Process Management (EBPM) methodologies to build a model that predicts the likelihood of closing a deal. This model identifies customers with the highest probability of closing a deal and provides guidance for making effective proposals to users.

[0047] This system enables users to implement a data-driven sales approach, allowing for effective resource allocation and action planning. For example, in marketing activities for a specific product, the system analyzes consumer reviews from social media and suggests that a price-competitive campaign would be effective for a particular segment. In this way, the success rate of sales activities can be increased.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The server collects the latest data from market databases, social network APIs, and customer relationship management systems. It automatically retrieves data using APIs and stores it in a standardized format.

[0051] Step 2:

[0052] The server inputs the collected data into a machine learning algorithm to score the likelihood of a potential customer closing a deal. This process uses a model based on past sales negotiations to calculate a quantified probability of closing a deal for each customer.

[0053] Step 3:

[0054] The terminal displays a scored list of potential customers sent from the server via a user interface. The list is sorted by likelihood of conversion, and detailed information for each customer is available.

[0055] Step 4:

[0056] The server analyzes each customer's purchase history and current market trends, and generates personalized approach strategies based on this analysis. Data mining techniques are used to clarify customer needs and characteristics.

[0057] Step 5:

[0058] The terminal graphically displays an approach strategy generated based on the analysis results to the user. The strategy includes a specific action plan, which the user uses to plan their sales activities.

[0059] Step 6:

[0060] The server monitors the progress of each stage of a sales opportunity, refers to past success stories to determine the next necessary actions, and generates alerts and suggestions based on the progress.

[0061] Step 7:

[0062] The terminal notifies the user in real time of action suggestions from the server. Based on this information, the user can quickly implement strategies to advance the business negotiation.

[0063] Step 8:

[0064] The server continuously updates the conversion prediction model using all the data. Leveraging business process management methodologies, the system enhances its ability to prioritize and propose leads with a high probability of success in future business negotiations.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] In traditional sales processes, it is difficult to efficiently collect, integrate, and analyze data from diverse sources. In particular, accurately assessing the likelihood of closing a deal with a potential customer and predicting customer purchasing needs based on market fluctuations is challenging. This inability to propose effective actions in negotiations in a timely manner hinders improved sales performance. Furthermore, accurately proposing the next steps based on progress is also difficult.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for integrating and processing market information, social network information, and customer management information collected using an information acquisition unit; means for evaluating the likelihood of a contract being concluded with a potential buyer based on the integrated information using a machine learning method; and means for generating a list of potential buyers based on the evaluation results and presenting it using a visual display device. This makes it possible to effectively collect data from diverse information sources, evaluate the likelihood of a potential customer closing a deal with high accuracy, and propose the optimal action based on the progress of the business negotiation.

[0070] The "Information Acquisition Unit" is a component that has the function of efficiently collecting market information, social network information, and customer management information from various sources.

[0071] "Integration processing" is the process of combining multiple acquired pieces of information into a single dataset while ensuring consistency.

[0072] "Machine learning techniques" are technologies that use algorithms to analyze large amounts of data and extract meaningful patterns and features from it.

[0073] A "potential buyer" refers to a customer who has not yet purchased a product but is likely to purchase it in the future.

[0074] "Contract completion probability" is an indicator that shows the degree to which a potential customer is likely to actually purchase the product.

[0075] A "visual display device" is a device used to present information to a user visually, and generally refers to computer displays or smartphone screens.

[0076] "Evaluation results" refer to the results of analysis obtained using data analysis and machine learning techniques, and serve as the foundation for making decisions based on those results.

[0077] "Business negotiation" refers to the process of conducting business transactions, specifically the activity of negotiating with customers regarding the purchase of products or services.

[0078] "Recommended actions" are guidelines designed to show users the optimal actions based on the analysis results.

[0079] This invention is an automated system designed to streamline the sales process and improve the closing rate. This system is primarily built around three core elements: a server, terminals, and users.

[0080] The server plays a role in collecting and integrating data from diverse sources. Specifically, it acquires market information, social network information, and customer management information through APIs and data feeds, and integrates this data using cloud computing platforms such as Amazon Web Services and Microsoft Azure. In this process, ETL processes and tools such as Talend and Apache Kafka are utilized.

[0081] The server uses an integrated dataset to assess the likelihood of a contract being concluded with a potential buyer. Machine learning frameworks such as TENSORFLOW® and PyTorch are used for this assessment. This allows for highly accurate estimation of the likelihood of a potential customer closing a deal from large datasets.

[0082] The evaluation results are presented to the user via a terminal. The terminal uses Tableau or Power BI to build dashboards that visually display the evaluation results and analytical data. Based on this information, users can efficiently develop sales strategies.

[0083] Furthermore, the server analyzes past purchase history and market trends, using SAS and R languages ​​to predict customer purchase requests. This predictive information is also provided to users via terminals and effectively utilized in actual business negotiations.

[0084] As a concrete example, when a user launches a new product into the market, the server collects consumer feedback from social networks and displays it on a dashboard on the user's device. This allows the user to plan and implement the most effective marketing campaigns for specific customer segments.

[0085] An example of a prompt using a generative AI model would be: "Please suggest an effective marketing strategy for a specific segment. Social network reviews are used as the data source." Through this prompt, the AI ​​model suggests optimal marketing methods and target audiences, which are then used in actual sales activities.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The server receives market information, social network information, and customer management information via APIs. This input data is stored on cloud storage. The server integrates this data through an ETL process (extract, transform, load). Data transformation uses Talend or Apache Kafka to unify and cleanse data in multiple formats, thereby generating a consistent dataset.

[0089] Step 2:

[0090] The server trains a machine learning model using an integrated dataset. Inputs include customer attribute data and historical transaction history. It builds the model using TensorFlow and PyTorch, performing data calculations to evaluate the likelihood of each potential buyer closing a deal. The output is a prioritized list of each potential customer's conversion probability.

[0091] Step 3:

[0092] The terminal receives a list of conversion probability sent from the server and displays it on the user interface. The input is conversion probability data from the server, and the output is a visual dashboard for the user. Power BI and Tableau are used to visualize the data and perform concrete actions to support the user's decision-making.

[0093] Step 4:

[0094] The server predicts customer purchasing demand based on past purchase history and market trend data. Input data includes historical transaction information and market change information. Data processing uses SAS and R languages, applying advanced statistical analysis techniques. The output results, including predicted purchasing needs, are generated and transferred to the terminal.

[0095] Step 5:

[0096] The terminal notifies the user of predicted purchasing needs and suggests the optimal next action in the sales negotiation. This notification function is implemented through chat applications and email systems. The user receives action suggestions sent from the server and confirms the specific steps to take in sales activities based on them.

[0097] Step 6:

[0098] The server monitors the progress of sales opportunities in real time. Input data includes information on each stage of the opportunity, and the progress data is processed using a business process management (BPM) tool. The output generates instructions indicating the next action based on past success patterns.

[0099] (Application Example 1)

[0100] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] Conventional commercial transaction support systems struggle to provide effective product recommendations tailored to individual consumer needs, resulting in insufficient improvements in closing rates. Furthermore, there is a need to properly manage the progress of commercial transactions and propose swift and accurate actions. In addition, a system is needed to efficiently process vast amounts of market information and streamline the identification of potential customers and the evaluation of their likelihood of closing a deal.

[0102] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0103] In this invention, the server includes means for aggregating information, means for integrating the aggregated information and evaluating the probability of a commercial transaction with a potential buyer, means for creating and presenting a list of potential buyers based on the evaluation results, and means for making individually tailored product proposals. This makes it possible to improve the closing rate through individually tailored product proposals, support optimal actions at each stage of the commercial transaction, and significantly improve overall sales efficiency.

[0104] "Means of aggregating information" refers to functions that efficiently collect and consolidate market data, social network data, and customer relationship management system data in one place.

[0105] "A means of integrating aggregated information and evaluating the probability of a commercial transaction for a potential buyer" refers to a process that uses algorithms to analyze various collected data and quantify the likelihood of a commercial transaction for each individual potential buyer.

[0106] "A means of creating and presenting a list of potential buyers based on evaluation results" refers to a function that generates a prioritized list of potential buyers based on the results of evaluating the probability of closing a deal, and displays that information in a format that is usable by the user.

[0107] "Means of providing individually tailored product suggestions" refers to a function that analyzes individual consumers' interests and past purchase history, and then suggests personalized products and services based on that analysis.

[0108] To implement this invention, three elements—a server, a terminal, and a user—must work together to perform their functions. The server utilizes AWS (registered trademark) cloud infrastructure and employs machine learning models using Python and TensorFlow to build a system foundation for aggregating and integrating information. First, the server takes in market data, social network data, and customer relationship management data and aggregates the information. Next, it integrates the aggregated data and applies a generative AI model to evaluate the probability of commercial conversion for potential buyers.

[0109] These evaluation results are displayed to the user through a smartphone app developed with React Native that runs on the device. The device presents the user with a list of potential buyers sent from the server and assists in providing product suggestions tailored to individual needs.

[0110] Users can conduct more efficient and strategic marketing activities based on data provided by their devices. For example, when a user opens the app on their way home, the app will analyze their past purchase history and market trends to recommend the most suitable products to them in a timely manner, and offer them with special benefits.

[0111] A concrete example of a prompt from a generative AI model would be, "Based on the user's purchase history data, please create the optimal sales campaign that suggests items the user is likely to purchase next." This improves the overall efficiency of the system and significantly increases the conversion rate of commercial activities.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server collects market data, social data, and customer management data. The input is raw information from multiple data sources, and the output is a database of this integrated information. Cloud services are used for data collection, and APIs are configured to gather data in real time.

[0115] Step 2:

[0116] The server applies machine learning algorithms based on aggregated data to evaluate the probability of a potential buyer closing a deal. The input is an integrated database, and the output is a list of potential buyers with their conversion probabilities. TensorFlow is used for data processing, and a generative AI model is used to predict conversion rates.

[0117] Step 3:

[0118] The server creates a list of potential buyers, each assigned a probability of conversion, and sends it to the device. The input is an evaluated list of potential buyers, and the output is a list of buyers ready to be sent. The list is prioritized within the server and delivered to the smartphone device via API.

[0119] Step 4:

[0120] The device reviews the received list and displays the most suitable product suggestions for the user on the screen. The input is a list of potential buyers sent from the server, and the output is product suggestions displayed in the user interface. The UI is created using React Native to display product offer information clearly in real time.

[0121] Step 5:

[0122] The user evaluates the presented product suggestions and makes a purchase decision. The input is the product suggestions displayed on the terminal, and the output is the purchase decision information. The user's feedback is sent back to the server and used to improve future suggestions.

[0123] Step 6:

[0124] The server updates the database based on user feedback and incorporates it into the next prediction model. The input is user feedback data, and the output is the updated prediction model. The system continuously improves its accuracy through this feedback loop.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention is an automated system for highly optimizing sales processes, and by combining it with an emotion engine, it enables customer service that takes into account the user's emotional state. This system consists of three main components: a server, a terminal, and a user, and is realized through their coordination.

[0127] Data collection and analysis:

[0128] The server continuously collects and integrates market data, social network data, and customer relationship management system data. Using this data, the server calculates a conversion probability score for potential customers. Furthermore, it analyzes past purchase history and market trend data to predict customer purchasing needs.

[0129] Emotion recognition by an emotion engine:

[0130] When a user interacts with a customer, the emotion engine on the device analyzes the user's voice tone and facial expressions in real time to recognize their current emotional state. The emotion engine combines voice recognition technology and facial expression analysis algorithms to provide highly reliable emotional data.

[0131] Integrating customer approach strategies:

[0132] The server dynamically adjusts its customer interaction approach strategy based on emotional data acquired by the emotion engine. This helps users implement the most appropriate communication strategy during their interactions with customers.

[0133] Feedback and improvements:

[0134] After the interaction ends, the terminal provides the user with feedback generated by the server. This feedback includes an analysis of how the user's emotional state influenced the sales negotiation. This allows the user to gain metrics to improve their sales skills.

[0135] Specific example:

[0136] For example, when a user is conducting a remote business negotiation with a customer, if the emotion engine detects the user's stress level, the server will immediately provide suggestions such as specific phrases to return the conversation to a calmer tone or highlight the importance of taking breaks. This allows the user to maintain a good relationship with the customer and increase the likelihood of closing a deal.

[0137] In this way, the present invention aims to enhance the entire sales process with a data-driven and emotion-recognition-based approach, thereby improving conversion rates and customer satisfaction.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The server collects market data, social network data, and customer relationship management system data. This ensures that all information related to sales activities is updated in real time and integrated into the database.

[0141] Step 2:

[0142] The server analyzes the collected data using machine learning algorithms to evaluate the likelihood of a potential customer converting into a customer. The evaluation results are quantified and stored as a conversion probability score for each customer.

[0143] Step 3:

[0144] The terminal displays a list of potential customers based on the evaluation results. The list is sorted in order of likelihood of closing a deal, and the user plans their sales activities based on this list.

[0145] Step 4:

[0146] When a user begins interacting with a customer, the device activates its emotion engine, analyzing the user's voice and facial expressions in real time. If a specific emotional state is detected, the data is sent to the server.

[0147] Step 5:

[0148] The server receives data from the emotion engine and adjusts the customer approach strategy according to the user's emotional state. For example, if the user is feeling stressed, it suggests ways to make the conversation more relaxing.

[0149] Step 6:

[0150] The terminal notifies the user of strategic suggestions provided by the server. Based on this information, the user takes the optimal approach to the customer and facilitates smooth communication.

[0151] Step 7:

[0152] After a business negotiation concludes, the server analyzes the emotional data collected during the conversation and the outcome of the negotiation to identify areas for improvement. The terminal then presents this feedback to the user, which can be used to improve future business negotiations.

[0153] Step 8:

[0154] The server continuously updates the sales prediction model using all the data. This allows the system to provide users with effective strategies for future sales negotiations.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] Traditional sales processes often failed to accurately assess the likelihood of closing a deal and made it difficult to consider customer emotions, thus hindering effective improvements in customer satisfaction and closing rates. Furthermore, inefficient data integration and analysis made it difficult to determine appropriate next steps.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes a device for collecting information, a device for integrating the collected information and evaluating the likelihood of conversion for potential customers, and a device for creating and displaying a list of potential customers based on the evaluation. This makes it possible to identify customers with a high likelihood of conversion and to conduct efficient sales activities. Furthermore, adjusting customer service strategies based on emotion recognition contributes to improving customer satisfaction.

[0160] A "device for collecting information" is a device for acquiring data from market information, network information, and customer management systems.

[0161] A "device that integrates collected information and evaluates the likelihood of conversion with potential customers" is a device that centralizes information obtained from various data sources and quantifies the customer's purchasing intent based on that information.

[0162] A "device for creating and displaying a list of potential customers" is a device that selects customers with a high probability of conversion and visualizes them in a list.

[0163] A "device that performs voice and image analysis to recognize the emotional state of a user" is a device that analyzes voice tone and visual data to identify the user's emotions.

[0164] A "device that adjusts customer service strategies based on emotional state" is a device that dynamically changes the optimal dialogue method and sales strategy based on identified emotional information.

[0165] This invention is an automated system that optimizes sales processes in a data-driven and sentiment-recognition-based manner. The system consists of three main components: a server, terminals, and users.

[0166] The server collects market information, network information, and customer management data through APIs and database connections. This includes CRM platforms such as Trello and Salesforce. The collected information is preprocessed using the Python pandas library and analyzed with machine learning algorithms using scikit-learn. This analysis identifies and scores potential customers with a high probability of conversion.

[0167] The device activates an emotion engine that identifies voice tone and facial expressions when the user communicates with a customer. This emotion engine uses speech recognition technology and OpenCV to recognize the user's emotional state in real time. The results are then used to determine appropriate customer service strategies.

[0168] Users receive conversion probability scores and sentiment-based feedback from the server, which they can use in their sales activities. This feedback includes supplementary information about the impact of emotions on sales opportunities, allowing users to objectively improve their sales approach.

[0169] For example, when a user is conducting an online business meeting, if the emotion engine built into the device detects the user's stress, the server immediately displays a suggestion on the device such as, "Please be mindful of speaking in a calm voice." This real-time feedback allows the user to implement an appropriate communication strategy with the customer.

[0170] As an example of a prompt to input into a generative AI model, it is possible to suggest appropriate dialogue methods in the form of, "Please tell me a phrase to use when emotional stress is detected in a sales conversation."

[0171] In this way, the system aims to improve sales performance and customer satisfaction by combining advanced data processing and emotion recognition technology.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The server collects data from market information, network information, and customer management systems. It takes raw data as input via APIs and database connections. Specifically, the server queries external data sources at regular intervals and saves the necessary information to the server in JSON format. The output is an integrated dataset.

[0175] Step 2:

[0176] The server integrates the collected information and evaluates the likelihood of conversion. It uses the dataset obtained from Step 1 as input. Data processing involves handling missing values ​​and normalizing the data using the pandas library, and then training a model with a machine learning algorithm using scikit-learn. The output is a conversion likelihood score calculated for each customer. Specifically, the server inputs the data into the trained model and scores the conversion likelihood for each customer.

[0177] Step 3:

[0178] The device collects audio and image data to recognize the user's emotional state. It uses real-time audio and video data acquired through a microphone and camera as input. Specifically, the device runs a speech recognition engine and the OpenCV library to analyze speech tone and perform facial expression recognition. The output is a real-time determination of the user's emotional state.

[0179] Step 4:

[0180] The server adjusts customer interaction strategies based on the emotional state. It uses the user's emotional data obtained in step 3 as input. As a data calculation, it combines the emotional data with the likelihood of conversion to generate appropriate dialogue scripts or sales strategies. In concrete action, the server generates situation-appropriate approaches and sends the information to the terminal. As output, a specific communication strategy for the user is formulated.

[0181] Step 5:

[0182] The terminal provides feedback to the user after the sales negotiation is completed. It receives feedback data sent from the server as input. Specifically, the terminal displays the analysis results on the screen, allowing the user to see how their emotional state influenced the negotiation. The output provides insights to help the user improve their sales skills.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] In recent years, there has been a growing demand for rapid responses to diverse customer needs, but traditional sales processes have made this difficult. In particular, the lack of consideration for customer emotions in communication has led to declining conversion rates and difficulties in improving customer satisfaction. Furthermore, even in areas such as electronic payment services, there is a growing need for flexible responses that reflect customer emotions in real time.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0187] In this invention, the server includes means for collecting market information, social network information, and customer information management system information; means for integrating the collected information and evaluating the likelihood of conversion for prospective customers; means for generating and displaying prospective customer lists based on the evaluation results; means for capturing customer voice or text and analyzing their emotional state; and means for dynamically presenting the optimal customer service strategy based on the emotional state. This enables the realization of an effective and flexible sales process that takes into account the emotional state of customers and the achievement of high customer satisfaction within electronic payment services.

[0188] "Market information" refers to numerical data, statistical information, and information on consumer behavior that reflect market trends and developments.

[0189] "Social network information" refers to information based on user posts and reactions collected on social media and online platforms.

[0190] "Customer information management system information" refers to information used to manage customer contact history, transaction history, and individual customer information.

[0191] A "prospective customer" is a potential customer who is expected to purchase a product or service in the future.

[0192] "Closing probability" is an indicator that shows the degree of likelihood that a business negotiation will be successful and a formal contract will be concluded.

[0193] "Means for capturing voice or text and analyzing emotional states" refers to methods for detecting and analyzing customer emotions using speech recognition or natural language processing technologies.

[0194] "A means of dynamically presenting the optimal customer service strategy" refers to a method of proposing appropriate responses and customer service methods in real time, according to the customer's emotional state.

[0195] To implement this invention, a system is used in which a server, a terminal, and a user work in cooperation. The server collects and integrates market information, social network information, and customer information management system information. This allows it to evaluate the likelihood of conversion for prospective customers and generate lists. The terminal uses speech recognition technology and natural language processing technology to capture customer voice and text and analyze their emotional state in real time. The analysis results are sent to the server, which generates an optimal customer response strategy, which is then dynamically presented to the terminal. The user then proceeds with the interaction with the customer based on this strategy. The hardware used includes a computer with a high-performance processor and a device equipped with state-of-the-art microphones and cameras. The software uses a speech recognition library (e.g., speech_recognition), a natural language processing library (e.g., nltk), and an emotion recognition algorithm.

[0196] As a concrete example, when a user provides customer support for an electronic payment service, if a customer inquiry comes in, the terminal analyzes the voice and recognizes that the customer's emotions indicate anxiety or dissatisfaction. This information is sent to the server, and a quick solution is generated. This allows the user to respond appropriately and increase customer satisfaction. An example of a prompt to the generating AI model would be, "Please suggest the best solution for this customer's problem. Also, please provide a hospitable response method based on the customer's emotional state."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The server periodically collects market information, social network information, and customer information management system information. It takes the latest data from various sources as input and integrates this data to build an information base. Based on this integrated data, it runs an algorithm to evaluate the likelihood of prospects converting into customers and generates a list of prospects.

[0200] Step 2:

[0201] When a user begins interacting with a customer, the device captures the customer's voice and facial expressions in real time using its microphone and camera. It receives voice and visual data as input and analyzes the customer's emotional state using natural language processing libraries and vision analysis algorithms. This process identifies whether the customer's current emotions are anxiety, dissatisfaction, or other similar states.

[0202] Step 3:

[0203] The server receives emotional state data acquired from the terminal. Based on this data, the server inputs prompts into a generative AI model to generate the optimal customer response strategy. Using emotional data as input, the AI ​​performs data calculations based on past success stories and customer profiles to generate the response.

[0204] Step 4:

[0205] The customer service strategy generated on the server is sent to the terminal. The terminal displays this strategy to the user and presents specific response methods. As output, it provides specific phrases and suggestions for the user to take action.

[0206] Step 5:

[0207] Users interact with customers based on strategies presented on their devices. The user's interactions and customer responses are then captured again on the device and sent to the server. The server analyzes this data as feedback and stores it as learning data to improve future interactions.

[0208] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0209] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0210] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0214] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0215] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0216] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0218] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0219] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0220] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0221] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0222] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0223] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0224] This invention is an automated system for streamlining the sales process and improving the closing rate. This system primarily consists of three elements: a server, terminals, and users.

[0225] Data collection and integration:

[0226] The server collects market data, social network data, and customer relationship management system data. This data is obtained from multiple sources and integrated to create detailed profiles of potential customers.

[0227] Assessing the likelihood of a potential customer converting into a customer:

[0228] The server uses machine learning algorithms to evaluate the likelihood of each potential customer converting, based on the integrated data. Based on the evaluation results, the server creates a list in order of priority and displays it on the terminal.

[0229] Predicting customer needs:

[0230] The server analyzes past purchase history data and market trend data to predict each customer's purchasing needs. These analysis results are then provided to the user via their terminal.

[0231] Sales negotiation progress management and action proposals:

[0232] The server monitors the progress of the sales negotiation in real time. This allows for the suggestion of the optimal action at each stage of the negotiation, based on past success stories. The terminal notifies the user of these suggestions, and the user takes the next action accordingly.

[0233] Deal prediction model:

[0234] The server utilizes Business Process Management (EBPM) methodologies to build a model that predicts the likelihood of closing a deal. This model identifies customers with the highest probability of closing a deal and provides guidance for making effective proposals to users.

[0235] This system enables users to implement a data-driven sales approach, allowing for effective resource allocation and action planning. For example, in marketing activities for a specific product, the system analyzes consumer reviews from social media and suggests that a price-competitive campaign would be effective for a particular segment. In this way, the success rate of sales activities can be increased.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The server collects the latest data from market databases, social network APIs, and customer relationship management systems. It automatically retrieves data using APIs and stores it in a standardized format.

[0239] Step 2:

[0240] The server inputs the collected data into a machine learning algorithm to score the likelihood of a potential customer closing a deal. This process uses a model based on past sales negotiations to calculate a quantified probability of closing a deal for each customer.

[0241] Step 3:

[0242] The terminal displays a scored list of potential customers sent from the server via a user interface. The list is sorted by likelihood of conversion, and detailed information for each customer is available.

[0243] Step 4:

[0244] The server analyzes each customer's purchase history and current market trends, and generates personalized approach strategies based on this analysis. Data mining techniques are used to clarify customer needs and characteristics.

[0245] Step 5:

[0246] The terminal graphically displays an approach strategy generated based on the analysis results to the user. The strategy includes a specific action plan, which the user uses to plan their sales activities.

[0247] Step 6:

[0248] The server monitors the progress of each stage of a sales opportunity, refers to past success stories to determine the next necessary actions, and generates alerts and suggestions based on the progress.

[0249] Step 7:

[0250] The terminal notifies the user in real time of action suggestions from the server. Based on this information, the user can quickly implement strategies to advance the business negotiation.

[0251] Step 8:

[0252] The server continuously updates the conversion prediction model using all the data. Leveraging business process management methodologies, the system enhances its ability to prioritize and propose leads with a high probability of success in future business negotiations.

[0253] (Example 1)

[0254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] In traditional sales processes, it is difficult to efficiently collect, integrate, and analyze data from diverse sources. In particular, accurately assessing the likelihood of closing a deal with a potential customer and predicting customer purchasing needs based on market fluctuations is challenging. This inability to propose effective actions in negotiations in a timely manner hinders improved sales performance. Furthermore, accurately proposing the next steps based on progress is also difficult.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes means for integrating and processing market information, social network information, and customer management information collected using an information acquisition unit; means for evaluating the likelihood of a contract being concluded with a potential buyer based on the integrated information using a machine learning method; and means for generating a list of potential buyers based on the evaluation results and presenting it using a visual display device. This makes it possible to effectively collect data from diverse information sources, evaluate the likelihood of a potential customer closing a deal with high accuracy, and propose the optimal action based on the progress of the business negotiation.

[0258] The "Information Acquisition Unit" is a component that has the function of efficiently collecting market information, social network information, and customer management information from various sources.

[0259] "Integration processing" is the process of combining multiple acquired pieces of information into a single dataset while ensuring consistency.

[0260] "Machine learning techniques" are technologies that use algorithms to analyze large amounts of data and extract meaningful patterns and features from it.

[0261] A "potential buyer" refers to a customer who has not yet purchased a product but is likely to purchase it in the future.

[0262] "Contract completion probability" is an indicator that shows the degree to which a potential customer is likely to actually purchase the product.

[0263] A "visual display device" is a device used to present information to a user visually, and generally refers to computer displays or smartphone screens.

[0264] "Evaluation results" refer to the results of analysis obtained using data analysis and machine learning techniques, and serve as the foundation for making decisions based on those results.

[0265] "Business negotiation" refers to the process of conducting business transactions, specifically the activity of negotiating with customers regarding the purchase of products or services.

[0266] "Recommended actions" are guidelines designed to show users the optimal actions based on the analysis results.

[0267] This invention is an automated system designed to streamline the sales process and improve the closing rate. This system is primarily built around three core elements: a server, terminals, and users.

[0268] The server plays a role in collecting and integrating data from diverse sources. Specifically, it acquires market information, social network information, and customer management information through APIs and data feeds, and then integrates and processes this data using cloud computing platforms such as Amazon Web Services and Microsoft Azure. In this process, ETL processes and tools such as Talend and Apache Kafka are utilized.

[0269] The server uses an integrated dataset to assess the likelihood of a deal being closed with a potential buyer. Machine learning frameworks such as TensorFlow and PyTorch are used for this assessment. This allows for highly accurate estimation of the likelihood of a potential customer closing a deal from large datasets.

[0270] The evaluation results are presented to the user via a terminal. The terminal uses Tableau or Power BI to build dashboards that visually display the evaluation results and analytical data. Based on this information, users can efficiently develop sales strategies.

[0271] Furthermore, the server analyzes past purchase history and market trends, using SAS and R languages ​​to predict customer purchase requests. This predictive information is also provided to users via terminals and effectively utilized in actual business negotiations.

[0272] As a concrete example, when a user launches a new product into the market, the server collects consumer feedback from social networks and displays it on a dashboard on the user's device. This allows the user to plan and implement the most effective marketing campaigns for specific customer segments.

[0273] An example of a prompt using a generative AI model would be: "Please suggest an effective marketing strategy for a specific segment. Social network reviews are used as the data source." Through this prompt, the AI ​​model suggests optimal marketing methods and target audiences, which are then used in actual sales activities.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The server receives market information, social network information, and customer management information via APIs. This input data is stored on cloud storage. The server integrates this data through an ETL process (extract, transform, load). Data transformation uses Talend or Apache Kafka to unify and cleanse data in multiple formats, thereby generating a consistent dataset.

[0277] Step 2:

[0278] The server trains a machine learning model using an integrated dataset. Inputs include customer attribute data and historical transaction history. It builds the model using TensorFlow and PyTorch, performing data calculations to evaluate the likelihood of each potential buyer closing a deal. The output is a prioritized list of each potential customer's conversion probability.

[0279] Step 3:

[0280] The terminal receives the contract probability list sent from the server and displays it on the user interface. The input is the contract probability data from the server, and the output is a visual dashboard for the user. By using Power BI or Tableau for visualization, specific operations are performed to assist the user's decision-making.

[0281] Step 4:

[0282] The server predicts the customer's purchase demand based on past purchase history and market trend data. The input data includes past transaction information and market change information. In data processing, SAS or R language is used to apply advanced statistical analysis methods. As the output result, the predicted purchase needs are generated and transferred to the terminal.

[0283] Step 5:

[0284] The terminal notifies the user of the predicted purchase needs and proposes the next optimal action in the negotiation. This notification function is implemented through a chat app or a mail system. The user receives the action proposal sent from the server and confirms the specific means to conduct business activities based on it.

[0285] Step 6:

[0286] The server monitors the progress of the negotiation in real time. The input data includes information on each stage of the negotiation, and business process management (BPM) tools are used to process the progress data. As the output, an instruction indicating the next action based on past successful patterns is generated.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] Conventional commercial transaction support systems struggle to provide effective product recommendations tailored to individual consumer needs, resulting in insufficient improvements in closing rates. Furthermore, there is a need to properly manage the progress of commercial transactions and propose swift and accurate actions. In addition, a system is needed to efficiently process vast amounts of market information and streamline the identification of potential customers and the evaluation of their likelihood of closing a deal.

[0290] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes means for aggregating information, means for integrating the aggregated information and evaluating the probability of a commercial transaction with a potential buyer, means for creating and presenting a list of potential buyers based on the evaluation results, and means for making individually tailored product proposals. This makes it possible to improve the closing rate through individually tailored product proposals, support optimal actions at each stage of the commercial transaction, and significantly improve overall sales efficiency.

[0292] "Means of aggregating information" refers to functions that efficiently collect and consolidate market data, social network data, and customer relationship management system data in one place.

[0293] "A means of integrating aggregated information and evaluating the probability of a commercial transaction for a potential buyer" refers to a process that uses algorithms to analyze various collected data and quantify the likelihood of a commercial transaction for each individual potential buyer.

[0294] "A means of creating and presenting a list of potential buyers based on evaluation results" refers to a function that generates a prioritized list of potential buyers based on the results of evaluating the probability of closing a deal, and displays that information in a format that is usable by the user.

[0295] "Means of providing individually tailored product suggestions" refers to a function that analyzes individual consumers' interests and past purchase history, and then suggests personalized products and services based on that analysis.

[0296] To implement this invention, three elements—a server, a terminal, and a user—must work together to perform their functions. The server utilizes AWS cloud infrastructure and employs machine learning models using Python and TensorFlow to build a system foundation for aggregating and integrating information. First, the server takes in market data, social network data, and customer relationship management data and aggregates the information. Next, it integrates the aggregated data and applies a generative AI model to evaluate the probability of commercial conversion for potential buyers.

[0297] These evaluation results are displayed to the user through a smartphone app developed with React Native that runs on the device. The device presents the user with a list of potential buyers sent from the server and assists in providing product suggestions tailored to individual needs.

[0298] Users can conduct more efficient and strategic marketing activities based on data provided by their devices. For example, when a user opens the app on their way home, the app will analyze their past purchase history and market trends to recommend the most suitable products to them in a timely manner, and offer them with special benefits.

[0299] A concrete example of a prompt from a generative AI model would be, "Based on the user's purchase history data, please create the optimal sales campaign that suggests items the user is likely to purchase next." This improves the overall efficiency of the system and significantly increases the conversion rate of commercial activities.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The server collects market data, social data, and customer management data. The input is raw information from multiple data sources, and the output is a database of integrated information from these data. Cloud services are used for data collection, and an API is configured to collect data in real time.

[0303] Step 2:

[0304] The server applies machine learning algorithms based on the aggregated data to evaluate the commercial contract conclusion probability of potential purchasers. The input is the integrated database, and the output is a list of potential purchasers with contract conclusion probabilities. TensorFlow is used for data processing, and a generative AI model is used to predict the conclusion rate.

[0305] Step 3:

[0306] The server creates a list of potential purchasers with contract conclusion probabilities and sends it to the terminal. The input is the evaluated list of potential purchasers, and the output is a list of purchasers that can be sent. The list is prioritized within the server and distributed to the smartphone terminal via the API.

[0307] Step 4:

[0308] The terminal checks the received list and displays the most suitable product recommendations for the user on the screen. The input is the list of potential purchasers sent from the server, and the output is the product recommendations displayed on the user interface. The UI is created using React Native and displays the product offer information clearly in real time.

[0309] Step 5:

[0310] The user evaluates the presented product recommendations and makes a purchase decision. The input is the product recommendations displayed on the terminal, and the output is the purchase decision information. The user's feedback is sent back to the server and utilized for the next recommendation.

[0311] Step 6:

[0312] The server updates the database based on user feedback and incorporates it into the next prediction model. The input is user feedback data, and the output is the updated prediction model. The system continuously improves its accuracy through this feedback loop.

[0313] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0314] This invention is an automated system for highly optimizing sales processes, and by combining it with an emotion engine, it enables customer service that takes into account the user's emotional state. This system consists of three main components: a server, a terminal, and a user, and is realized through their coordination.

[0315] Data collection and analysis:

[0316] The server continuously collects and integrates market data, social network data, and customer relationship management system data. Using this data, the server calculates a conversion probability score for potential customers. Furthermore, it analyzes past purchase history and market trend data to predict customer purchasing needs.

[0317] Emotion recognition by an emotion engine:

[0318] When a user interacts with a customer, the emotion engine on the device analyzes the user's voice tone and facial expressions in real time to recognize their current emotional state. The emotion engine combines voice recognition technology and facial expression analysis algorithms to provide highly reliable emotional data.

[0319] Integrating customer approach strategies:

[0320] The server dynamically adjusts its customer interaction approach strategy based on emotional data acquired by the emotion engine. This helps users implement the most appropriate communication strategy during their interactions with customers.

[0321] Feedback and improvements:

[0322] After the interaction ends, the terminal provides the user with feedback generated by the server. This feedback includes an analysis of how the user's emotional state influenced the sales negotiation. This allows the user to gain metrics to improve their sales skills.

[0323] Specific example:

[0324] For example, when a user is conducting a remote business negotiation with a customer, if the emotion engine detects the user's stress level, the server will immediately provide suggestions such as specific phrases to return the conversation to a calmer tone or highlight the importance of taking breaks. This allows the user to maintain a good relationship with the customer and increase the likelihood of closing a deal.

[0325] In this way, the present invention aims to enhance the entire sales process with a data-driven and emotion-recognition-based approach, thereby improving conversion rates and customer satisfaction.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The server collects market data, social network data, and customer relationship management system data. This ensures that all information related to sales activities is updated in real time and integrated into the database.

[0329] Step 2:

[0330] The server analyzes the collected data using machine learning algorithms to evaluate the likelihood of a potential customer converting into a customer. The evaluation results are quantified and stored as a conversion probability score for each customer.

[0331] Step 3:

[0332] The terminal displays a list of potential customers based on the evaluation results. The list is sorted in order of likelihood of closing a deal, and the user plans their sales activities based on this list.

[0333] Step 4:

[0334] When a user begins interacting with a customer, the device activates its emotion engine, analyzing the user's voice and facial expressions in real time. If a specific emotional state is detected, the data is sent to the server.

[0335] Step 5:

[0336] The server receives data from the emotion engine and adjusts the customer approach strategy according to the user's emotional state. For example, if the user is feeling stressed, it suggests ways to make the conversation more relaxing.

[0337] Step 6:

[0338] The terminal notifies the user of strategic suggestions provided by the server. Based on this information, the user takes the optimal approach to the customer and facilitates smooth communication.

[0339] Step 7:

[0340] After a business negotiation concludes, the server analyzes the emotional data collected during the conversation and the outcome of the negotiation to identify areas for improvement. The terminal then presents this feedback to the user, which can be used to improve future business negotiations.

[0341] Step 8:

[0342] The server continuously updates the sales prediction model using all the data. This allows the system to provide users with effective strategies for future sales negotiations.

[0343] (Example 2)

[0344] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0345] Traditional sales processes often failed to accurately assess the likelihood of closing a deal and made it difficult to consider customer emotions, thus hindering effective improvements in customer satisfaction and closing rates. Furthermore, inefficient data integration and analysis made it difficult to determine appropriate next steps.

[0346] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0347] In this invention, the server includes a device for collecting information, a device for integrating the collected information and evaluating the likelihood of conversion for potential customers, and a device for creating and displaying a list of potential customers based on the evaluation. This makes it possible to identify customers with a high likelihood of conversion and to conduct efficient sales activities. Furthermore, adjusting customer service strategies based on emotion recognition contributes to improving customer satisfaction.

[0348] A "device for collecting information" is a device for acquiring data from market information, network information, and customer management systems.

[0349] A "device that integrates collected information and evaluates the likelihood of conversion with potential customers" is a device that centralizes information obtained from various data sources and quantifies the customer's purchasing intent based on that information.

[0350] A "device for creating and displaying a list of potential customers" is a device that selects customers with a high probability of conversion and visualizes them in a list.

[0351] A "device that performs voice and image analysis to recognize the emotional state of a user" is a device that analyzes voice tone and visual data to identify the user's emotions.

[0352] A "device that adjusts customer service strategies based on emotional state" is a device that dynamically changes the optimal dialogue method and sales strategy based on identified emotional information.

[0353] This invention is an automated system that optimizes sales processes in a data-driven and sentiment-recognition-based manner. The system consists of three main components: a server, terminals, and users.

[0354] The server collects market information, network information, and customer management data through APIs and database connections. This includes CRM platforms such as Trello and Salesforce. The collected information is preprocessed using the Python pandas library and analyzed with machine learning algorithms using scikit-learn. This analysis identifies and scores potential customers with a high probability of conversion.

[0355] The device activates an emotion engine that identifies voice tone and facial expressions when the user communicates with a customer. This emotion engine uses speech recognition technology and OpenCV to recognize the user's emotional state in real time. The results are then used to determine appropriate customer service strategies.

[0356] Users receive conversion probability scores and sentiment-based feedback from the server, which they can use in their sales activities. This feedback includes supplementary information about the impact of emotions on sales opportunities, allowing users to objectively improve their sales approach.

[0357] For example, when a user is conducting an online business meeting, if the emotion engine built into the device detects the user's stress, the server immediately displays a suggestion on the device such as, "Please be mindful of speaking in a calm voice." This real-time feedback allows the user to implement an appropriate communication strategy with the customer.

[0358] As an example of a prompt to input into a generative AI model, it is possible to suggest appropriate dialogue methods in the form of, "Please tell me a phrase to use when emotional stress is detected in a sales conversation."

[0359] In this way, the system aims to improve sales performance and customer satisfaction by combining advanced data processing and emotion recognition technology.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The server collects data from market information, network information, and customer management systems. It takes raw data as input via APIs and database connections. Specifically, the server queries external data sources at regular intervals and saves the necessary information to the server in JSON format. The output is an integrated dataset.

[0363] Step 2:

[0364] The server integrates the collected information and evaluates the likelihood of conversion. It uses the dataset obtained from Step 1 as input. Data processing involves handling missing values ​​and normalizing the data using the pandas library, and then training a model with a machine learning algorithm using scikit-learn. The output is a conversion likelihood score calculated for each customer. Specifically, the server inputs the data into the trained model and scores the conversion likelihood for each customer.

[0365] Step 3:

[0366] The device collects audio and image data to recognize the user's emotional state. It uses real-time audio and video data acquired through a microphone and camera as input. Specifically, the device runs a speech recognition engine and the OpenCV library to analyze speech tone and perform facial expression recognition. The output is a real-time determination of the user's emotional state.

[0367] Step 4:

[0368] The server adjusts customer interaction strategies based on the emotional state. It uses the user's emotional data obtained in step 3 as input. As a data calculation, it combines the emotional data with the likelihood of conversion to generate appropriate dialogue scripts or sales strategies. In concrete action, the server generates situation-appropriate approaches and sends the information to the terminal. As output, a specific communication strategy for the user is formulated.

[0369] Step 5:

[0370] The terminal provides feedback to the user after the sales negotiation is completed. It receives feedback data sent from the server as input. Specifically, the terminal displays the analysis results on the screen, allowing the user to see how their emotional state influenced the negotiation. The output provides insights to help the user improve their sales skills.

[0371] (Application Example 2)

[0372] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0373] In recent years, there has been a growing demand for rapid responses to diverse customer needs, but traditional sales processes have made this difficult. In particular, the lack of consideration for customer emotions in communication has led to declining conversion rates and difficulties in improving customer satisfaction. Furthermore, even in areas such as electronic payment services, there is a growing need for flexible responses that reflect customer emotions in real time.

[0374] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0375] In this invention, the server includes means for collecting market information, social network information, and customer information management system information; means for integrating the collected information and evaluating the likelihood of conversion for prospective customers; means for generating and displaying prospective customer lists based on the evaluation results; means for capturing customer voice or text and analyzing their emotional state; and means for dynamically presenting the optimal customer service strategy based on the emotional state. This enables the realization of an effective and flexible sales process that takes into account the emotional state of customers and the achievement of high customer satisfaction within electronic payment services.

[0376] "Market information" refers to numerical data, statistical information, and information on consumer behavior that reflect market trends and developments.

[0377] "Social network information" refers to information based on user posts and reactions collected on social media and online platforms.

[0378] "Customer information management system information" refers to information used to manage customer contact history, transaction history, and individual customer information.

[0379] A "prospective customer" is a potential customer who is expected to purchase a product or service in the future.

[0380] "Closing probability" is an indicator that shows the degree of likelihood that a business negotiation will be successful and a formal contract will be concluded.

[0381] "Means for capturing voice or text and analyzing emotional states" refers to methods for detecting and analyzing customer emotions using speech recognition or natural language processing technologies.

[0382] "A means of dynamically presenting the optimal customer service strategy" refers to a method of proposing appropriate responses and customer service methods in real time, according to the customer's emotional state.

[0383] To implement this invention, a system is used in which a server, a terminal, and a user work in cooperation. The server collects and integrates market information, social network information, and customer information management system information. This allows it to evaluate the likelihood of conversion for prospective customers and generate lists. The terminal uses speech recognition technology and natural language processing technology to capture customer voice and text and analyze their emotional state in real time. The analysis results are sent to the server, which generates an optimal customer response strategy, which is then dynamically presented to the terminal. The user then proceeds with the interaction with the customer based on this strategy. The hardware used includes a computer with a high-performance processor and a device equipped with state-of-the-art microphones and cameras. The software uses a speech recognition library (e.g., speech_recognition), a natural language processing library (e.g., nltk), and an emotion recognition algorithm.

[0384] As a concrete example, when a user provides customer support for an electronic payment service, if a customer inquiry comes in, the terminal analyzes the voice and recognizes that the customer's emotions indicate anxiety or dissatisfaction. This information is sent to the server, and a quick solution is generated. This allows the user to respond appropriately and increase customer satisfaction. An example of a prompt to the generating AI model would be, "Please suggest the best solution for this customer's problem. Also, please provide a hospitable response method based on the customer's emotional state."

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The server periodically collects market information, social network information, and customer information management system information. It takes the latest data from various sources as input and integrates this data to build an information base. Based on this integrated data, it runs an algorithm to evaluate the likelihood of prospects converting into customers and generates a list of prospects.

[0388] Step 2:

[0389] When a user begins interacting with a customer, the device captures the customer's voice and facial expressions in real time using its microphone and camera. It receives voice and visual data as input and analyzes the customer's emotional state using natural language processing libraries and vision analysis algorithms. This process identifies whether the customer's current emotions are anxiety, dissatisfaction, or other similar states.

[0390] Step 3:

[0391] The server receives emotional state data acquired from the terminal. Based on this data, the server inputs prompts into a generative AI model to generate the optimal customer response strategy. Using emotional data as input, the AI ​​performs data calculations based on past success stories and customer profiles to generate the response.

[0392] Step 4:

[0393] The customer service strategy generated on the server is sent to the terminal. The terminal displays this strategy to the user and presents specific response methods. As output, it provides specific phrases and suggestions for the user to take action.

[0394] Step 5:

[0395] Users interact with customers based on strategies presented on their devices. The user's interactions and customer responses are then captured again on the device and sent to the server. The server analyzes this data as feedback and stores it as learning data to improve future interactions.

[0396] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0397] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0402] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0403] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0404] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0405] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0406] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0407] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0408] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0409] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0410] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0411] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0412] This invention is an automated system for streamlining the sales process and improving the closing rate. This system primarily consists of three elements: a server, terminals, and users.

[0413] Data collection and integration:

[0414] The server collects market data, social network data, and customer relationship management system data. This data is obtained from multiple sources and integrated to create detailed profiles of potential customers.

[0415] Assessing the likelihood of a potential customer converting into a customer:

[0416] The server uses machine learning algorithms to evaluate the likelihood of each potential customer converting, based on the integrated data. Based on the evaluation results, the server creates a list in order of priority and displays it on the terminal.

[0417] Predicting customer needs:

[0418] The server analyzes past purchase history data and market trend data to predict each customer's purchasing needs. These analysis results are then provided to the user via their terminal.

[0419] Sales negotiation progress management and action proposals:

[0420] The server monitors the progress of the sales negotiation in real time. This allows for the suggestion of the optimal action at each stage of the negotiation, based on past success stories. The terminal notifies the user of these suggestions, and the user takes the next action accordingly.

[0421] Deal prediction model:

[0422] The server utilizes Business Process Management (EBPM) methodologies to build a model that predicts the likelihood of closing a deal. This model identifies customers with the highest probability of closing a deal and provides guidance for making effective proposals to users.

[0423] This system enables users to implement a data-driven sales approach, allowing for effective resource allocation and action planning. For example, in marketing activities for a specific product, the system analyzes consumer reviews from social media and suggests that a price-competitive campaign would be effective for a particular segment. In this way, the success rate of sales activities can be increased.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The server collects the latest data from market databases, social network APIs, and customer relationship management systems. It automatically retrieves data using APIs and stores it in a standardized format.

[0427] Step 2:

[0428] The server inputs the collected data into a machine learning algorithm to score the likelihood of a potential customer closing a deal. This process uses a model based on past sales negotiations to calculate a quantified probability of closing a deal for each customer.

[0429] Step 3:

[0430] The terminal displays a scored list of potential customers sent from the server via a user interface. The list is sorted by likelihood of conversion, and detailed information for each customer is available.

[0431] Step 4:

[0432] The server analyzes each customer's purchase history and current market trends, and generates personalized approach strategies based on this analysis. Data mining techniques are used to clarify customer needs and characteristics.

[0433] Step 5:

[0434] The terminal graphically displays an approach strategy generated based on the analysis results to the user. The strategy includes a specific action plan, which the user uses to plan their sales activities.

[0435] Step 6:

[0436] The server monitors the progress of each stage of a sales opportunity, refers to past success stories to determine the next necessary actions, and generates alerts and suggestions based on the progress.

[0437] Step 7:

[0438] The terminal notifies the user in real time of action suggestions from the server. Based on this information, the user can quickly implement strategies to advance the business negotiation.

[0439] Step 8:

[0440] The server continuously updates the conversion prediction model using all the data. Leveraging business process management methodologies, the system enhances its ability to prioritize and propose leads with a high probability of success in future business negotiations.

[0441] (Example 1)

[0442] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0443] In traditional sales processes, it is difficult to efficiently collect, integrate, and analyze data from diverse sources. In particular, accurately assessing the likelihood of closing a deal with a potential customer and predicting customer purchasing needs based on market fluctuations is challenging. This inability to propose effective actions in negotiations in a timely manner hinders improved sales performance. Furthermore, accurately proposing the next steps based on progress is also difficult.

[0444] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0445] In this invention, the server includes means for integrating and processing market information, social network information, and customer management information collected using an information acquisition unit; means for evaluating the likelihood of a contract being concluded with a potential buyer based on the integrated information using a machine learning method; and means for generating a list of potential buyers based on the evaluation results and presenting it using a visual display device. This makes it possible to effectively collect data from diverse information sources, evaluate the likelihood of a potential customer closing a deal with high accuracy, and propose the optimal action based on the progress of the business negotiation.

[0446] The "Information Acquisition Unit" is a component that has the function of efficiently collecting market information, social network information, and customer management information from various sources.

[0447] "Integration processing" is the process of combining multiple acquired pieces of information into a single dataset while ensuring consistency.

[0448] "Machine learning techniques" are technologies that use algorithms to analyze large amounts of data and extract meaningful patterns and features from it.

[0449] A "potential buyer" refers to a customer who has not yet purchased a product but is likely to purchase it in the future.

[0450] "Contract completion probability" is an indicator that shows the degree to which a potential customer is likely to actually purchase the product.

[0451] A "visual display device" is a device used to present information to a user visually, and generally refers to computer displays or smartphone screens.

[0452] "Evaluation results" refer to the results of analysis obtained using data analysis and machine learning techniques, and serve as the foundation for making decisions based on those results.

[0453] "Business negotiation" refers to the process of conducting business transactions, specifically the activity of negotiating with customers regarding the purchase of products or services.

[0454] "Recommended actions" are guidelines designed to show users the optimal actions based on the analysis results.

[0455] This invention is an automated system designed to streamline the sales process and improve the closing rate. This system is primarily built around three core elements: a server, terminals, and users.

[0456] The server plays a role in collecting and integrating data from diverse sources. Specifically, it acquires market information, social network information, and customer management information through APIs and data feeds, and then integrates and processes this data using cloud computing platforms such as Amazon Web Services and Microsoft Azure. In this process, ETL processes and tools such as Talend and Apache Kafka are utilized.

[0457] The server uses an integrated dataset to assess the likelihood of a deal being closed with a potential buyer. Machine learning frameworks such as TensorFlow and PyTorch are used for this assessment. This allows for highly accurate estimation of the likelihood of a potential customer closing a deal from large datasets.

[0458] The evaluation results are presented to the user via a terminal. The terminal uses Tableau or Power BI to build dashboards that visually display the evaluation results and analytical data. Based on this information, users can efficiently develop sales strategies.

[0459] Furthermore, the server analyzes past purchase history and market trends, using SAS and R languages ​​to predict customer purchase requests. This predictive information is also provided to users via terminals and effectively utilized in actual business negotiations.

[0460] As a concrete example, when a user launches a new product into the market, the server collects consumer feedback from social networks and displays it on a dashboard on the user's device. This allows the user to plan and implement the most effective marketing campaigns for specific customer segments.

[0461] An example of a prompt using a generative AI model would be: "Please suggest an effective marketing strategy for a specific segment. Social network reviews are used as the data source." Through this prompt, the AI ​​model suggests optimal marketing methods and target audiences, which are then used in actual sales activities.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The server receives market information, social network information, and customer management information via APIs. This input data is stored on cloud storage. The server integrates this data through an ETL process (extract, transform, load). Data transformation uses Talend or Apache Kafka to unify and cleanse data in multiple formats, thereby generating a consistent dataset.

[0465] Step 2:

[0466] The server trains a machine learning model using an integrated dataset. Inputs include customer attribute data and historical transaction history. It builds the model using TensorFlow and PyTorch, performing data calculations to evaluate the likelihood of each potential buyer closing a deal. The output is a prioritized list of each potential customer's conversion probability.

[0467] Step 3:

[0468] The terminal receives a list of conversion probability sent from the server and displays it on the user interface. The input is conversion probability data from the server, and the output is a visual dashboard for the user. Power BI and Tableau are used to visualize the data and perform concrete actions to support the user's decision-making.

[0469] Step 4:

[0470] The server predicts customer purchasing demand based on past purchase history and market trend data. Input data includes historical transaction information and market change information. Data processing uses SAS and R languages, applying advanced statistical analysis techniques. The output results, including predicted purchasing needs, are generated and transferred to the terminal.

[0471] Step 5:

[0472] The terminal notifies the user of predicted purchasing needs and suggests the optimal next action in the sales negotiation. This notification function is implemented through chat applications and email systems. The user receives action suggestions sent from the server and confirms the specific steps to take in sales activities based on them.

[0473] Step 6:

[0474] The server monitors the progress of sales opportunities in real time. Input data includes information on each stage of the opportunity, and the progress data is processed using a business process management (BPM) tool. The output generates instructions indicating the next action based on past success patterns.

[0475] (Application Example 1)

[0476] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0477] Conventional commercial transaction support systems struggle to provide effective product recommendations tailored to individual consumer needs, resulting in insufficient improvements in closing rates. Furthermore, there is a need to properly manage the progress of commercial transactions and propose swift and accurate actions. In addition, a system is needed to efficiently process vast amounts of market information and streamline the identification of potential customers and the evaluation of their likelihood of closing a deal.

[0478] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0479] In this invention, the server includes means for aggregating information, means for integrating the aggregated information and evaluating the probability of a commercial transaction with a potential buyer, means for creating and presenting a list of potential buyers based on the evaluation results, and means for making individually tailored product proposals. This makes it possible to improve the closing rate through individually tailored product proposals, support optimal actions at each stage of the commercial transaction, and significantly improve overall sales efficiency.

[0480] "Means of aggregating information" refers to functions that efficiently collect and consolidate market data, social network data, and customer relationship management system data in one place.

[0481] "A means of integrating aggregated information and evaluating the probability of a commercial transaction for a potential buyer" refers to a process that uses algorithms to analyze various collected data and quantify the likelihood of a commercial transaction for each individual potential buyer.

[0482] "A means of creating and presenting a list of potential buyers based on evaluation results" refers to a function that generates a prioritized list of potential buyers based on the results of evaluating the probability of closing a deal, and displays that information in a format that is usable by the user.

[0483] "Means of providing individually tailored product suggestions" refers to a function that analyzes individual consumers' interests and past purchase history, and then suggests personalized products and services based on that analysis.

[0484] To implement this invention, three elements—a server, a terminal, and a user—must work together to perform their functions. The server utilizes AWS cloud infrastructure and employs machine learning models using Python and TensorFlow to build a system foundation for aggregating and integrating information. First, the server takes in market data, social network data, and customer relationship management data and aggregates the information. Next, it integrates the aggregated data and applies a generative AI model to evaluate the probability of commercial conversion for potential buyers.

[0485] These evaluation results are displayed to the user through a smartphone app developed with React Native that runs on the device. The device presents the user with a list of potential buyers sent from the server and assists in providing product suggestions tailored to individual needs.

[0486] Users can conduct more efficient and strategic marketing activities based on data provided by their devices. For example, when a user opens the app on their way home, the app will analyze their past purchase history and market trends to recommend the most suitable products to them in a timely manner, and offer them with special benefits.

[0487] A concrete example of a prompt from a generative AI model would be, "Based on the user's purchase history data, please create the optimal sales campaign that suggests items the user is likely to purchase next." This improves the overall efficiency of the system and significantly increases the conversion rate of commercial activities.

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] The server collects market data, social data, and customer management data. The input is raw information from multiple data sources, and the output is a database of this integrated information. Cloud services are used for data collection, and APIs are configured to gather data in real time.

[0491] Step 2:

[0492] The server applies machine learning algorithms based on aggregated data to evaluate the probability of a potential buyer closing a deal. The input is an integrated database, and the output is a list of potential buyers with their conversion probabilities. TensorFlow is used for data processing, and a generative AI model is used to predict conversion rates.

[0493] Step 3:

[0494] The server creates a list of potential buyers, each assigned a probability of conversion, and sends it to the device. The input is an evaluated list of potential buyers, and the output is a list of buyers ready to be sent. The list is prioritized within the server and delivered to the smartphone device via API.

[0495] Step 4:

[0496] The device reviews the received list and displays the most suitable product suggestions for the user on the screen. The input is a list of potential buyers sent from the server, and the output is product suggestions displayed in the user interface. The UI is created using React Native to display product offer information clearly in real time.

[0497] Step 5:

[0498] The user evaluates the presented product suggestions and makes a purchase decision. The input is the product suggestions displayed on the terminal, and the output is the purchase decision information. The user's feedback is sent back to the server and used to improve future suggestions.

[0499] Step 6:

[0500] The server updates the database based on user feedback and incorporates it into the next prediction model. The input is user feedback data, and the output is the updated prediction model. The system continuously improves its accuracy through this feedback loop.

[0501] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0502] This invention is an automated system for highly optimizing sales processes, and by combining it with an emotion engine, it enables customer service that takes into account the user's emotional state. This system consists of three main components: a server, a terminal, and a user, and is realized through their coordination.

[0503] Data collection and analysis:

[0504] The server continuously collects and integrates market data, social network data, and customer relationship management system data. Using this data, the server calculates a conversion probability score for potential customers. Furthermore, it analyzes past purchase history and market trend data to predict customer purchasing needs.

[0505] Emotion recognition by an emotion engine:

[0506] When a user interacts with a customer, the emotion engine on the device analyzes the user's voice tone and facial expressions in real time to recognize their current emotional state. The emotion engine combines voice recognition technology and facial expression analysis algorithms to provide highly reliable emotional data.

[0507] Integrating customer approach strategies:

[0508] The server dynamically adjusts its customer interaction approach strategy based on emotional data acquired by the emotion engine. This helps users implement the most appropriate communication strategy during their interactions with customers.

[0509] Feedback and improvements:

[0510] After the interaction ends, the terminal provides the user with feedback generated by the server. This feedback includes an analysis of how the user's emotional state influenced the sales negotiation. This allows the user to gain metrics to improve their sales skills.

[0511] Specific example:

[0512] For example, when a user is conducting a remote business negotiation with a customer, if the emotion engine detects the user's stress level, the server will immediately provide suggestions such as specific phrases to return the conversation to a calmer tone or highlight the importance of taking breaks. This allows the user to maintain a good relationship with the customer and increase the likelihood of closing a deal.

[0513] In this way, the present invention aims to enhance the entire sales process with a data-driven and emotion-recognition-based approach, thereby improving conversion rates and customer satisfaction.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The server collects market data, social network data, and customer relationship management system data. This ensures that all information related to sales activities is updated in real time and integrated into the database.

[0517] Step 2:

[0518] The server analyzes the collected data using machine learning algorithms to evaluate the likelihood of a potential customer converting into a customer. The evaluation results are quantified and stored as a conversion probability score for each customer.

[0519] Step 3:

[0520] The terminal displays a list of potential customers based on the evaluation results. The list is sorted in order of likelihood of closing a deal, and the user plans their sales activities based on this list.

[0521] Step 4:

[0522] When a user begins interacting with a customer, the device activates its emotion engine, analyzing the user's voice and facial expressions in real time. If a specific emotional state is detected, the data is sent to the server.

[0523] Step 5:

[0524] The server receives data from the emotion engine and adjusts the customer approach strategy according to the user's emotional state. For example, if the user is feeling stressed, it suggests ways to make the conversation more relaxing.

[0525] Step 6:

[0526] The terminal notifies the user of strategic suggestions provided by the server. Based on this information, the user takes the optimal approach to the customer and facilitates smooth communication.

[0527] Step 7:

[0528] After a business negotiation concludes, the server analyzes the emotional data collected during the conversation and the outcome of the negotiation to identify areas for improvement. The terminal then presents this feedback to the user, which can be used to improve future business negotiations.

[0529] Step 8:

[0530] The server continuously updates the sales prediction model using all the data. This allows the system to provide users with effective strategies for future sales negotiations.

[0531] (Example 2)

[0532] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0533] Traditional sales processes often failed to accurately assess the likelihood of closing a deal and made it difficult to consider customer emotions, thus hindering effective improvements in customer satisfaction and closing rates. Furthermore, inefficient data integration and analysis made it difficult to determine appropriate next steps.

[0534] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0535] In this invention, the server includes a device for collecting information, a device for integrating the collected information and evaluating the likelihood of conversion for potential customers, and a device for creating and displaying a list of potential customers based on the evaluation. This makes it possible to identify customers with a high likelihood of conversion and to conduct efficient sales activities. Furthermore, adjusting customer service strategies based on emotion recognition contributes to improving customer satisfaction.

[0536] A "device for collecting information" is a device for acquiring data from market information, network information, and customer management systems.

[0537] A "device that integrates collected information and evaluates the likelihood of conversion with potential customers" is a device that centralizes information obtained from various data sources and quantifies the customer's purchasing intent based on that information.

[0538] A "device for creating and displaying a list of potential customers" is a device that selects customers with a high probability of conversion and visualizes them in a list.

[0539] A "device that performs voice and image analysis to recognize the emotional state of a user" is a device that analyzes voice tone and visual data to identify the user's emotions.

[0540] A "device that adjusts customer service strategies based on emotional state" is a device that dynamically changes the optimal dialogue method and sales strategy based on identified emotional information.

[0541] This invention is an automated system that optimizes sales processes in a data-driven and sentiment-recognition-based manner. The system consists of three main components: a server, terminals, and users.

[0542] The server collects market information, network information, and customer management data through APIs and database connections. This includes CRM platforms such as Trello and Salesforce. The collected information is preprocessed using the Python pandas library and analyzed with machine learning algorithms using scikit-learn. This analysis identifies and scores potential customers with a high probability of conversion.

[0543] The device activates an emotion engine that identifies voice tone and facial expressions when the user communicates with a customer. This emotion engine uses speech recognition technology and OpenCV to recognize the user's emotional state in real time. The results are then used to determine appropriate customer service strategies.

[0544] Users receive conversion probability scores and sentiment-based feedback from the server, which they can use in their sales activities. This feedback includes supplementary information about the impact of emotions on sales opportunities, allowing users to objectively improve their sales approach.

[0545] For example, when a user is conducting an online business meeting, if the emotion engine built into the device detects the user's stress, the server immediately displays a suggestion on the device such as, "Please be mindful of speaking in a calm voice." This real-time feedback allows the user to implement an appropriate communication strategy with the customer.

[0546] As an example of a prompt to input into a generative AI model, it is possible to suggest appropriate dialogue methods in the form of, "Please tell me a phrase to use when emotional stress is detected in a sales conversation."

[0547] In this way, the system aims to improve sales performance and customer satisfaction by combining advanced data processing and emotion recognition technology.

[0548] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0549] Step 1:

[0550] The server collects data from market information, network information, and customer management systems. It takes raw data as input via APIs and database connections. Specifically, the server queries external data sources at regular intervals and saves the necessary information to the server in JSON format. The output is an integrated dataset.

[0551] Step 2:

[0552] The server integrates the collected information and evaluates the likelihood of conversion. It uses the dataset obtained from Step 1 as input. Data processing involves handling missing values ​​and normalizing the data using the pandas library, and then training a model with a machine learning algorithm using scikit-learn. The output is a conversion likelihood score calculated for each customer. Specifically, the server inputs the data into the trained model and scores the conversion likelihood for each customer.

[0553] Step 3:

[0554] The device collects audio and image data to recognize the user's emotional state. It uses real-time audio and video data acquired through a microphone and camera as input. Specifically, the device runs a speech recognition engine and the OpenCV library to analyze speech tone and perform facial expression recognition. The output is a real-time determination of the user's emotional state.

[0555] Step 4:

[0556] The server adjusts customer interaction strategies based on the emotional state. It uses the user's emotional data obtained in step 3 as input. As a data calculation, it combines the emotional data with the likelihood of conversion to generate appropriate dialogue scripts or sales strategies. In concrete action, the server generates situation-appropriate approaches and sends the information to the terminal. As output, a specific communication strategy for the user is formulated.

[0557] Step 5:

[0558] The terminal provides feedback to the user after the sales negotiation is completed. It receives feedback data sent from the server as input. Specifically, the terminal displays the analysis results on the screen, allowing the user to see how their emotional state influenced the negotiation. The output provides insights to help the user improve their sales skills.

[0559] (Application Example 2)

[0560] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0561] In recent years, there has been a growing demand for rapid responses to diverse customer needs, but traditional sales processes have made this difficult. In particular, the lack of consideration for customer emotions in communication has led to declining conversion rates and difficulties in improving customer satisfaction. Furthermore, even in areas such as electronic payment services, there is a growing need for flexible responses that reflect customer emotions in real time.

[0562] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0563] In this invention, the server includes means for collecting market information, social network information, and customer information management system information; means for integrating the collected information and evaluating the likelihood of conversion for prospective customers; means for generating and displaying prospective customer lists based on the evaluation results; means for capturing customer voice or text and analyzing their emotional state; and means for dynamically presenting the optimal customer service strategy based on the emotional state. This enables the realization of an effective and flexible sales process that takes into account the emotional state of customers and the achievement of high customer satisfaction within electronic payment services.

[0564] "Market information" refers to numerical data, statistical information, and information on consumer behavior that reflect market trends and developments.

[0565] "Social network information" refers to information based on user posts and reactions collected on social media and online platforms.

[0566] "Customer information management system information" refers to information used to manage customer contact history, transaction history, and individual customer information.

[0567] A "prospective customer" is a potential customer who is expected to purchase a product or service in the future.

[0568] "Closing probability" is an indicator that shows the degree of likelihood that a business negotiation will be successful and a formal contract will be concluded.

[0569] "Means for capturing voice or text and analyzing emotional states" refers to methods for detecting and analyzing customer emotions using speech recognition or natural language processing technologies.

[0570] "A means of dynamically presenting the optimal customer service strategy" refers to a method of proposing appropriate responses and customer service methods in real time, according to the customer's emotional state.

[0571] To implement this invention, a system is used in which a server, a terminal, and a user work in cooperation. The server collects and integrates market information, social network information, and customer information management system information. This allows it to evaluate the likelihood of conversion for prospective customers and generate lists. The terminal uses speech recognition technology and natural language processing technology to capture customer voice and text and analyze their emotional state in real time. The analysis results are sent to the server, which generates an optimal customer response strategy, which is then dynamically presented to the terminal. The user then proceeds with the interaction with the customer based on this strategy. The hardware used includes a computer with a high-performance processor and a device equipped with state-of-the-art microphones and cameras. The software uses a speech recognition library (e.g., speech_recognition), a natural language processing library (e.g., nltk), and an emotion recognition algorithm.

[0572] As a concrete example, when a user provides customer support for an electronic payment service, if a customer inquiry comes in, the terminal analyzes the voice and recognizes that the customer's emotions indicate anxiety or dissatisfaction. This information is sent to the server, and a quick solution is generated. This allows the user to respond appropriately and increase customer satisfaction. An example of a prompt to the generating AI model would be, "Please suggest the best solution for this customer's problem. Also, please provide a hospitable response method based on the customer's emotional state."

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The server periodically collects market information, social network information, and customer information management system information. It takes the latest data from various sources as input and integrates this data to build an information base. Based on this integrated data, it runs an algorithm to evaluate the likelihood of prospects converting into customers and generates a list of prospects.

[0576] Step 2:

[0577] When a user begins interacting with a customer, the device captures the customer's voice and facial expressions in real time using its microphone and camera. It receives voice and visual data as input and analyzes the customer's emotional state using natural language processing libraries and vision analysis algorithms. This process identifies whether the customer's current emotions are anxiety, dissatisfaction, or other similar states.

[0578] Step 3:

[0579] The server receives emotional state data acquired from the terminal. Based on this data, the server inputs prompts into a generative AI model to generate the optimal customer response strategy. Using emotional data as input, the AI ​​performs data calculations based on past success stories and customer profiles to generate the response.

[0580] Step 4:

[0581] The customer service strategy generated on the server is sent to the terminal. The terminal displays this strategy to the user and presents specific response methods. As output, it provides specific phrases and suggestions for the user to take action.

[0582] Step 5:

[0583] Users interact with customers based on strategies presented on their devices. The user's interactions and customer responses are then captured again on the device and sent to the server. The server analyzes this data as feedback and stores it as learning data to improve future interactions.

[0584] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0585] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0586] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0587] [Fourth Embodiment]

[0588] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0589] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0590] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0591] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0592] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0593] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0594] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0595] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0596] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0597] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0598] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0599] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0600] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0601] This invention is an automated system for streamlining the sales process and improving the closing rate. This system primarily consists of three elements: a server, terminals, and users.

[0602] Data collection and integration:

[0603] The server collects market data, social network data, and customer relationship management system data. This data is obtained from multiple sources and integrated to create detailed profiles of potential customers.

[0604] Assessing the likelihood of a potential customer converting into a customer:

[0605] The server uses machine learning algorithms to evaluate the likelihood of each potential customer converting, based on the integrated data. Based on the evaluation results, the server creates a list in order of priority and displays it on the terminal.

[0606] Predicting customer needs:

[0607] The server analyzes past purchase history data and market trend data to predict each customer's purchasing needs. These analysis results are then provided to the user via their terminal.

[0608] Sales negotiation progress management and action proposals:

[0609] The server monitors the progress of the sales negotiation in real time. This allows for the suggestion of the optimal action at each stage of the negotiation, based on past success stories. The terminal notifies the user of these suggestions, and the user takes the next action accordingly.

[0610] Deal prediction model:

[0611] The server utilizes Business Process Management (EBPM) methodologies to build a model that predicts the likelihood of closing a deal. This model identifies customers with the highest probability of closing a deal and provides guidance for making effective proposals to users.

[0612] This system enables users to implement a data-driven sales approach, allowing for effective resource allocation and action planning. For example, in marketing activities for a specific product, the system analyzes consumer reviews from social media and suggests that a price-competitive campaign would be effective for a particular segment. In this way, the success rate of sales activities can be increased.

[0613] The following describes the processing flow.

[0614] Step 1:

[0615] The server collects the latest data from market databases, social network APIs, and customer relationship management systems. It automatically retrieves data using APIs and stores it in a standardized format.

[0616] Step 2:

[0617] The server inputs the collected data into a machine learning algorithm to score the likelihood of a potential customer closing a deal. This process uses a model based on past sales negotiations to calculate a quantified probability of closing a deal for each customer.

[0618] Step 3:

[0619] The terminal displays a scored list of potential customers sent from the server via a user interface. The list is sorted by likelihood of conversion, and detailed information for each customer is available.

[0620] Step 4:

[0621] The server analyzes each customer's purchase history and current market trends, and generates personalized approach strategies based on this analysis. Data mining techniques are used to clarify customer needs and characteristics.

[0622] Step 5:

[0623] The terminal graphically displays an approach strategy generated based on the analysis results to the user. The strategy includes a specific action plan, which the user uses to plan their sales activities.

[0624] Step 6:

[0625] The server monitors the progress of each stage of a sales opportunity, refers to past success stories to determine the next necessary actions, and generates alerts and suggestions based on the progress.

[0626] Step 7:

[0627] The terminal notifies the user in real time of action suggestions from the server. Based on this information, the user can quickly implement strategies to advance the business negotiation.

[0628] Step 8:

[0629] The server continuously updates the conversion prediction model using all the data. Leveraging business process management methodologies, the system enhances its ability to prioritize and propose leads with a high probability of success in future business negotiations.

[0630] (Example 1)

[0631] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0632] In traditional sales processes, it is difficult to efficiently collect, integrate, and analyze data from diverse sources. In particular, accurately assessing the likelihood of closing a deal with a potential customer and predicting customer purchasing needs based on market fluctuations is challenging. This inability to propose effective actions in negotiations in a timely manner hinders improved sales performance. Furthermore, accurately proposing the next steps based on progress is also difficult.

[0633] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0634] In this invention, the server includes means for integrating and processing market information, social network information, and customer management information collected using an information acquisition unit; means for evaluating the likelihood of a contract being concluded with a potential buyer based on the integrated information using a machine learning method; and means for generating a list of potential buyers based on the evaluation results and presenting it using a visual display device. This makes it possible to effectively collect data from diverse information sources, evaluate the likelihood of a potential customer closing a deal with high accuracy, and propose the optimal action based on the progress of the business negotiation.

[0635] The "Information Acquisition Unit" is a component that has the function of efficiently collecting market information, social network information, and customer management information from various sources.

[0636] "Integration processing" is the process of combining multiple acquired pieces of information into a single dataset while ensuring consistency.

[0637] "Machine learning techniques" are technologies that use algorithms to analyze large amounts of data and extract meaningful patterns and features from it.

[0638] A "potential buyer" refers to a customer who has not yet purchased a product but is likely to purchase it in the future.

[0639] "Contract completion probability" is an indicator that shows the degree to which a potential customer is likely to actually purchase the product.

[0640] A "visual display device" is a device used to present information to a user visually, and generally refers to computer displays or smartphone screens.

[0641] "Evaluation results" refer to the results of analysis obtained using data analysis and machine learning techniques, and serve as the foundation for making decisions based on those results.

[0642] "Business negotiation" refers to the process of conducting business transactions, specifically the activity of negotiating with customers regarding the purchase of products or services.

[0643] "Recommended actions" are guidelines designed to show users the optimal actions based on the analysis results.

[0644] This invention is an automated system designed to streamline the sales process and improve the closing rate. This system is primarily built around three core elements: a server, terminals, and users.

[0645] The server plays a role in collecting and integrating data from diverse sources. Specifically, it acquires market information, social network information, and customer management information through APIs and data feeds, and then integrates and processes this data using cloud computing platforms such as Amazon Web Services and Microsoft Azure. In this process, ETL processes and tools such as Talend and Apache Kafka are utilized.

[0646] The server uses an integrated dataset to assess the likelihood of a deal being closed with a potential buyer. Machine learning frameworks such as TensorFlow and PyTorch are used for this assessment. This allows for highly accurate estimation of the likelihood of a potential customer closing a deal from large datasets.

[0647] The evaluation results are presented to the user via a terminal. The terminal uses Tableau or Power BI to build dashboards that visually display the evaluation results and analytical data. Based on this information, users can efficiently develop sales strategies.

[0648] Furthermore, the server analyzes past purchase history and market trends, using SAS and R languages ​​to predict customer purchase requests. This predictive information is also provided to users via terminals and effectively utilized in actual business negotiations.

[0649] As a concrete example, when a user launches a new product into the market, the server collects consumer feedback from social networks and displays it on a dashboard on the user's device. This allows the user to plan and implement the most effective marketing campaigns for specific customer segments.

[0650] An example of a prompt using a generative AI model would be: "Please suggest an effective marketing strategy for a specific segment. Social network reviews are used as the data source." Through this prompt, the AI ​​model suggests optimal marketing methods and target audiences, which are then used in actual sales activities.

[0651] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0652] Step 1:

[0653] The server receives market information, social network information, and customer management information via APIs. This input data is stored on cloud storage. The server integrates this data through an ETL process (extract, transform, load). Data transformation uses Talend or Apache Kafka to unify and cleanse data in multiple formats, thereby generating a consistent dataset.

[0654] Step 2:

[0655] The server trains a machine learning model using an integrated dataset. Inputs include customer attribute data and historical transaction history. It builds the model using TensorFlow and PyTorch, performing data calculations to evaluate the likelihood of each potential buyer closing a deal. The output is a prioritized list of each potential customer's conversion probability.

[0656] Step 3:

[0657] The terminal receives a list of conversion probability sent from the server and displays it on the user interface. The input is conversion probability data from the server, and the output is a visual dashboard for the user. Power BI and Tableau are used to visualize the data and perform concrete actions to support the user's decision-making.

[0658] Step 4:

[0659] The server predicts customer purchasing demand based on past purchase history and market trend data. Input data includes historical transaction information and market change information. Data processing uses SAS and R languages, applying advanced statistical analysis techniques. The output results, including predicted purchasing needs, are generated and transferred to the terminal.

[0660] Step 5:

[0661] The terminal notifies the user of predicted purchasing needs and suggests the optimal next action in the sales negotiation. This notification function is implemented through chat applications and email systems. The user receives action suggestions sent from the server and confirms the specific steps to take in sales activities based on them.

[0662] Step 6:

[0663] The server monitors the progress of sales opportunities in real time. Input data includes information on each stage of the opportunity, and the progress data is processed using a business process management (BPM) tool. The output generates instructions indicating the next action based on past success patterns.

[0664] (Application Example 1)

[0665] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] Conventional commercial transaction support systems struggle to provide effective product recommendations tailored to individual consumer needs, resulting in insufficient improvements in closing rates. Furthermore, there is a need to properly manage the progress of commercial transactions and propose swift and accurate actions. In addition, a system is needed to efficiently process vast amounts of market information and streamline the identification of potential customers and the evaluation of their likelihood of closing a deal.

[0667] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0668] In this invention, the server includes means for aggregating information, means for integrating the aggregated information and evaluating the probability of a commercial transaction with a potential buyer, means for creating and presenting a list of potential buyers based on the evaluation results, and means for making individually tailored product proposals. This makes it possible to improve the closing rate through individually tailored product proposals, support optimal actions at each stage of the commercial transaction, and significantly improve overall sales efficiency.

[0669] "Means of aggregating information" refers to functions that efficiently collect and consolidate market data, social network data, and customer relationship management system data in one place.

[0670] "A means of integrating aggregated information and evaluating the probability of a commercial transaction for a potential buyer" refers to a process that uses algorithms to analyze various collected data and quantify the likelihood of a commercial transaction for each individual potential buyer.

[0671] "A means of creating and presenting a list of potential buyers based on evaluation results" refers to a function that generates a prioritized list of potential buyers based on the results of evaluating the probability of closing a deal, and displays that information in a format that is usable by the user.

[0672] "Means of providing individually tailored product suggestions" refers to a function that analyzes individual consumers' interests and past purchase history, and then suggests personalized products and services based on that analysis.

[0673] To implement this invention, three elements—a server, a terminal, and a user—must work together to perform their functions. The server utilizes AWS cloud infrastructure and employs machine learning models using Python and TensorFlow to build a system foundation for aggregating and integrating information. First, the server takes in market data, social network data, and customer relationship management data and aggregates the information. Next, it integrates the aggregated data and applies a generative AI model to evaluate the probability of commercial conversion for potential buyers.

[0674] These evaluation results are displayed to the user through a smartphone app developed with React Native that runs on the device. The device presents the user with a list of potential buyers sent from the server and assists in providing product suggestions tailored to individual needs.

[0675] Users can conduct more efficient and strategic marketing activities based on data provided by their devices. For example, when a user opens the app on their way home, the app will analyze their past purchase history and market trends to recommend the most suitable products to them in a timely manner, and offer them with special benefits.

[0676] A concrete example of a prompt from a generative AI model would be, "Based on the user's purchase history data, please create the optimal sales campaign that suggests items the user is likely to purchase next." This improves the overall efficiency of the system and significantly increases the conversion rate of commercial activities.

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] The server collects market data, social data, and customer management data. The input is raw information from multiple data sources, and the output is a database of this integrated information. Cloud services are used for data collection, and APIs are configured to gather data in real time.

[0680] Step 2:

[0681] The server applies machine learning algorithms based on aggregated data to evaluate the probability of a potential buyer closing a deal. The input is an integrated database, and the output is a list of potential buyers with their conversion probabilities. TensorFlow is used for data processing, and a generative AI model is used to predict conversion rates.

[0682] Step 3:

[0683] The server creates a list of potential buyers, each assigned a probability of conversion, and sends it to the device. The input is an evaluated list of potential buyers, and the output is a list of buyers ready to be sent. The list is prioritized within the server and delivered to the smartphone device via API.

[0684] Step 4:

[0685] The device reviews the received list and displays the most suitable product suggestions for the user on the screen. The input is a list of potential buyers sent from the server, and the output is product suggestions displayed in the user interface. The UI is created using React Native to display product offer information clearly in real time.

[0686] Step 5:

[0687] The user evaluates the presented product suggestions and makes a purchase decision. The input is the product suggestions displayed on the terminal, and the output is the purchase decision information. The user's feedback is sent back to the server and used to improve future suggestions.

[0688] Step 6:

[0689] The server updates the database based on user feedback and incorporates it into the next prediction model. The input is user feedback data, and the output is the updated prediction model. The system continuously improves its accuracy through this feedback loop.

[0690] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0691] This invention is an automated system for highly optimizing sales processes, and by combining it with an emotion engine, it enables customer service that takes into account the user's emotional state. This system consists of three main components: a server, a terminal, and a user, and is realized through their coordination.

[0692] Data collection and analysis:

[0693] The server continuously collects and integrates market data, social network data, and customer relationship management system data. Using this data, the server calculates a conversion probability score for potential customers. Furthermore, it analyzes past purchase history and market trend data to predict customer purchasing needs.

[0694] Emotion recognition by an emotion engine:

[0695] When a user interacts with a customer, the emotion engine on the device analyzes the user's voice tone and facial expressions in real time to recognize their current emotional state. The emotion engine combines voice recognition technology and facial expression analysis algorithms to provide highly reliable emotional data.

[0696] Integrating customer approach strategies:

[0697] The server dynamically adjusts its customer interaction approach strategy based on emotional data acquired by the emotion engine. This helps users implement the most appropriate communication strategy during their interactions with customers.

[0698] Feedback and improvements:

[0699] After the interaction ends, the terminal provides the user with feedback generated by the server. This feedback includes an analysis of how the user's emotional state influenced the sales negotiation. This allows the user to gain metrics to improve their sales skills.

[0700] Specific example:

[0701] For example, when a user is conducting a remote business negotiation with a customer, if the emotion engine detects the user's stress level, the server will immediately provide suggestions such as specific phrases to return the conversation to a calmer tone or highlight the importance of taking breaks. This allows the user to maintain a good relationship with the customer and increase the likelihood of closing a deal.

[0702] In this way, the present invention aims to enhance the entire sales process with a data-driven and emotion-recognition-based approach, thereby improving conversion rates and customer satisfaction.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] The server collects market data, social network data, and customer relationship management system data. This ensures that all information related to sales activities is updated in real time and integrated into the database.

[0706] Step 2:

[0707] The server analyzes the collected data using machine learning algorithms to evaluate the likelihood of a potential customer converting into a customer. The evaluation results are quantified and stored as a conversion probability score for each customer.

[0708] Step 3:

[0709] The terminal displays a list of potential customers based on the evaluation results. The list is sorted in order of likelihood of closing a deal, and the user plans their sales activities based on this list.

[0710] Step 4:

[0711] When a user begins interacting with a customer, the device activates its emotion engine, analyzing the user's voice and facial expressions in real time. If a specific emotional state is detected, the data is sent to the server.

[0712] Step 5:

[0713] The server receives data from the emotion engine and adjusts the customer approach strategy according to the user's emotional state. For example, if the user is feeling stressed, it suggests ways to make the conversation more relaxing.

[0714] Step 6:

[0715] The terminal notifies the user of strategic suggestions provided by the server. Based on this information, the user takes the optimal approach to the customer and facilitates smooth communication.

[0716] Step 7:

[0717] After a business negotiation concludes, the server analyzes the emotional data collected during the conversation and the outcome of the negotiation to identify areas for improvement. The terminal then presents this feedback to the user, which can be used to improve future business negotiations.

[0718] Step 8:

[0719] The server continuously updates the sales prediction model using all the data. This allows the system to provide users with effective strategies for future sales negotiations.

[0720] (Example 2)

[0721] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0722] Traditional sales processes often failed to accurately assess the likelihood of closing a deal and made it difficult to consider customer emotions, thus hindering effective improvements in customer satisfaction and closing rates. Furthermore, inefficient data integration and analysis made it difficult to determine appropriate next steps.

[0723] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0724] In this invention, the server includes a device for collecting information, a device for integrating the collected information and evaluating the likelihood of conversion for potential customers, and a device for creating and displaying a list of potential customers based on the evaluation. This makes it possible to identify customers with a high likelihood of conversion and to conduct efficient sales activities. Furthermore, adjusting customer service strategies based on emotion recognition contributes to improving customer satisfaction.

[0725] A "device for collecting information" is a device for acquiring data from market information, network information, and customer management systems.

[0726] A "device that integrates collected information and evaluates the likelihood of conversion with potential customers" is a device that centralizes information obtained from various data sources and quantifies the customer's purchasing intent based on that information.

[0727] A "device for creating and displaying a list of potential customers" is a device that selects customers with a high probability of conversion and visualizes them in a list.

[0728] A "device that performs voice and image analysis to recognize the emotional state of a user" is a device that analyzes voice tone and visual data to identify the user's emotions.

[0729] A "device that adjusts customer service strategies based on emotional state" is a device that dynamically changes the optimal dialogue method and sales strategy based on identified emotional information.

[0730] This invention is an automated system that optimizes sales processes in a data-driven and sentiment-recognition-based manner. The system consists of three main components: a server, terminals, and users.

[0731] The server collects market information, network information, and customer management data through APIs and database connections. This includes CRM platforms such as Trello and Salesforce. The collected information is preprocessed using the Python pandas library and analyzed with machine learning algorithms using scikit-learn. This analysis identifies and scores potential customers with a high probability of conversion.

[0732] The device activates an emotion engine that identifies voice tone and facial expressions when the user communicates with a customer. This emotion engine uses speech recognition technology and OpenCV to recognize the user's emotional state in real time. The results are then used to determine appropriate customer service strategies.

[0733] Users receive conversion probability scores and sentiment-based feedback from the server, which they can use in their sales activities. This feedback includes supplementary information about the impact of emotions on sales opportunities, allowing users to objectively improve their sales approach.

[0734] For example, when a user is conducting an online business meeting, if the emotion engine built into the device detects the user's stress, the server immediately displays a suggestion on the device such as, "Please be mindful of speaking in a calm voice." This real-time feedback allows the user to implement an appropriate communication strategy with the customer.

[0735] As an example of a prompt to input into a generative AI model, it is possible to suggest appropriate dialogue methods in the form of, "Please tell me a phrase to use when emotional stress is detected in a sales conversation."

[0736] In this way, the system aims to improve sales performance and customer satisfaction by combining advanced data processing and emotion recognition technology.

[0737] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0738] Step 1:

[0739] The server collects data from market information, network information, and customer management systems. It takes raw data as input via APIs and database connections. Specifically, the server queries external data sources at regular intervals and saves the necessary information to the server in JSON format. The output is an integrated dataset.

[0740] Step 2:

[0741] The server integrates the collected information and evaluates the likelihood of conversion. It uses the dataset obtained from Step 1 as input. Data processing involves handling missing values ​​and normalizing the data using the pandas library, and then training a model with a machine learning algorithm using scikit-learn. The output is a conversion likelihood score calculated for each customer. Specifically, the server inputs the data into the trained model and scores the conversion likelihood for each customer.

[0742] Step 3:

[0743] The device collects audio and image data to recognize the user's emotional state. It uses real-time audio and video data acquired through a microphone and camera as input. Specifically, the device runs a speech recognition engine and the OpenCV library to analyze speech tone and perform facial expression recognition. The output is a real-time determination of the user's emotional state.

[0744] Step 4:

[0745] The server adjusts customer interaction strategies based on the emotional state. It uses the user's emotional data obtained in step 3 as input. As a data calculation, it combines the emotional data with the likelihood of conversion to generate appropriate dialogue scripts or sales strategies. In concrete action, the server generates situation-appropriate approaches and sends the information to the terminal. As output, a specific communication strategy for the user is formulated.

[0746] Step 5:

[0747] The terminal provides feedback to the user after the sales negotiation is completed. It receives feedback data sent from the server as input. Specifically, the terminal displays the analysis results on the screen, allowing the user to see how their emotional state influenced the negotiation. The output provides insights to help the user improve their sales skills.

[0748] (Application Example 2)

[0749] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0750] In recent years, there has been a growing demand for rapid responses to diverse customer needs, but traditional sales processes have made this difficult. In particular, the lack of consideration for customer emotions in communication has led to declining conversion rates and difficulties in improving customer satisfaction. Furthermore, even in areas such as electronic payment services, there is a growing need for flexible responses that reflect customer emotions in real time.

[0751] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0752] In this invention, the server includes means for collecting market information, social network information, and customer information management system information; means for integrating the collected information and evaluating the likelihood of conversion for prospective customers; means for generating and displaying prospective customer lists based on the evaluation results; means for capturing customer voice or text and analyzing their emotional state; and means for dynamically presenting the optimal customer service strategy based on the emotional state. This enables the realization of an effective and flexible sales process that takes into account the emotional state of customers and the achievement of high customer satisfaction within electronic payment services.

[0753] "Market information" refers to numerical data, statistical information, and information on consumer behavior that reflect market trends and developments.

[0754] "Social network information" refers to information based on user posts and reactions collected on social media and online platforms.

[0755] "Customer information management system information" refers to information used to manage customer contact history, transaction history, and individual customer information.

[0756] A "prospective customer" is a potential customer who is expected to purchase a product or service in the future.

[0757] "Closing probability" is an indicator that shows the degree of likelihood that a business negotiation will be successful and a formal contract will be concluded.

[0758] "Means for capturing voice or text and analyzing emotional states" refers to methods for detecting and analyzing customer emotions using speech recognition or natural language processing technologies.

[0759] "A means of dynamically presenting the optimal customer service strategy" refers to a method of proposing appropriate responses and customer service methods in real time, according to the customer's emotional state.

[0760] To implement this invention, a system is used in which a server, a terminal, and a user work in cooperation. The server collects and integrates market information, social network information, and customer information management system information. This allows it to evaluate the likelihood of conversion for prospective customers and generate lists. The terminal uses speech recognition technology and natural language processing technology to capture customer voice and text and analyze their emotional state in real time. The analysis results are sent to the server, which generates an optimal customer response strategy, which is then dynamically presented to the terminal. The user then proceeds with the interaction with the customer based on this strategy. The hardware used includes a computer with a high-performance processor and a device equipped with state-of-the-art microphones and cameras. The software uses a speech recognition library (e.g., speech_recognition), a natural language processing library (e.g., nltk), and an emotion recognition algorithm.

[0761] As a concrete example, when a user provides customer support for an electronic payment service, if a customer inquiry comes in, the terminal analyzes the voice and recognizes that the customer's emotions indicate anxiety or dissatisfaction. This information is sent to the server, and a quick solution is generated. This allows the user to respond appropriately and increase customer satisfaction. An example of a prompt to the generating AI model would be, "Please suggest the best solution for this customer's problem. Also, please provide a hospitable response method based on the customer's emotional state."

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The server periodically collects market information, social network information, and customer information management system information. It takes the latest data from various sources as input and integrates this data to build an information base. Based on this integrated data, it runs an algorithm to evaluate the likelihood of prospects converting into customers and generates a list of prospects.

[0765] Step 2:

[0766] When a user begins interacting with a customer, the device captures the customer's voice and facial expressions in real time using its microphone and camera. It receives voice and visual data as input and analyzes the customer's emotional state using natural language processing libraries and vision analysis algorithms. This process identifies whether the customer's current emotions are anxiety, dissatisfaction, or other similar states.

[0767] Step 3:

[0768] The server receives emotional state data acquired from the terminal. Based on this data, the server inputs prompts into a generative AI model to generate the optimal customer response strategy. Using emotional data as input, the AI ​​performs data calculations based on past success stories and customer profiles to generate the response.

[0769] Step 4:

[0770] The customer service strategy generated on the server is sent to the terminal. The terminal displays this strategy to the user and presents specific response methods. As output, it provides specific phrases and suggestions for the user to take action.

[0771] Step 5:

[0772] Users interact with customers based on strategies presented on their devices. The user's interactions and customer responses are then captured again on the device and sent to the server. The server analyzes this data as feedback and stores it as learning data to improve future interactions.

[0773] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0774] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0775] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0776] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0777] Figure 9 shows an emotion map 400 in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0778] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0779] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0780] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0781] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0782] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0783] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0784] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0785] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0786] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0787] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0788] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0789] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0790] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0791] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0792] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0793] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0794] The following is further disclosed regarding the embodiments described above.

[0795] (Claim 1)

[0796] Means for collecting market data, social network data, and customer relationship management system data,

[0797] A means of integrating collected data and evaluating the likelihood of conversion from potential customers,

[0798] A method for creating and displaying a list of potential customers based on the evaluation results,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, further comprising means for analyzing past purchase history and market trend data to estimate customer purchasing needs.

[0802] (Claim 3)

[0803] The system according to claim 1, further comprising means for monitoring progress at each stage of closing a deal and proposing the next action based on past success stories.

[0804] "Example 1"

[0805] (Claim 1)

[0806] A means for integrating and processing market information, social network information, and customer management information collected using an information acquisition unit,

[0807] A means of evaluating the likelihood of a contract being concluded with a potential buyer based on integrated information using machine learning techniques,

[0808] A means of generating a list of potential buyers based on the evaluation results and presenting it using a visual display device,

[0809] A means of notifying users of recommended actions based on analysis results,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, further comprising a function that analyzes purchase history information and market fluctuation information to predict the purchase demands of buyers.

[0813] (Claim 3)

[0814] The system according to claim 1, further comprising means for monitoring progress at each stage of the contract formation process and a function for providing recommended actions based on past successful examples.

[0815] "Application Example 1"

[0816] (Claim 1)

[0817] Means of aggregating information,

[0818] A means of integrating aggregated information and evaluating the probability of a commercial transaction with a potential buyer,

[0819] A means of creating and presenting a list of potential buyers based on the evaluation results,

[0820] A means of providing individually tailored product proposals,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, further comprising means for analyzing past purchase history and market trend information to predict individual purchase requests.

[0824] (Claim 3)

[0825] The system according to claim 1, further comprising means for tracking progress at each stage of a commercial transaction and suggesting optimal actions based on past success stories.

[0826] "Example 2 of combining an emotion engine"

[0827] (Claim 1)

[0828] A device for collecting information,

[0829] A device that integrates collected information to evaluate the likelihood of conversion with potential customers,

[0830] A device that creates and displays a list of potential customers based on evaluations,

[0831] A device that performs voice analysis and image analysis to recognize the emotional state of the user,

[0832] A device that adjusts customer service strategies based on emotional state,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, further comprising a device for analyzing past purchase history and market trend information to estimate customer purchasing needs.

[0836] (Claim 3)

[0837] The system according to claim 1, further comprising a device that monitors the progress at each stage of closing a deal and suggests the next action based on past success stories.

[0838] "Application example 2 when combining with an emotional engine"

[0839] (Claim 1)

[0840] Means for collecting market information, social network information, and customer information management system information,

[0841] A means of integrating collected information and evaluating the likelihood of conversion for prospective customers,

[0842] A means of generating and displaying a list of potential customers based on the evaluation results,

[0843] A means of capturing customer voice or text and analyzing their emotional state,

[0844] A means of dynamically presenting the optimal customer response strategy based on emotional state,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, further comprising means for analyzing past purchase history and market trend information to estimate customer purchasing needs.

[0848] (Claim 3)

[0849] The system according to claim 1, further comprising means for monitoring progress at each stage of closing a deal and suggesting the next course of action based on past success stories. [Explanation of Symbols]

[0850] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of aggregating information, A means of integrating aggregated information and evaluating the probability of a commercial transaction with a potential buyer, A means of creating and presenting a list of potential buyers based on the evaluation results, A means of providing individually tailored product proposals, A system that includes this.

2. The system according to claim 1, further comprising means for analyzing past purchase history and market trend information to predict individual purchase demands.

3. The system according to claim 1, further comprising means for tracking the progress at each stage of a commercial transaction and suggesting the optimal course of action based on past success stories.

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