system

Abstract

We provide the system. [Solution] A means of receiving product information, industry information, and customer environment information from input sources, and organizing this information in a standardized format. A means for generating multiple sales scenarios and creating proposal patterns based on the aforementioned organized information, Means for providing the aforementioned proposed patterns and sales scenarios to a display device, A means for receiving user feedback and analyzing information to improve the accuracy of the proposed patterns and sales scenarios, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, 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] In business activities, there is a problem that the success rate of business negotiations decreases due to insufficient preparation of proposal content and delays in customer response. In addition, due to insufficient knowledge of sales staff and unclear lead times of internal processes, it may not be possible to quickly and accurately respond to customer needs. These problems are factors that reduce the efficiency and effectiveness of business activities.

Means for Solving the Problems

[0005] To address the above challenges, the present invention provides a system that uses AI technology to generate multiple sales scenarios and proposal patterns based on product information, industry information, and customer environment information. This system standardizes the input information and performs simulations based on past success stories and industry best practices, allowing users to confirm the optimal strategy in advance before making actual proposals. Furthermore, by presenting common problems and solutions related to the generated proposal patterns, it enables sales representatives to respond quickly and accurately to customer inquiries. In addition, by utilizing user feedback to improve the accuracy of the system, it enhances the efficiency and success rate of sales activities.

[0006] "Input source" refers to the means by which data such as product information, industry information, and customer environment information is received from users.

[0007] A "standardized format" refers to a data format in which input information is unified and organized according to certain rules in order to facilitate data processing and analysis within a system.

[0008] A "sales scenario" refers to a pattern of specific proposals and strategies for sales activities that is generated by AI based on the input information.

[0009] A "proposal pattern" refers to a template that describes specific proposal content and tactics tailored to a sales scenario.

[0010] A "display device" refers to a device that presents sales scenarios and proposal patterns generated by the system to the user in a visually understandable format.

[0011] "Feedback" refers to information that users provide to a system based on their experience and evaluation, and is useful for improving the system's performance and accuracy.

[0012] A "success story" refers to a specific instance in the past where sales activities were particularly effective and a deal was successfully concluded.

[0013] "Industry best practices" refer to a collection of methods and techniques that are generally considered effective within a particular industry.

[0014] "Common problems" refer to issues that are likely to occur in common during customer interactions in sales activities.

[0015] A "solution" refers to a specific method or proposal designed to address a particular problem. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0020] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The system for implementing this invention uses AI technology to perform simulations of sales activities. First, the user inputs product information, industry information, and customer environment information into the system via a terminal. This information is received by a server, analyzed, and converted into a standardized format.

[0038] The server uses AI to generate multiple sales scenarios based on standardized data. This takes into account past success stories and industry best practices. Based on the generated sales scenarios, the server creates specific proposal patterns, allowing users to preview the optimal proposal tactics.

[0039] As a concrete example, let's assume a user is planning to sell a new software product. The user inputs information into the system, such as that the target industry is manufacturing and the specific features required by a particular customer. Based on this information, the server simulates effective implementation examples and proposal strategies for the manufacturing industry.

[0040] The simulation results are presented to the user as proposed patterns and associated sales scenarios. Furthermore, the server also presents common problems and their solutions, helping users quickly address potential challenges they might face in customer interactions.

[0041] Based on the information provided, users prepare optimal proposals before actual sales activities and send feedback to the server, contributing to the improvement of the system's accuracy. In this way, the efficiency of sales activities can be increased, and the success rate of business negotiations can be improved.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users input product information, industry information, and customer environment information via their terminals. This information is registered in the system as data necessary for sales activities.

[0045] Step 2:

[0046] The server receives information sent by the user and normalizes the data. It organizes the data in a normalized format and prepares it for analysis.

[0047] Step 3:

[0048] The server uses an AI engine to generate multiple sales scenarios based on normalized data. In doing so, it consults past success stories and industry best practices to find the optimal scenario.

[0049] Step 4:

[0050] The server creates specific proposal patterns tailored to each case based on the generated sales scenarios. This ensures that proposals are available that can address different situations.

[0051] Step 5:

[0052] The server sends proposal patterns and sales scenarios to the terminal and provides the user with a visualized simulation result. The user then reviews this and confirms the proposal strategy.

[0053] Step 6:

[0054] Users input feedback on their proposals and simulation results into the system via their terminals. This feedback is used to improve future scenario generation and proposal patterns.

[0055] Step 7:

[0056] The server analyzes user feedback and uses AI to make adjustments to improve the overall accuracy and efficiency of the system. This optimizes the system to further increase the success rate of sales activities.

[0057] (Example 1)

[0058] 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."

[0059] In existing sales activities, there is a challenge in effectively utilizing product information, industry information, and customer environment information to derive optimal sales scenarios and proposal patterns. Furthermore, there is a need to strengthen sales activities by efficiently utilizing past success stories and best practices in the industry.

[0060] 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.

[0061] In this invention, the server includes means for receiving product data, industry data, and customer information from an input device and organizing this information in a standardized format; means for generating multiple sales plans and forming proposal patterns using a generative model; and means for receiving feedback and analyzing data to improve the accuracy of the proposal patterns and sales plans. This makes it possible to efficiently generate an optimal sales strategy and support sales activities based on it.

[0062] An "input device" is a device used by users to transmit product data, industry data, and customer information to a system.

[0063] "Product data" refers to information about goods and services that are being bought and sold.

[0064] "Industry data" refers to information about a specific industry or market.

[0065] "Customer information" refers to information about customers who are the target of sales activities.

[0066] A "standardized format" is a method of unifying data in different formats into a consistent format.

[0067] A "generative model" is an algorithm used to generate new information based on given data.

[0068] A "sales plan" is a set of strategies and procedures designed to efficiently carry out sales activities.

[0069] A "proposal pattern" refers to the elements that make up the specific proposal content presented to the customer.

[0070] "Feedback" refers to opinions and information returned by users to improve the accuracy of proposal patterns and sales plans.

[0071] "Data for improving accuracy" refers to information that contributes to improving the precision of proposals and sales strategies.

[0072] This system uses a generative AI model to generate multiple sales scenarios to support efficient decision-making in sales activities. Users first input product data, industry data, and customer information into the system using a terminal. This input is typically done via dedicated user interface software. Personal computers and tablet devices are used as terminals.

[0073] Information entered by the user is received by the server. The server analyzes the information and processes it to standardize it into a regular format. General-purpose programming languages ​​such as Python and Java (registered trademark) are used for data cleaning and format conversion.

[0074] Subsequently, the server uses a generative AI model to generate multiple sales plans based on past success stories and industry best practices. This AI model can utilize common libraries for natural language processing and machine learning. The generated sales plans form specific proposal patterns, which are then provided to the user. This allows the user to simulate sales activities and consider optimal proposal tactics in advance.

[0075] As a concrete example, consider a scenario where a user is trying to sell a new software product to a manufacturing customer. The user inputs product features, target industry trends, and specific customer requirements via a terminal. Based on this information, the server generates a sales scenario with a high probability of success. The presented sales plan includes how to approach the customer and the content of the proposal. The user can then use this as a reference to conduct actual sales activities.

[0076] An example of a prompt message would be a specific instruction such as, "Generate effective proposal scenarios for introducing new software for the manufacturing industry." Such prompt messages enable the generating AI model to present more accurate sales plans.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users use a terminal to input product information, industry information, and customer information. This input includes product name, target industry, and specific customer requests. The information entered on the terminal is sent to a dedicated input form and then transferred to the server.

[0080] Step 2:

[0081] The server analyzes the information received from the terminal and standardizes it into a regular format. This process involves imputing missing data values ​​and converting different data formats to the standard format. Libraries in programming languages ​​such as Python are used to prepare the cleaned data.

[0082] Step 3:

[0083] The server utilizes a generation AI model based on standardized data to generate multiple sales scenarios. Prompt messages are fed into the model, and various scenarios are output while referencing past success data and industry best practices. As a result, a diverse range of sales plans are formed.

[0084] Step 4:

[0085] The server creates specific proposal patterns based on the generated scenarios. At this stage, a proposal document is generated that includes the advantages of the proposed product and appropriate approaches to customers. The created proposal patterns are then organized in a way that users can refer to.

[0086] Step 5:

[0087] The server sends back proposal patterns and sales scenarios to the user. The user receives this information on their terminal and uses it as a basis for developing sales activity plans. Furthermore, by collecting feedback on actual customer interactions and sending it back to the server, the system continuously improves its accuracy.

[0088] (Application Example 1)

[0089] 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."

[0090] Current sales support systems lack the technology to analyze the conditions of visited locations and the needs of individual customers in real time and present optimal sales strategies. Sales representatives often cannot obtain immediately effective proposals at the locations they visit, making timely responses difficult. Furthermore, there is a need for methods that effectively utilize visual information from the field during sales activities to provide more accurate sales strategies.

[0091] 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.

[0092] In this invention, the server includes means for acquiring product information, activity group information, and customer status information from an input device and organizing this information in a standardized format; means for generating multiple commercial scenarios and creating proposed methods based on the organized information; and means for providing the proposed methods and commercial scenarios to a designated device. It also includes means for acquiring on-site information using a portable visual device and providing commercial strategies based on the on-site information. This enables sales representatives to instantly obtain appropriate commercial strategies tailored to the actual situation on-site, thereby realizing effective and timely sales activities.

[0093] An "input device" is a device used to acquire and input product information, activity group information, and customer status information, and plays the role of providing information to the user.

[0094] A "standardized format" is an effort to convert information provided in different formats into a unified format, with the aim of facilitating the consistency and understanding of the information.

[0095] "Means of organization" refers to the function of organizing acquired information, arranging it into a usable format, and preparing it so that the next processing step can be carried out efficiently.

[0096] A "commercial scenario" is a set of hypotheses used to derive the optimal sales strategy in a specific sales situation, and serves as a foundation for planning specific sales actions.

[0097] The "proposal method" involves selecting the most effective approach from the generated commercial scenarios and presenting a specific strategy for its use in sales activities.

[0098] "Designated equipment" refers to display and communication devices used to provide information to sales representatives and related parties, and is used by users to confirm the information they have been presented with.

[0099] A "portable visual device" is a device carried by sales representatives to collect visual information on-site, and is a device that supports information acquisition in the field.

[0100] A "commercial strategy based on on-site information" is a sales policy that is applied in real time by utilizing acquired on-site visual information, and is a strategy that enables responsive sales activities that are adapted to the environment.

[0101] The system for implementing this invention aims to efficiently collect information at the sales site and provide optimal commercial strategies. The server receives product information, activity group information, and customer status information from users through an input device. This information is converted into a standardized format and organized. Based on this organized information, the server generates multiple commercial scenarios and formulates proposed strategies.

[0102] Using portable visual devices, users collect on-site information at their destinations. These devices include smart glasses and head-mounted displays. Based on the on-site information acquired by these devices, a server generates commercial strategies in real time. This allows sales representatives to obtain optimal information on the spot and conduct commercial activities quickly and accurately.

[0103] For example, based on store information acquired through smart glasses during a visit, the server generates a commercial strategy such as "Industry: Retail, Characteristics: Localized sales promotion is effective." This information is displayed on the portable visual device used by the sales representative.

[0104] Furthermore, this system can use a generative AI model to provide an example prompt message such as, "Please generate the optimal sales proposal based on successful case studies related to this industry and its characteristics." This prompt enables the system to provide an appropriate proposal method based on past success stories and industry best practices.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server receives product information, activity group information, and customer status information from the terminal. This information is data entered by the user, and the server first converts it into a standard data format. This allows for consistent handling of the data in subsequent processing.

[0108] Step 2:

[0109] The server generates multiple commercial scenarios based on the compiled data. Specifically, it uses a generative AI model to perform calculations to derive the optimal commercial strategy from past success stories and industry best practices. This process develops effective proposal methods for each sales activity.

[0110] Step 3:

[0111] Users collect on-site information using portable visual devices, such as smart glasses. These devices capture location data and image data from within stores and transmit this information to a server. The server uses this new information to analyze, update, and apply commercial strategies tailored to the local situation in real time.

[0112] Step 4:

[0113] The server transmits and displays commercial scenarios based on acquired field information to the user's portable visual device. Here, an example prompt message, "Please generate the optimal sales proposal based on successful case studies related to this industry and its characteristics," is used to provide specific action guidelines for the sales representative.

[0114] Step 5:

[0115] Users conduct sales activities on-site, following commercial strategies displayed using visual aids. The results and feedback from these sales activities are sent back to the server, accumulating data that contributes to improving the overall accuracy of the system. This data is then used to generate future commercial scenarios.

[0116] 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.

[0117] This invention enables more personalized proposals by combining an emotion engine with a simulation system used in sales activities. Specifically, the user inputs product information, industry information, and customer environment information into the system via a terminal, and at the same time, the user's emotional state is analyzed by the emotion engine.

[0118] The server first standardizes the received information and then uses AI technology to generate multiple sales scenarios. Here, data from the emotion engine is taken into consideration to generate suggestion patterns that are appropriate for the user's emotions. In this process, the server not only refers to past success stories and industry best practices but also reflects the user's current emotional state to propose more effective sales strategies.

[0119] For example, when a user proposes a new product to a customer, the emotion engine may detect feelings such as tension or anxiety. In this case, the server will provide proposal patterns that emphasize positive success stories and reassurance to the customer in order to alleviate these feelings.

[0120] The server also optimizes feedback analysis based on the user's emotions detected by the emotion engine. This allows the AI ​​to receive feedback tailored to the user's individual needs and emotional state, and to make adjustments that improve the overall accuracy and efficiency of the system.

[0121] In this way, the system can effectively support sales activities and increase the success rate of deals. For users, it helps them achieve better sales results by tailoring proposals to their emotions.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] Users input product information, industry information, and customer environment information via a terminal. At the same time, emotional data is collected from the user's facial expressions and voice through an emotion recognition device connected to the terminal.

[0125] Step 2:

[0126] The server receives product information, industry information, and customer environment information transmitted from the terminal, converts the data into a standardized format, and organizes it. At the same time, it also receives emotional data and determines the user's current emotional state.

[0127] Step 3:

[0128] The server uses an AI engine to generate multiple sales scenarios based on organized information and emotional data. During this process, the suggested scenarios are adjusted according to the emotional data. For example, if the user is feeling anxious, a scenario is generated that takes that emotion into account and provides a sense of reassurance.

[0129] Step 4:

[0130] The server sends the generated sales scenarios and proposal patterns to the terminal, allowing the user to visually review them. The user then uses these as a reference to prepare for actual proposals.

[0131] Step 5:

[0132] Users input feedback into the system from their terminal based on the practical results and experiences gained when making actual proposals. Emotional data is also acquired again and sent to the server along with information on the effectiveness of the proposals.

[0133] Step 6:

[0134] The server analyzes feedback and newly acquired sentiment data to make improvements to enhance the accuracy of sales scenarios and proposal patterns. This ensures that the system continues to provide proposals optimized for the user's emotions.

[0135] This entire process enables users to make appropriate suggestions based on their emotions, thereby improving the success rate of business negotiations.

[0136] (Example 2)

[0137] 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".

[0138] In sales activities, there is a challenge in that the proposals offered by sales representatives do not adequately address customer needs or market conditions. Furthermore, the emotions and state of mind of sales representatives are often not reflected in the scenarios, resulting in limited effectiveness of proposals. This leads to a decrease in sales success rates and hinders efficient sales activities.

[0139] 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.

[0140] In this invention, the server includes means for receiving product information, industry information, and customer environment information from an input source and organizing this information in a standardized format; means for analyzing the user's emotional state; and means for adjusting the proposal patterns and sales scenarios to reflect the user's emotional state. This makes it possible to provide personalized sales proposals based on the user's emotional state and improve the success rate of sales.

[0141] "Input source" refers to the source or system that provides information such as product information, industry information, and customer environment information.

[0142] "Standardization" refers to the process of converting data provided in different formats or units into a consistent format.

[0143] "Product information" refers to detailed information about the products or services being bought or sold.

[0144] "Industry information" refers to information about a specific industry or business field.

[0145] "Customer environment information" refers to information about the customer's business situation and market environment.

[0146] A "sales scenario" refers to a plan or strategy created for a specific sales objective.

[0147] A "proposal pattern" refers to the structure and format of a specific proposal made to a customer.

[0148] "Emotional analysis methods" refer to processes and tools for evaluating a user's emotional state.

[0149] An "information display device" is a device that visually displays generated proposals and scenarios to the user.

[0150] "Feedback" refers to information received as an evaluation or reaction to proposals or sales activities.

[0151] This invention is a sales simulation system incorporating sentiment analysis, aimed at improving the quality of proposals in sales activities. The system consists of a server, terminals, and users.

[0152] The terminal provides an interface for receiving product information, industry information, and customer environment information from the user. Furthermore, the terminal uses a specific application called "emotion analysis software" to analyze the user's emotional state from voice and input patterns. This analyzed emotional data is used to understand the user's mental state.

[0153] The server uses a "data standardization module" to convert various types of information received from terminals into a standardized data format. This standardization enables consistent analysis of different data. Subsequently, the server uses a "sales scenario generation AI model" to automatically generate multiple sales scenarios based on this standardized data. This model takes into account the user's current emotional state while referencing past success stories and industry best practices to form the optimal proposal pattern.

[0154] To give a specific example, when a user tries to propose a new product to a customer, the information entered on the terminal is sent to the server. At the same time, if the emotion analysis software detects that the user is in a state of tension, the server selects a scenario that emphasizes reassuring success stories and presents that scenario on the terminal's information display.

[0155] An example of a prompt message might be, "When a user is nervous about being presented with a new product, what kind of presentation pattern would be effective?" In this way, the system adjusts to provide appropriate sales proposals based on the user's emotions.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The user inputs product information, industry information, and customer environment information via a terminal. This information is temporarily stored on the terminal. Based on the input information, the terminal uses emotion analysis software to analyze the user's emotional state. Specifically, it analyzes data such as input speed and voice tone to identify the user's current emotions. The input is raw data, and the output is the analyzed emotional state.

[0159] Step 2:

[0160] The terminal transmits received product and industry information to the server. Simultaneously, the sentiment analysis results are also transferred to the server. The server, using a "data standardization module," converts the received data into a standard format. For example, it standardizes currency units and date formats. The input is raw data from the terminal, while the output is in a standardized data format.

[0161] Step 3:

[0162] The server runs an "AI sales scenario generation model" based on standardized data to generate multiple sales scenarios. This process references past cases and industry knowledge databases, and also incorporates sentiment analysis results into the scenarios. For example, if the user's emotion is determined to be "anxiety," the system prioritizes proposals that provide reassurance to alleviate that anxiety. The input consists of standardized data and sentiment data, and the output is an adjusted sales scenario.

[0163] Step 4:

[0164] The server sends the generated sales scenario to the terminal and presents it visually to the user via an information display device. This allows the user to understand what kind of scenario to propose to the customer. Sentimental data is taken into consideration to ensure that the presented scenario is optimal according to the user's state.

[0165] Step 5:

[0166] After a proposal is implemented, the user provides feedback via their terminal. This feedback is sent to the server and analyzed by FeedbackOptimizer. The analysis results are used to improve the system's accuracy and make adjustments for future proposals. The input is the user's feedback data, and the output is the analysis results, including areas for improvement.

[0167] (Application Example 2)

[0168] 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".

[0169] Traditional sales activities often struggled to take customers' emotional states into account, resulting in generic proposals. Consequently, sales efforts failed to accurately address individual customer needs, leading to a lower success rate for proposals. Furthermore, there was a lack of support for sales representatives in stores to effectively communicate with customers in real time and make appropriate proposals.

[0170] 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.

[0171] In this invention, the server includes means for receiving various information from an input source and organizing this information in a standardized format; means for generating multiple dialogue scenarios and creating presentation content based on the organized information and detected emotion data; and means for identifying the user's emotional state using an emotion analysis device and applying it to the presentation content. This makes it possible to analyze the customer's emotions in real time and provide suggestions tailored to their individual emotional state.

[0172] An "input source" refers to a medium or device used to provide various types of information.

[0173] "Information" refers to a collection of data received from input sources for standardization purposes, which is then organized and analyzed based on specific applications.

[0174] Standardization is the process of organizing information into a consistent format, enabling consistent processing within a system.

[0175] "Organization" is the process of compiling received information into an easily understandable format, forming the foundation for analysis and processing.

[0176] "Emotional data" refers to information that indicates a user's emotional state and is acquired by an emotion analysis device.

[0177] A "dialogue scenario" is a plan or script generated based on informational and emotional data to guide a conversation with a user.

[0178] "Presented content" refers to the information or message determined by the dialogue scenario, and is the specific content of the proposal presented to the user.

[0179] An "emotion analysis device" is a device used to recognize and analyze a user's emotional state and to acquire emotional data.

[0180] "User emotional state" refers to the type and intensity of emotions a user experiences, and is used to optimize customer service and sales.

[0181] The system for realizing this application consists of a display device such as smart glasses, an emotion analysis device, and a central processing unit. A server receives customer information from input sources via this group of devices and standardizes it. Based on the standardized information and emotion data, a generative AI model is used to generate multiple dialogue scenarios. The presentation content included in these generated dialogue scenarios is provided to the smart glasses' display. The emotion analysis device analyzes the user's emotional state in real time, enabling dynamically optimal suggestions. Microsoft® Azure® Face API and other emotion analysis software are used to identify the emotional state.

[0182] As a concrete example, suppose a sales representative in a store is explaining a new product. If the customer shows interest in the product but is concerned about the price, the server detects the customer's emotional state and, based on a generative AI model (e.g., OpenAI®'s GPT-3®), displays something like, "This product is durable and could ultimately reduce your costs," on the smart glasses' display. In this example, the generative AI model could be instructed with a prompt such as, "If the customer's emotions are 'interested' and 'concerned,' generate a suggestion that emphasizes the value of the product in terms of cost reduction."

[0183] In this way, users can flexibly change their suggestions according to the customer's emotions, resulting in more personalized customer service.

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The server transmits the customer's visual information and audio data from the terminal (such as smart glasses) to the emotion analysis device. The data input at this time is real-time data on changes in the customer's facial expressions and voice. Based on this, the emotion analysis device uses Microsoft Azure Face API, etc., to analyze the customer's emotional state and extract emotional data.

[0187] Step 2:

[0188] The server integrates and standardizes emotional data with customer-related data such as purchase history and interest levels. This integrated dataset is then input into a generative AI model, which generates a series of dialogue scenarios optimized for the customer's emotional state. OpenAI's GPT-3 is used as the generative AI model for this process.

[0189] Step 3:

[0190] The server selects the most appropriate presentation from the generated dialogue scenarios and communicates it to the terminal. This selection is made using prompt statements, such as, "If the customer's emotions are 'interested' and 'anxious,' generate a proposal that emphasizes the value of a product that leads to cost savings." The output proposal optimizes communication with the customer.

[0191] Step 4:

[0192] The terminal displays the information received from the server on its screen, making it available for the sales representative to review. The sales representative can instantly provide appropriate suggestions and information during conversations with customers through the display on their smart glasses. This allows customers to receive information tailored to their individual needs.

[0193] By following these steps, users can dynamically and effectively provide customer-centric suggestions, thereby improving sales performance.

[0194] 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.

[0195] Data generation model 58 is a 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.

[0196] 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.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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".

[0210] The system for implementing this invention uses AI technology to perform simulations of sales activities. First, the user inputs product information, industry information, and customer environment information into the system via a terminal. This information is received by a server, analyzed, and converted into a standardized format.

[0211] The server uses AI to generate multiple sales scenarios based on standardized data. This takes into account past success stories and industry best practices. Based on the generated sales scenarios, the server creates specific proposal patterns, allowing users to preview the optimal proposal tactics.

[0212] As a concrete example, let's assume a user is planning to sell a new software product. The user inputs information into the system, such as that the target industry is manufacturing and the specific features required by a particular customer. Based on this information, the server simulates effective implementation examples and proposal strategies for the manufacturing industry.

[0213] The simulation results are presented to the user as proposed patterns and associated sales scenarios. Furthermore, the server also presents common problems and their solutions, helping users quickly address potential challenges they might face in customer interactions.

[0214] Based on the information provided, users prepare optimal proposals before actual sales activities and send feedback to the server, contributing to the improvement of the system's accuracy. In this way, the efficiency of sales activities can be increased, and the success rate of business negotiations can be improved.

[0215] The following describes the processing flow.

[0216] Step 1:

[0217] Users input product information, industry information, and customer environment information via their terminals. This information is registered in the system as data necessary for sales activities.

[0218] Step 2:

[0219] The server receives information sent by the user and normalizes the data. It organizes the data in a normalized format and prepares it for analysis.

[0220] Step 3:

[0221] The server uses an AI engine to generate multiple sales scenarios based on normalized data. In doing so, it consults past success stories and industry best practices to find the optimal scenario.

[0222] Step 4:

[0223] The server creates specific proposal patterns tailored to each case based on the generated sales scenarios. This ensures that proposals are available that can address different situations.

[0224] Step 5:

[0225] The server sends proposal patterns and sales scenarios to the terminal and provides the user with a visualized simulation result. The user then reviews this and confirms the proposal strategy.

[0226] Step 6:

[0227] Users input feedback on their proposals and simulation results into the system via their terminals. This feedback is used to improve future scenario generation and proposal patterns.

[0228] Step 7:

[0229] The server analyzes user feedback and uses AI to make adjustments to improve the overall accuracy and efficiency of the system. This optimizes the system to further increase the success rate of sales activities.

[0230] (Example 1)

[0231] 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."

[0232] In existing sales activities, there is a challenge in effectively utilizing product information, industry information, and customer environment information to derive optimal sales scenarios and proposal patterns. Furthermore, there is a need to strengthen sales activities by efficiently utilizing past success stories and best practices in the industry.

[0233] 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.

[0234] In this invention, the server includes means for receiving product data, industry data, and customer information from an input device and organizing this information in a standardized format; means for generating multiple sales plans and forming proposal patterns using a generative model; and means for receiving feedback and analyzing data to improve the accuracy of the proposal patterns and sales plans. This makes it possible to efficiently generate an optimal sales strategy and support sales activities based on it.

[0235] An "input device" is a device used by users to transmit product data, industry data, and customer information to a system.

[0236] "Product data" refers to information about goods and services that are being bought and sold.

[0237] "Industry data" refers to information about a specific industry or market.

[0238] "Customer information" refers to information about customers who are the target of sales activities.

[0239] A "standardized format" is a method of unifying data in different formats into a consistent format.

[0240] A "generative model" is an algorithm used to generate new information based on given data.

[0241] A "sales plan" is a set of strategies and procedures designed to efficiently carry out sales activities.

[0242] A "proposal pattern" refers to the elements that make up the specific proposal content presented to the customer.

[0243] "Feedback" refers to opinions and information returned by users to improve the accuracy of proposal patterns and sales plans.

[0244] "Data for improving accuracy" refers to information that contributes to improving the precision of proposals and sales strategies.

[0245] This system uses a generative AI model to generate multiple sales scenarios to support efficient decision-making in sales activities. Users first input product data, industry data, and customer information into the system using a terminal. This input is typically done via dedicated user interface software. Personal computers and tablet devices are used as terminals.

[0246] Information entered by the user is received by the server. The server analyzes the information and processes it to standardize it into a regular format. General-purpose programming languages ​​such as Python and Java are used for data cleaning and format conversion.

[0247] Subsequently, the server uses a generative AI model to generate multiple sales plans based on past success stories and industry best practices. This AI model can utilize common libraries for natural language processing and machine learning. The generated sales plans form specific proposal patterns, which are then provided to the user. This allows the user to simulate sales activities and consider optimal proposal tactics in advance.

[0248] As a concrete example, consider a scenario where a user is trying to sell a new software product to a manufacturing customer. The user inputs product features, target industry trends, and specific customer requirements via a terminal. Based on this information, the server generates a sales scenario with a high probability of success. The presented sales plan includes how to approach the customer and the content of the proposal. The user can then use this as a reference to conduct actual sales activities.

[0249] An example of a prompt message would be a specific instruction such as, "Generate effective proposal scenarios for introducing new software for the manufacturing industry." Such prompt messages enable the generating AI model to present more accurate sales plans.

[0250] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0251] Step 1:

[0252] Users use a terminal to input product information, industry information, and customer information. This input includes product name, target industry, and specific customer requests. The information entered on the terminal is sent to a dedicated input form and then transferred to the server.

[0253] Step 2:

[0254] The server analyzes the information received from the terminal and standardizes it into a regular format. This process involves imputing missing data values ​​and converting different data formats to the standard format. Libraries in programming languages ​​such as Python are used to prepare the cleaned data.

[0255] Step 3:

[0256] The server utilizes a generation AI model based on standardized data to generate multiple sales scenarios. Prompt messages are fed into the model, and various scenarios are output while referencing past success data and industry best practices. As a result, a diverse range of sales plans are formed.

[0257] Step 4:

[0258] The server creates specific proposal patterns based on the generated scenarios. At this stage, a proposal document is generated that includes the advantages of the proposed product and appropriate approaches to customers. The created proposal patterns are then organized in a way that users can refer to.

[0259] Step 5:

[0260] The server sends back proposal patterns and sales scenarios to the user. The user receives this information on their terminal and uses it as a basis for developing sales activity plans. Furthermore, by collecting feedback on actual customer interactions and sending it back to the server, the system continuously improves its accuracy.

[0261] (Application Example 1)

[0262] 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 glasses 214 will be referred to as the "terminal."

[0263] Current sales support systems lack the technology to analyze the conditions of visited locations and the needs of individual customers in real time and present optimal sales strategies. Sales representatives often cannot obtain immediately effective proposals at the locations they visit, making timely responses difficult. Furthermore, there is a need for methods that effectively utilize visual information from the field during sales activities to provide more accurate sales strategies.

[0264] 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.

[0265] In this invention, the server includes means for acquiring product information, activity group information, and customer status information from an input device and organizing this information in a standardized format; means for generating multiple commercial scenarios and creating proposed methods based on the organized information; and means for providing the proposed methods and commercial scenarios to a designated device. It also includes means for acquiring on-site information using a portable visual device and providing commercial strategies based on the on-site information. This enables sales representatives to instantly obtain appropriate commercial strategies tailored to the actual situation on-site, thereby realizing effective and timely sales activities.

[0266] An "input device" is a device used to acquire and input product information, activity group information, and customer status information, and plays the role of providing information to the user.

[0267] A "standardized format" is an effort to convert information provided in different formats into a unified format, with the aim of facilitating the consistency and understanding of the information.

[0268] "Means of organization" refers to the function of organizing acquired information, arranging it into a usable format, and preparing it so that the next processing step can be carried out efficiently.

[0269] A "commercial scenario" is a set of hypotheses used to derive the optimal sales strategy in a specific sales situation, and serves as a foundation for planning specific sales actions.

[0270] The "proposal method" involves selecting the most effective approach from the generated commercial scenarios and presenting a specific strategy for its use in sales activities.

[0271] "Designated equipment" refers to display and communication devices used to provide information to sales representatives and related parties, and is used by users to confirm the information they have been presented with.

[0272] A "portable visual device" is a device carried by sales representatives to collect visual information on-site, and is a device that supports information acquisition in the field.

[0273] A "commercial strategy based on on-site information" is a sales policy that is applied in real time by utilizing acquired on-site visual information, and is a strategy that enables responsive sales activities that are adapted to the environment.

[0274] The system for implementing this invention aims to efficiently collect information at the sales site and provide optimal commercial strategies. The server receives product information, activity group information, and customer status information from users through an input device. This information is converted into a standardized format and organized. Based on this organized information, the server generates multiple commercial scenarios and formulates proposed strategies.

[0275] Using portable visual devices, users collect on-site information at their destinations. These devices include smart glasses and head-mounted displays. Based on the on-site information acquired by these devices, a server generates commercial strategies in real time. This allows sales representatives to obtain optimal information on the spot and conduct commercial activities quickly and accurately.

[0276] For example, based on store information acquired through smart glasses during a visit, the server generates a commercial strategy such as "Industry: Retail, Characteristics: Localized sales promotion is effective." This information is displayed on the portable visual device used by the sales representative.

[0277] Furthermore, this system can use a generative AI model to provide an example prompt message such as, "Please generate the optimal sales proposal based on successful case studies related to this industry and its characteristics." This prompt enables the system to provide an appropriate proposal method based on past success stories and industry best practices.

[0278] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0279] Step 1:

[0280] The server receives product information, activity group information, and customer status information from the terminal. This information is data entered by the user, and the server first converts it into a standard data format. This allows for consistent handling of the data in subsequent processing.

[0281] Step 2:

[0282] The server generates a plurality of business scenarios based on the refined data. Specifically, using a generation AI model, calculations are performed to derive an optimal business strategy from past success cases and industry best practices. Through this process, an effective proposal method for each business activity is formulated.

[0283] Step 3:

[0284] The user collects on-site information using a portable visual device, such as smart glasses. The visual device captures location information and in-store image data and transmits them to the server. The server uses this new information to analyze, update, and apply a business strategy in real time according to the local situation.

[0285] Step 4:

[0286] The server transmits the business scenario based on the acquired on-site information to the user's portable visual device for display. Here, using an example of a prompt sentence "Please generate an optimal business proposal by referring to success cases based on this industry type and characteristics", guidelines for actions that sales staff should specifically take are shown.

[0287] Step 5:

[0288] The user conducts business activities according to the business strategy displayed using the visual device on-site. The results and feedback of the business activities are transmitted to the server again, and data for contributing to improving the accuracy of the entire system is accumulated. This data is utilized for generating business scenarios in subsequent times.

[0289] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0290] This invention enables more personalized proposals by combining an emotion engine with a simulation system used in sales activities. Specifically, the user inputs product information, industry information, and customer environment information into the system via a terminal, and at the same time, the user's emotional state is analyzed by the emotion engine.

[0291] The server first standardizes the received information and then uses AI technology to generate multiple sales scenarios. Here, data from the emotion engine is taken into consideration to generate suggestion patterns that are appropriate for the user's emotions. In this process, the server not only refers to past success stories and industry best practices but also reflects the user's current emotional state to propose more effective sales strategies.

[0292] For example, when a user proposes a new product to a customer, the emotion engine may detect feelings such as tension or anxiety. In this case, the server will provide proposal patterns that emphasize positive success stories and reassurance to the customer in order to alleviate these feelings.

[0293] The server also optimizes feedback analysis based on the user's emotions detected by the emotion engine. This allows the AI ​​to receive feedback tailored to the user's individual needs and emotional state, and to make adjustments that improve the overall accuracy and efficiency of the system.

[0294] In this way, the system can effectively support sales activities and increase the success rate of deals. For users, it helps them achieve better sales results by tailoring proposals to their emotions.

[0295] The following describes the processing flow.

[0296] Step 1:

[0297] The user inputs merchandise information, industry type information, and customer environment information via the terminal. At this time, emotional data is also collected from the user's expression, voice, etc. through the emotion recognition device connected to the terminal.

[0298] Step 2:

[0299] The server receives the merchandise information, industry type information, and customer environment information sent from the terminal, converts the data into a standardized format, and organizes it. At the same time, the emotional data is also received to judge the user's current emotional state.

[0300] Step 3:

[0301] Based on the organized information and emotional data, the server uses the AI engine to generate multiple sales scenarios. In this process, the proposed pattern is adjusted according to the emotional data. For example, when the user is nervous, a scenario that gives a sense of security considering that emotion is generated.

[0302] Step 4:

[0303] The server sends the generated sales scenarios and proposed patterns to the terminal so that the user can visually confirm them. The user prepares for the actual proposal based on these.

[0304] Step 5:

[0305] The user inputs feedback from the terminal to the system based on the practical results and experiences obtained when making the actual proposal. Emotional data is also obtained again and sent to the server together with information regarding the effectiveness of the proposal.

[0306] Step 6:

[0307] The server analyzes the feedback and the emotional data obtained again, and makes improvements to improve the accuracy of the sales scenarios and proposed patterns. As a result, the system continues to provide proposals optimized for the user's emotions.

[0308] This entire process enables users to make appropriate suggestions based on their emotions, thereby improving the success rate of business negotiations.

[0309] (Example 2)

[0310] 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".

[0311] In sales activities, there is a challenge in that the proposals offered by sales representatives do not adequately address customer needs or market conditions. Furthermore, the emotions and state of mind of sales representatives are often not reflected in the scenarios, resulting in limited effectiveness of proposals. This leads to a decrease in sales success rates and hinders efficient sales activities.

[0312] 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.

[0313] In this invention, the server includes means for receiving product information, industry information, and customer environment information from an input source and organizing this information in a standardized format; means for analyzing the user's emotional state; and means for adjusting the proposal patterns and sales scenarios to reflect the user's emotional state. This makes it possible to provide personalized sales proposals based on the user's emotional state and improve the success rate of sales.

[0314] "Input source" refers to the source or system that provides information such as product information, industry information, and customer environment information.

[0315] "Standardization" refers to the process of converting data provided in different formats or units into a consistent format.

[0316] "Product information" refers to detailed information about the products or services being bought or sold.

[0317] "Industry information" refers to information about a specific industry or business field.

[0318] "Customer environment information" refers to information about the customer's business situation and market environment.

[0319] A "sales scenario" refers to a plan or strategy created for a specific sales objective.

[0320] A "proposal pattern" refers to the structure and format of a specific proposal made to a customer.

[0321] "Emotional analysis methods" refer to processes and tools for evaluating a user's emotional state.

[0322] An "information display device" is a device that visually displays generated proposals and scenarios to the user.

[0323] "Feedback" refers to information received as an evaluation or reaction to proposals or sales activities.

[0324] This invention is a sales simulation system incorporating sentiment analysis, aimed at improving the quality of proposals in sales activities. The system consists of a server, terminals, and users.

[0325] The terminal provides an interface for receiving product information, industry information, and customer environment information from the user. Furthermore, the terminal uses a specific application called "emotion analysis software" to analyze the user's emotional state from voice and input patterns. This analyzed emotional data is used to understand the user's mental state.

[0326] The server uses a "data standardization module" to convert various types of information received from terminals into a standardized data format. This standardization enables consistent analysis of different data. Subsequently, the server uses a "sales scenario generation AI model" to automatically generate multiple sales scenarios based on this standardized data. This model takes into account the user's current emotional state while referencing past success stories and industry best practices to form the optimal proposal pattern.

[0327] To give a specific example, when a user tries to propose a new product to a customer, the information entered on the terminal is sent to the server. At the same time, if the emotion analysis software detects that the user is in a state of tension, the server selects a scenario that emphasizes reassuring success stories and presents that scenario on the terminal's information display.

[0328] An example of a prompt message might be, "When a user is nervous about being presented with a new product, what kind of presentation pattern would be effective?" In this way, the system adjusts to provide appropriate sales proposals based on the user's emotions.

[0329] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0330] Step 1:

[0331] The user inputs product information, industry information, and customer environment information via a terminal. This information is temporarily stored on the terminal. Based on the input information, the terminal uses emotion analysis software to analyze the user's emotional state. Specifically, it analyzes data such as input speed and voice tone to identify the user's current emotions. The input is raw data, and the output is the analyzed emotional state.

[0332] Step 2:

[0333] The terminal transmits received product and industry information to the server. Simultaneously, the sentiment analysis results are also transferred to the server. The server, using a "data standardization module," converts the received data into a standard format. For example, it standardizes currency units and date formats. The input is raw data from the terminal, while the output is in a standardized data format.

[0334] Step 3:

[0335] The server runs an "AI sales scenario generation model" based on standardized data to generate multiple sales scenarios. This process references past cases and industry knowledge databases, and also incorporates sentiment analysis results into the scenarios. For example, if the user's emotion is determined to be "anxiety," the system prioritizes proposals that provide reassurance to alleviate that anxiety. The input consists of standardized data and sentiment data, and the output is an adjusted sales scenario.

[0336] Step 4:

[0337] The server sends the generated sales scenario to the terminal and presents it visually to the user via an information display device. This allows the user to understand what kind of scenario to propose to the customer. Sentimental data is taken into consideration to ensure that the presented scenario is optimal according to the user's state.

[0338] Step 5:

[0339] After a proposal is implemented, the user provides feedback via their terminal. This feedback is sent to the server and analyzed by FeedbackOptimizer. The analysis results are used to improve the system's accuracy and make adjustments for future proposals. The input is the user's feedback data, and the output is the analysis results, including areas for improvement.

[0340] (Application Example 2)

[0341] 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."

[0342] Traditional sales activities often struggled to take customers' emotional states into account, resulting in generic proposals. Consequently, sales efforts failed to accurately address individual customer needs, leading to a lower success rate for proposals. Furthermore, there was a lack of support for sales representatives in stores to effectively communicate with customers in real time and make appropriate proposals.

[0343] 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.

[0344] In this invention, the server includes means for receiving various information from an input source and organizing this information in a standardized format; means for generating multiple dialogue scenarios and creating presentation content based on the organized information and detected emotion data; and means for identifying the user's emotional state using an emotion analysis device and applying it to the presentation content. This makes it possible to analyze the customer's emotions in real time and provide suggestions tailored to their individual emotional state.

[0345] An "input source" refers to a medium or device used to provide various types of information.

[0346] "Information" refers to a collection of data received from input sources for standardization purposes, which is then organized and analyzed based on specific applications.

[0347] Standardization is the process of organizing information into a consistent format, enabling consistent processing within a system.

[0348] "Organization" is the process of compiling received information into an easily understandable format, forming the foundation for analysis and processing.

[0349] "Emotional data" refers to information that indicates a user's emotional state and is acquired by an emotion analysis device.

[0350] A "dialogue scenario" is a plan or script generated based on informational and emotional data to guide a conversation with a user.

[0351] "Presented content" refers to the information or message determined by the dialogue scenario, and is the specific content of the proposal presented to the user.

[0352] An "emotion analysis device" is a device used to recognize and analyze a user's emotional state and to acquire emotional data.

[0353] "User emotional state" refers to the type and intensity of emotions a user experiences, and is used to optimize customer service and sales.

[0354] The system for realizing this application consists of a display device such as smart glasses, an emotion analysis device, and a central processing unit. A server receives customer information from input sources via this group of devices and standardizes it. Based on the standardized information and emotion data, a generative AI model is used to generate multiple dialogue scenarios. The presentation content included in these generated dialogue scenarios is provided to the smart glasses' display. The emotion analysis device analyzes the user's emotional state in real time, enabling dynamically optimal suggestions. Microsoft Azure Face API and other emotion analysis software are used to identify the emotional state.

[0355] As a concrete example, suppose a sales representative in a store is explaining a new product. If the customer shows interest in the product but is concerned about the price, the server detects the customer's emotional state and, based on a generative AI model (e.g., OpenAI's GPT-3), displays something like, "This product is durable and could ultimately reduce your costs," on the smart glasses' display. In this example, the prompt given to the generative AI model could be, "If the customer's emotions are 'interested' and 'concerned,' generate a suggestion that emphasizes the value of the product in terms of cost reduction."

[0356] In this way, users can flexibly change their suggestions according to the customer's emotions, resulting in more personalized customer service.

[0357] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0358] Step 1:

[0359] The server transmits the customer's visual information and audio data from the terminal (such as smart glasses) to the emotion analysis device. The data input at this time is real-time data on changes in the customer's facial expressions and voice. Based on this, the emotion analysis device uses Microsoft Azure Face API, etc., to analyze the customer's emotional state and extract emotional data.

[0360] Step 2:

[0361] The server integrates and standardizes emotional data with customer-related data such as purchase history and interest levels. This integrated dataset is then input into a generative AI model, which generates a series of dialogue scenarios optimized for the customer's emotional state. OpenAI's GPT-3 is used as the generative AI model for this process.

[0362] Step 3:

[0363] The server selects the most appropriate presentation from the generated dialogue scenarios and communicates it to the terminal. This selection is made using prompt statements, such as, "If the customer's emotions are 'interested' and 'anxious,' generate a proposal that emphasizes the value of a product that leads to cost savings." The output proposal optimizes communication with the customer.

[0364] Step 4:

[0365] The terminal displays the information received from the server on its screen, making it available for the sales representative to review. The sales representative can instantly provide appropriate suggestions and information during conversations with customers through the display on their smart glasses. This allows customers to receive information tailored to their individual needs.

[0366] By following these steps, users can dynamically and effectively provide customer-centric suggestions, thereby improving sales performance.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] [Third Embodiment]

[0371] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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).

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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".

[0383] The system for implementing this invention uses AI technology to perform simulations of sales activities. First, the user inputs product information, industry information, and customer environment information into the system via a terminal. This information is received by a server, analyzed, and converted into a standardized format.

[0384] The server uses AI to generate multiple sales scenarios based on standardized data. This takes into account past success stories and industry best practices. Based on the generated sales scenarios, the server creates specific proposal patterns, allowing users to preview the optimal proposal tactics.

[0385] As a concrete example, let's assume a user is planning to sell a new software product. The user inputs information into the system, such as that the target industry is manufacturing and the specific features required by a particular customer. Based on this information, the server simulates effective implementation examples and proposal strategies for the manufacturing industry.

[0386] The simulation results are presented to the user as proposed patterns and associated sales scenarios. Furthermore, the server also presents common problems and their solutions, helping users quickly address potential challenges they might face in customer interactions.

[0387] Based on the information provided, users prepare optimal proposals before actual sales activities and send feedback to the server, contributing to the improvement of the system's accuracy. In this way, the efficiency of sales activities can be increased, and the success rate of business negotiations can be improved.

[0388] The following describes the processing flow.

[0389] Step 1:

[0390] Users input product information, industry information, and customer environment information via their terminals. This information is registered in the system as data necessary for sales activities.

[0391] Step 2:

[0392] The server receives information sent by the user and normalizes the data. It organizes the data in a normalized format and prepares it for analysis.

[0393] Step 3:

[0394] The server uses an AI engine to generate multiple sales scenarios based on normalized data. In doing so, it consults past success stories and industry best practices to find the optimal scenario.

[0395] Step 4:

[0396] The server creates specific proposal patterns tailored to each case based on the generated sales scenarios. This ensures that proposals are available that can address different situations.

[0397] Step 5:

[0398] The server sends proposal patterns and sales scenarios to the terminal and provides the user with a visualized simulation result. The user then reviews this and confirms the proposal strategy.

[0399] Step 6:

[0400] Users input feedback on their proposals and simulation results into the system via their terminals. This feedback is used to improve future scenario generation and proposal patterns.

[0401] Step 7:

[0402] The server analyzes user feedback and uses AI to make adjustments to improve the overall accuracy and efficiency of the system. This optimizes the system to further increase the success rate of sales activities.

[0403] (Example 1)

[0404] 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."

[0405] In existing sales activities, there is a challenge in effectively utilizing product information, industry information, and customer environment information to derive optimal sales scenarios and proposal patterns. Furthermore, there is a need to strengthen sales activities by efficiently utilizing past success stories and best practices in the industry.

[0406] 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.

[0407] In this invention, the server includes means for receiving product data, industry data, and customer information from an input device and organizing this information in a standardized format; means for generating multiple sales plans and forming proposal patterns using a generative model; and means for receiving feedback and analyzing data to improve the accuracy of the proposal patterns and sales plans. This makes it possible to efficiently generate an optimal sales strategy and support sales activities based on it.

[0408] An "input device" is a device used by users to transmit product data, industry data, and customer information to a system.

[0409] "Product data" refers to information about goods and services that are being bought and sold.

[0410] "Industry data" refers to information about a specific industry or market.

[0411] "Customer information" refers to information about customers who are the target of sales activities.

[0412] A "standardized format" is a method of unifying data in different formats into a consistent format.

[0413] A "generative model" is an algorithm used to generate new information based on given data.

[0414] A "sales plan" is a set of strategies and procedures designed to efficiently carry out sales activities.

[0415] A "proposal pattern" refers to the elements that make up the specific proposal content presented to the customer.

[0416] "Feedback" refers to opinions and information returned by users to improve the accuracy of proposal patterns and sales plans.

[0417] "Data for improving accuracy" refers to information that contributes to improving the precision of proposals and sales strategies.

[0418] This system uses a generative AI model to generate multiple sales scenarios to support efficient decision-making in sales activities. Users first input product data, industry data, and customer information into the system using a terminal. This input is typically done via dedicated user interface software. Personal computers and tablet devices are used as terminals.

[0419] Information entered by the user is received by the server. The server analyzes the information and processes it to standardize it into a regular format. General-purpose programming languages ​​such as Python and Java are used for data cleaning and format conversion.

[0420] Subsequently, the server uses a generative AI model to generate multiple sales plans based on past success stories and industry best practices. This AI model can utilize common libraries for natural language processing and machine learning. The generated sales plans form specific proposal patterns, which are then provided to the user. This allows the user to simulate sales activities and consider optimal proposal tactics in advance.

[0421] As a concrete example, consider a scenario where a user is trying to sell a new software product to a manufacturing customer. The user inputs product features, target industry trends, and specific customer requirements via a terminal. Based on this information, the server generates a sales scenario with a high probability of success. The presented sales plan includes how to approach the customer and the content of the proposal. The user can then use this as a reference to conduct actual sales activities.

[0422] An example of a prompt message would be a specific instruction such as, "Generate effective proposal scenarios for introducing new software for the manufacturing industry." Such prompt messages enable the generating AI model to present more accurate sales plans.

[0423] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0424] Step 1:

[0425] Users use a terminal to input product information, industry information, and customer information. This input includes product name, target industry, and specific customer requests. The information entered on the terminal is sent to a dedicated input form and then transferred to the server.

[0426] Step 2:

[0427] The server analyzes the information received from the terminal and standardizes it into a regular format. This process involves imputing missing data values ​​and converting different data formats to the standard format. Libraries in programming languages ​​such as Python are used to prepare the cleaned data.

[0428] Step 3:

[0429] The server utilizes a generation AI model based on standardized data to generate multiple sales scenarios. Prompt messages are fed into the model, and various scenarios are output while referencing past success data and industry best practices. As a result, a diverse range of sales plans are formed.

[0430] Step 4:

[0431] The server creates specific proposal patterns based on the generated scenarios. At this stage, a proposal document is generated that includes the advantages of the proposed product and appropriate approaches to customers. The created proposal patterns are then organized in a way that users can refer to.

[0432] Step 5:

[0433] The server sends back proposal patterns and sales scenarios to the user. The user receives this information on their terminal and uses it as a basis for developing sales activity plans. Furthermore, by collecting feedback on actual customer interactions and sending it back to the server, the system continuously improves its accuracy.

[0434] (Application Example 1)

[0435] 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."

[0436] Current sales support systems lack the technology to analyze the conditions of visited locations and the needs of individual customers in real time and present optimal sales strategies. Sales representatives often cannot obtain immediately effective proposals at the locations they visit, making timely responses difficult. Furthermore, there is a need for methods that effectively utilize visual information from the field during sales activities to provide more accurate sales strategies.

[0437] 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.

[0438] In this invention, the server includes means for acquiring product information, activity group information, and customer status information from an input device and organizing this information in a standardized format; means for generating multiple commercial scenarios and creating proposed methods based on the organized information; and means for providing the proposed methods and commercial scenarios to a designated device. It also includes means for acquiring on-site information using a portable visual device and providing commercial strategies based on the on-site information. This enables sales representatives to instantly obtain appropriate commercial strategies tailored to the actual situation on-site, thereby realizing effective and timely sales activities.

[0439] An "input device" is a device used to acquire and input product information, activity group information, and customer status information, and plays the role of providing information to the user.

[0440] A "standardized format" is an effort to convert information provided in different formats into a unified format, with the aim of facilitating the consistency and understanding of the information.

[0441] "Means of organization" refers to the function of organizing acquired information, arranging it into a usable format, and preparing it so that the next processing step can be carried out efficiently.

[0442] A "commercial scenario" is a set of hypotheses used to derive the optimal sales strategy in a specific sales situation, and serves as a foundation for planning specific sales actions.

[0443] The "proposal method" involves selecting the most effective approach from the generated commercial scenarios and presenting a specific strategy for its use in sales activities.

[0444] "Designated equipment" refers to display and communication devices used to provide information to sales representatives and related parties, and is used by users to confirm the information they have been presented with.

[0445] A "portable visual device" is a device carried by sales representatives to collect visual information on-site, and is a device that supports information acquisition in the field.

[0446] A "commercial strategy based on on-site information" is a sales policy that is applied in real time by utilizing acquired on-site visual information, and is a strategy that enables responsive sales activities that are adapted to the environment.

[0447] The system for implementing this invention aims to efficiently collect information at the sales site and provide optimal commercial strategies. The server receives product information, activity group information, and customer status information from users through an input device. This information is converted into a standardized format and organized. Based on this organized information, the server generates multiple commercial scenarios and formulates proposed strategies.

[0448] Using portable visual devices, users collect on-site information at their destinations. These devices include smart glasses and head-mounted displays. Based on the on-site information acquired by these devices, a server generates commercial strategies in real time. This allows sales representatives to obtain optimal information on the spot and conduct commercial activities quickly and accurately.

[0449] For example, based on store information acquired through smart glasses during a visit, the server generates a commercial strategy such as "Industry: Retail, Characteristics: Localized sales promotion is effective." This information is displayed on the portable visual device used by the sales representative.

[0450] Furthermore, this system can use a generative AI model to provide an example prompt message such as, "Please generate the optimal sales proposal based on successful case studies related to this industry and its characteristics." This prompt enables the system to provide an appropriate proposal method based on past success stories and industry best practices.

[0451] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0452] Step 1:

[0453] The server receives product information, activity group information, and customer status information from the terminal. This information is data entered by the user, and the server first converts it into a standard data format. This allows for consistent handling of the data in subsequent processing.

[0454] Step 2:

[0455] The server generates multiple commercial scenarios based on the compiled data. Specifically, it uses a generative AI model to perform calculations to derive the optimal commercial strategy from past success stories and industry best practices. This process develops effective proposal methods for each sales activity.

[0456] Step 3:

[0457] Users collect on-site information using portable visual devices, such as smart glasses. These devices capture location data and image data from within stores and transmit this information to a server. The server uses this new information to analyze, update, and apply commercial strategies tailored to the local situation in real time.

[0458] Step 4:

[0459] The server transmits and displays commercial scenarios based on acquired field information to the user's portable visual device. Here, an example prompt message, "Please generate the optimal sales proposal based on successful case studies related to this industry and its characteristics," is used to provide specific action guidelines for the sales representative.

[0460] Step 5:

[0461] Users conduct sales activities on-site, following commercial strategies displayed using visual aids. The results and feedback from these sales activities are sent back to the server, accumulating data that contributes to improving the overall accuracy of the system. This data is then used to generate future commercial scenarios.

[0462] 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.

[0463] This invention enables more personalized proposals by combining an emotion engine with a simulation system used in sales activities. Specifically, the user inputs product information, industry information, and customer environment information into the system via a terminal, and at the same time, the user's emotional state is analyzed by the emotion engine.

[0464] The server first standardizes the received information and then uses AI technology to generate multiple sales scenarios. Here, data from the emotion engine is taken into consideration to generate suggestion patterns that are appropriate for the user's emotions. In this process, the server not only refers to past success stories and industry best practices but also reflects the user's current emotional state to propose more effective sales strategies.

[0465] For example, when a user proposes a new product to a customer, the emotion engine may detect feelings such as tension or anxiety. In this case, the server will provide proposal patterns that emphasize positive success stories and reassurance to the customer in order to alleviate these feelings.

[0466] The server also optimizes feedback analysis based on the user's emotions detected by the emotion engine. This allows the AI ​​to receive feedback tailored to the user's individual needs and emotional state, and to make adjustments that improve the overall accuracy and efficiency of the system.

[0467] In this way, the system can effectively support sales activities and increase the success rate of deals. For users, it helps them achieve better sales results by tailoring proposals to their emotions.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] Users input product information, industry information, and customer environment information via a terminal. At the same time, emotional data is collected from the user's facial expressions and voice through an emotion recognition device connected to the terminal.

[0471] Step 2:

[0472] The server receives product information, industry information, and customer environment information transmitted from the terminal, converts the data into a standardized format, and organizes it. At the same time, it also receives emotional data and determines the user's current emotional state.

[0473] Step 3:

[0474] The server uses an AI engine to generate multiple sales scenarios based on organized information and emotional data. During this process, the suggested scenarios are adjusted according to the emotional data. For example, if the user is feeling anxious, a scenario is generated that takes that emotion into account and provides a sense of reassurance.

[0475] Step 4:

[0476] The server sends the generated sales scenarios and proposal patterns to the terminal, allowing the user to visually review them. The user then uses these as a reference to prepare for actual proposals.

[0477] Step 5:

[0478] Users input feedback into the system from their terminal based on the practical results and experiences gained when making actual proposals. Emotional data is also acquired again and sent to the server along with information on the effectiveness of the proposals.

[0479] Step 6:

[0480] The server analyzes feedback and newly acquired sentiment data to make improvements to enhance the accuracy of sales scenarios and proposal patterns. This ensures that the system continues to provide proposals optimized for the user's emotions.

[0481] This entire process enables users to make appropriate suggestions based on their emotions, thereby improving the success rate of business negotiations.

[0482] (Example 2)

[0483] 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."

[0484] In sales activities, there is a challenge in that the proposals offered by sales representatives do not adequately address customer needs or market conditions. Furthermore, the emotions and state of mind of sales representatives are often not reflected in the scenarios, resulting in limited effectiveness of proposals. This leads to a decrease in sales success rates and hinders efficient sales activities.

[0485] 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.

[0486] In this invention, the server includes means for receiving product information, industry information, and customer environment information from an input source and organizing this information in a standardized format; means for analyzing the user's emotional state; and means for adjusting the proposal patterns and sales scenarios to reflect the user's emotional state. This makes it possible to provide personalized sales proposals based on the user's emotional state and improve the success rate of sales.

[0487] "Input source" refers to the source or system that provides information such as product information, industry information, and customer environment information.

[0488] "Standardization" refers to the process of converting data provided in different formats or units into a consistent format.

[0489] "Product information" refers to detailed information about the products or services being bought or sold.

[0490] "Industry information" refers to information about a specific industry or business field.

[0491] "Customer environment information" refers to information about the customer's business situation and market environment.

[0492] A "sales scenario" refers to a plan or strategy created for a specific sales objective.

[0493] A "proposal pattern" refers to the structure and format of a specific proposal made to a customer.

[0494] "Emotional analysis methods" refer to processes and tools for evaluating a user's emotional state.

[0495] An "information display device" is a device that visually displays generated proposals and scenarios to the user.

[0496] "Feedback" refers to information received as an evaluation or reaction to proposals or sales activities.

[0497] This invention is a sales simulation system incorporating sentiment analysis, aimed at improving the quality of proposals in sales activities. The system consists of a server, terminals, and users.

[0498] The terminal provides an interface for receiving product information, industry information, and customer environment information from the user. Furthermore, the terminal uses a specific application called "emotion analysis software" to analyze the user's emotional state from voice and input patterns. This analyzed emotional data is used to understand the user's mental state.

[0499] The server uses a "data standardization module" to convert various types of information received from terminals into a standardized data format. This standardization enables consistent analysis of different data. Subsequently, the server uses a "sales scenario generation AI model" to automatically generate multiple sales scenarios based on this standardized data. This model takes into account the user's current emotional state while referencing past success stories and industry best practices to form the optimal proposal pattern.

[0500] To give a specific example, when a user tries to propose a new product to a customer, the information entered on the terminal is sent to the server. At the same time, if the emotion analysis software detects that the user is in a state of tension, the server selects a scenario that emphasizes reassuring success stories and presents that scenario on the terminal's information display.

[0501] An example of a prompt message might be, "When a user is nervous about being presented with a new product, what kind of presentation pattern would be effective?" In this way, the system adjusts to provide appropriate sales proposals based on the user's emotions.

[0502] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0503] Step 1:

[0504] The user inputs product information, industry information, and customer environment information via a terminal. This information is temporarily stored on the terminal. Based on the input information, the terminal uses emotion analysis software to analyze the user's emotional state. Specifically, it analyzes data such as input speed and voice tone to identify the user's current emotions. The input is raw data, and the output is the analyzed emotional state.

[0505] Step 2:

[0506] The terminal transmits received product and industry information to the server. Simultaneously, the sentiment analysis results are also transferred to the server. The server, using a "data standardization module," converts the received data into a standard format. For example, it standardizes currency units and date formats. The input is raw data from the terminal, while the output is in a standardized data format.

[0507] Step 3:

[0508] The server runs an "AI sales scenario generation model" based on standardized data to generate multiple sales scenarios. This process references past cases and industry knowledge databases, and also incorporates sentiment analysis results into the scenarios. For example, if the user's emotion is determined to be "anxiety," the system prioritizes proposals that provide reassurance to alleviate that anxiety. The input consists of standardized data and sentiment data, and the output is an adjusted sales scenario.

[0509] Step 4:

[0510] The server sends the generated sales scenario to the terminal and presents it visually to the user via an information display device. This allows the user to understand what kind of scenario to propose to the customer. Sentimental data is taken into consideration to ensure that the presented scenario is optimal according to the user's state.

[0511] Step 5:

[0512] After a proposal is implemented, the user provides feedback via their terminal. This feedback is sent to the server and analyzed by FeedbackOptimizer. The analysis results are used to improve the system's accuracy and make adjustments for future proposals. The input is the user's feedback data, and the output is the analysis results, including areas for improvement.

[0513] (Application Example 2)

[0514] 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."

[0515] Traditional sales activities often struggled to take customers' emotional states into account, resulting in generic proposals. Consequently, sales efforts failed to accurately address individual customer needs, leading to a lower success rate for proposals. Furthermore, there was a lack of support for sales representatives in stores to effectively communicate with customers in real time and make appropriate proposals.

[0516] 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.

[0517] In this invention, the server includes means for receiving various information from an input source and organizing this information in a standardized format; means for generating multiple dialogue scenarios and creating presentation content based on the organized information and detected emotion data; and means for identifying the user's emotional state using an emotion analysis device and applying it to the presentation content. This makes it possible to analyze the customer's emotions in real time and provide suggestions tailored to their individual emotional state.

[0518] An "input source" refers to a medium or device used to provide various types of information.

[0519] "Information" refers to a collection of data received from input sources for standardization purposes, which is then organized and analyzed based on specific applications.

[0520] Standardization is the process of organizing information into a consistent format, enabling consistent processing within a system.

[0521] "Organization" is the process of compiling received information into an easily understandable format, forming the foundation for analysis and processing.

[0522] "Emotional data" refers to information that indicates a user's emotional state and is acquired by an emotion analysis device.

[0523] A "dialogue scenario" is a plan or script generated based on informational and emotional data to guide a conversation with a user.

[0524] "Presented content" refers to the information or message determined by the dialogue scenario, and is the specific content of the proposal presented to the user.

[0525] An "emotion analysis device" is a device used to recognize and analyze a user's emotional state and to acquire emotional data.

[0526] "User emotional state" refers to the type and intensity of emotions a user experiences, and is used to optimize customer service and sales.

[0527] The system for realizing this application consists of a display device such as smart glasses, an emotion analysis device, and a central processing unit. A server receives customer information from input sources via this group of devices and standardizes it. Based on the standardized information and emotion data, a generative AI model is used to generate multiple dialogue scenarios. The presentation content included in these generated dialogue scenarios is provided to the smart glasses' display. The emotion analysis device analyzes the user's emotional state in real time, enabling dynamically optimal suggestions. Microsoft Azure Face API and other emotion analysis software are used to identify the emotional state.

[0528] As a concrete example, suppose a sales representative in a store is explaining a new product. If the customer shows interest in the product but is concerned about the price, the server detects the customer's emotional state and, based on a generative AI model (e.g., OpenAI's GPT-3), displays something like, "This product is durable and could ultimately reduce your costs," on the smart glasses' display. In this example, the prompt given to the generative AI model could be, "If the customer's emotions are 'interested' and 'concerned,' generate a suggestion that emphasizes the value of the product in terms of cost reduction."

[0529] In this way, users can flexibly change their suggestions according to the customer's emotions, resulting in more personalized customer service.

[0530] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0531] Step 1:

[0532] The server transmits the customer's visual information and audio data from the terminal (such as smart glasses) to the emotion analysis device. The data input at this time is real-time data on changes in the customer's facial expressions and voice. Based on this, the emotion analysis device uses Microsoft Azure Face API, etc., to analyze the customer's emotional state and extract emotional data.

[0533] Step 2:

[0534] The server integrates and standardizes emotional data with customer-related data such as purchase history and interest levels. This integrated dataset is then input into a generative AI model, which generates a series of dialogue scenarios optimized for the customer's emotional state. OpenAI's GPT-3 is used as the generative AI model for this process.

[0535] Step 3:

[0536] The server selects the most appropriate presentation from the generated dialogue scenarios and communicates it to the terminal. This selection is made using prompt statements, such as, "If the customer's emotions are 'interested' and 'anxious,' generate a proposal that emphasizes the value of a product that leads to cost savings." The output proposal optimizes communication with the customer.

[0537] Step 4:

[0538] The terminal displays the information received from the server on its screen, making it available for the sales representative to review. The sales representative can instantly provide appropriate suggestions and information during conversations with customers through the display on their smart glasses. This allows customers to receive information tailored to their individual needs.

[0539] By following these steps, users can dynamically and effectively provide customer-centric suggestions, thereby improving sales performance.

[0540] 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.

[0541] 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.

[0542] 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.

[0543] [Fourth Embodiment]

[0544] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0545] 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.

[0546] 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).

[0547] 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.

[0548] 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.

[0549] 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).

[0550] 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.

[0551] 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.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] 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".

[0557] The system for implementing this invention uses AI technology to perform simulations of sales activities. First, the user inputs product information, industry information, and customer environment information into the system via a terminal. This information is received by a server, analyzed, and converted into a standardized format.

[0558] The server uses AI to generate multiple sales scenarios based on standardized data. This takes into account past success stories and industry best practices. Based on the generated sales scenarios, the server creates specific proposal patterns, allowing users to preview the optimal proposal tactics.

[0559] As a concrete example, let's assume a user is planning to sell a new software product. The user inputs information into the system, such as that the target industry is manufacturing and the specific features required by a particular customer. Based on this information, the server simulates effective implementation examples and proposal strategies for the manufacturing industry.

[0560] The simulation results are presented to the user as proposed patterns and associated sales scenarios. Furthermore, the server also presents common problems and their solutions, helping users quickly address potential challenges they might face in customer interactions.

[0561] Based on the information provided, users prepare optimal proposals before actual sales activities and send feedback to the server, contributing to the improvement of the system's accuracy. In this way, the efficiency of sales activities can be increased, and the success rate of business negotiations can be improved.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] Users input product information, industry information, and customer environment information via their terminals. This information is registered in the system as data necessary for sales activities.

[0565] Step 2:

[0566] The server receives information sent by the user and normalizes the data. It organizes the data in a normalized format and prepares it for analysis.

[0567] Step 3:

[0568] The server uses an AI engine to generate multiple sales scenarios based on normalized data. In doing so, it consults past success stories and industry best practices to find the optimal scenario.

[0569] Step 4:

[0570] The server creates specific proposal patterns tailored to each case based on the generated sales scenarios. This ensures that proposals are available that can address different situations.

[0571] Step 5:

[0572] The server sends proposal patterns and sales scenarios to the terminal and provides the user with a visualized simulation result. The user then reviews this and confirms the proposal strategy.

[0573] Step 6:

[0574] Users input feedback on their proposals and simulation results into the system via their terminals. This feedback is used to improve future scenario generation and proposal patterns.

[0575] Step 7:

[0576] The server analyzes user feedback and uses AI to make adjustments to improve the overall accuracy and efficiency of the system. This optimizes the system to further increase the success rate of sales activities.

[0577] (Example 1)

[0578] 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".

[0579] In existing sales activities, there is a challenge in effectively utilizing product information, industry information, and customer environment information to derive optimal sales scenarios and proposal patterns. Furthermore, there is a need to strengthen sales activities by efficiently utilizing past success stories and best practices in the industry.

[0580] 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.

[0581] In this invention, the server includes means for receiving product data, industry data, and customer information from an input device and organizing this information in a standardized format; means for generating multiple sales plans and forming proposal patterns using a generative model; and means for receiving feedback and analyzing data to improve the accuracy of the proposal patterns and sales plans. This makes it possible to efficiently generate an optimal sales strategy and support sales activities based on it.

[0582] An "input device" is a device used by users to transmit product data, industry data, and customer information to a system.

[0583] "Product data" refers to information about goods and services that are being bought and sold.

[0584] "Industry data" refers to information about a specific industry or market.

[0585] "Customer information" refers to information about customers who are the target of sales activities.

[0586] A "standardized format" is a method of unifying data in different formats into a consistent format.

[0587] A "generative model" is an algorithm used to generate new information based on given data.

[0588] A "sales plan" is a set of strategies and procedures designed to efficiently carry out sales activities.

[0589] A "proposal pattern" refers to the elements that make up the specific proposal content presented to the customer.

[0590] "Feedback" refers to opinions and information returned by users to improve the accuracy of proposal patterns and sales plans.

[0591] "Data for improving accuracy" refers to information that contributes to improving the precision of proposals and sales strategies.

[0592] This system uses a generative AI model to generate multiple sales scenarios to support efficient decision-making in sales activities. Users first input product data, industry data, and customer information into the system using a terminal. This input is typically done via dedicated user interface software. Personal computers and tablet devices are used as terminals.

[0593] Information entered by the user is received by the server. The server analyzes the information and processes it to standardize it into a regular format. General-purpose programming languages ​​such as Python and Java are used for data cleaning and format conversion.

[0594] Subsequently, the server uses a generative AI model to generate multiple sales plans based on past success stories and industry best practices. This AI model can utilize common libraries for natural language processing and machine learning. The generated sales plans form specific proposal patterns, which are then provided to the user. This allows the user to simulate sales activities and consider optimal proposal tactics in advance.

[0595] As a concrete example, consider a scenario where a user is trying to sell a new software product to a manufacturing customer. The user inputs product features, target industry trends, and specific customer requirements via a terminal. Based on this information, the server generates a sales scenario with a high probability of success. The presented sales plan includes how to approach the customer and the content of the proposal. The user can then use this as a reference to conduct actual sales activities.

[0596] An example of a prompt message would be a specific instruction such as, "Generate effective proposal scenarios for introducing new software for the manufacturing industry." Such prompt messages enable the generating AI model to present more accurate sales plans.

[0597] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0598] Step 1:

[0599] Users use a terminal to input product information, industry information, and customer information. This input includes product name, target industry, and specific customer requests. The information entered on the terminal is sent to a dedicated input form and then transferred to the server.

[0600] Step 2:

[0601] The server analyzes the information received from the terminal and standardizes it into a regular format. This process involves imputing missing data values ​​and converting different data formats to the standard format. Libraries in programming languages ​​such as Python are used to prepare the cleaned data.

[0602] Step 3:

[0603] The server utilizes a generation AI model based on standardized data to generate multiple sales scenarios. Prompt messages are fed into the model, and various scenarios are output while referencing past success data and industry best practices. As a result, a diverse range of sales plans are formed.

[0604] Step 4:

[0605] The server creates specific proposal patterns based on the generated scenarios. At this stage, a proposal document is generated that includes the advantages of the proposed product and appropriate approaches to customers. The created proposal patterns are then organized in a way that users can refer to.

[0606] Step 5:

[0607] The server sends back proposal patterns and sales scenarios to the user. The user receives this information on their terminal and uses it as a basis for developing sales activity plans. Furthermore, by collecting feedback on actual customer interactions and sending it back to the server, the system continuously improves its accuracy.

[0608] (Application Example 1)

[0609] 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".

[0610] Current sales support systems lack the technology to analyze the conditions of visited locations and the needs of individual customers in real time and present optimal sales strategies. Sales representatives often cannot obtain immediately effective proposals at the locations they visit, making timely responses difficult. Furthermore, there is a need for methods that effectively utilize visual information from the field during sales activities to provide more accurate sales strategies.

[0611] 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.

[0612] In this invention, the server includes means for acquiring product information, activity group information, and customer status information from an input device and organizing this information in a standardized format; means for generating multiple commercial scenarios and creating proposed methods based on the organized information; and means for providing the proposed methods and commercial scenarios to a designated device. It also includes means for acquiring on-site information using a portable visual device and providing commercial strategies based on the on-site information. This enables sales representatives to instantly obtain appropriate commercial strategies tailored to the actual situation on-site, thereby realizing effective and timely sales activities.

[0613] An "input device" is a device used to acquire and input product information, activity group information, and customer status information, and plays the role of providing information to the user.

[0614] A "standardized format" is an effort to convert information provided in different formats into a unified format, with the aim of facilitating the consistency and understanding of the information.

[0615] "Means of organization" refers to the function of organizing acquired information, arranging it into a usable format, and preparing it so that the next processing step can be carried out efficiently.

[0616] A "commercial scenario" is a set of hypotheses used to derive the optimal sales strategy in a specific sales situation, and serves as a foundation for planning specific sales actions.

[0617] The "proposal method" involves selecting the most effective approach from the generated commercial scenarios and presenting a specific strategy for its use in sales activities.

[0618] "Designated equipment" refers to display and communication devices used to provide information to sales representatives and related parties, and is used by users to confirm the information they have been presented with.

[0619] A "portable visual device" is a device carried by sales representatives to collect visual information on-site, and is a device that supports information acquisition in the field.

[0620] A "commercial strategy based on on-site information" is a sales policy that is applied in real time by utilizing acquired on-site visual information, and is a strategy that enables responsive sales activities that are adapted to the environment.

[0621] The system for implementing this invention aims to efficiently collect information at the sales site and provide optimal commercial strategies. The server receives product information, activity group information, and customer status information from users through an input device. This information is converted into a standardized format and organized. Based on this organized information, the server generates multiple commercial scenarios and formulates proposed strategies.

[0622] Using portable visual devices, users collect on-site information at their destinations. These devices include smart glasses and head-mounted displays. Based on the on-site information acquired by these devices, a server generates commercial strategies in real time. This allows sales representatives to obtain optimal information on the spot and conduct commercial activities quickly and accurately.

[0623] For example, based on store information acquired through smart glasses during a visit, the server generates a commercial strategy such as "Industry: Retail, Characteristics: Localized sales promotion is effective." This information is displayed on the portable visual device used by the sales representative.

[0624] Furthermore, this system can use a generative AI model to provide an example prompt message such as, "Please generate the optimal sales proposal based on successful case studies related to this industry and its characteristics." This prompt enables the system to provide an appropriate proposal method based on past success stories and industry best practices.

[0625] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0626] Step 1:

[0627] The server receives product information, activity group information, and customer status information from the terminal. This information is data entered by the user, and the server first converts it into a standard data format. This allows for consistent handling of the data in subsequent processing.

[0628] Step 2:

[0629] The server generates multiple commercial scenarios based on the compiled data. Specifically, it uses a generative AI model to perform calculations to derive the optimal commercial strategy from past success stories and industry best practices. This process develops effective proposal methods for each sales activity.

[0630] Step 3:

[0631] Users collect on-site information using portable visual devices, such as smart glasses. These devices capture location data and image data from within stores and transmit this information to a server. The server uses this new information to analyze, update, and apply commercial strategies tailored to the local situation in real time.

[0632] Step 4:

[0633] The server transmits and displays commercial scenarios based on acquired field information to the user's portable visual device. Here, an example prompt message, "Please generate the optimal sales proposal based on successful case studies related to this industry and its characteristics," is used to provide specific action guidelines for the sales representative.

[0634] Step 5:

[0635] Users conduct sales activities on-site, following commercial strategies displayed using visual aids. The results and feedback from these sales activities are sent back to the server, accumulating data that contributes to improving the overall accuracy of the system. This data is then used to generate future commercial scenarios.

[0636] 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.

[0637] This invention enables more personalized proposals by combining an emotion engine with a simulation system used in sales activities. Specifically, the user inputs product information, industry information, and customer environment information into the system via a terminal, and at the same time, the user's emotional state is analyzed by the emotion engine.

[0638] The server first standardizes the received information and then uses AI technology to generate multiple sales scenarios. Here, data from the emotion engine is taken into consideration to generate suggestion patterns that are appropriate for the user's emotions. In this process, the server not only refers to past success stories and industry best practices but also reflects the user's current emotional state to propose more effective sales strategies.

[0639] For example, when a user proposes a new product to a customer, the emotion engine may detect feelings such as tension or anxiety. In this case, the server will provide proposal patterns that emphasize positive success stories and reassurance to the customer in order to alleviate these feelings.

[0640] The server also optimizes feedback analysis based on the user's emotions detected by the emotion engine. This allows the AI ​​to receive feedback tailored to the user's individual needs and emotional state, and to make adjustments that improve the overall accuracy and efficiency of the system.

[0641] In this way, the system can effectively support sales activities and increase the success rate of deals. For users, it helps them achieve better sales results by tailoring proposals to their emotions.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] Users input product information, industry information, and customer environment information via a terminal. At the same time, emotional data is collected from the user's facial expressions and voice through an emotion recognition device connected to the terminal.

[0645] Step 2:

[0646] The server receives product information, industry information, and customer environment information transmitted from the terminal, converts the data into a standardized format, and organizes it. At the same time, it also receives emotional data and determines the user's current emotional state.

[0647] Step 3:

[0648] The server uses an AI engine to generate multiple sales scenarios based on organized information and emotional data. During this process, the suggested scenarios are adjusted according to the emotional data. For example, if the user is feeling anxious, a scenario is generated that takes that emotion into account and provides a sense of reassurance.

[0649] Step 4:

[0650] The server sends the generated sales scenarios and proposal patterns to the terminal, allowing the user to visually review them. The user then uses these as a reference to prepare for actual proposals.

[0651] Step 5:

[0652] Users input feedback into the system from their terminal based on the practical results and experiences gained when making actual proposals. Emotional data is also acquired again and sent to the server along with information on the effectiveness of the proposals.

[0653] Step 6:

[0654] The server analyzes feedback and newly acquired sentiment data to make improvements to enhance the accuracy of sales scenarios and proposal patterns. This ensures that the system continues to provide proposals optimized for the user's emotions.

[0655] This entire process enables users to make appropriate suggestions based on their emotions, thereby improving the success rate of business negotiations.

[0656] (Example 2)

[0657] 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".

[0658] In sales activities, there is a challenge in that the proposals offered by sales representatives do not adequately address customer needs or market conditions. Furthermore, the emotions and state of mind of sales representatives are often not reflected in the scenarios, resulting in limited effectiveness of proposals. This leads to a decrease in sales success rates and hinders efficient sales activities.

[0659] 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.

[0660] In this invention, the server includes means for receiving product information, industry information, and customer environment information from an input source and organizing this information in a standardized format; means for analyzing the user's emotional state; and means for adjusting the proposal patterns and sales scenarios to reflect the user's emotional state. This makes it possible to provide personalized sales proposals based on the user's emotional state and improve the success rate of sales.

[0661] "Input source" refers to the source or system that provides information such as product information, industry information, and customer environment information.

[0662] "Standardization" refers to the process of converting data provided in different formats or units into a consistent format.

[0663] "Product information" refers to detailed information about the products or services being bought or sold.

[0664] "Industry information" refers to information about a specific industry or business field.

[0665] "Customer environment information" refers to information about the customer's business situation and market environment.

[0666] A "sales scenario" refers to a plan or strategy created for a specific sales objective.

[0667] A "proposal pattern" refers to the structure and format of a specific proposal made to a customer.

[0668] "Emotional analysis methods" refer to processes and tools for evaluating a user's emotional state.

[0669] An "information display device" is a device that visually displays generated proposals and scenarios to the user.

[0670] "Feedback" refers to information received as an evaluation or reaction to proposals or sales activities.

[0671] This invention is a sales simulation system incorporating sentiment analysis, aimed at improving the quality of proposals in sales activities. The system consists of a server, terminals, and users.

[0672] The terminal provides an interface for receiving product information, industry information, and customer environment information from the user. Furthermore, the terminal uses a specific application called "emotion analysis software" to analyze the user's emotional state from voice and input patterns. This analyzed emotional data is used to understand the user's mental state.

[0673] The server uses a "data standardization module" to convert various types of information received from terminals into a standardized data format. This standardization enables consistent analysis of different data. Subsequently, the server uses a "sales scenario generation AI model" to automatically generate multiple sales scenarios based on this standardized data. This model takes into account the user's current emotional state while referencing past success stories and industry best practices to form the optimal proposal pattern.

[0674] To give a specific example, when a user tries to propose a new product to a customer, the information entered on the terminal is sent to the server. At the same time, if the emotion analysis software detects that the user is in a state of tension, the server selects a scenario that emphasizes reassuring success stories and presents that scenario on the terminal's information display.

[0675] An example of a prompt message might be, "When a user is nervous about being presented with a new product, what kind of presentation pattern would be effective?" In this way, the system adjusts to provide appropriate sales proposals based on the user's emotions.

[0676] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0677] Step 1:

[0678] The user inputs product information, industry information, and customer environment information via a terminal. This information is temporarily stored on the terminal. Based on the input information, the terminal uses emotion analysis software to analyze the user's emotional state. Specifically, it analyzes data such as input speed and voice tone to identify the user's current emotions. The input is raw data, and the output is the analyzed emotional state.

[0679] Step 2:

[0680] The terminal transmits received product and industry information to the server. Simultaneously, the sentiment analysis results are also transferred to the server. The server, using a "data standardization module," converts the received data into a standard format. For example, it standardizes currency units and date formats. The input is raw data from the terminal, while the output is in a standardized data format.

[0681] Step 3:

[0682] The server runs an "AI sales scenario generation model" based on standardized data to generate multiple sales scenarios. This process references past cases and industry knowledge databases, and also incorporates sentiment analysis results into the scenarios. For example, if the user's emotion is determined to be "anxiety," the system prioritizes proposals that provide reassurance to alleviate that anxiety. The input consists of standardized data and sentiment data, and the output is an adjusted sales scenario.

[0683] Step 4:

[0684] The server sends the generated sales scenario to the terminal and presents it visually to the user via an information display device. This allows the user to understand what kind of scenario to propose to the customer. Sentimental data is taken into consideration to ensure that the presented scenario is optimal according to the user's state.

[0685] Step 5:

[0686] After a proposal is implemented, the user provides feedback via their terminal. This feedback is sent to the server and analyzed by FeedbackOptimizer. The analysis results are used to improve the system's accuracy and make adjustments for future proposals. The input is the user's feedback data, and the output is the analysis results, including areas for improvement.

[0687] (Application Example 2)

[0688] 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".

[0689] Traditional sales activities often struggled to take customers' emotional states into account, resulting in generic proposals. Consequently, sales efforts failed to accurately address individual customer needs, leading to a lower success rate for proposals. Furthermore, there was a lack of support for sales representatives in stores to effectively communicate with customers in real time and make appropriate proposals.

[0690] 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.

[0691] In this invention, the server includes means for receiving various information from an input source and organizing this information in a standardized format; means for generating multiple dialogue scenarios and creating presentation content based on the organized information and detected emotion data; and means for identifying the user's emotional state using an emotion analysis device and applying it to the presentation content. This makes it possible to analyze the customer's emotions in real time and provide suggestions tailored to their individual emotional state.

[0692] An "input source" refers to a medium or device used to provide various types of information.

[0693] "Information" refers to a collection of data received from input sources for standardization purposes, which is then organized and analyzed based on specific applications.

[0694] Standardization is the process of organizing information into a consistent format, enabling consistent processing within a system.

[0695] "Organization" is the process of compiling received information into an easily understandable format, forming the foundation for analysis and processing.

[0696] "Emotional data" refers to information that indicates a user's emotional state and is acquired by an emotion analysis device.

[0697] A "dialogue scenario" is a plan or script generated based on informational and emotional data to guide a conversation with a user.

[0698] "Presented content" refers to the information or message determined by the dialogue scenario, and is the specific content of the proposal presented to the user.

[0699] An "emotion analysis device" is a device used to recognize and analyze a user's emotional state and to acquire emotional data.

[0700] "User emotional state" refers to the type and intensity of emotions a user experiences, and is used to optimize customer service and sales.

[0701] The system for realizing this application consists of a display device such as smart glasses, an emotion analysis device, and a central processing unit. A server receives customer information from input sources via this group of devices and standardizes it. Based on the standardized information and emotion data, a generative AI model is used to generate multiple dialogue scenarios. The presentation content included in these generated dialogue scenarios is provided to the smart glasses' display. The emotion analysis device analyzes the user's emotional state in real time, enabling dynamically optimal suggestions. Microsoft Azure Face API and other emotion analysis software are used to identify the emotional state.

[0702] As a concrete example, suppose a sales representative in a store is explaining a new product. If the customer shows interest in the product but is concerned about the price, the server detects the customer's emotional state and, based on a generative AI model (e.g., OpenAI's GPT-3), displays something like, "This product is durable and could ultimately reduce your costs," on the smart glasses' display. In this example, the prompt given to the generative AI model could be, "If the customer's emotions are 'interested' and 'concerned,' generate a suggestion that emphasizes the value of the product in terms of cost reduction."

[0703] In this way, users can flexibly change their suggestions according to the customer's emotions, resulting in more personalized customer service.

[0704] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0705] Step 1:

[0706] The server transmits the customer's visual information and audio data from the terminal (such as smart glasses) to the emotion analysis device. The data input at this time is real-time data on changes in the customer's facial expressions and voice. Based on this, the emotion analysis device uses Microsoft Azure Face API, etc., to analyze the customer's emotional state and extract emotional data.

[0707] Step 2:

[0708] The server integrates and standardizes emotional data with customer-related data such as purchase history and interest levels. This integrated dataset is then input into a generative AI model, which generates a series of dialogue scenarios optimized for the customer's emotional state. OpenAI's GPT-3 is used as the generative AI model for this process.

[0709] Step 3:

[0710] The server selects the most appropriate presentation from the generated dialogue scenarios and communicates it to the terminal. This selection is made using prompt statements, such as, "If the customer's emotions are 'interested' and 'anxious,' generate a proposal that emphasizes the value of a product that leads to cost savings." The output proposal optimizes communication with the customer.

[0711] Step 4:

[0712] The terminal displays the information received from the server on its screen, making it available for the sales representative to review. The sales representative can instantly provide appropriate suggestions and information during conversations with customers through the display on their smart glasses. This allows customers to receive information tailored to their individual needs.

[0713] By following these steps, users can dynamically and effectively provide customer-centric suggestions, thereby improving sales performance.

[0714] 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.

[0715] 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.

[0716] In the above embodiment, an example was given in which the 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.

[0717] 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.

[0718] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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."

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] The following is further disclosed regarding the embodiments described above.

[0736] (Claim 1)

[0737] A means of receiving product information, industry information, and customer environment information from input sources, and organizing this information in a standardized format.

[0738] A means for generating multiple sales scenarios and creating proposal patterns based on the aforementioned organized information,

[0739] Means for providing the aforementioned proposed patterns and sales scenarios to a display device,

[0740] A means for receiving user feedback and analyzing information to improve the accuracy of the proposed patterns and sales scenarios,

[0741] A system that includes this.

[0742] (Claim 2)

[0743] The system according to claim 1, further comprising means for presenting a general problem related to the proposed pattern and providing a solution thereto.

[0744] (Claim 3)

[0745] The system according to claim 1, further comprising means for generating the sales scenario based on past success stories and industry best practices.

[0746] "Example 1"

[0747] (Claim 1)

[0748] A means of receiving product data, industry data, and customer information from an input device, and organizing this information in a standardized format.

[0749] A means for generating multiple sales plans and forming proposal patterns using a generative model based on the organized information described above,

[0750] Means for providing the aforementioned proposed patterns and sales plans to a display,

[0751] A means for receiving feedback from users and analyzing data to improve the accuracy of the proposed patterns and sales plans,

[0752] A system that includes this.

[0753] (Claim 2)

[0754] The system according to claim 1, further comprising means for presenting common problems related to the proposed pattern and providing solutions thereto.

[0755] (Claim 3)

[0756] The system according to claim 1, further comprising means for generating the sales plan based on past success stories and best practices in the industry.

[0757] "Application Example 1"

[0758] (Claim 1)

[0759] A means for acquiring product information, activity group information, and customer status information from an input device, and organizing this information in a standardized format.

[0760] A means for generating multiple commercial scenarios based on the aforementioned compiled information and creating a proposed method,

[0761] A means for providing the proposed method and commercial scenario in the designated device,

[0762] A means for obtaining user evaluations and analyzing information to improve the accuracy of the proposed method and commercial scenario,

[0763] A means of acquiring on-site information using portable visual devices and providing a commercial strategy based on said on-site information,

[0764] A system that includes this.

[0765] (Claim 2)

[0766] The system according to claim 1, further comprising means for presenting general problems related to the proposed method and providing solutions thereto.

[0767] (Claim 3)

[0768] The system according to claim 1, further comprising means for generating the commercial scenario based on past success stories and best practices of activity groups.

[0769] "Example 2 of combining an emotion engine"

[0770] (Claim 1)

[0771] A means of receiving product information, industry information, and customer environment information from input sources, and organizing this information in a standardized format.

[0772] A means for generating multiple sales scenarios and creating proposal patterns based on the aforementioned organized information,

[0773] A means of analyzing the emotional state of a user,

[0774] A means for adjusting the aforementioned proposal patterns and sales scenarios to reflect the user's emotional state,

[0775] Means for providing the aforementioned proposed patterns and sales scenarios to an information display device,

[0776] A means for receiving user feedback and analyzing information to improve the accuracy of the proposed patterns and sales scenarios,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, further comprising means for presenting a general problem related to the proposed pattern and providing a solution thereto.

[0780] (Claim 3)

[0781] The system according to claim 1, further comprising means for generating the sales scenario based on past success stories and industry best practices, and means for adjusting the generated proposal based on sentiment analysis results.

[0782] "Application example 2 when combining with an emotional engine"

[0783] (Claim 1)

[0784] A means of receiving various types of information from an input source and organizing this information in a standardized format,

[0785] A means for generating multiple dialogue scenarios and creating presentation content based on the organized information and detected emotion data,

[0786] Means for providing the aforementioned presentation content and dialogue scenario to a display device,

[0787] A means for receiving user responses and analyzing information to improve the accuracy of the presented content and dialogue scenarios,

[0788] A means for identifying the user's emotional state using an emotion analysis device and applying it to the presented content,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, further comprising means for presenting general problems related to the aforementioned content and providing solutions thereto.

[0792] (Claim 3)

[0793] The system according to claim 1, further comprising means for generating the dialogue scenario based on past success stories and best practices in the industry. [Explanation of Symbols]

[0794] 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. A means of receiving product information, industry information, and customer environment information from input sources, and organizing this information in a standardized format. A means for generating multiple sales scenarios and creating proposal patterns based on the aforementioned organized information, Means for providing the aforementioned proposed patterns and sales scenarios to a display device, A means for receiving user feedback and analyzing information to improve the accuracy of the proposed patterns and sales scenarios, A system that includes this.

2. The system according to claim 1, further comprising means for presenting a general problem related to the proposed pattern and providing a solution thereto.

3. The system according to claim 1, further comprising means for generating the sales scenario based on past success stories and industry best practices.

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